structured problem solving

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The Basics of Structured Problem-Solving Methodologies: DMAIC & 8D

Topics: Minitab Engage

When it comes to solving a problem, organizations want to get to the root cause of the problem, as quickly as possible. They also want to ensure that they find the most effective solution to that problem, make sure the solution is implemented fully, and is sustained into the future so that the problem no longer occurs. The best way to do this is by implementing structured problem-solving. In this blog post, we’ll briefly cover structured problem-solving and the best improvement methodologies to achieve operational excellence. Before we dive into ways Minitab can help, let’s first cover the basics of problem-solving.

WHAT IS STRUCTURED PROBLEM-SOLVING?

Structured problem-solving is a disciplined approach that breaks down the problem-solving process into discrete steps with clear objectives. This method enables you to tackle complex problems, while ensuring you’re resolving the right ones. It also ensures that you fully understand those problems, you've considered the reasonable solutions, and are effectively implementing and sustaining them.

WHAT IS A STRUCTURED PROBLEM-SOLVING METHODOLOGY?

A structured problem-solving methodology is a technique that consists of a series of phases that a project must pass through before it gets completed. The goal of a methodology is to highlight the intention behind solving a particular problem and offers a strategic way to resolve it. WHAT ARE THE BEST PROBLEM-SOLVING METHODOLOGIES?

That depends on the problem you’re trying to solve for your improvement initiative. The structure and discipline of completing all the steps in each methodology is more important than the specific methodology chosen. To help you easily visualize these methodologies, we’ve created the Periodic Table of Problem-Solving Methodologies. Now let’s cover two important methodologies for successful process improvement and problem prevention: DMAIC and 8D .

8D is known as the Eight Disciplines of problem-solving. It consists of eight steps to solve difficult, recurring, or critical problems. The methodology consists of problem-solving tools to help you identify, correct, and eliminate the source of problems within your organization. If the problem you’re trying to solve is complex and needs to be resolved quickly, 8D might be the right methodology to implement for your organization. Each methodology could be supported with a project template, where its roadmap corresponds to the set of phases in that methodology. It is a best practice to complete each step of a given methodology, before moving on to the next one.

MINITAB ENGAGE, YOUR SOLUTION TO EFFECTIVE PROBLEM-SOLVING

Minitab Engage TM was built to help organizations drive innovation and improvement initiatives. What makes our solution unique is that it combines structured problem-solving methodologies with tools and dashboards to help you plan, execute, and measure your innovation initiatives! There are many problem-solving methodologies and tools to help you get started. We have the ultimate end-to-end improvement solution to help you reach innovation success.

Ready to explore structured problem-solving?

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The Complete Guide to Structured Problem Solving

When you are looking to thoroughly solve a pesky problem, structured problem solving is the way to go. Structured problem solving allows you to explore the problem, get to the heart of the issue, and develop a creative solution that finally solves the issue.

To illustrate this example, Takashi Amano was a nature photographer and avid aquarist. He started developing art in the form of fish tanks – which he called nature aquariums. The problem was algae would grow in his tanks and ruin his art. Not deterred, Mr. Amano found a shrimp distributor who bred, small, and clear micro-shrimp which were various algae eaters. Mr. Amano ordered thousands of them and promoted them in the hobby – to the point where the shrimp are now called Amano Shrimp.

He got creative. Knowing he needed a lasting solution to his algae problem, a clear shrimp that would eat the algae and not detract from his art was perfect.

The Basic idea of Structured Problem Solving

Professionals who solve complex problems for a living all start from the same place. They need to understand the actual problem they are solving. They ask themselves questions to get to the heart of the problem.

Usually promoting thinking with questions like what is the real problem, how can we gather data about the root problem, brainstorm solutions, test a solution and monitor it?

Why Structured Problem-Solving Works

Often, we are eager to jump into solving the first apparent problem with a variety of solutions. Why structured problem-solving works is because it forces us to slow down. By slowing down, we understand the problem first, without leaping into “fix-it” mode with preconceived notions of how the problem should be solved.

Studies have also found that having explicit techniques (methods for problem-solving) in the structured problem-solving workflow not only improves the problem-solving process but also increases the knowledge base all individuals can pull from. Basically, using structured problem solving allows better solutions to be developed while ensuring everyone participates in sharing their own unique knowledge.

The two ideas translate into the problem-solving principles of:

Seek to understand before we seek to solve
Search early, search often

By understanding the problem inside and out, the individual, or team can make more informed decisions and generate appropriate solutions.

