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The PDCA cycle: more success with the Deming cycle

The PDCA cycle: more success with the Deming cycle

Whether you’re an employer, employee, or self-employed, everyone wants to do their job as efficiently as possible. Increased productivity not only leads to better results, but can also improve personal satisfaction. Increasing productivity is a continuous process. You can keep on working to improve processes: it can be an everlasting cycle if you get involved.

The physicist Walter Andrew Shewhart already had this insight in the 1930s. He developed a cyclic method for quality assurance. His student William Edwards Deming refined the theory, which is why we often speak of the Deming cycle today. Others are most familiar with the term PDCA: the sequence of plan, do, check, and act.

The overriding goal is to learn continuously. This is why the PDCA cycle is so versatile : Management can benefit from the circular model, making work processes in production or in everyday office life more efficient. But the life of each individual can also benefit from the application of PDCA.

PDCA – a definition

A pdca example, advantages and disadvantages of pdca.

The PDCA cycle was designed with the aim of establishing a continuous model for the continuous improvement of processes: quality assurance that is efficient and continuous. However, the model can be applied in many contexts, especially through the extensions of Deming. Behind PDCA is a model that is useful for any learning process and improvement.

To do this, you follow the four steps plan, do, check, act. This can be applied not just to work processes, but also to the resulting products and services, as well as to the people themselves. PDCA therefore helps, for example, to improve teamwork like the stability of a sales item.

The PDCA cycle is a popular tool for implementing a continuous improvement process (CIP) . This way of thinking is based on the assumption that a company must continue to improve in order to compete in the market. The Deming cycle serves as a concrete plan for implementing the idea of CIP.

PDCA has a lot in common with the Japanese philosophy Kaizen and the ideas behind continuous integration and continuous delivery , which are well known in software development.

PDCA procedure: plan, do, check, act

The model is divided into four phases that form a circular, repetitive process.

You start with a planning phase: What problems have you identified and how can you best address them? To do this, you first determine the current situation . The problem is then outlined so that you can determine exactly how the goal should be achieved. This also includes the concrete planning of the required resources. Here, too, you first determine the current state and then scale to what is additionally required.

After all, the team also has to agree on success factors . What must happen for the changes to be considered successfully completed? Only when you have defined the goals concretely can you also measure whether you have achieved an acceptable result. This also includes choosing goals that are realistically achievable. There is no point in defining utopian successes that cannot be achieved within a reasonable timeframe and with justifiable effort anyway.

After planning, the implementation phase begins. The team or the individual now realizes what they planned in the first phase. It is best to proceed in small steps and question the implementation again and again. In this way, you can ensure that you do not lose control during implementation and stick to the plan. In practice, it has also proven successful to test the change process only on a small scale – e.g. first on a product, not on the complete product range, or only in one department and not in the entire company.

Therefore, this second step can also be regarded as a test phase . You use this time to gather knowledge: Just because you’ve planned something through properly doesn’t mean it will work in practice. The experiences you gain in the do phase directly initiate the third phase.

During the review, the collected results are compared with the objectives set. You look critically at what worked and what went differently than expected. It is important to look objectively at the plan and its implementation. It doesn’t help the improvement process to gloss over results in order not to endanger your own strategy. Problems in the do phase are not to be seen as setbacks, but as opportunities to learn from them – because that is what this phase is meant for.

In the check phase, the results are not only summarized , but also analyzed : Why didn’t everything go according to plan? Once you have found out how the problems came about, you can change the plan accordingly and achieve better results next time.

Now that the problems are known and the causes have been identified, the plan can be adapted and finally implemented . While the do phase was a test run and carried out on a small scale, the fourth step comprises the overall picture. Depending on the framework in which you use the PDCA cycle, you extend the application.

Once the transformation is completed, the new state is considered standard. You should not let the quality standard deteriorate. Therefore, you need to install a form of control . You can always question yourself and make sure that you don’t fall back into old patterns; someone else – a mentor, a supervisor etc. – can also take over this control function. It is important for the further development that you don’t step back again. PDCA begins again in the new state.

Let’s take a furniture factory as an example. The management wants to increase the output of cabinets. They notice that most of the cupboard is finished quickly, but they regularly wait for their round feet. At this point, a PDCA cycle should help.

During the planning phase (plan) , you notice that the lathe used to produce round objects is prone to errors. Often the excess has to be disposed of, which not only slows down the production chain, but also leads to unnecessary additional expenditure. So they are planning to buy a more modern machine. Instead of directly replacing all the relevant machines, you start with just one to test the success.

In the second step (do), the new machine is tested in practice. The work completed with the new machine is checked for one month. At the same time, however, the older machines continue to run. This gives those responsible the advantage that they can now see exactly whether the investment in the new machine is worthwhile.

One notices that although the production error has been contained, the production speed has hardly increased at all. In the third phase (check) , this problem is analyzed and it is recognized that the employees are so used to the old machine that they still have difficulties using the new one just as efficiently.

Therefore, the plan will now be amended and then fully implemented in the final phase (act) : All machines are now replaced and at the same time, the employees receive detailed instruction on the new equipment. As a result, production of the cabinets is significantly accelerated and scrap is minimized. The company now accepts the new production speed as standard.

PDCA is a wonderful tool for introducing improvements in a sustainable and thoughtful way. Instead of changing the usual procedures with a spontaneous hair-jerk procedure, one proceeds with small steps and always under close observations. However, this is also one of the big disadvantages of the Deming cycle: You have to plan enough time for the model. PDCA does not allow for rapid problem solving.

Are you interested in further methods to increase your productivity? Then take a look at the detailed articles on the models Kanban and Scrum . However, good project management helps you to be more successful in your company.

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Plan do check act examples

How to apply the Plan-Do-Check-Act (PDCA) model to improve your business

Reading time: about 7 min

  • Professional development
  • Project management

Most businesses want to improve. But when it comes to actually making needed changes, many fall short. Bureaucracy, silos, and even culture can block progress and stall innovation.

The Plan-Do-Check-Act model helps break companies out of stagnancy and transition to a system of continuous improvement. Learn how the PDCA cycle works and what benefits you can gain from using it at your company.

plan-do-check-act example

What is PDCA?

The Plan-Do-Check-Act (PDCA) model, also known as the Deming wheel or the Deming cycle, is an iterative method for continual improvement of processes, products, or services and is a key element of lean management.

The PDCA model was developed in the 1950s by William Deming as a learning or improvement process based on the scientific method of problem-solving. Deming himself called it by another term—the Shewhart cycle—because he created the model based on an idea from his mentor, Walter Shewhart.

As all of these names suggest, the PDCA cycle is a loop rather than an end-to-end process. The goal is to improve on each improvement in an ongoing process of learning and growth.

When should you use the PDCA process?

The Plan-Do-Check-Act model is a helpful tool that can be used for a number of applications:

  • Exploring and testing multiple solutions in a small, controlled trial
  • Avoiding waste by catching and adapting ineffective solutions before rolling them out on a large scale
  • Implementing Total Quality Management or Six Sigma initiatives
  • Developing or improving a process

What is great about the PDCA cycle is that it can be applied across industries and organizational types.

Pros and cons of PDCA

The PDCA cycle has a number of advantages and disadvantages. Consider both before you decide to apply Plan-Do-Check-Act to different projects.

Versatile: You can use PDCA in a variety of business environments and for a number of applications. Potential use cases include project management, change management, product development, and resource management.

Simple and powerful: The PDCA model is simple and easy to understand, yet it is a powerful driver for meaningful change and improvement while minimizing waste and increasing efficiency.

Hard to do: Though the model is simple, the work isn’t easy. Because PDCA breaks process improvements into smaller steps, it can be slow and probably isn’t a great solution for urgent projects.

Requires commitment: PDCA is not a one-time event. It is an ongoing, continuous process and therefore requires commitment and buy-in from the top down. Without committed leadership, the PDCA cycle can’t work effectively for the long term.

The PDCA model

Sold? Learn the four stages in the PDCA cycle (which you can probably guess from the name) to start using it.

The planning stage is for mapping out what you are going to do to try to solve a problem or otherwise change a process. During this step, you will identify and analyze the problem or opportunity for change, develop hypotheses for what the underlying issues or causes are, and decide on one hypothesis to test first.

As you plan, consider the following questions:

  • What is the core problem we need to solve?
  • Is this the right problem to work on?
  • What information do we need to fully understand the problem and its root cause?
  • Is it feasible to solve it?
  • What resources do we need?
  • What resources do we have?
  • What are some viable solutions?
  • What are the measures of success?
  • How will the results from a small trial translate to a full-scale implementation?

During this stage, an affinity diagram can help you and your colleagues organize a large number of ideas into groups. Once you have determined your course of action, write down your expected results. You will check your results against your hypothesis and expectations in the “Check” stage.

super header affinity diagram example

The next step is to test your hypothesis (i.e., your proposed solution). The PDCA cycle focuses on smaller, incremental changes that help improve processes with minimal disruption.

Test your hypothesis with a small-scale project, preferably in a controlled environment, so you can evaluate the results without interrupting the rest of your operation. You might want to test the solution on one team or within a certain demographic.

Once you have completed your trial, it’s time to review and analyze the results. This stage is important because it allows you to evaluate your solution and revise your plans as necessary. Did the plan actually work? If so, were there any hiccups in the process? What steps could be improved or need to be eliminated from future iterations?

Your evaluation at this stage will guide your decisions in the next step, so it is important to consider your results carefully.

Finally, it is time to act. If all went according to plan, you can now implement your tried-and-tested plan. This new process now becomes your baseline for future PDCA iterations.

Consider the following questions before you act:

  • What resources do you need to implement the solution at full scale?
  • What training is needed for successful implementation and adoption?
  • How can you measure and track the performance of the solution?
  • What opportunities are there for improvement?
  • What have we learned that can be applied to other projects?

If the plan did not pan out as expected, you can cycle back to the planning stage to make adjustments and prepare for a new trial.

Plan-Do-Check-Act example

So what does the PDCA model look like in action? 

In 2019, the Department of Obstetrics and Gynecology at the Ningbo Women and Children’s Hospital in China applied the Plan-Do-Check-Act model to shorten the emergency decision to delivery interval (DDI) time. This is the time it takes between the decision to conduct a caesarean section and the delivery of a newborn. Shortening this time period in emergency situations is critical to saving lives and improving patient outcomes. 

Here’s how they did it:

Plan: In 2019, the hospital had an average DDI time of 14.40 minutes. Their process analysis identified three main causes impacting DDI time: 

  • A defective process
  • Lack of first-aid experience
  • Poor cooperation among departments

Do: The team developed improvement measures for each cause including: 

  • Simplifying the surgical process to speed up the pre-op routine
  • Establishing a special DDI team to respond to emergency situations
  • Standardizing DDI team working processes
  • Creating an emergency treatment team, featuring senior doctors with clinical first-aid experience
  • Implementing a variety of regular training, such as obstetrical safety meetings, emergency C-section process classes, and practical and theoretical trainings
  • Conducting multi-department emergency treatment drills

Check: The hospital monitored and analyzed progress monthly, creating regular evaluation summaries and refining the cause analysis and improvement measures over time. 

Act: After refining their processes, the hospital’s improvements optimized C-section delivery processes, increased collaboration across departments, and shortened the average emergency DDI to 12.18 minutes in 2020.

Supporting Kaizen with the PDCA cycle

The Plan-Do-Check-Act model is a particularly useful tool for companies who follow the Kaizen method . Kaizen is an organizational mindset and culture focused on small, frequent changes that lead to significant improvements over time.

The PDCA cycle supports the Kaizen philosophy by providing the framework for developing and implementing continuous improvements.

Using Lucidchart to continuously improve

Lucidchart is a visual workspace that helps teams and companies map out their processes and visualize their data in new ways. Use Lucidchart to visualize your PDCA cycle and help you through each step of the process. As you plan your project, you can create a fishbone (cause-and-effect) diagram to visualize problems and potential causes.

fishbone diagram example

During the “Do” stage, map out the new processes you plan to try. Use data linking to connect real-time data to your diagrams and keep track of your results. Once you have a solution you’re ready to implement, use Lucidchart to create diagrams visualizing the new processes. Lucidchart makes it easy to share documents with your team so they can quickly learn and understand the changes.

flowchart with swimlanes

If you’re continuously improving, it can be easy to lose track of your changes over time. Keep everyone on the same page by documenting your continual progress in Lucidchart.

Plan do check act examples

Learn more about how to elevate your business.

Lucidchart, a cloud-based intelligent diagramming application, is a core component of Lucid Software's Visual Collaboration Suite. This intuitive, cloud-based solution empowers teams to collaborate in real-time to build flowcharts, mockups, UML diagrams, customer journey maps, and more. Lucidchart propels teams forward to build the future faster. Lucid is proud to serve top businesses around the world, including customers such as Google, GE, and NBC Universal, and 99% of the Fortune 500. Lucid partners with industry leaders, including Google, Atlassian, and Microsoft. Since its founding, Lucid has received numerous awards for its products, business, and workplace culture. For more information, visit lucidchart.com.

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PDCA Cycle: The Plan-do-check-act Cycle In A Nutshell

The PDCA (Plan-Do-Check-Act) cycle was first proposed by American physicist and engineer Walter A. Shewhart in the 1920s. The PDCA cycle is a continuous process and product improvement method and an essential component of the lean manufacturing philosophy.

Table of Contents

Understanding the PDCA cycle

It was later popularised by fellow engineer and statistician W. Edwards Deming, widely attributed as the father of modern quality control.

Deming called it the Shewhart Cycle after his mentor and applied its principles to improving production processes during the Second World War. 

Today, the PDCA cycle is useful in any industry or setting in multiple contexts. These include:

  • Continuous improvement or the establishment of a new improvement project.
  • Developing or refining the design of a product, service, or process.
  • Clarifying a repetitive work process.
  • Data collection and analysis to verify or identify problems or root causes.
  • Change implementation.

The four components of the PDCA cycle

The PDCA cycle is an iterative, systematic, four-stage approach.

Following is a look at each stage:

Plan (P) – in the first stage, a plan is created from a recognized opportunity for improvement or change

Here, decision-makers need to clarify core problems by developing hypotheses for each.

They must also determine what resources they have and what they are lacking.

In other words, is the initiative feasible? Could it be scaled? Lastly, goals must be established – under what circumstances would the initiative be considered successful?

Do (D) – then, it is time to develop, implement, and test the solution

Unforeseen problems may occur during implementation, so it is useful to start small and in a controlled environment.

Before work is carried out, standardization of roles, responsibilities, and methods must also be established.

Check (C) – arguably the most important stage. Do the test results accept or reject the hypotheses?

Furthermore, do the tests support initiative or project objectives? Even successful tests may have problems or inefficiencies that offer room for improvement.

Consult a variety of relevant stakeholders to encourage diverse opinions.

Act (A) – in the final stage, a refined initiative is implemented and becomes the new baseline for any future PDCA cycle

Required resources and employee training should be quantified for organization-wide scaling.

Metrics that measure and track the performance of the initiative overtime should also be clarified. Failed initiatives move back to the first stage and are adjusted to prepare for a new cycle.

Advantages of the PDCA cycle

Versatility.

As noted earlier, the PDCA cycle can be used wherever change or continuous improvement is required.

Applications are possible in change management, product development, project management, and quality improvement.

The Mayo Clinic used quality improvement to reduce wait times for candidates qualifying for cochlear implant surgery.

Using the cycle, the hospital and research center, it was able to reduce the median cycle time for testing from 7.3 to 3 hours.

Intuitiveness

The PDCA cycle is also relatively simple to understand and implement.

This reduces inefficiencies arising from misunderstandings or misuse and facilitates buy-in from key stakeholders.

Disadvantages of the PDCA Cycle

Requires commitment.

The PDCA cycle is not something a business can perform once and then file away in a cabinet.

This continuous and cyclical process requires commitment which must be demonstrated from senior management to permeate down through the organization.

The PDCA cycle is an effective but rather time-consuming process.

Some businesses will see its effectiveness as a major advantage, but it is nevertheless unsuitable for urgent problems, emergencies, or other initiatives requiring speedier resolution.

The cycle is also somewhat reactive since it assumes everything starts with planning.

The basic philosophy of PDCA is planning and performing an activity first and responding to drawbacks later.

This approach of correcting (and not pre-empting) mistakes discourage innovation , dynamism, and creativity.

Ultimately, this makes it unsuitable for many modern business environments that demand proactive thinking.

When to Use the PDCA Cycle

Ideal use cases:.

  • Continuous Improvement Projects: Particularly useful in quality management and continuous improvement initiatives where incremental changes are implemented over time.
  • Well-Defined Problems: Effective for addressing well-defined, straightforward problems where the goals and methods are clear.

Strategic Application:

  • Process Optimization: Can be strategically applied to optimize and standardize processes in manufacturing, operations, and service delivery.
  • Quality Control: Useful in scenarios requiring rigorous quality control and systematic documentation of changes and results.

How to Use the PDCA Cycle

Implementing the methodology:.

  • Plan: Identify a problem or opportunity for improvement, and develop a plan to address it, including setting objectives and deciding on actions.
  • Do: Implement the plan on a small scale to test its effectiveness.
  • Check: Assess the results of the test implementation, comparing them against the expected outcomes to identify any discrepancies.
  • Act: Based on the assessment, make adjustments to the plan and either standardize the solution if successful or return to the planning stage for further refinement.

Best Practices:

  • Iterative Approach: Embrace the iterative nature of PDCA, understanding that multiple cycles may be needed to achieve the desired results.
  • Data-Driven Decisions: Base decisions on data and factual information gathered during the Check phase.
  • Flexibility and Adaptation: Be prepared to adapt the plan based on findings during the cycle, maintaining flexibility in the approach.

What to Expect from Implementing the PDCA Cycle

Enhanced problem-solving and quality:.

  • Improved Processes and Quality: PDCA facilitates systematic problem-solving that can lead to improved processes and product quality.
  • Structured Improvement Approach: Provides a structured approach to continuous improvement, making processes more efficient over time.

Organizational Impact:

  • Cultural Shift Towards Continuous Improvement: Can help foster a culture of continuous improvement and learning within an organization.
  • Better Documentation and Standardization: Encourages thorough documentation and standardization of best practices.

Potential Challenges:

  • Need for Patience and Persistence: The iterative nature of PDCA requires patience and persistence, as immediate results may not always be evident.
  • Balancing Structure with Flexibility: It’s important to balance the structured approach of PDCA with the need for flexibility and adaptability to new information or changing circumstances.

In summary, the PDCA Cycle is a valuable tool for systematic problem-solving and continuous improvement, particularly effective in structured environments where incremental progress is essential.

While it offers a disciplined approach to tackling problems and enhancing quality, it may not be as suitable for highly dynamic or complex issues that require rapid responses or creative solutions.

Successful implementation of PDCA involves a commitment to iterative learning and improvement, data-driven decision-making, and the flexibility to adapt plans based on evolving insights and results.

PDCA cycle examples

Here are a few ways the PDCA cycle could be used in a real-world setting.

Health care establishment

Consider the example of a hospital that forms a team to improve patient care and outcomes.

Once the task ahead of them is properly understood, the team expects to use the PDCA cycle to improve patient feedback scores by 55%.

To achieve this in practice, the team identifies various contributing factors such as the hospital air filtration system, nurse training, visiting hours, and access to facilities.

Members decide that nurse training is the factor most likely to influence patient care in the hospital.

With this in mind, the PDCA team implements a revised nurse development program and tests its efficacy on new recruits.

In the months after implementation, the team routinely evaluates the impact of the new program by collecting patient feedback and comparing it to the stated improvement level of 55%.

At some point, the new influx of nurses and re-training of existing staff help the hospital achieve its objective.

Moving forward, the hospital plans to introduce the initiative to other departments with periodic reviews to ensure it remains successful.

Hiring agency

Now imagine a hiring agency whose primary function is to review job applications and schedule interviews for eligible candidates.

After six months in operation, the hiring agency realizes that candidates who are penciled in for an interview often find jobs with other providers beforehand.

Since the viability of the hiring agency relies on providing talent or labor for its clients, a team uses the PDCA cycle to make the process more efficient.

Understanding that reviewing applications takes longer than it should, the HR team proposes that a new administrator position be created.

This individual would be tasked with filtering applications or establishing an applicant tracking system (ATS).

Both options are tested with a team member playing the part of an HR administrator and ATS user, with the new system ultimately determined to be the more salient choice.

The hiring agency then monitors and refines this system to reduce wait times and ensure that candidates are more likely to choose one of its own clients as an employer. 

Bricks-and-mortar retail

In the final example, a retailer wants to open a new fashion store but is unsure of which product lines are best suited to its customers.

Using the PDCA cycle, the retailer decides to introduce three new products every month.

At the end of this month, they assesses sales data to determine which products sold best. This process is repeated for six months with the best performing lines incorporated into store-only promotions. 

Sales, customer preferences, and any other added benefits are quantified every month to ensure introduced products continue to be successful.

In the “Act” stage of the PDCA cycle, the retailer decides which product lines it will sell permanently and enters into talks with suppliers to establish an ongoing relationship.

Case Studies

  • Reducing Defects : A manufacturing company uses PDCA to continually reduce defects in its production process.
  • Cycle Time Reduction : A factory implements PDCA to decrease cycle times for production steps.
  • Patient Safety : A hospital employs PDCA to enhance patient safety by addressing medication errors.
  • Emergency Response : An ambulance service uses PDCA to improve response times and emergency protocols.
  • Curriculum Enhancement : A school applies PDCA to regularly review and update its curriculum to meet educational goals.
  • Teacher Training : An educational institution uses PDCA for ongoing teacher training and development programs.
  • Software Testing : A software development team employs PDCA to refine its testing processes for software quality improvement.
  • Bug Resolution : A tech company uses PDCA to prioritize and resolve software bugs efficiently.
  • Inventory Optimization : A logistics company applies PDCA to optimize inventory levels for cost reduction.
  • Supplier Performance : A manufacturer uses PDCA to assess and improve supplier performance.
  • Complaint Resolution : A customer support center utilizes PDCA to enhance complaint resolution processes.
  • Call Center Efficiency : A call center applies PDCA to improve call handling times and customer satisfaction.
  • Waste Reduction : A company uses PDCA to reduce waste generation and improve sustainability.
  • Energy Efficiency : An organization applies PDCA to optimize energy consumption in its facilities.
  • Project Planning : A project management team uses PDCA for effective project planning, including scope, timeline, and resource allocation.
  • Risk Mitigation : PDCA is employed to identify and mitigate risks throughout a project’s lifecycle.
  • Sales Strategy : A sales team utilizes PDCA to refine its sales strategies and tactics for increased revenue.
  • Marketing Campaigns : A marketing department applies PDCA to assess and enhance the effectiveness of marketing campaigns.
  • Quality Control : A quality control department uses PDCA to maintain and improve product quality.
  • ISO Certification : An organization employs PDCA to achieve and maintain ISO certification standards.
  • Menu Enhancement : A restaurant uses PDCA to regularly update its menu based on customer preferences and feedback.
  • Kitchen Efficiency : A fast-food chain applies PDCA to streamline kitchen operations for faster service.
  • Expense Reduction : A financial team employs PDCA to identify and reduce unnecessary expenses.
  • Investment Strategy : An investment firm uses PDCA for ongoing portfolio optimization.
  • Accident Prevention : A construction company applies PDCA to prevent workplace accidents and improve safety protocols.
  • Health and Wellness Programs : An HR department uses PDCA to assess and refine employee wellness programs.
  • Stock Rotation : A retail store uses PDCA to manage inventory turnover and reduce stock wastage.
  • Store Layout : PDCA is employed to optimize store layouts for better customer flow and product visibility.
  • Policy Evaluation : Government agencies use PDCA to assess the effectiveness of public policies and make necessary adjustments.
  • City Planning : PDCA is applied to urban planning to enhance city infrastructure and services.

Key takeaways

  • The PDCA cycle is an iterative, four-step problem-solving and continuous improvement methodology developed by Walter A. Shewhart in the 1920s. It was later refined by the father of modern quality control, W. Edwards Deming.
  • The PDCA cycle is an acronym of four distinct stages: plan, do, check, and act. Collectively, the four stages form a cyclical process where initiatives are planned, tested, evaluated, and refined if necessary.
  • The PDCA cycle is a versatile process useful in any scenario requiring change or improvement. However, it is an exhaustive process and requires a display of commitment from upper management. In some cases, it may also be reactive and discourage out-of-the-box thinking.

Key Highlights

  • Origins and Evolution: Proposed by Walter A. Shewhart in the 1920s and popularized by W. Edwards Deming, the PDCA cycle is a continuous improvement method applicable across industries and contexts.
  • Versatility: PDCA is useful in various scenarios such as continuous improvement projects, product design , process refinement, data analysis , change implementation, and more.
  • Plan (P): Identify opportunities, set goals, allocate resources, and create a plan.
  • Do (D): Execute the plan on a small scale, implement changes, and gather data.
  • Check (C): Evaluate outcomes, compare results to goals, and analyze data.
  • Act (A): Implement refined changes, standardize processes, and prepare for the next cycle.
  • Adaptable and applicable to diverse situations.
  • Emphasizes empirical evidence and continuous improvement.
  • Encourages collaboration and engagement among stakeholders.
  • Requires commitment to continuous cycles.
  • Time-consuming and may not be suitable for urgent issues.
  • Can be somewhat reactive and limit proactive innovation .
  • Healthcare: Improving patient care scores through nurse training.
  • Hiring Agency: Enhancing application review and candidate scheduling.
  • Retail: Optimizing product selection and sales in a fashion store.
  • Iterative Approach: The PDCA cycle is an iterative and cyclical process, making it suitable for ongoing improvement efforts and problem-solving.
  • Rooted in Quality Management: Developed within the quality management context, PDCA emphasizes evidence-based decision-making and continuous learning.
  • Simple and Understandable: The straightforward PDCA framework is easy to comprehend, implement, and communicate among stakeholders.
  • Commitment and Leadership: Successful application of PDCA requires commitment from senior management and alignment with the organization’s goals and culture.
  • Continuous Improvement: PDCA is aligned with the principles of continuous improvement, allowing organizations to refine processes and achieve better outcomes over time.
  • Applicability: PDCA can be used across industries, including manufacturing, healthcare, services, and technology, making it a versatile approach for improvement initiatives.

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  • What is the Plan-Do-Check-Act (PDCA) cy ...

What is the Plan-Do-Check-Act (PDCA) cycle?

Julia Martins contributor headshot

The PDCA cycle is a useful technique for addressing, analyzing, and solving business problems. Because the PDCA cycle is built on the process of continuous improvement, it offers a level of flexibility and iterative improvement. 

PDCA cycle history

The PDCA cycle was first introduced by Walter Shewhart , the father of statistical quality control. In his book, Economic control of quality manufactured product , Shewhart applied the scientific method to economic quality control. 

Shewhart’s thesis was further developed by W. Edwards Deming , who championed Shewhart’s work. Deming expanded on Shewhart’s idea and used the scientific method not only for quality control but also process improvement.

Deming went on to teach the method—which he called the Shewhart cycle—to Japanese engineers. There, the Shewhart cycle mixed with kaizen (the Japanese principle of continuous improvement , which was developed by Kaoru Ishikawa ), the Toyota production system, and lean manufacturing to become what we now call the Plan-Do-Check-Act (PDCA) cycle. 

Nowadays, the Plan-Do-Check-Act cycle is commonly used as part of lean project management .

This methodology has many names, including:

Plan-Do-Check-Act cycle, or PDCA cycle

Deming cycle or Deming wheel

Shewhart cycle

Control cycle

Plan-Do-Study-Act cycle or PDSA cycle 

When should you use the PDCA cycle?

The PDCA cycle is a framework for how to approach and resolve project management and process improvement problems. As a result, it can be implemented for a wide variety of projects. Teams that use the PDCA cycle effectively embrace the element of continuous improvement—rather than using the cycle for an end-to-end process, the PDCA cycle is a way to ensure continuous improvement and implement the iterative process . 

The Plan-Do-Check-Act cycle is particularly useful when you want to:

Streamline and improve a repetitive work process

Develop a new business process

Get started with continuous improvement

Rapidly iterate on change and see immediate results

Minimize errors and maximize outcomes

Test multiple solutions quickly

4 steps to use the PDCA cycle

The four steps of the PDCA process are in the name: planning, doing, checking, and actioning. Notably, this process is a cycle, so as soon as you reach the end, you can start over from the beginning again. 

The first step to any process improvement or project planning is to figure out what you need to do. Like any project plan , this includes a variety of information, including:

The project objectives

Success metrics

The project deliverables or end result

Project stakeholders

The project timeline

Any relevant project risks or constraints

You can use the PDCA cycle for a wide variety of projects. Whether you’re building a new project from scratch or using the PDCA as a quality improvement project, investing in a robust planning phase is a great way to set the project on the right track. 

Keep in mind that PDCA is a cycle. It’s okay if you don’t have all of the answers the first time around, since you’ll probably run this cycle multiple times. Each time you re-run the PDCA cycle, evaluate your project plan to ensure it’s up-to-date and accurate towards your project goals. 

Once you’ve ironed out your project plan, the next step is to try it out. Like most types of lean project management, PDCA embraces small, incremental changes. During the Do phase of the PDCA cycle, implement the project plan on a small scale to ensure it works. 

Review the test you ran during the Do phase of the PDCA cycle to ensure everything went according to plan. More likely than not, you will identify things to improve on during the Do phase. After all, it isn’t called continuous improvement for nothing! The Check phase is critical to finding these small things before they get too big and problematic. 

If necessary, revisit your project plan to ensure your project is still hitting your project objectives. Alternatively, if you realized you need to make a change to the project plan, you can also do so now. 

After the check, move to the Act phase, which includes rolling out the full project or process improvement. Don’t forget that the PDCA cycle is a cycle. If you need to, return to the Plan phase to continuously improve your project or processes. 

Pros and cons of the Plan-Do-Check-Act cycle

The PDCA cycle is a powerful tool to continuously improve, but there are also some disadvantages to using this system as well. Take a look at the pros and cons of the PDCA cycle: 

Helpful for teams looking to get started with continuous improvement

Flexible methodology for virtually any project

Quickly implement change and see results

Use the PDCA as your standard operating procedure to increase org-wide standardization without the use of a project management office (PMO)

Proven continuous improvement methodology

You need support from senior management in order for the PDCA cycle to be particularly effective

Value comes from running the cycle over and over again. Not an effective methodology if you only plan on doing it once. 

Requires time to implement and learn

Isn’t a great solution for urgent projects, since you typically expect to run the cycle multiple times

Planned-Done-Checked-Actioned

The PDCA cycle is an effective way to implement continuous improvement and problem solving. To get the most out of the PDCA cycle, set your projects up for success with project planning tools . Plan, manage, and track your team’s projects to hit your deliverables on time. 

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The problem with Plan-Do-Study-Act cycles

Julie e reed.

1 NIHR CLAHRC NWL, Imperial College London, London, UK

Alan J Card

2 Department of Management, University of Notre Dame, Notre Dame, Indiana, USA

3 Evidence-Based Health Solutions, LLC, Notre Dame, Indiana, USA

Introduction

Quality improvement (QI) methods have been introduced to healthcare to support the delivery of care that is safe, timely, effective, efficient, equitable and cost effective. Of the many QI tools and methods, the Plan-Do-Study-Act (PDSA) cycle is one of the few that focuses on the crux of change, the translation of ideas and intentions into action. As such, the PDSA cycle and the concept of iterative tests of change are central to many QI approaches, including the model for improvement, 1 lean, 2 six sigma 3 and total quality management. 4

PDSA provides a structured experimental learning approach to testing changes. Previously, concerns have been raised regarding the fidelity of application of PDSA method, which may undermine learning efforts, 5 the complexity of its use in practice 5 6 and as to the appropriateness of the PDSA method to address the significant challenges of healthcare improvement. 7

This article presents our reflections on the full potential of using PDSA in healthcare, but in doing so we explore the inherent complexity and multiple challenges of executing PDSA well. Ultimately, we argue that the problem with PDSA is the oversimplification of the method as it has been translated into healthcare and the failure to invest in a rigorous and tailored application of the approach.

The value of PDSA in healthcare improvement

The purpose of the PDSA method lies in learning as quickly as possible whether an intervention works in a particular setting and to making adjustments accordingly to increase the chances of delivering and sustaining the desired improvement. In contrast to controlled trials, PDSAs allow new learning to be built in to this experimental process. If problems are identified with the original plan, then the theory can be revised to build on this learning and a subsequent experiment conducted to see if it has resolved the problem, and to identify if any further problems also need to be addressed. In the complex social systems of healthcare, this flexibility and adaptability of PDSA are important features that support the adaption of interventions to work in local settings.

A successful PDSA process does not equal a successful QI project or programme. The intended output of PDSA is learning and informed action. Successful application of the PDSA methodology may enable users to achieve their QI goals more efficiently or to reach QI goals they would otherwise not have achieved. But it is also successful if it saves wasted effort by revealing QI goals that cannot be achieved under realistic constraints or if it identifies new problems to tackle instead of the originally identified issue. A well-conducted PDSA promises learning. But it does not, and cannot, promise that users will achieve their desired outcomes.

As PDSA has been translated into healthcare from industrial settings, an emphasis has been placed on rapid small-scale tests of change, often on one, three and then five patients in ‘ramps’ of increasing scale, and responsibility delegated to frontline staff and improvement or quality managers. This pragmatic approach has been embraced and has been seen as providing a new freedom for healthcare staff to lead change and improvement in local care settings.

However, the process of change rarely progresses in simple linear ramps. 6 8 The conduct of PDSAs can reveal other related issues that need to be addressed in order to achieve the improvement goal. Such issues may relate to minor changes to current practices or processes of care, but can often reveal larger cultural or organisational issues that need to be addressed and overcome.

Recent evaluations have reported on the failure of the PDSA method to help frontline staff address the multiple improvement challenges they faced as the scale of investigation and range of issues they needed to address increased. 7 9 A report evaluating the Safer Clinical Systems programme in the UK identified ‘the need for clarity about when improvement approaches based on PDSA cycles are appropriate and when they are not’, viewing some challenges as ‘too big and hairy’ for the PDSA method and beyond the scope of small-scale tests of change run by local clinical teams. 7

We argue that any improvement situation, no matter how big and hairy, is conducive to application of the PDSA method. The four stages of PDSA mirror the scientific experimental method of formulating a hypothesis, collecting data to test this hypothesis, analysing and interpreting the results and making inferences to iterate the hypothesis. 5 10

Whether improvement initiatives have been planned at national level to support standardisation of care or planned over a cup of coffee to solve a minor local problem, we believe there will always be a role for PDSA. In moving from planning to implementing a change in practice, PDSA provides a structure for experimental learning to know whether a change has worked or not, and to learn and act upon any new information as a result.

But it is not a magic bullet. Increasingly complex problems require increasingly sophisticated application of the PDSA method, and this is where we believe the problem with the PDSA method lies.

Its simplicity belies its sophistication

One of the main narratives surrounding the use of PDSA in healthcare is that it is easy, and can be applied in practice by anyone. At one level this is true, and the simplicity of the PDSA method and its applicability to many different situations can be viewed as one of its main strengths. However, this simplicity also creates some of the greatest challenges to using PDSA successfully. Users need to understand how to adapt the use of PDSA to address different problems and different stages in the lifecycle of each improvement project. This requires an extensive repertoire of skills and knowledge to be used in conjunction with the basic PDSA model.

One of the main problems encountered in using PDSA is the misperception that it can be used as a standalone method. PDSA needs to be used as part of a suite of QI methods, the exact nature of which may be influenced by the broader methodological approach that is being followed (eg, model for improvement, lean). An important role of the wider methodological approach is to conduct investigations prior to starting the use of PDSA to ensure that the problem is correctly understood and framed. Investigations can include process mapping, failure mode effects analysis, cause and effect analysis, stakeholder engagement and interviews, data analysis and review of existing evidence.

A second misperception is that the PDSA is limited to small-scale tests of change on one, three and five patients. PDSA is an extremely flexible method that can be adapted to support the scale up of interventions and used in conjunction with monitoring activities to support sustainability. But, this flexibility gives rise to a number of key dimensions that require careful consideration. For instance, the scope and scale of change, the amount of preparation prior to use, rigour of the evaluation, time, expertise, management support and funding must be carefully aligned. Often these needs must be rebalanced over the project's lifecycle. If managed well, these adjustments enable the use of PDSA to adapt to new learning and support the design and conduct of ‘tests of change’ as they increase in scale, and often complexity, to achieve the desired improvement goal.

Using PDSA as an iterative design framework to help solve ‘big hairy problems’ or ‘big hairy audacious goals’ 11 is, therefore, entirely appropriate. In fact, developing solutions to large-scale ‘wicked problems’ 12 may require ‘an iterative explorative and generative’ 13 approach of the sort PDSA provides, in which ‘knowledge is built through designing’. 13 The key is to understand that this framework will need to be implemented (and resourced) very differently for large and complex problems than for smaller and more ‘tame’ problems. One size does not fit all.

While frontline staff with little training or support may successfully address some quality problems, the complexity of many problems demands greater organisational support, with direct involvement of senior managers to facilitate adequate planning. Projects in which frontline staff must fend for themselves also run the risk of insufficient usage of theory and existing evidence to develop the intervention and a suboptimal evaluation.

Quick (not dirty) tests of change

In healthcare, PDSA training often overemphasises the conceptual simplicity of the framework and underemphasises the different ways in which the method can be adapted to solve increasingly complex problems. This frequently leads people to leap into PDSA with insufficient prior investigation and framing of the problem, to delegate management of the process to frontline staff who have little influence over broader systemic concerns that need to be addressed, and to provide these staff with little support to overcome the obstacles and barriers they face. The resources, skills and expertise required to apply PDSA in the real world are often significantly underestimated, leading to projects that are destined to fail.

This has led to the impression that PDSA cycles involve ‘quick and dirty’ tests of change. In the rush to empower healthcare staff, there is a danger that the scientific rigour of the PDSA method is frequently compromised. A systematic review 5 revealed that the core principles of PDSA are often not executed in practice, with ‘substantial variability with which they are designed, executed and reported in the healthcare literature’. 6 A failure to properly execute PDSAs can undermine learning efforts… ‘if data collection does not occur frequently enough, if iterative cycles are few, and if system-level changes are not apparent as a result of these cycles, the improvement work is less likely to succeed’. 6 While its scientific principles differ from those of controlled trials, rigour in the application of PDSA is still required for PDSA to maximise the learning obtained from tests of change.

In addition to a lack of fidelity with PDSA guiding principles, there is the need to ensure that each stage of the cycle is conducted well. But the frenetic culture endemic in healthcare organisations can make it difficult to achieve sustained engagement in the deliberative processes of PDSA.

Just get on with it

While ‘planning paralysis’ can be an issue in healthcare organisations, the more common problem is a serious underinvestment in the planning phase. The pervasive cultural compulsion to ‘just get on with it’ 14 leads many teams to move too quickly from ‘plan’ to ‘do.’ The consequences of skipping this up-front work can include wasted PDSA cycles or projects that fail altogether. Table 1 describes some of the key failure modes for the planning and preplanning (ie, investigation and problem-framing) steps of the PDSA process.

Table 1

Key failure modes for the investigation/problem framing and plan steps

PDSA, Plan-Do-Study-Act.

Why do planning failures present such a challenge to the successful use of PDSA? It is much more difficult to correctly execute and learn from a plan that has not been well thought out. And even perfect execution cannot ensure success if the plan, itself, is wrong.

The iterative nature of PDSA enables course corrections, but this feature of the approach is much more effective if there was a clear and reasoned course in the first place. Many of the barriers to success in the do, study and act phases can be predicted and mitigated through more effective planning.

Overcoming the prevailing culture of ‘Do, Do, Do’

The structured, reflective practice required for PDSA runs counter to the main mode of operation in healthcare organisations, ‘doing’, with the time required for planning and reflection regarded as a luxury rather than a necessity. As a result, teams often get ‘stuck’ in the ‘do’ phase, failing to progress to the ‘study’ phase. While these problems may reflect poor planning, they may also be caused by problems beyond the control of the project team, such as the challenges of creating time to conduct tests of change, staff turnover and changing or competing priorities. To stop at the ‘do’ phase is to throw away the core contribution of PDSA: its support for iterative design as a way of making improvement interventions more successful. 15 Another important but frequently overlooked part of the ‘do’ phase is inductive learning, noticing the unexpected and feeding these observations into the study phase.

Poor planning or conduct of the ‘do’ phase in turn can significantly undermine the ‘study’ phase. In some cases, improvement teams appear to bypass the ‘study’ phase altogether, moving directly from ‘do’ to ‘act’. 5 In other cases, the ‘study’ phase may collect insufficient data or may not collect the right type of data to answer questions about the intervention's effectiveness and acceptability. For instance, quantitative data can assess the impact of a given change, without qualitative feedback; the reasons for the results or staff attitudes and ideas about what could be improved will remain unknown. It is also possible that teams draw the wrong conclusions from the data they have collected or fail to notice unanticipated consequences, which may lead to incorrect actions.

Failure to take appropriate action based on what was learned from the ‘study’ phase and previous PDSA cycles is another common concern. 5 Inappropriate actions may include adopting or scaling up an intervention that has not proven effective and acceptable, 16 or ending a project that has proved successful, or is on track to do so. An important part of the act phase consists of reviewing and revising the theory of how the intervention is intended to achieve its desired impact. This iterative refinement of theory is a key component of PDSA methodology, which is often overlooked in practice.

Effectively managing the PDSA process is about more than individual PDSA steps or cycles. Connecting PDSA cycles together is a messier and far more complicated endeavour than most of the literature on the approach suggests. 6 Progression across cycles is seldom linear, and double-loop learning 17 may lead to revised goals, as well as revised interventions, and requires significant oversight to manage emergent learning and coordination of PDSA activities over time.

Table 2 describes some of the key failure modes for the execution of the do, study and act steps of the PDSA process.

Table 2

Key failure modes for executing the do, study and act steps

PDSA, Plan-Do-Study-Act; QI, quality improvement.

The problem with PDSA: failure to invest in rigorous and tailored application

While the PDSA method is conceptually simple, simple does not mean easy. That said, PDSA is a powerful approach, and projects that make successful use of PDSA can solve specific quality problems and also help shape the culture of healthcare organisations for the better. So, the effort required to apply PDSA successfully has a substantial return on investment. But the resources and supportive context required for success (including funding, methodological expertise, buy-in and sustained effort) 18 are often underestimated. Inadequate human resources and financial support doom many projects to fail and also undermine organisational culture, contributing to change fatigue and disillusionment as yet another project produces no real improvement. It is therefore crucial, at both the project level and the programmatic level, that the resource requirements for successful application of PDSA for a given project are well understood and that the process is well managed.

The barriers to ensuring this type of practice in a healthcare culture of ‘just get on with it’ and ‘do, do, do’ are difficult to overcome. To be successful, the use of PDSA must be supported by a significant investment in leadership, expertise and resources for change.

Academia and researchers have a potential role to play to support appropriate rigour of planning and studying and understanding how to manage emergent learning while engaging diverse stakeholder groups. Working in partnership will be beneficial to support effective use of PDSA and is essential to establish genuine learning organisations. 19 20

Twitter: Follow Julie Reed at @julie4clahrc and Alan Card at @AlanJCard

Competing interests: None declared.

Disclaimer: This article presents independent research commissioned by the National Institute for Health Research (NIHR) under the Collaborations for Leadership in Applied Health Research and Care (CLAHRC) programme for North West London. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Provenance and peer review: Commissioned; internally peer reviewed.

problem solving cycle advantages and disadvantages

PDCA cycle - Structuring goal achievement

Market needs change daily, which means services or products also have to be adapted quickly. Regardless of agility level, specific structural guidelines within a process can be valuable in approaching such needs with a certain level of professionalism. One concept that stands out in this regard is the PDCA cycle. In this article, we will tell you how it works, how it can help your company, and whether it is suitable for quick success. 

What is the PDCA cycle?

Also known as the Deming circle or Deming cycle, the PDCA cycle is a concept for achieving a continuous improvement process, both in product development and in root cause failure analysis. The PDCA cycle is made up of a continuous loop of four stages: Plan, Do, Check, and Act. The particular strength of the PDCA cycle lies in its ease of application. The cycle is designed for long-term use and is intended to improve a company’s quality management instead of simply achieving short-term success.

The origin of the PDCA cycle dates back over 400 years to the time of Galileo and Francis Bacon, who were known for their application of inductive reasoning. The form we know today was developed by physicist Andrew Shewhart in the 1930s, under the name the Shewhart cycle. It was then further developed by W. Edwards Deming and used by Japanese companies, among others, during reconstruction following World War II.

The difference between PDCA and PDSA

In addition to the PDCA cycle, there is also a similarly sounding approach: the PDSA cycle. In this case, the “Check” stage is replaced with “Study.” This can be traced back to Deming, who rejected the “Check” stage because he saw it as a misunderstood modification of his idea. For him, it was not about creating a comparison between the current and target state. Instead, he was more concerned with learning from the obtained analyses and, as a result, sustainably improving the quality of the process. In his mind, the method could be illustrated using a spiral rather than a circle that is constantly spinning.

What are the stages of the PDCA cycle?

The four stages of the PDCA cycle are set in stone and do not change.

Within this stage, the analysis of both the current and desired state takes place. During this time, areas of improvement are identified and corresponding actions that should lead to the achievement of the desired goal are derived.

Subsequently, the pre-planned actions are implemented into the appropriate departments.

The results of the actions are then evaluated to determine whether or not they led to achievement of the defined goal.

If the actions led to a satisfactory result, they become standard practice for the future. Activities that did not lead to—or not sufficiently lead to—the desired goal are used as starting points to repeat the cycle until the goal is achieved.

problem solving cycle advantages and disadvantages

Practical example: PDCA with an OKR cycle

Despite its age, the PDCA cycle still finds its way into the processes of companies and organizations today. The Deming cycle can also be featured in connection with other methods, such as OKRs.

OKR is a goal-setting method for companies used at all organizational levels. The abbreviation stands for Objectives (O), or the value proposition for internal and external customers, and Key Results (KR), which measure whether a defined value proposition has been achieved.

When applying the OKR cycle from Workpath , a company is also going through a cycle similar to that of the PDCA.

problem solving cycle advantages and disadvantages

Based on the four stages of the Deming cycle, the OKR cycle’s individual steps are as follows:

PDCA cycle in Workpath goal cycle

Before the implementation of the OKR framework begins, the company’s management announces the priorities for the upcoming quarter, which provides a rough strategic direction for the cycle.

  • Plan = Team OKR drafting

As a reminder, the first stage of the PDCA cycle includes the analysis of the desired state and the definition of respective actions. In an OKR implementation context, the individual teams and departments define their own goals and how they can contribute to the communicated priorities. The different OKRs are drafted at this time, which is one of the most important tasks of the cycle. After all, well-written Objectives and Key Results can be the deciding factor when it comes to success or failure.

In the Alignment Workshop that follows, all responsible individuals from each team discuss the predefined OKRs, prioritizing them more precisely and identifying specific dependencies. The final OKRs are then formulated within the teams.

  • Do = OKR Kick-off

Final OKRs are then presented to all employees and the work beings. The focus throughout the entire quarter is placed on the tasks that contribute to the defined Objective. Short meetings are conducted during this time to ensure a short track for regular updates and discussions on insights and support possibilities.

  • Check = Retrospective und review

Next, the processes around the OKRs and how they can be improved are discussed in a retrospective. Two weeks before the end of the cycle, all progress and success are documented and evaluated during a review. Would you like to know the differences between a retrospective and review? You can find out here .

  • Act = Adjusting priorities and OKRs

OKRs that have not achieved the predefined goal or processes that require a certain amount of attention are then used in the subsequent OKR workshops to define new priorities and OKRs for the next cycle. The next cycle then begins.

Advantages and disadvantages of PDCA

The PDCA cycle comes with a variety of advantages and disadvantages organizations should be aware of before integrating it into their work processes. This prevents them from getting different results than expected.

Advantages of the PDCA cycle

  • Integration of all employees

If an organization uses the PDCA cycle, all employees are automatically pulled into the project. This can have a positive impact on team chemistry and give employees a sense of belonging.

  • Versatile application

The cycle can also be applied in many different ways. Not only can problem solving be simplified with the PDCA cycle, it can also be used in product manufacturing for both production and quality control.

  • Unlimited application

In addition, unlimited application of the PDCA cycle is possible. As soon as it ends, the fourth and final stage directly sets the foundation for the new cycle. This leads to continuous process improvement.

Disadvantages of the PDCA cycle

Despite its advantages, PDCA is not perfect. There are also negative aspects to consider before deciding to apply the concept.

  • A fixed principle

First and foremost, the PDCA cycle operates on a fixed principle and leaves little room for other variables during implementation.

  • Slow progress

Since it has four stages, progress is also slow and quick results cannot be expected.

  • Requires a lot of time

The cycle also requires a big time commitment, especially when it comes to planning and analyzing. Real actions do not take place often. If too much time is invested in analysis, projects can quickly come to a standstill.

PDCA cycle FAQ

‍ what does pdca mean.

‍ The PDCA cycle is a concept for continuous improvement processes. PDCA is mainly used in product development and root cause failure analysis. The PDCA cycle consists of the four stages Plan, Do, Check and Act, which are completed one after the other in a fixed sequence to achieve the desired result.

What is the difference between PDCA and PDSA?

‍ The only difference between PDCA and PDSA is the third stage. Instead of a “Check” stage, PDSA uses “Study.” This can be traced back to W. Edwards Deming, the developer of today’s version of the concept. Deming rejected the Check stage as, in his mind, the stage should focus on the learning effect and improvement of quality instead of comparison between the current and future state.

What are the individual stages of the cycle?

‍ “Plan” refers to the first analysis of the current state and desired goal state. In the “Do” stage, the predefined actions for goal achievement are implemented. Next, the results from the actions are evaluated in the “Check” stage to determine if they led to goal achievement. In the final stage, the “Act” stage, satisfactory results are set as standard and results that fell short are used as starting points for the following cycle.

What are the advantages and disadvantages of PDCA?

‍ The advantages of PDCA include the integration of all company team members, which has a positive effect on the atmosphere within the organization. The cycle is also versatile and has an unlimited number of applications. It can be useful for both failure analysis and product manufacturing, and can be repeated again after completion of the final stage. Disadvantages of PDCA lie mainly in its fixed structure of stages, which often provide little room for other ideas or variables. It also takes a lot of time to plan the concept because the four stages lead to slow progress.

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Problem-Solving Strategies and Obstacles

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

problem solving cycle advantages and disadvantages

Sean is a fact-checker and researcher with experience in sociology, field research, and data analytics.

problem solving cycle advantages and disadvantages

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From deciding what to eat for dinner to considering whether it's the right time to buy a house, problem-solving is a large part of our daily lives. Learn some of the problem-solving strategies that exist and how to use them in real life, along with ways to overcome obstacles that are making it harder to resolve the issues you face.

What Is Problem-Solving?

In cognitive psychology , the term 'problem-solving' refers to the mental process that people go through to discover, analyze, and solve problems.

A problem exists when there is a goal that we want to achieve but the process by which we will achieve it is not obvious to us. Put another way, there is something that we want to occur in our life, yet we are not immediately certain how to make it happen.

Maybe you want a better relationship with your spouse or another family member but you're not sure how to improve it. Or you want to start a business but are unsure what steps to take. Problem-solving helps you figure out how to achieve these desires.

The problem-solving process involves:

  • Discovery of the problem
  • Deciding to tackle the issue
  • Seeking to understand the problem more fully
  • Researching available options or solutions
  • Taking action to resolve the issue

Before problem-solving can occur, it is important to first understand the exact nature of the problem itself. If your understanding of the issue is faulty, your attempts to resolve it will also be incorrect or flawed.

Problem-Solving Mental Processes

Several mental processes are at work during problem-solving. Among them are:

  • Perceptually recognizing the problem
  • Representing the problem in memory
  • Considering relevant information that applies to the problem
  • Identifying different aspects of the problem
  • Labeling and describing the problem

Problem-Solving Strategies

There are many ways to go about solving a problem. Some of these strategies might be used on their own, or you may decide to employ multiple approaches when working to figure out and fix a problem.

An algorithm is a step-by-step procedure that, by following certain "rules" produces a solution. Algorithms are commonly used in mathematics to solve division or multiplication problems. But they can be used in other fields as well.

In psychology, algorithms can be used to help identify individuals with a greater risk of mental health issues. For instance, research suggests that certain algorithms might help us recognize children with an elevated risk of suicide or self-harm.

One benefit of algorithms is that they guarantee an accurate answer. However, they aren't always the best approach to problem-solving, in part because detecting patterns can be incredibly time-consuming.

There are also concerns when machine learning is involved—also known as artificial intelligence (AI)—such as whether they can accurately predict human behaviors.

Heuristics are shortcut strategies that people can use to solve a problem at hand. These "rule of thumb" approaches allow you to simplify complex problems, reducing the total number of possible solutions to a more manageable set.

If you find yourself sitting in a traffic jam, for example, you may quickly consider other routes, taking one to get moving once again. When shopping for a new car, you might think back to a prior experience when negotiating got you a lower price, then employ the same tactics.

While heuristics may be helpful when facing smaller issues, major decisions shouldn't necessarily be made using a shortcut approach. Heuristics also don't guarantee an effective solution, such as when trying to drive around a traffic jam only to find yourself on an equally crowded route.

Trial and Error

A trial-and-error approach to problem-solving involves trying a number of potential solutions to a particular issue, then ruling out those that do not work. If you're not sure whether to buy a shirt in blue or green, for instance, you may try on each before deciding which one to purchase.

This can be a good strategy to use if you have a limited number of solutions available. But if there are many different choices available, narrowing down the possible options using another problem-solving technique can be helpful before attempting trial and error.

In some cases, the solution to a problem can appear as a sudden insight. You are facing an issue in a relationship or your career when, out of nowhere, the solution appears in your mind and you know exactly what to do.

Insight can occur when the problem in front of you is similar to an issue that you've dealt with in the past. Although, you may not recognize what is occurring since the underlying mental processes that lead to insight often happen outside of conscious awareness .

Research indicates that insight is most likely to occur during times when you are alone—such as when going on a walk by yourself, when you're in the shower, or when lying in bed after waking up.

How to Apply Problem-Solving Strategies in Real Life

If you're facing a problem, you can implement one or more of these strategies to find a potential solution. Here's how to use them in real life:

  • Create a flow chart . If you have time, you can take advantage of the algorithm approach to problem-solving by sitting down and making a flow chart of each potential solution, its consequences, and what happens next.
  • Recall your past experiences . When a problem needs to be solved fairly quickly, heuristics may be a better approach. Think back to when you faced a similar issue, then use your knowledge and experience to choose the best option possible.
  • Start trying potential solutions . If your options are limited, start trying them one by one to see which solution is best for achieving your desired goal. If a particular solution doesn't work, move on to the next.
  • Take some time alone . Since insight is often achieved when you're alone, carve out time to be by yourself for a while. The answer to your problem may come to you, seemingly out of the blue, if you spend some time away from others.

Obstacles to Problem-Solving

Problem-solving is not a flawless process as there are a number of obstacles that can interfere with our ability to solve a problem quickly and efficiently. These obstacles include:

  • Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options.
  • Functional fixedness : This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be available to find a solution.
  • Irrelevant or misleading information: When trying to solve a problem, it's important to distinguish between information that is relevant to the issue and irrelevant data that can lead to faulty solutions. The more complex the problem, the easier it is to focus on misleading or irrelevant information.
  • Mental set: A mental set is a tendency to only use solutions that have worked in the past rather than looking for alternative ideas. A mental set can work as a heuristic, making it a useful problem-solving tool. However, mental sets can also lead to inflexibility, making it more difficult to find effective solutions.

How to Improve Your Problem-Solving Skills

In the end, if your goal is to become a better problem-solver, it's helpful to remember that this is a process. Thus, if you want to improve your problem-solving skills, following these steps can help lead you to your solution:

  • Recognize that a problem exists . If you are facing a problem, there are generally signs. For instance, if you have a mental illness , you may experience excessive fear or sadness, mood changes, and changes in sleeping or eating habits. Recognizing these signs can help you realize that an issue exists.
  • Decide to solve the problem . Make a conscious decision to solve the issue at hand. Commit to yourself that you will go through the steps necessary to find a solution.
  • Seek to fully understand the issue . Analyze the problem you face, looking at it from all sides. If your problem is relationship-related, for instance, ask yourself how the other person may be interpreting the issue. You might also consider how your actions might be contributing to the situation.
  • Research potential options . Using the problem-solving strategies mentioned, research potential solutions. Make a list of options, then consider each one individually. What are some pros and cons of taking the available routes? What would you need to do to make them happen?
  • Take action . Select the best solution possible and take action. Action is one of the steps required for change . So, go through the motions needed to resolve the issue.
  • Try another option, if needed . If the solution you chose didn't work, don't give up. Either go through the problem-solving process again or simply try another option.

You can find a way to solve your problems as long as you keep working toward this goal—even if the best solution is simply to let go because no other good solution exists.

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. doi:10.3389/fnhum.2018.00261

Dunbar K. Problem solving . A Companion to Cognitive Science . 2017. doi:10.1002/9781405164535.ch20

Stewart SL, Celebre A, Hirdes JP, Poss JW. Risk of suicide and self-harm in kids: The development of an algorithm to identify high-risk individuals within the children's mental health system . Child Psychiat Human Develop . 2020;51:913-924. doi:10.1007/s10578-020-00968-9

Rosenbusch H, Soldner F, Evans AM, Zeelenberg M. Supervised machine learning methods in psychology: A practical introduction with annotated R code . Soc Personal Psychol Compass . 2021;15(2):e12579. doi:10.1111/spc3.12579

Mishra S. Decision-making under risk: Integrating perspectives from biology, economics, and psychology . Personal Soc Psychol Rev . 2014;18(3):280-307. doi:10.1177/1088868314530517

Csikszentmihalyi M, Sawyer K. Creative insight: The social dimension of a solitary moment . In: The Systems Model of Creativity . 2015:73-98. doi:10.1007/978-94-017-9085-7_7

Chrysikou EG, Motyka K, Nigro C, Yang SI, Thompson-Schill SL. Functional fixedness in creative thinking tasks depends on stimulus modality .  Psychol Aesthet Creat Arts . 2016;10(4):425‐435. doi:10.1037/aca0000050

Huang F, Tang S, Hu Z. Unconditional perseveration of the short-term mental set in chunk decomposition .  Front Psychol . 2018;9:2568. doi:10.3389/fpsyg.2018.02568

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Plan-Do-Check-Act Cycle

Plan-do-check-act (PDCA) is a four step cycle that allows you to implement change, solve problems, and continuously improve processes. Its cyclical nature allows it to be utilized in a continuous manner for ongoing improvement.

When implementing change.

For problem solving.

For continuous improvement.

To develop a design.

1. PLAN the change or improvement.

2. DO: Conduct a pilot test of the change.

3. CHECK: Gather data about the pilot change to ensure the change was successful.

4. ACT: Implement the change on a broader scale. Continue to monitor the change and iterate as necessary by repeating the cycle.

Makes sure that all appropriate steps are followed.

Offers a systematic improvement method.

Is an effective process improvement guide.

Informs future improvement by providing feedback.

Maintains order during problem solving.

Requires significant commitment over time.

Yeager K. Program evaluation: this is rocket science. In: Roberts A, Yeager K, editors. Evidence-based practice manual: research and outcome measures in health and human services. New York, NY: Oxford University Press; 2004. p. 647-53.

American Society for Quality. Project planning and implementing tools: Plan-Do-Check-Act Cycle. 2009 [cited 2009 July 23]; Available from: http://www.asq.org/learn-about-quality/project-planning-tools/overview/pdca-cycle.html

Silimperi D, Zanten V, Franco L. Framework for institutionalizing quality assurance. In: Roberts A, Yeager K, editors. Evidence-based practice manual: research and outcome measures in health and human services. New York, NY: Oxford University Press; 2004. p. 867-81.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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  • http://orcid.org/0000-0002-9974-2017 Julie E Reed 1 ,
  • Alan J Card 2 , 3
  • 1 NIHR CLAHRC NWL , Imperial College London , London , UK
  • 2 Department of Management, University of Notre Dame, Notre Dame, Indiana, USA
  • 3 Evidence-Based Health Solutions, LLC, Notre Dame, Indiana, USA
  • Correspondence to Dr Julie E Reed, NIHR CLAHRC NWL, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, UK; julie.reed02{at}imperial.ac.uk

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  • Quality improvement
  • Healthcare quality improvement
  • Quality measurement

Introduction

Quality improvement (QI) methods have been introduced to healthcare to support the delivery of care that is safe, timely, effective, efficient, equitable and cost effective. Of the many QI tools and methods, the Plan-Do-Study-Act (PDSA) cycle is one of the few that focuses on the crux of change, the translation of ideas and intentions into action. As such, the PDSA cycle and the concept of iterative tests of change are central to many QI approaches, including the model for improvement, 1 lean, 2 six sigma 3 and total quality management. 4

This article presents our reflections on the full potential of using PDSA in healthcare, but in doing so we explore the inherent complexity and multiple challenges of executing PDSA well. Ultimately, we argue that the problem with PDSA is the oversimplification of the method as it has been translated into healthcare and the failure to invest in a rigorous and tailored application of the approach.

The value of PDSA in healthcare improvement

The purpose of the PDSA method lies in learning as quickly as possible whether an intervention works in a particular setting and to making adjustments accordingly to increase the chances of delivering and sustaining the desired improvement. In contrast to controlled trials, PDSAs allow new learning to be built in to this experimental process. If problems are identified with the original plan, then the theory can be revised to build on this learning and a subsequent experiment conducted to see if it has resolved the problem, and to identify if any further problems also need to be addressed. In the complex social systems of healthcare, this flexibility and adaptability of PDSA are important features that support the adaption of interventions to work in local settings.

A successful PDSA process does not equal a successful QI project or programme. The intended output of PDSA is learning and informed action. Successful application of the PDSA methodology may enable users to achieve their QI goals more efficiently or to reach QI goals they would otherwise not have achieved. But it is also successful if it saves wasted effort by revealing QI goals that cannot be achieved under realistic constraints or if it identifies new problems to tackle instead of the originally identified issue. A well-conducted PDSA promises learning. But it does not, and cannot, promise that users will achieve their desired outcomes.

As PDSA has been translated into healthcare from industrial settings, an emphasis has been placed on rapid small-scale tests of change, often on one, three and then five patients in ‘ramps’ of increasing scale, and responsibility delegated to frontline staff and improvement or quality managers. This pragmatic approach has been embraced and has been seen as providing a new freedom for healthcare staff to lead change and improvement in local care settings.

However, the process of change rarely progresses in simple linear ramps. 6 , 8 The conduct of PDSAs can reveal other related issues that need to be addressed in order to achieve the improvement goal. Such issues may relate to minor changes to current practices or processes of care, but can often reveal larger cultural or organisational issues that need to be addressed and overcome.

Recent evaluations have reported on the failure of the PDSA method to help frontline staff address the multiple improvement challenges they faced as the scale of investigation and range of issues they needed to address increased. 7 , 9 A report evaluating the Safer Clinical Systems programme in the UK identified ‘the need for clarity about when improvement approaches based on PDSA cycles are appropriate and when they are not’, viewing some challenges as ‘too big and hairy’ for the PDSA method and beyond the scope of small-scale tests of change run by local clinical teams. 7

We argue that any improvement situation, no matter how big and hairy, is conducive to application of the PDSA method. The four stages of PDSA mirror the scientific experimental method of formulating a hypothesis, collecting data to test this hypothesis, analysing and interpreting the results and making inferences to iterate the hypothesis. 5 , 10

Whether improvement initiatives have been planned at national level to support standardisation of care or planned over a cup of coffee to solve a minor local problem, we believe there will always be a role for PDSA. In moving from planning to implementing a change in practice, PDSA provides a structure for experimental learning to know whether a change has worked or not, and to learn and act upon any new information as a result.

But it is not a magic bullet. Increasingly complex problems require increasingly sophisticated application of the PDSA method, and this is where we believe the problem with the PDSA method lies.

Its simplicity belies its sophistication

One of the main narratives surrounding the use of PDSA in healthcare is that it is easy, and can be applied in practice by anyone. At one level this is true, and the simplicity of the PDSA method and its applicability to many different situations can be viewed as one of its main strengths. However, this simplicity also creates some of the greatest challenges to using PDSA successfully. Users need to understand how to adapt the use of PDSA to address different problems and different stages in the lifecycle of each improvement project. This requires an extensive repertoire of skills and knowledge to be used in conjunction with the basic PDSA model.

One of the main problems encountered in using PDSA is the misperception that it can be used as a standalone method. PDSA needs to be used as part of a suite of QI methods, the exact nature of which may be influenced by the broader methodological approach that is being followed (eg, model for improvement, lean). An important role of the wider methodological approach is to conduct investigations prior to starting the use of PDSA to ensure that the problem is correctly understood and framed. Investigations can include process mapping, failure mode effects analysis, cause and effect analysis, stakeholder engagement and interviews, data analysis and review of existing evidence.

A second misperception is that the PDSA is limited to small-scale tests of change on one, three and five patients. PDSA is an extremely flexible method that can be adapted to support the scale up of interventions and used in conjunction with monitoring activities to support sustainability. But, this flexibility gives rise to a number of key dimensions that require careful consideration. For instance, the scope and scale of change, the amount of preparation prior to use, rigour of the evaluation, time, expertise, management support and funding must be carefully aligned. Often these needs must be rebalanced over the project's lifecycle. If managed well, these adjustments enable the use of PDSA to adapt to new learning and support the design and conduct of ‘tests of change’ as they increase in scale, and often complexity, to achieve the desired improvement goal.

Using PDSA as an iterative design framework to help solve ‘big hairy problems’ or ‘big hairy audacious goals’ 11 is, therefore, entirely appropriate. In fact, developing solutions to large-scale ‘wicked problems’ 12 may require ‘an iterative explorative and generative’ 13 approach of the sort PDSA provides, in which ‘knowledge is built through designing’. 13 The key is to understand that this framework will need to be implemented (and resourced) very differently for large and complex problems than for smaller and more ‘tame’ problems. One size does not fit all.

While frontline staff with little training or support may successfully address some quality problems, the complexity of many problems demands greater organisational support, with direct involvement of senior managers to facilitate adequate planning. Projects in which frontline staff must fend for themselves also run the risk of insufficient usage of theory and existing evidence to develop the intervention and a suboptimal evaluation.

Quick (not dirty) tests of change

In healthcare, PDSA training often overemphasises the conceptual simplicity of the framework and underemphasises the different ways in which the method can be adapted to solve increasingly complex problems. This frequently leads people to leap into PDSA with insufficient prior investigation and framing of the problem, to delegate management of the process to frontline staff who have little influence over broader systemic concerns that need to be addressed, and to provide these staff with little support to overcome the obstacles and barriers they face. The resources, skills and expertise required to apply PDSA in the real world are often significantly underestimated, leading to projects that are destined to fail.

This has led to the impression that PDSA cycles involve ‘quick and dirty’ tests of change. In the rush to empower healthcare staff, there is a danger that the scientific rigour of the PDSA method is frequently compromised. A systematic review 5 revealed that the core principles of PDSA are often not executed in practice, with ‘substantial variability with which they are designed, executed and reported in the healthcare literature’. 6 A failure to properly execute PDSAs can undermine learning efforts… ‘if data collection does not occur frequently enough, if iterative cycles are few, and if system-level changes are not apparent as a result of these cycles, the improvement work is less likely to succeed’. 6 While its scientific principles differ from those of controlled trials, rigour in the application of PDSA is still required for PDSA to maximise the learning obtained from tests of change.

In addition to a lack of fidelity with PDSA guiding principles, there is the need to ensure that each stage of the cycle is conducted well. But the frenetic culture endemic in healthcare organisations can make it difficult to achieve sustained engagement in the deliberative processes of PDSA.

Just get on with it

While ‘planning paralysis’ can be an issue in healthcare organisations, the more common problem is a serious underinvestment in the planning phase. The pervasive cultural compulsion to ‘just get on with it’ 14 leads many teams to move too quickly from ‘plan’ to ‘do.’ The consequences of skipping this up-front work can include wasted PDSA cycles or projects that fail altogether. Table 1 describes some of the key failure modes for the planning and preplanning (ie, investigation and problem-framing) steps of the PDSA process.

  • View inline

Key failure modes for the investigation/problem framing and plan steps

Why do planning failures present such a challenge to the successful use of PDSA? It is much more difficult to correctly execute and learn from a plan that has not been well thought out. And even perfect execution cannot ensure success if the plan, itself, is wrong.

The iterative nature of PDSA enables course corrections, but this feature of the approach is much more effective if there was a clear and reasoned course in the first place. Many of the barriers to success in the do, study and act phases can be predicted and mitigated through more effective planning.

Overcoming the prevailing culture of ‘Do, Do, Do’

The structured, reflective practice required for PDSA runs counter to the main mode of operation in healthcare organisations, ‘doing’, with the time required for planning and reflection regarded as a luxury rather than a necessity. As a result, teams often get ‘stuck’ in the ‘do’ phase, failing to progress to the ‘study’ phase. While these problems may reflect poor planning, they may also be caused by problems beyond the control of the project team, such as the challenges of creating time to conduct tests of change, staff turnover and changing or competing priorities. To stop at the ‘do’ phase is to throw away the core contribution of PDSA: its support for iterative design as a way of making improvement interventions more successful. 15 Another important but frequently overlooked part of the ‘do’ phase is inductive learning, noticing the unexpected and feeding these observations into the study phase.

Poor planning or conduct of the ‘do’ phase in turn can significantly undermine the ‘study’ phase. In some cases, improvement teams appear to bypass the ‘study’ phase altogether, moving directly from ‘do’ to ‘act’. 5 In other cases, the ‘study’ phase may collect insufficient data or may not collect the right type of data to answer questions about the intervention's effectiveness and acceptability. For instance, quantitative data can assess the impact of a given change, without qualitative feedback; the reasons for the results or staff attitudes and ideas about what could be improved will remain unknown. It is also possible that teams draw the wrong conclusions from the data they have collected or fail to notice unanticipated consequences, which may lead to incorrect actions.

Failure to take appropriate action based on what was learned from the ‘study’ phase and previous PDSA cycles is another common concern. 5 Inappropriate actions may include adopting or scaling up an intervention that has not proven effective and acceptable, 16 or ending a project that has proved successful, or is on track to do so. An important part of the act phase consists of reviewing and revising the theory of how the intervention is intended to achieve its desired impact. This iterative refinement of theory is a key component of PDSA methodology, which is often overlooked in practice.

Effectively managing the PDSA process is about more than individual PDSA steps or cycles. Connecting PDSA cycles together is a messier and far more complicated endeavour than most of the literature on the approach suggests. 6 Progression across cycles is seldom linear, and double-loop learning 17 may lead to revised goals, as well as revised interventions, and requires significant oversight to manage emergent learning and coordination of PDSA activities over time.

Table 2 describes some of the key failure modes for the execution of the do, study and act steps of the PDSA process.

Key failure modes for executing the do, study and act steps

The problem with PDSA: failure to invest in rigorous and tailored application

While the PDSA method is conceptually simple, simple does not mean easy. That said, PDSA is a powerful approach, and projects that make successful use of PDSA can solve specific quality problems and also help shape the culture of healthcare organisations for the better. So, the effort required to apply PDSA successfully has a substantial return on investment. But the resources and supportive context required for success (including funding, methodological expertise, buy-in and sustained effort) 18 are often underestimated. Inadequate human resources and financial support doom many projects to fail and also undermine organisational culture, contributing to change fatigue and disillusionment as yet another project produces no real improvement. It is therefore crucial, at both the project level and the programmatic level, that the resource requirements for successful application of PDSA for a given project are well understood and that the process is well managed.

The barriers to ensuring this type of practice in a healthcare culture of ‘just get on with it’ and ‘do, do, do’ are difficult to overcome. To be successful, the use of PDSA must be supported by a significant investment in leadership, expertise and resources for change.

Academia and researchers have a potential role to play to support appropriate rigour of planning and studying and understanding how to manage emergent learning while engaging diverse stakeholder groups. Working in partnership will be beneficial to support effective use of PDSA and is essential to establish genuine learning organisations. 19 , 20

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Twitter Follow Julie Reed at @julie4clahrc and Alan Card at @AlanJCard

Competing interests None declared.

Disclaimer This article presents independent research commissioned by the National Institute for Health Research (NIHR) under the Collaborations for Leadership in Applied Health Research and Care (CLAHRC) programme for North West London. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

Provenance and peer review Commissioned; internally peer reviewed.

Linked Articles

  • Narrative review Value of small sample sizes in rapid-cycle quality improvement projects E Etchells M Ho K G Shojania BMJ Quality & Safety 2015; 25 202-206 Published Online First: 30 Dec 2015. doi: 10.1136/bmjqs-2015-005094
  • Miscellaneous Correction: Value of small sample sizes in rapid-cycle quality improvement projects BMJ Publishing Group Ltd BMJ Quality & Safety 2020; 29 e1-e1 Published Online First: 19 Aug 2020. doi: 10.1136/bmjqs-2015-005094corr1

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Fishbone diagram: Solving problems properly

Fishbone diagram: Solving problems properly

Every company faces its own problems. Often managing directors and employees are forced to deal with new challenges every day. But how wise is it having to overcome the same obstacles every day? Instead of dealing with the symptoms, the root of the problem must be addressed. But finding the cause of a problem is not an easy task. The fishbone diagram assists in identifying the cause of a problem.

What is the fishbone diagram?

Step 1: stating the problem, step 2: defining the main influencing factors, step 3: stating the causes, step 4: setting priorities, step 5: taking measures, advantages and disadvantages of the fishbone method.

The fishbone diagram, invented by Ishikawa Kaoru, a Japanese chemist, (hence why it is also referred to as the Ishikawa diagram) aims to help companies find solutions to problems and their causes in a structured way . It is therefore also called the cause-effect diagram . Every problem, requiring a sustainable solution, is graphically illustrated with its respective causes in the diagram.

The problem that needs to be addressed is on the right side of the diagram. It is written down before any other observations are made. Be as accurate as possible when describing the problem and write it on the right hand side of the flip chart or blackboard. You then draw a line or an arrow to the left i.e. pointing towards the problem. Several other lines branch off from this main line: the possible causes of the problem. When specifying the potential causes of the problem, you can use several methods: the 4M method, and its extensions the 5M and 8M methods, are frequently used. They refer to the main influencing factors of processes, which often lead to problems.

The 4M method uses the following main influencing factors:

The 5M method also uses the additional factor of:

  • Mother Nature

When using the 8M method , three additional factors are used:

  • Measurement

It is not always mandatory to use 4, 5 or 8 factors for the fishbone diagram. Instead, all relevant factors for the problem should be addressed. And you can of course use other terms that do not begin with M.

The lines branching off from the main line list the actual causes of the problem and are arranged according to the appropriate categories. These causes should be identified very explicitly as opposed to the rather broadly defined main influencing factors.

This graph resembles a fish skeleton, which is why the Ishikawa diagram is also known as the fishbone diagram.

problem solving cycle advantages and disadvantages

Balanced Scorecard: A tool for effective strategy implementation

Is your business strategy working out? The balanced scorecard by Kaplan and Norton can help you answer this question. It enables a comprehensive analysis of the company from various perspectives; it also provides useful key indicators on where there may be shortcomings in the company. Identify the success factors at all levels of your company to ensure long-term success.

CIP – Continuous Improvement for Your Company

CIP – Continuous Improvement for Your Company

Constant increases in quality: That works best with ongoing, small improvements. CIP – the continuous improvement process – provides the right framework for this. Every employee should see it as their task to recommend improvements for their area of work. And that can be quite simple: Even reorganising the workplace can have a great impact.

The Six Sigma Method: How it Works and What it Does

The Six Sigma Method: How it Works and What it Does

Six Sigma is one of the most commonly used process optimisation and quality management methods in companies. This method sets itself apart through its foundation in math and its clear focus on being able to measure change. In the following article, we show you how the Six Sigma method works, what benefits you can gain from it, and how to put it into practice.

The PDCA cycle: more success with the Deming cycle

The PDCA cycle: more success with the Deming cycle

Continuous improvement and continuous learning: with PDCA, you ensure sustainable changes in both work and private life. The model, also known as the Deming cycle, helps to improve all possible situations – through an iterative, cyclical, and controlled process. How do you apply PDCA correctly?

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By Denis G.

The DMAIC Model

In this article:

The DMAIC model is a problem-solving method used to identify flaws and improve inefficiencies in business processes.  

One challenge of day-to-day business is resolving problems. Imagine you run a small business that sells products online, and a quarterly review reveals a significant drop in orders.  

In a situation like this, you might look at customer feedback to find and fix the problem. You could also use your experience to try to resolve the problem. But what happens if the problem is complex and resurfaces because you haven’t properly resolved it?

Some problems are more complex than we first think. To solve the problem and get the business back on track, we need a structured problem-solving approach.

This is where DMAIC comes in. DMAIC stands for Define, Measure, Analyze, Improve, and Control. It is a data-driven iterative approach that you can use to improve a process or fix a problem. 

DMAIC is Part of Six-Sigma

DMAIC is a core part of the six-sigma quality improvement methodology.  

Six-Sigma is a business management strategy that aims to improve the output of processes by removing variation and errors in both manufacturing and business processes. With a focus on continuous improvement, it uses a series of quality management methods. DMAIC is one of those methods.

Bill Smith created Six-Sigma whilst working for Motorola in 1980. The methodology became popular after Bill Welch made it the central focus of the business strategy at General Electric.

The DMAIC Model

The five steps of the DMAIC model are: 

  • Define phase: where you understand the problem you want to address.
  • Measure phase: where you collect data to help you make an informed decision about what’s causing the problem.
  • Analyze phase: where you examine and interpret the collected data to attempt to identify the root cause of the problem.
  • Improve phase: where you fix the root cause of the problem.
  • Control phase: where you put in place a mechanism to ensure the improvements you’ve made are sustained.

Each step builds on the previous step in the sequence. As you use the DMAIC model and come to understand the bigger picture of an improvement project, you may need to iterate through the whole process or a subset of it several times.

Let’s take a closer look at each step of the DMAIC framework and how to use it in day to day business.

1. Define Phase 

The define phase aims to understand the problem and determine the need for a project that resolves the challenge the problem presents. A business problem is most often represented by some financial or performance challenges.  

Some questions to ask to define the project are:

  • What is the problem?
  • How often does it happen?
  • What are the current inputs and outputs?
  • What is the impact of the problem?
  • Who is the customer?
  • What is the customer saying?
  • Who are the stakeholders?
  • Who is on the project team to address the issue?
  • What are the goals of this project?

The gathered information is used to create a high-level project plan. This plan becomes the guiding document for the project. 

2. Measure Phase 

The measure phase aims to gather reliable information to quantify the problem. The data collected in this phase will establish a baseline to compare against improvement later.

Some questions to ask in this phase include:

  • How do we measure the problem?
  • What is the current process performance?
  • What data do we collect to measure it?
  • Who or what needs to be surveyed?
  • Is the data consistent?

Some useful tools to assist in this phase are basic Pareto charts, trend charts, and process flowcharts.

3. Analyze Phase 

This phase aims to find the root cause of the problem under investigation. Some questions to ask when analyzing the project are:

  • How does the process work?
  • What does the data say?
  • How can the data help us understand the root cause?
  • How does this affect performance? 
  • Why are we having problems?

In this phase, you can use tools like brainstorming, fishbone diagrams,  or the 5-Whys to help you find the root cause.

4. Improve Phase

The aim of the improve phase is to implement one or more solutions to address the root cause of the problem.

Many different factors can influence a solution, so a well-designed solution most likely to resolve the root cause is what you’re looking for.

5. Control Phase 

The control phase aims to ensure that the improvements continue to meet the performance objectives over time.  

Ongoing monitoring ensures the process continues to meet your performance expectations in the future.  

DMAIC Model Example

Imagine you are a luxury watch manufacturer. Following a quarterly review, you learn that your profit margins are lower than you’d expect but you are not sure why. 

You decide to use the DMAIC model to help you to get to the bottom of the problem. 

Step 1: Define

The first step is to define the problem as best as you can.

For our example, we know that margins are going down even though the price we charge hasn’t changed. Our aim is to find and rectify the situation quickly.

During this step we also decide to put a team together, representing all the different parts of the business, to investigate the problem and ensure that no stone is left unturned.

Step 2: Measure 

Next, we need to collect the data we think will give us the most insight into what’s causing the problem. 

For our watch manufacturer, we decide to map out the entire production process and monitor the time it takes to move between each and every step.

Step 3: Analyze 

Once we’ve collected all of the data the next step is to analyze it with the aim of identifying the root cause of the problem.

Tools that are frequently used during this step include The 5 Whys and Fishbone diagrams . Both of these tools are useful for finding the root cause of a problem.

For our example, we decide to hold a brainstorming session and use the 5 Whys technique to attempt to identify the root cause of the problem. During this process, we uncover that a stage in the middle of the manufacturing process is taking much longer than we would expect. 

Upon further investigation, we find that this is happening because a small but necessary component is often out of stock. When this happens the team creating the watches has to wait until more stock arrives before they can resume the manufacturing process. According to the team, this is happening because the supplier is unreliable.

Step 4: Improve

In this step, we decide how to fix the root cause of the problem. For our example, there are many ways in which this could be done, including:

  • Hold more stock to iron out the delivery inconsistencies.
  • Switch to a different supplier.
  • Create an SLA (Service Level Agreement) with the supplier to ensure you always get stock from the supplier when you expect.

You decide to always hold a high level of stock for the component so that the problem doesn’t happen again.

Step 5: Control

The final step is to ensure that the problem doesn’t happen again, or that if it does happen you know about it almost immediately.

To do this, you decide to hold a weekly review meeting to monitor all of the data regarding how long each part of the manufacturing process takes. If there is a large negative change from one week to another then it will be obvious that there has been a problem and you can step in and take corrective action.

DMAIC Model Template

We have created a DMAIC Model Template in PDF format, which you can use to perform your own DMAIC project. You can download it here .

DMAIC Model Template

Advantages and Disadvantages

The main advantages of the DMAIC method are:

  • The structured approach is useful in any situation where you need to improve a complex process.
  • The DMAIC method aims to analyze a process before implementation, and this reduces the chance of fixing the wrong issue.
  • It helps to improve team and organization communications. This leads to improved performance overall, and ultimately this can filter through to happier customers.

The main disadvantages of the DMAIC method are:

  • A one size fits all approach does not suit an organization that relies on creativity.
  • Without awareness, during the implementation of DMAIC, it is easy to become too focused on using the tool, rather than finding the right solution.
  • It is cumbersome for simple and obvious problems.

DMAIC Model Summary 

The DMAIC model is a framework for fixing a business problem or improving a business process. DMAIC stands for Define, Measure, Analyse, Improve, and Control. It is part of the six-sigma continuous improvement methodology.

Each step follows and builds upon the previous step to define the problem, and then resolve it in a way that will result in lasting improvement.  

Cite this article

Minute Tools Content Team, The DMAIC Model, Minute Tools, Apr, 2020 https://expertprogrammanagement.com/2020/04/the-dmaic-model/

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Originally hailing from Dublin, Denis has always been interested in all things business and started EPM in 2009. Before EPM, Denis held a leadership position at Nokia, owned a sports statistics business, and was a member of the PMI's (Project Management Institute’s) Global Executive Council for two years. Denis now spends his days helping others understand complex business topics.

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Team Problem Solving: Advantages and Disadvantages

Most of the time in personal and professional life, executing work or task with the help of a team or with the team can deliver some sort of positive as well as negative impression. But it is necessary for a person to understand both sides of the coin and in this case, it is about team problem solving skills.

Apparently, all the positive and negative terms or else, in other words, these advantages and disadvantages of team problem solving will help in reducing the contrast behaviour of certain task handling.

Team Problem Solving

Tips to Strengthen Team Problem Solving Skills:

Problem-solving skill is one of the best known and important trait every employee should possess. Most of the time even a small team problem gets bigger, as no employee would be willing to take an extra step to solve it or to stand for it.

“ As far as it’s not our own problem, we do not have to worry ” is the cheat code most of us have. And the main reason for developing such a tendency are our employers. Though they allow employees to think creatively and motivate to do things accordingly, but will metaphorically bite people’s heads off if anything goes wrong. This propensity makes their employees work like robots who can just act upon commands.

Now even if the above-mentioned one is considered as a part of training and coaching, whenever any group or team problem arises, managers need to install few approaches and strategies to tackle it effectively. This would allow the employee to join in a group and work on it.

How to Strengthen Your Team’s Problem Solving Skills?

  • Start working on team problem-solving by making smaller victories.
  • Do not act with anger when you have failed
  • Provide enough freedom to the employees for solving issues accordingly
  • Set up high expectations
  • Teach your group on how to frame problems
  • Motivate the group to take extra expertise
  • Motivate all the employees to form a group when solving team-related problems
  • Celebrate accomplishments and success

Advantages of Team Problem Solving:

When it comes to problem-solving in a team, the organization heads prefer their best people to solve that issue. And while solving those issues the board of members or a team of people work on that particular issue and conclude with the best possible solution for an issue.

1. Better thinking:

During team problem-solving process, a person might think of certain solutions which can be used to solve such issue for a temporary basis. But in case of team problem-solving sessions a team or group of people will try to put in all their individual thinking in that particular matter so that they can get an answer and solution for their problem.

Therefore, team problem-solving techniques helps to conclude with a better solution with better thinking.

2. Better risk handling:

It might seem a bit confusing for people that when it comes to risk, more people can handle a higher amount of risk. And that is very true because when a group person tries to handle the severity of the risk, it is quite possible that they can grow better in their professional outputs.

Therefore, team problem-solving methods can increase the risk factor in a very positive manner and moreover because of such team problem-solving ability, one can create better growth.

3. Better communication:

Problems can be solved in a better way with proper communication between people. And when it comes to a team or a group of persons, then it is easy to understand that they can communicate better as compared to others.

Therefore, team problem-solving method increases communication and better understanding between a group of people and this ultimately helps to solve the issues as soon as possible.

4. Increases understanding:

As it is explained earlier that team problem-solving methods can increase better communication between people from the same group and apparently such behavior can lead them to build better understanding between teammates.

Therefore, such type of understanding can help all the people from the group and their problem-solving ability. Moreover, this can turn itself as one of the advantages for the sake of organization and their growth.

5. Increased number of solutions:

Most of the time while working in an organization whenever some sort of problem occurs, then the organization heads will consider their experienced employees to understand the problem and try to get the possible solution for such problems.

In this course of action, most of the people prefer their best working team to come with some best solution and that is why people and most of the companies prefer their team problem-solving abilities to look at the wide range of possible solution for a single issue.

6. Helps to increase the team’s potential:

There are some situations wherein which a person can face some uncertain situation in terms of their professional parameters, but at that point of time, that person needs to think wisely regarding the issue. And the level of thinking can increase the chances of his or her potential and ability in relation to a problem.

Similarly, when it comes to team problem solving, then it is quite clear that the level of problem-solving with the help of a team can definitely increase the chances of the team’s potential.

7. Higher commitment:

Most probably when a team or a group of people working on a project experiences some sort of professional problems, then it is the team’s responsibility to solve that problem as soon as possible. And eventually, the problem might possibly reach its end with the efforts of the team.

Therefore, at the end of every single issue solving process, the team presents its higher-level commitment towards the problem’s solution.

8. Reduces the possibility of bias:

When a team performs a job or a task, then the efforts that the team have indulged in that task or project would be mainly considered as a team effort than an individual person effort. And eventually, the organization will reduce their bias behaviour with certain employees of their company.

Therefore, it is definitely understandable that by involving team problem-solving technique the employee and employers of the company both can be comfortable with the working environment.

9. Greater productive output:

As it is definitely expected that when a company or an organization works with their team efforts, then the company or an organization can experience a greater amount of productive output in terms of their profit margin.

Therefore, involving in good problem-solving skills and techniques can be beneficial for both the company and its directors. And eventually, this increases the profit ratio of the company which can ultimately increase the growth of the company.

10. Encourages creative ideas:

As most of the team members working in a particular team will be equally provided a chance of presenting their own creative ideas while discussing something necessary for the welfare of the company. And in that process of creative ideas, a team baring potential employees can present their problem-solving ideas for the sake of the overall growth of the company.

Therefore, as it has been explained earlier that because of the team problem-solving behaviour, the company can benefit in terms of their profit margin as compared to the other company working in the same field.

Disadvantages of Team Problem Solving:

As compared to the advantages of team problem solving, the disadvantages can deliberately present the difference of opinion within the working behaviour of the team members.

1. Increased competition:

Most of the time while working in a team a person’s individual efforts can be ignored because of the team. And that is because every team maintains its own team leader and every time if that team achieves some sort of excellence, then it is quite clear that the team leader will be acknowledged first.

In this process of acknowledging, the team members can be left out and that eventually brings up competition within the members of the team.

2. Level of confirmation:

When it comes to confirmation of it regarding a certain task or project, a person needs to understand that it never helps if a person is involved in a group or is a member of a group.

Therefore, it is very much necessary for every single person that he or she should know more about these team problem-solving abilities. Moreover, team problem solving is capable of a distinguished level of conformity.

3. Lack of objective guidelines:

Most of the time it happens in the team working behaviour that all the objective direction need not be followed because of the team leaders direction. In a team working behaviour, it is clear to everyone that if a team needs to work according to the prescribed way, then they need to follow a certain type of objective direction.

That direction will not be available with the team problem-solving ability as the team leader leads all the decisions in the process.

4. Time constraints:

Because of the team problem-solving methods, a person might not think about or bother about its timing. And that is necessary to understand, clear out all the fogs in a way. Time constraints are one of the disadvantages in the team problem-solving ability and method that never depend upon the number of people in the group.

People think that if a single group have a maximum number of members, then time constraints will be eliminated.

5. Unequal participation:

It is not necessary that if a team or a group of a certain number of people are part of the group, then they need to participate in all types of work or task. Most of the time it happens such as a team member might be interested in being a part of something interesting, but the team doesn’t let him or her participate in such an event.

Similarly, this type of behaviour creates unequal participation within the group and which eventually grow into something big in terms of drift and loss.

6. Unwillingness to participate:

Most of the while being a part of a team or a group some member might not feel interested to participate in all sorts of events or task assigned to that particular group, but the actual truth is that such type of behaviour can bring up some sort of communal issue within the group.

Therefore, unwillingness to participate can be considered as a decision of a person who is a part of the group, but because of that individual decision, the whole team benefits some great loss.

7. Lack of team spirit:

Working in a team or a group takes a lot of team spirit, but some people totally lack behind in such type of criteria and because of that the team or a group damages its reputation.

And that is why a group must contain those members who are more than interested in the team working behaviour and its environment. Therefore, while choosing a member of a team, a team leader must consider the level of comfortability in the participant about being a team player.

Components of Effective Team Problem Solving:

There are certain components when it comes to team problem-solving methods. And those components bring up all sorts of solution to any type of team issues or problems. These components also help to improve problem-solving skills. Therefore, it is necessary to understand all those components first to go ahead with a solution without any understanding.

1. An undesirable situation:

It is a very common component in team problem solving and that is because a person might not experience any type of trouble or problem with the desirable situation. And these desirable situations, eliminate a process of problem-solving on its own without any extra efforts.

Therefore, while considering this undesirable situation component, a person needs to understand all things about undesirable objectives.

2. Desired situation:

Most of the time it is a contrasting behaviour that people might experience threatened with the desired situation and that is common for everyone. Because most of the time in a common daily life people face all sorts of people and all those people might not experience the same desired situation as one, and that is why it gets a little weird with the team problem-solving.

Therefore, it is very necessary to understand the desired situation as well in the team problem-solving ways.

3. The difference between the desired and undesired situation:

That is because of the team playing availabilities. Most probably while being a part of a team a person or a member need to understand a thin line between desired and undesired situation.

And it is very much necessary for all the team members to clear out all the doubts with the desired and undesired situation. Therefore, things which create a difference between desired and undesired need to solved by the team altogether.

Conclusion:

Finally, the bottom line is that here we have provided all the advantages and disadvantages of team problem solving along with its own components which brings up all the necessary study materials regarding team problem-solving activity. Therefore, if anyone is interested to understand more about team problem solving, then they can refer all the above-mentioned points to continue their study in the same field of work.

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A Comprehensive Guide on the Problem Solving Cycle – Step-by-Step Approach with Real-Life Example

  • Post author By bicycle-u
  • Post date 08.12.2023

Solving problems is an essential skill that we encounter in various aspects of our lives. Whether it’s a personal problem or a professional challenge, having a structured approach can greatly help in finding effective solutions. The problem solving cycle provides a systematic framework to analyze, evaluate, and improve upon existing solutions.

The first step of the problem solving cycle is to identify and define the problem. This involves clearly understanding the issue at hand, its root causes, and the desired outcomes. Once the problem is defined, the next step is to analyze it in detail. This involves gathering relevant information, examining different perspectives, and identifying possible solutions. It is important to be thorough in this step to ensure that all possible angles are considered.

After analyzing the problem, the next step is to develop and implement a solution. This requires creativity and innovation to come up with an effective approach. A well-thought-out solution should address the root causes of the problem and have a clear plan of action. Once the solution is implemented, it is necessary to evaluate its effectiveness. This involves monitoring the progress, gathering feedback, and making any necessary adjustments.

An example of the problem solving cycle in action is in the context of a team project. Let’s say a team is facing a communication problem that is hindering progress. The first step would be to analyze the problem by identifying the main barriers to effective communication. The team then comes up with a solution, such as implementing regular team meetings and using collaborative software. After implementing this solution, they evaluate its effectiveness by monitoring communication patterns and gathering feedback from team members. If improvements are needed, they can go back to the analysis phase and refine their approach.

Understanding the Problem

Before you can solve a problem, it’s important to fully understand what the problem entails. This involves taking the time to evaluate and analyze the situation at hand. By thoroughly understanding the problem, you can then develop a plan of action and implement a solution.

When faced with a problem, take the time to define it clearly. What is the nature of the problem? What are its causes and effects? This step is crucial in order to identify the root cause and develop an effective solution.

Analyze the problem by breaking it down into smaller components. This will help you gain a deeper understanding of its complexities. By identifying the different factors at play, you can better assess the problem and develop a more targeted approach.

Once you have a clear understanding of the problem, it’s time to implement solutions. This involves brainstorming ideas and evaluating potential solutions. Consider the advantages and disadvantages of each option, and choose the best course of action.

Remember, problem solving is a cycle. After implementing a solution, it’s important to evaluate its effectiveness. Did it solve the problem? Did it improve the situation? If not, go back to the beginning of the cycle and re-evaluate the problem. This iterative process allows for continuous improvement and ensures that the problem is fully solved.

Analyzing the Situation

Before implementing the problem solving cycle, it is crucial to thoroughly analyze the situation at hand. This step plays a vital role in identifying the problem and understanding its root causes. Here are the key steps to follow when analyzing a problem:

  • Analyze the problem: Begin by clearly defining the problem and understanding its scope and impact. Break down the problem into manageable components to identify the specific issues.
  • Gather information: Collect relevant data and facts about the problem. This could involve conducting research, surveying stakeholders, or examining past examples or experiences.
  • Identify the root causes: Dig deeper to uncover the underlying reasons behind the problem. This requires examining the contributing factors and identifying any patterns or trends.
  • Develop hypotheses: Based on the information gathered, generate potential hypotheses about what might be causing the problem. These hypotheses will serve as a starting point for further investigation.
  • Verify the hypotheses: Test the hypotheses through further analysis or experimentation. This step helps to validate or rule out potential causes and narrow down the focus.
  • Evaluate the information: Assess the findings from the analysis and determine their significance. Consider the potential impact of each possible cause on the problem.

By following this analytical process, you can gain a deep understanding of the problem and its underlying causes. This knowledge will guide you towards effectively implementing the problem solving cycle and developing an appropriate solution. Let’s consider an example to illustrate this:

Example: Imagine a manufacturing company facing a sudden decrease in product quality. To analyze the situation, the company would first define the problem as a decline in quality control. They would then gather data on production processes, equipment, and employee feedback. Upon analysis, they might discover that faulty equipment or inadequate training are the root causes. They would then develop hypotheses to test these potential causes and evaluate the impact of each on the problem.

By carefully analyzing the situation, you can uncover valuable insights and set the foundation for effective problem solving. This step ensures that the subsequent steps of implementing, evaluating, and refining the solution are based on a comprehensive understanding of the problem at hand.

Gathering Relevant Information

As part of the problem-solving cycle, gathering relevant information is an essential step in finding a solution. Without a clear understanding of the problem at hand, it becomes challenging to analyze and find an appropriate solution.

Analyze the Problem

Before attempting to gather information, it is crucial to thoroughly analyze the problem. This involves breaking down the problem into smaller components and identifying any underlying issues or causes. By doing so, it becomes easier to determine what kind of information is needed to address the problem effectively.

Identify the Information Needed

Once the problem has been analyzed, the next step is to identify the specific information that is relevant to finding a solution. This may include data, statistics, reports, expert opinions, or any other relevant sources of information. It is important to be selective and focus on gathering the most pertinent information to avoid being overwhelmed or distracted by irrelevant details.

By gathering relevant information, you can gain a deeper understanding of the problem you are facing. This will allow you to evaluate potential solutions more effectively and make informed decisions about the best course of action to take.

In addition to gathering information, it is also important to consider the source and reliability of the information. This will help ensure that the data you are using to analyze the problem and develop a solution is accurate and trustworthy.

Once you have gathered the necessary information, the next steps in the problem-solving cycle include evaluating potential solutions, implementing the chosen solution, and monitoring its effectiveness. Gathering relevant information is an ongoing process, as new information may arise during the problem-solving journey that can help improve the chosen solution or address any unforeseen challenges.

Overall, gathering relevant information is a critical step in the problem-solving cycle. It sets the foundation for effectively analyzing the problem, finding the best solution, and continuously improving upon that solution.

Identifying the Root Cause

In the problem solving cycle, it is important to accurately identify the root cause of the problem. This step involves evaluating all possible factors that may contribute to the problem and analyzing them to determine the underlying cause.

For example, if a company is experiencing low sales, it may be tempting to assume that the problem lies in the sales team’s performance. However, a deeper analysis may reveal that the root cause is actually a lack of marketing efforts to attract new customers.

Identifying the root cause allows for a targeted approach to problem solving. It enables the implementation of strategies and solutions that directly address the underlying issue, leading to long-term improvements. Without a thorough analysis and identification of the root cause, any solutions implemented may only provide temporary relief and fail to address the problem at its core.

Generating Solutions

Once you have completed the analyze step of the problem solving cycle and have a clear understanding of the problem, it is time to generate potential solutions. This step involves coming up with different ideas and options that could potentially solve the problem at hand.

Brainstorming

One of the most common techniques for generating solutions is brainstorming. This involves getting a group of people together and encouraging them to come up with as many ideas as possible, without judgment or critique. The goal is to generate a large quantity of ideas, as even seemingly unrelated or outlandish suggestions can often spark new and innovative solutions.

Considering Example Solutions

Another approach for generating solutions is to look at example solutions that have been successful in similar situations. By learning from past experiences and successes, you can gain insights that may help you generate potential solutions for your current problem. This could involve researching case studies, speaking with experts in the field, or reviewing best practices.

When generating solutions, it is important to keep in mind that no idea is too small or too big. The goal is to think creatively and come up with as many options as possible. It is also helpful to involve different perspectives and expertise to ensure a diverse range of ideas.

Once you have generated a list of potential solutions, it is time to evaluate and select the best options to implement. This will involve analyzing the pros and cons of each solution, considering feasibility and potential impact. The goal is to choose the solution(s) that have the highest likelihood of solving the problem and improving the situation.

During this step, it is important to remember that generating solutions is just one part of the problem solving cycle. The next steps involve implementing the chosen solution, monitoring its effectiveness, and making improvements as needed. The problem solving cycle is an iterative process, and solutions may need to be refined and adjusted based on ongoing evaluation.

Creative Idea Generation

Problem solving is an essential skill in various areas of life, including work, relationships, and personal growth. To effectively solve a problem, it is important to go through a problem-solving cycle that involves several steps such as problem identification, analysis, solution generation, and evaluation. One crucial step in this cycle is creative idea generation.

Why is Creative Idea Generation Important?

Creative idea generation is essential as it allows individuals to think outside the box and come up with innovative and unique solutions. It goes beyond traditional thinking and encourages individuals to explore new possibilities and perspectives.

By engaging in creative idea generation, individuals can expand their problem-solving skills and come up with unconventional solutions that can greatly improve the outcome of a problem. It allows for a more comprehensive evaluation of potential options and helps in identifying the best possible solution.

How to Generate Creative Ideas?

Here are some strategies that can help in generating creative ideas:

  • Brainstorming: Gather a group of individuals and encourage them to freely share their ideas without judgment. This can lead to the generation of diverse and fresh perspectives.
  • Mind Mapping: Create a visual representation of the problem and brainstorm various related ideas and concepts. This can help in identifying connections and generating new ideas.
  • Reverse Thinking: Instead of focusing on the problem, try to think of the opposite or reverse of the problem. This can trigger unique and unconventional solutions.
  • Combining Ideas: Take different ideas and concepts and try to combine them in new and creative ways. This can result in innovative and out-of-the-box solutions.
  • Role Playing: Imagine yourself as a different person, such as a famous personality or a fictional character, and think about how they would approach the problem. This can provide fresh insights and ideas.

Let’s consider an example to better understand the process of creative idea generation within the problem-solving cycle:

A company is facing a decline in sales and wants to improve its market position. The problem is identified, analyzed, and the solution generation phase begins.

During the creative idea generation phase, the team engages in brainstorming sessions and mind mapping exercises. They generate ideas such as launching a new product line, targeting a different demographic, implementing a unique marketing campaign, or partnering with influential industry leaders.

The team evaluates these ideas, considering factors such as feasibility, potential impact, and resources required. After thorough evaluation, they decide to implement a combination of launching a new product line and targeting a different demographic. This solution is expected to improve sales and strengthen the company’s market position.

In conclusion, creative idea generation plays a crucial role in the problem-solving cycle. By encouraging out-of-the-box thinking, individuals can come up with innovative solutions that can greatly improve the outcome of a problem. By utilizing strategies such as brainstorming, mind mapping, reverse thinking, combining ideas, and role-playing, individuals can enhance their creative idea generation skills and effectively solve problems.

Evaluating Possible Approaches

Once you have identified and analyzed the problem you are trying to solve, it is important to consider various possible approaches to finding a solution. Evaluating these approaches will help you determine the most effective and efficient way to solve the problem.

To evaluate the possible approaches, you should:

  • Analyze each approach in detail: Take the time to thoroughly understand each approach and its potential benefits and drawbacks. Consider factors such as feasibility, cost, time, and resources required.
  • Consider the example and context: Think about how each approach relates specifically to the example problem you are trying to solve. Consider if the approach is suitable and applicable to your situation.
  • Compare the approaches: Look at the pros and cons of each approach and weigh them against each other. Consider the potential outcomes and impact of each approach on solving the problem.
  • Implement and test the approaches: Consider implementing a small-scale test of each approach to see how well it works in practice. This can help you gain insights into the potential effectiveness of each approach and help you make an informed decision.

By evaluating and testing the possible approaches, you can make an informed decision about which approach is the best fit for solving the problem at hand. Remember that the problem-solving cycle is iterative, so if the chosen approach does not yield the desired results, you can go back and analyze the problem again, and evaluate and test different approaches until a suitable solution is found.

Selecting the Best Solution

Once you have gone through the problem solving cycle, which includes steps such as analyzing the problem, brainstorming solutions, and evaluating their feasibility, it’s time to select the best solution to implement.

To select the best solution, you’ll need to carefully evaluate each option based on its potential effectiveness and feasibility in solving the problem at hand. Consider the example situation where a company is facing decreased productivity due to outdated technology. Possible solutions may include upgrading the existing technology, implementing new software, or retraining employees on the current system.

Evaluating Effectiveness

The first step in selecting the best solution is to evaluate its potential effectiveness in solving the problem. Consider the example problem of the company facing decreased productivity due to outdated technology. Ask yourself questions such as:

  • Will this solution address the root cause of the problem?
  • Does it have the potential to improve productivity?
  • Will it align with the goals and values of the company?

By analyzing the potential effectiveness of each solution, you can narrow down your options and focus on those that have the highest likelihood of success.

Assessing Feasibility

In addition to effectiveness, it’s important to assess the feasibility of each solution. This involves considering factors such as:

  • Available resources: Does the solution require significant financial investment or specialized skills?
  • Time constraints: Can the solution be implemented within a reasonable timeframe?
  • Impact on stakeholders: How will the solution affect different stakeholders, such as employees, customers, or shareholders?

By evaluating the feasibility of each solution, you can determine whether it is realistic and practical to implement.

Once you have evaluated the potential effectiveness and feasibility of each solution, you can make an informed decision and select the best solution to implement. However, it’s important to keep in mind that problem solving is an iterative process. You may need to re-evaluate and improve your chosen solution as you go along to ensure its success in effectively solving the problem.

Weighing Pros and Cons

Once you have identified a potential solution to the problem, the next step in the problem solving cycle is to weigh the pros and cons of that solution. This step is crucial in order to evaluate the feasibility and effectiveness of the solution before implementing it.

Analyze the Solution

First, you need to analyze the proposed solution in depth. Look at how it addresses the problem and how it can improve the current situation. Consider the resources required and any potential risks or drawbacks that may arise.

For example, let’s say you’re trying to solve the problem of communication breakdown in a team. One potential solution could be to implement a project management software that allows team members to easily collaborate and share updates. When analyzing this solution, you would evaluate how it addresses the problem of communication breakdown and whether it would effectively improve communication within the team.

Evaluate the Pros and Cons

Next, you need to weigh the pros and cons of the solution. Consider the potential benefits that the solution offers and balance them against any drawbacks or limitations it may have. This evaluation will help you determine whether the solution is a viable option or if you need to explore alternative solutions.

Continuing with the example of solving communication breakdown in a team, some pros of implementing a project management software could include improved communication, better organization, and increased productivity. On the other hand, some cons could be the cost of the software, the time required for training, and potential resistance from team members to adopt a new tool.

By evaluating the pros and cons, you can make an informed decision about whether the benefits outweigh the drawbacks and if the solution is worth pursuing.

Remember, the goal of weighing the pros and cons is not only to make the best decision for solving the problem, but also to learn from the process. Sometimes, a solution may have more cons than pros, but the analysis itself can provide valuable insights for future problem solving.

Consideration of Feasibility

Once a potential solution has been identified, the next step in the problem solving cycle is to evaluate the feasibility of that solution. This involves considering whether the proposed solution is practical, realistic, and achievable given the resources, time, and limitations at hand.

During this stage, it is important to thoroughly assess the potential solution and determine whether it is a viable option for addressing the problem. Various factors need to be taken into consideration, such as the availability of resources, the time required for implementation, and any potential risks or obstacles that may arise.

Evaluation of Resources

One key aspect to consider is the availability of resources. This includes both financial resources and human resources. Can the necessary funding be secured to implement the solution? Are there enough skilled individuals available to carry out the required tasks? Evaluating resources helps determine whether the proposed solution is financially and practically feasible.

Risk Assessment

Another important consideration is conducting a thorough risk assessment. This involves identifying potential risks or obstacles that may arise during implementation and determining their likelihood and impact. By anticipating and addressing these risks in advance, potential challenges can be mitigated or avoided altogether, increasing the feasibility of the proposed solution.

Overall, the consideration of feasibility is a crucial step in the problem solving cycle. It helps ensure that the proposed solution is not only effective in addressing the problem, but also practical and achievable within the given constraints. By carefully evaluating the resources, assessing potential risks, and identifying any limitations, organizations can improve their problem solving efforts and implement solutions that yield positive results.

Implementing the Solution

Once you have analyzed the problem and developed a solution, it is time to implement it. Implementing the solution involves putting the plan into action and making the necessary changes to improve the situation.

For example, let’s say the problem you identified was a slow response time for customer inquiries. After analyzing the problem, you determined that hiring additional customer service representatives would help improve the response time. The solution you developed was to hire three new representatives.

Now, it is time to implement the solution. This involves going through the hiring process, such as posting job advertisements, conducting interviews, and selecting the candidates. Once the new representatives are hired, they can be trained and integrated into the customer service team.

By implementing the solution, you are taking concrete steps to address the problem and improve the overall situation. However, it is important to monitor the effectiveness of the solution and make adjustments if necessary. This may involve analyzing response times after the new representatives have been working for a while to ensure that the desired improvement has been achieved.

The implementation phase is a crucial part of the problem-solving cycle. It is where the solution becomes a reality and tangible changes are made. By following this cycle of analyzing the problem, developing a solution, and implementing it, you can effectively solve problems and improve various aspects of your work or personal life.

Creating an Action Plan

Once you have identified and analyzed the problem, it’s time to create an action plan to solve it. An action plan outlines the steps you need to take to address the problem and implement a solution.

Evaluate the Solution Options: Start by evaluating the potential solutions you identified during the problem-solving cycle. Consider the advantages and disadvantages of each solution, and assess their feasibility and potential impact.

Choose the Best Solution: Based on your evaluation, choose the solution that is most likely to solve the problem effectively. Consider the resources required, the time frame, and the expected outcomes.

Create a Timeline: Develop a timeline that outlines the specific actions you need to take and the deadlines for each action. This will help you stay organized and ensure that you are making progress towards your goal.

Determine the Necessary Resources: Identify the resources you need to implement your solution. This could include tools, equipment, funding, or additional personnel. Make sure you have access to all the necessary resources before proceeding.

Assign Responsibilities: Determine who will be responsible for each action or task in the action plan. Clearly define roles and responsibilities to ensure that everyone understands what is expected of them.

Implement the Action Plan: Begin implementing the action plan by executing the steps outlined. Regularly monitor progress and make adjustments as needed.

Track Results: Monitor the results of your actions and assess whether they are having the desired impact. If necessary, make changes or adjustments to improve the effectiveness of your solution.

Seek Feedback: Throughout the implementation process, seek feedback from relevant stakeholders. This feedback can help you refine your approach and ensure that you are addressing the problem in the most effective way.

Document Lessons Learned: Once the problem is solved, take the time to document what you have learned from the experience. This can help you improve future problem-solving efforts and build upon your analytical and problem-solving skills.

Let’s say you have identified a problem in your workplace where employees are feeling overwhelmed due to high workloads. You analyze the problem and identify potential solutions, such as hiring additional staff, implementing new processes, or redistributing tasks. After evaluating the options, you choose to implement a combination of hiring additional staff and redistributing tasks to alleviate the workload. You create a timeline with specific deadlines for hiring new staff and reassigning tasks. You assign responsibilities to the HR department for hiring and to department managers for task redistribution. As the action plan is implemented, you track the progress and seek feedback from employees to ensure the solution is effective. Once the workload is successfully reduced, you document the lessons learned, such as the importance of proactive workload management and the benefits of effective communication within the team.

Executing the Plan

Once a problem has been thoroughly analyzed and a solution has been developed, it is time to move forward with implementing the plan. This stage of the problem-solving cycle is crucial, as it is where the proposed solution is put into action.

When executing the plan, it is important to have a clear understanding of the steps that need to be taken and the resources required. This may involve coordinating with other individuals or teams, gathering necessary materials or equipment, and allocating time and budget effectively.

During the implementation phase, it is essential to maintain open lines of communication and provide clear instructions to those involved. This helps to ensure that everyone is on the same page and working towards the same goals. Any issues or obstacles that arise should be addressed promptly and effectively to keep the execution on track.

As the solution is being implemented, it is also important to continuously evaluate and monitor progress. This allows for any adjustments or modifications to be made as needed, ensuring that the solution remains effective and aligned with the desired outcome. Regularly analyzing and evaluating the execution of the plan helps to identify any areas for improvement or potential issues that may arise.

Throughout the execution stage, it is crucial to stay focused and committed to the problem-solving cycle. By following the steps of the cycle – analyze, develop, implement, evaluate – in a systematic and disciplined manner, the chances of successfully solving the problem and achieving the desired solution are greatly increased.

In summary, executing the plan is a vital part of the problem-solving cycle. It involves putting the proposed solution into action, coordinating with others, addressing any issues that arise, and continuously evaluating and monitoring progress. By diligently following the steps of the problem-solving cycle, individuals and teams can improve their problem-solving skills and increase their chances of finding effective solutions.

Monitoring Progress

After implementing a solution to a problem, it is important to continuously monitor the progress and evaluate its effectiveness. This allows for improvements to be made if necessary and ensures that the problem is fully solved.

To monitor progress, it is important to regularly analyze the results of the implemented solution. This can be done by comparing the current state to the desired outcome. If the desired outcome has been achieved, then the problem has been successfully solved. If not, further evaluation is required to identify any areas that need improvement.

An example of monitoring progress in a problem solving cycle can be seen in a customer service scenario. Let’s say a company is experiencing a high number of customer complaints. The problem is analyzed, and a solution is implemented to improve customer service training and communication skills.

To monitor progress, the company sets a goal of reducing customer complaints by 50% within six months. They regularly collect data on the number of complaints and compare it to the initial baseline. If the number of complaints decreases over time and reaches the desired reduction goal, then the solution is effective.

However, if the number of complaints does not decrease, further analysis is needed to identify any gaps in the implemented solution. The company may need to re-evaluate the training program, analyze customer feedback, or make adjustments to their communication processes. This ongoing monitoring and evaluation allows for continuous improvement and ensures that the problem is fully solved.

Making Adjustments as Needed

Once you have gone through the problem solving cycle and implemented a solution, it is important to evaluate the results and make any necessary adjustments. The problem solving cycle is an iterative process, and making adjustments is a key part of refining your solution.

Evaluate the Solution

After implementing your solution, take the time to evaluate its effectiveness. Did it solve the problem you identified? Is the solution meeting the desired goals and objectives? Gather data and feedback to help you make an informed evaluation. Consider using quantitative and qualitative analysis to measure the impact of your solution.

Analyze the Results

Once you have evaluated the solution, analyze the results to understand why the solution worked or did not work as expected. Look for patterns and trends in the data and feedback you gathered. This analysis will help you identify any adjustments that need to be made.

For example, if the solution did not meet the desired goals, identify the areas where it fell short and determine why. Is there a flaw in the solution itself, or did you encounter unforeseen obstacles during implementation? This analysis will help you pinpoint the root cause of any issues and guide you in making the necessary adjustments.

Remember that the problem solving cycle is an iterative process, and making adjustments is part of that process. Do not be discouraged if your initial solution did not solve the problem entirely. Use the evaluation and analysis of the results to inform your next steps and make the necessary adjustments.

Implement Adjustments

Based on your evaluation and analysis, develop a plan to implement the necessary adjustments. This may involve modifying your solution, revising your approach, or seeking additional resources or expertise. Take into account the lessons learned from the previous implementation and apply them to the adjustments you are making.

Document the adjustments you are making and communicate them to the relevant stakeholders. This will ensure that everyone is aware of the changes being made and why they are being made. It will also help you track the progress of the adjustments and evaluate their effectiveness.

Continue to cycle through the problem solving process, making adjustments as needed, until you have achieved a satisfactory solution. Each iteration of the cycle will bring you closer to solving the problem effectively and efficiently.

Evaluating the Outcome

Once you have implemented a solution to a problem, it is important to evaluate the outcome to determine if it effectively solved the problem. This step is an essential part of the problem-solving cycle, as it allows you to analyze the results and make improvements for future problem-solving efforts.

When evaluating the outcome, it is important to assess whether the problem was fully solved or if there are any lingering issues. This involves comparing the initial problem with the results achieved after implementing the solution. Look for any discrepancies or areas where the implemented solution may have fallen short.

One way to evaluate the outcome is to gather feedback from stakeholders or those directly impacted by the problem. Their insights can provide valuable information on the effectiveness of the solution. Additionally, consider conducting a post-implementation analysis to identify any unexpected consequences or side effects.

Based on the evaluation, you can determine if any further improvements are necessary. This may involve tweaking the implemented solution, seeking additional resources, or revisiting the problem-solving cycle to develop a more effective approach. Remember that problem-solving is a continuous process, and there is always room for improvement.

For example, let’s say a company implemented a new customer service system to address customer complaints and improve overall satisfaction. After the system was implemented, they collected feedback from customers and conducted an analysis of support tickets. It was found that while customer complaints decreased, some customers still experienced longer wait times and difficulty reaching customer service representatives. Based on this evaluation, the company decided to make adjustments to the system and allocate additional resources to address these issues and further improve customer satisfaction.

In conclusion, evaluating the outcome is a crucial step in the problem-solving cycle. It allows you to analyze the effectiveness of the implemented solution, identify any remaining issues, and make improvements for future problem-solving efforts. By continuously evaluating and refining your problem-solving approach, you can enhance your ability to effectively solve problems and achieve desired outcomes.

Assessing Results

After implementing a solution to a problem, it is important to assess the results achieved through the problem-solving cycle. This step allows you to analyze the effectiveness of the solution and identify any areas for improvement.

One way to assess the results is to compare the actual outcomes with the expected outcomes. Did the solution successfully resolve the problem at hand? Did it achieve the desired goals and objectives? This analysis will help determine the overall success of the problem-solving cycle.

It is also important to gather feedback from those involved in the problem-solving process. This can be done through surveys, interviews, or discussions. By gathering input from different perspectives, you can gain a comprehensive understanding of the solution’s impact and identify any potential areas for improvement.

An assessment of the results can also involve evaluating the implementation process itself. Did the solution follow the problem-solving cycle effectively? Were there any deviations or challenges encountered along the way? This evaluation can help identify any weaknesses in the problem-solving process and provide insights on how to improve it in the future.

Finally, it is important to document the results of the problem-solving cycle. This can involve creating a report or summary that outlines the problem, the solution implemented, and the results achieved. This documentation will serve as a reference for future problem-solving efforts and can help facilitate continuous improvement.

For example, let’s say a company identified a problem with their customer service response time. Through the problem-solving cycle, they developed a solution that involved implementing a new ticketing system and providing additional training to customer service representatives. After the solution was implemented, they assessed the results by comparing the average response time before and after the changes. If they saw a significant improvement in response time, they would consider the solution successful and identify any areas where further improvements could be made.

In conclusion, assessing the results of a problem-solving cycle is crucial to determine the effectiveness of the solution and identify areas for improvement. By analyzing the outcomes, gathering feedback, evaluating the implementation process, and documenting the results, you can ensure that the problem-solving cycle leads to continuous improvement.

Learning from the Experience

Once you have gone through the problem-solving cycle and have come up with a solution, it is important to take the time to learn from the experience. This step is crucial in improving your problem-solving skills and preventing similar issues from arising in the future.

First, it is important to analyze the solution that you have come up with. Ask yourself if it effectively addresses the problem at hand. Did it provide a practical solution? Were there any unexpected outcomes or side effects?

Next, evaluate the entire problem-solving cycle. Look back at each step you took, from identifying the problem to implementing the solution. Were there any areas where you could have approached the problem differently? Were there any steps that could have been skipped or were unnecessary?

By analyzing both the solution and the problem-solving cycle, you can gain valuable insights and learn from your experience. You may discover areas where you can improve your problem-solving skills or identify patterns that can help you handle similar problems more effectively in the future.

It is also important to reflect on the example you have just encountered. Consider how this problem-solving cycle can be applied to different situations. Reflecting on past experiences and considering different scenarios can help you become a more adaptable problem solver.

Remember, problem-solving is an ongoing process, and there is always room for improvement. By learning from each experience and continuously refining your problem-solving skills, you can become more effective in finding solutions and overcoming challenges.

Questions and answers:

What is the problem solving cycle.

The problem solving cycle is a step-by-step process used to solve a problem or find a solution to a complex issue. It typically involves identifying the problem, brainstorming possible solutions, evaluating those solutions, implementing the best solution, and finally, reflecting on the effectiveness of the chosen solution.

Why is the problem solving cycle important?

The problem solving cycle is important because it provides a structured approach to addressing problems and finding solutions. It helps individuals or teams break down complex problems into manageable parts, encourages creative thinking and innovation, and ensures that solutions are evaluated and refined for optimal effectiveness.

Can you give an example of the problem solving cycle in action?

Sure! Let’s say a team is faced with the problem of declining sales. They would first identify the problem by analyzing sales data and customer feedback. Then, they would brainstorm possible solutions, such as offering discounts, launching a new marketing campaign, or improving customer service. Next, they would evaluate each solution based on feasibility, cost, and potential impact. After selecting the best solution, they would implement it and closely monitor its effectiveness. Finally, they would reflect on whether the chosen solution effectively addressed the problem and make any necessary adjustments.

What are the benefits of using the problem solving cycle?

Using the problem solving cycle offers several benefits. It helps individuals or teams become more effective problem solvers, encourages collaboration and communication, fosters creativity and innovation, and ensures that solutions are well-thought-out and implemented in a systematic manner. Additionally, it can improve decision making, build critical thinking skills, and lead to continuous improvement within organizations.

Are there any potential challenges when using the problem solving cycle?

Yes, there can be potential challenges when using the problem solving cycle. One challenge is ensuring that all team members have the necessary skills and knowledge to effectively analyze and solve the problem at hand. Another challenge is managing time and resources efficiently throughout the problem solving process. Additionally, resistance to change or a lack of commitment to implementing solutions can hinder the effectiveness of the problem solving cycle. However, with proper planning, communication, and support, these challenges can be overcome.

The problem solving cycle is a systematic approach used to solve problems and make decisions. It involves several steps, including identifying the problem, gathering information, generating possible solutions, evaluating those solutions, and implementing the best solution.

Can you give me an example of the problem solving cycle in action?

Certainly! Let’s say you are experiencing a technical issue with your computer. The first step in the problem solving cycle would be to identify the problem, which is the computer not turning on. Then, you would gather information about the problem, such as whether there are any error messages or if the computer was working fine before. Next, you would generate possible solutions, such as checking the power source or restarting the computer. After evaluating these solutions, you might decide to try restarting the computer first. Finally, you would implement the solution by restarting the computer and observing whether it solves the problem.

The problem solving cycle is important because it provides a structured approach to addressing and resolving problems. It helps individuals and teams think critically, consider multiple perspectives, and make informed decisions. By following the problem solving cycle, problems can be solved more efficiently and effectively.

The benefits of using the problem solving cycle include increased efficiency in problem solving, improved decision making, and better outcomes. By following a systematic approach, individuals and teams can thoroughly analyze problems, consider different solutions, and evaluate their effectiveness. This can lead to more effective problem resolution and a higher likelihood of achieving desired outcomes.

What are some common challenges in the problem solving cycle?

Some common challenges in the problem solving cycle include difficulty in identifying the root cause of the problem, lack of relevant information or data, limited creativity in generating solutions, and resistance to change when implementing solutions. It is important to address these challenges by using tools and techniques such as root cause analysis, data gathering methods, brainstorming, and change management strategies.

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  • The Comprehensive Guide to the Problem Solving Cycle in PDF Format
  • A Comprehensive Guide to the Problem Solving Cycle in Psychology – Strategies, Techniques, and Applications
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5 Advantages and Disadvantages of Problem-Based Learning [+ Activity Design Steps]

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Written by Marcus Guido

Easily differentiate learning and engage your students with Prodigy Math.

  • Teaching Strategies

Advantages of Problem-Based Learning

Disadvantages of problem-based learning, steps to designing problem-based learning activities.

Used since the 1960s, many teachers express concerns about the effectiveness of problem-based learning (PBL) in certain classroom settings.

Whether you introduce the student-centred pedagogy as a one-time activity or mainstay exercise, grouping students together to solve open-ended problems can present pros and cons.

Below are five advantages and disadvantages of problem-based learning to help you determine if it can work in your classroom.

If you decide to introduce an activity, there are also design creation steps and a downloadable guide to keep at your desk for easy reference.

1. Development of Long-Term Knowledge Retention

Students who participate in problem-based learning activities can improve their abilities to retain and recall information, according to a literature review of studies about the pedagogy .

The literature review states “elaboration of knowledge at the time of learning” -- by sharing facts and ideas through discussion and answering questions -- “enhances subsequent retrieval.” This form of elaborating reinforces understanding of subject matter , making it easier to remember.

Small-group discussion can be especially beneficial -- ideally, each student will get chances to participate.

But regardless of group size, problem-based learning promotes long-term knowledge retention by encouraging students to discuss -- and answer questions about -- new concepts as they’re learning them.

2. Use of Diverse Instruction Types

problem solving cycle advantages and disadvantages

You can use problem-based learning activities to the meet the diverse learning needs and styles of your students, effectively engaging a diverse classroom in the process. In general, grouping students together for problem-based learning will allow them to:

  • Address real-life issues that require real-life solutions, appealing to students who struggle to grasp abstract concepts
  • Participate in small-group and large-group learning, helping students who don’t excel during solo work grasp new material
  • Talk about their ideas and challenge each other in a constructive manner, giving participatory learners an avenue to excel
  • Tackle a problem using a range of content you provide -- such as videos, audio recordings, news articles and other applicable material -- allowing the lesson to appeal to distinct learning styles

Since running a problem-based learning scenario will give you a way to use these differentiated instruction approaches , it can be especially worthwhile if your students don’t have similar learning preferences.

3. Continuous Engagement

problem solving cycle advantages and disadvantages

Providing a problem-based learning challenge can engage students by acting as a break from normal lessons and common exercises.

It’s not hard to see the potential for engagement, as kids collaborate to solve real-world problems that directly affect or heavily interest them.

Although conducted with post-secondary students, a study published by the Association for the Study of Medical Education reported increased student attendance to -- and better attitudes towards -- courses that feature problem-based learning.

These activities may lose some inherent engagement if you repeat them too often, but can certainly inject excitement into class.

4. Development of Transferable Skills

Problem-based learning can help students develop skills they can transfer to real-world scenarios, according to a 2015 book that outlines theories and characteristics of the pedagogy .

The tangible contexts and consequences presented in a problem-based learning activity “allow learning to become more profound and durable.” As you present lessons through these real-life scenarios, students should be able to apply learnings if they eventually face similar issues.

For example, if they work together to address a dispute within the school, they may develop lifelong skills related to negotiation and communicating their thoughts with others.

As long as the problem’s context applies to out-of-class scenarios, students should be able to build skills they can use again.

5. Improvement of Teamwork and Interpersonal Skills

problem solving cycle advantages and disadvantages

Successful completion of a problem-based learning challenge hinges on interaction and communication, meaning students should also build transferable skills based on teamwork and collaboration . Instead of memorizing facts, they get chances to present their ideas to a group, defending and revising them when needed.

What’s more, this should help them understand a group dynamic. Depending on a given student, this can involve developing listening skills and a sense of responsibility when completing one’s tasks. Such skills and knowledge should serve your students well when they enter higher education levels and, eventually, the working world.

1. Potentially Poorer Performance on Tests

problem solving cycle advantages and disadvantages

Devoting too much time to problem-based learning can cause issues when students take standardized tests, as they may not have the breadth of knowledge needed to achieve high scores. Whereas problem-based learners develop skills related to collaboration and justifying their reasoning, many tests reward fact-based learning with multiple choice and short answer questions. Despite offering many advantages, you could spot this problem develop if you run problem-based learning activities too regularly.

2. Student Unpreparedness

problem solving cycle advantages and disadvantages

Problem-based learning exercises can engage many of your kids, but others may feel disengaged as a result of not being ready to handle this type of exercise for a number of reasons. On a class-by-class and activity-by-activity basis, participation may be hindered due to:

  • Immaturity  -- Some students may not display enough maturity to effectively work in a group, not fulfilling expectations and distracting other students.
  • Unfamiliarity  -- Some kids may struggle to grasp the concept of an open problem, since they can’t rely on you for answers.
  • Lack of Prerequisite Knowledge  -- Although the activity should address a relevant and tangible problem, students may require new or abstract information to create an effective solution.

You can partially mitigate these issues by actively monitoring the classroom and distributing helpful resources, such as guiding questions and articles to read. This should keep students focused and help them overcome knowledge gaps. But if you foresee facing these challenges too frequently, you may decide to avoid or seldom introduce problem-based learning exercises.

3. Teacher Unpreparedness

If supervising a problem-based learning activity is a new experience, you may have to prepare to adjust some teaching habits . For example, overtly correcting students who make flawed assumptions or statements can prevent them from thinking through difficult concepts and questions. Similarly, you shouldn’t teach to promote the fast recall of facts. Instead, you should concentrate on:

  • Giving hints to help fix improper reasoning
  • Questioning student logic and ideas in a constructive manner
  • Distributing content for research and to reinforce new concepts
  • Asking targeted questions to a group or the class, focusing their attention on a specific aspect of the problem

Depending on your teaching style, it may take time to prepare yourself to successfully run a problem-based learning lesson.

4. Time-Consuming Assessment

problem solving cycle advantages and disadvantages

If you choose to give marks, assessing a student’s performance throughout a problem-based learning exercise demands constant monitoring and note-taking. You must take factors into account such as:

  • Completed tasks
  • The quality of those tasks
  • The group’s overall work and solution
  • Communication among team members
  • Anything you outlined on the activity’s rubric

Monitoring these criteria is required for each student, making it time-consuming to give and justify a mark for everyone.

5. Varying Degrees of Relevancy and Applicability

It can be difficult to identify a tangible problem that students can solve with content they’re studying and skills they’re mastering. This introduces two clear issues. First, if it is easy for students to divert from the challenge’s objectives, they may miss pertinent information. Second, you could veer off the problem’s focus and purpose as students run into unanticipated obstacles. Overcoming obstacles has benefits, but may compromise the planning you did. It can also make it hard to get back on track once the activity is complete. Because of the difficulty associated with keeping activities relevant and applicable, you may see problem-based learning as too taxing.

If the advantages outweigh the disadvantages -- or you just want to give problem-based learning a shot -- follow these steps:

1. Identify an Applicable Real-Life Problem

problem solving cycle advantages and disadvantages

Find a tangible problem that’s relevant to your students, allowing them to easily contextualize it and hopefully apply it to future challenges. To identify an appropriate real-world problem, look at issues related to your:

  • Students’ shared interests

You must also ensure that students understand the problem and the information around it. So, not all problems are appropriate for all grade levels.

2. Determine the Overarching Purpose of the Activity

Depending on the problem you choose, determine what you want to accomplish by running the challenge. For example, you may intend to help your students improve skills related to:

  • Collaboration
  • Problem-solving
  • Curriculum-aligned topics
  • Processing diverse content

A more precise example, you may prioritize collaboration skills by assigning specific tasks to pairs of students within each team. In doing so, students will continuously develop communication and collaboration abilities by working as a couple and part of a small group. By defining a clear purpose, you’ll also have an easier time following the next step.

3. Create and Distribute Helpful Material

problem solving cycle advantages and disadvantages

Handouts and other content not only act as a set of resources, but help students stay focused on the activity and its purpose. For example, if you want them to improve a certain math skill , you should make material that highlights the mathematical aspects of the problem. You may decide to provide items such as:

  • Data that helps quantify and add context to the problem
  • Videos, presentations and other audio-visual material
  • A list of preliminary questions to investigate

Providing a range of resources can be especially important for elementary students and struggling students in higher grades, who may not have self-direction skills to work without them.

4. Set Goals and Expectations for Your Students

Along with the aforementioned materials, give students a guide or rubric that details goals and expectations. It will allow you to further highlight the purpose of the problem-based learning exercise, as you can explain what you’re looking for in terms of collaboration, the final product and anything else. It should also help students stay on track by acting as a reference throughout the activity.

5. Participate

problem solving cycle advantages and disadvantages

Although explicitly correcting students may be discouraged, you can still help them and ask questions to dig into their thought processes. When you see an opportunity, consider if it’s worthwhile to:

  • Fill gaps in knowledge
  • Provide hints, not answers
  • Question a student’s conclusion or logic regarding a certain point, helping them think through tough spots

By participating in these ways, you can provide insight when students need it most, encouraging them to effectively analyze the problem.

6. Have Students Present Ideas and Findings

If you divided them into small groups, requiring students to present their thoughts and results in front the class adds a large-group learning component to the lesson. Encourage other students to ask questions, allowing the presenting group to elaborate and provide evidence for their thoughts. This wraps up the activity and gives your class a final chance to find solutions to the problem.

Wrapping Up

The effectiveness of problem-based learning may differ between classrooms and individual students, depending on how significant specific advantages and disadvantages are to you. Evaluative research consistently shows value in giving students a question and letting them take control of their learning. But the extent of this value can depend on the difficulties you face.It may be wise to try a problem-based learning activity, and go forward based on results.

Create or log into your teacher account on Prodigy -- an adaptive math game that adjusts content to accommodate player trouble spots and learning speeds. Aligned to US and Canadian curricula, it’s used by more than 350,000 teachers and 10 million students. It may be wise to try a problem-based learning activity, and go forward based on results.

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    Allocate Resources Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue. The best strategy for solving a problem depends largely on the unique situation.

  5. How to Apply the Plan-Do-Check-Act (PDCA) Model

    Most businesses want to improve. But when it comes to actually making needed changes, many fall short. Bureaucracy, silos, and even culture can block progress and stall innovation. The Plan-Do-Check-Act model helps break companies out of stagnancy and transition to a system of continuous improvement.

  6. PDCA Cycle: The Plan-do-check-act Cycle In A Nutshell

    The PDCA cycle is an iterative, four-step problem-solving and continuous improvement methodology developed by Walter A. Shewhart in the 1920s. It was later refined by the father of modern quality control, W. Edwards Deming. The PDCA cycle is an acronym of four distinct stages: plan, do, check, and act. Collectively, the four stages form a ...

  7. What is the Plan-Do-Check-Act (PDCA) cycle?

    Jump to section. The PDCA (Plan-Do-Check-Act) cycle is an interactive problem-solving strategy to improve processes and implement change. The PDCA cycle is a method for continuous improvement. Rather than representing a one-and-done process, the Plan-Do-Check-Act cycle is an ongoing feedback loop for iterations and process improvements.

  8. The problem with Plan-Do-Study-Act cycles

    Of the many QI tools and methods, the Plan-Do-Study-Act (PDSA) cycle is one of the few that focuses on the crux of change, the translation of ideas and intentions into action. As such, the PDSA cycle and the concept of iterative tests of change are central to many QI approaches, including the model for improvement, 1 lean, 2 six sigma 3 and ...

  9. PDCA cycle

    Disadvantages of the PDCA cycle. Despite its advantages, PDCA is not perfect. There are also negative aspects to consider before deciding to apply the concept. A fixed principle; First and foremost, the PDCA cycle operates on a fixed principle and leaves little room for other variables during implementation. Slow progress

  10. Problem-Solving Strategies and Obstacles

    Assumptions: When dealing with a problem, people can make assumptions about the constraints and obstacles that prevent certain solutions. Thus, they may not even try some potential options. Functional fixedness: This term refers to the tendency to view problems only in their customary manner. Functional fixedness prevents people from fully seeing all of the different options that might be ...

  11. Plan-Do-Check-Act Cycle

    Plan-do-check-act (PDCA) is a four step cycle that allows you to implement change, solve problems, and continuously improve processes. Its cyclical nature allows it to be utilized in a continuous manner for ongoing improvement. ... Maintains order during problem solving. Disadvantages. Requires significant commitment over time.

  12. How to master the seven-step problem-solving process

    When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that's very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use ...

  13. Problem Solving Cycle and Communication: A Guide

    The second step of the problem solving cycle is to generate and evaluate possible solutions to the problem. This means exploring different options, weighing the pros and cons, and choosing the ...

  14. What is a problem-solving cycle? With 9 steps to create one

    How to create a cycle. By following these steps, you can create your cycle: 1. Identify the problem. The first step is to identify what the problem is. It may seem obvious. But it's essential to be as specific as possible, as this ensures that you work on the right issue.

  15. The problem with Plan-Do-Study-Act cycles

    Quality improvement (QI) methods have been introduced to healthcare to support the delivery of care that is safe, timely, effective, efficient, equitable and cost effective. Of the many QI tools and methods, the Plan-Do-Study-Act (PDSA) cycle is one of the few that focuses on the crux of change, the translation of ideas and intentions into action. As such, the PDSA cycle and the concept of ...

  16. Fishbone diagram

    Advantages Disadvantages ; Encourages creativity when searching for the causes of a problem Requires discipline and simplicity in order to render the diagram useful : Categorises possible causes Predefined categories (e.g. 5M) can limit creative problem-solving avenues : Working in a team opens up new perspectives

  17. The DMAIC Model

    4 The DMAIC Model The DMAIC model is a problem-solving method used to identify flaws and improve inefficiencies in business processes. One challenge of day-to-day business is resolving problems. Imagine you run a small business that sells products online, and a quarterly review reveals a significant drop in orders.

  18. Team Problem Solving: Advantages and Disadvantages

    1. Better thinking: During team problem-solving process, a person might think of certain solutions which can be used to solve such issue for a temporary basis.

  19. Problem Solving Cycle: A Guide with Example

    The problem solving cycle is an iterative process, and solutions may need to be refined and adjusted based on ongoing evaluation. Creative Idea Generation. ... Consider the advantages and disadvantages of each solution, and assess their feasibility and potential impact. Choose the Best Solution: ...

  20. 5 Advantages and Disadvantages of Problem-Based Learning [+ Activity

    1. Development of Long-Term Knowledge Retention Students who participate in problem-based learning activities can improve their abilities to retain and recall information, according to a literature review of studies about the pedagogy.

  21. The advantages and disadvantages of problem-solving practice when

    ABSTRACT. How children learn to retrieve answers to basic (single-digit) addition problems and how teachers can support children's learning of retrieval has captivated my attention as a researcher and teacher educator for the last 25 years. In this chapter, I describe this research and explain how I got started with the help of Professor Mike ...

  22. What is Case-Based Reasoning (CBR)? Definition from WhatIs.com

    Gathering from memory an experience closest to the current problem. Reuse. Suggesting a solution based on the experience and adapting it to meet the demands of the new situation. Revision. Evaluating the use of the solution in the new context. Retaining. Storing this new problem-solving method in the memory system. Comparison to other techniques