There are a variety of techniques to work through the process. Below are some sample ways to do structured problem solving before getting into the walk-through further down in the article.

Multiple Ways of Structured Problem Solving

There are many techniques to perform structured problem solving, or at least get more in-depth in certain aspects of the process. Some of my favorite ones include

Pre-mortem analysis: Instead of working through a project and assessing what went wrong at the end, run through a simulation of the project to see where the project could fail before you even start. Where and why did it fail? Then brainstorm solutions to avoid those issues without creating new ones.

The Hat Technique: There are 6 colored hats, all with different roles. Whether alone or in a group, assign some time or a specific person to that role. Having a person designated to each role means that all ideas are validated through six different lenses. Plus, everyone has a designated role which helps keep people engaged, and limits feelings getting hurt since everyone is simply doing their assigned role.

PDCA Cycle: An easy way to remember the process is the PDCA cycle. Which stands for Plan-Do-Check-Act. PDCA is a high-level way to remember how the structured problem-solving process works.

You can also use the PDCA Model to manage your personal development too !

Get the Creative Juices Flowing

I like to start all my structured problem-solving sessions with some fun at the beginning of the session to get everyone’s creative juices flowing. By taking the 5 minutes to have a little fun, it is surprising how much more creative and engaged people are with the structured problem-solving process!

Problem-solving can be a stressful process, and it can even be high-stakes with the future of the group’s work hanging in the balance. However, laughing together helps relieve stress, makes people more creative, and improves social bonding.

The New Idea: One creative thinking exercise to start your session in a fun way, the goal is to split into two groups. Each group generates two dissimilar words. Then they swap words. For instance, “bug” & “sky-diving” and “winter” and “bikinis” for the other. Then the groups must devise the best ideas for those two words. For the bugs, you could make parachute designs that are themed after a different butterfly, and for the other, you could make a winter work-out with the goal have bikini-ready bodies by the summer. Silly ideas but shows there is a solution to even the weirdest problems.

Horrible idea challenge: Think of your problem. Then have everyone compete to come up with the worst idea. The practical part is that it helps to see what not to do – plus, part of the fun is seeing how creative people can be!

Beyond the two creative ideas, there are also 13 mental models which make work easier overall as well.

The Structured Problem Solving Process

1. define the problem statement.

The first step is defining what the real problem is. Below are some prompts to get the right decision-makers and problem-solvers sent in the right direction to tackle the challenge.

Is the problem many problems?
What requirements must a solution meet?
Which problem solvers should we engage?
What information and language should the problem statement include?
Tip: To engage the largest number of solvers from the widest variety of fields, a problem statement must meet the twin goals of being extremely specific but not necessarily technical.
What do solvers need to submit?
What incentive do solvers need?
How will solutions be evaluated, and success measured?

Problem statements are a statement of a current issue or problem. For example , Problem: Voter turnout in the southwest region of Florida has been significantly decreasing over the past decade, while other areas of the state continue to see increasing numbers of voters at the polls.

Writing one or two sentences takes the whole issue and makes it very clear what the issue is.

2. Root Cause Analysis

After getting the foundation set, an understanding of the root problem is critical. If you want to go through all the effort of structured problem solving, you might as well get to the real problem in the end.

Think of weeds in a garden. A potential solution is to mow over the weeds and they are gone. However, every few days the weeds keep coming back. That is because the root is the root issue in this scenario. You need to get the whole root system of the weed out to stop those pesky weeds in your garden.

Below are three techniques to help with Root-Cause Analysis

5 whys: When a problem occurs, drill down to its root cause by asking “Why?” five or more times. Then, when a counter-measure becomes apparent, you follow it through to prevent the issues from recurring.

Fishbone diagram: (Also called Ishikawa diagram named after Kaoru Ishikawa) is a cause-and-effect diagram that helps managers track down the reasons for imperfections, variations, defects, or failures.

Cause mapping: a cause map provides a visual explanation of why an incident occurred. It connects individual cause-and-effect relationships to reveal the system of cause within an issue.

3. Gather Data

After analyzing the problem and getting to the root cause – you need to gather information to understand why the problem and situation are happening. Doing the research and understanding how the different forces are interacting lets you understand why the problem is happening and how the overall solution is occurring.

Below are three different methods for gathering data to understand the context and interplaying forces in the current problem.

Gemba walk: The purpose is to allow managers and leaders to observe actual work process, engage with employees, gain knowledge about the work process, and explore opportunities for continuous improvement

Process mapping: A process map is a planning and management tool that visually describes the flow of work. Allowing you to see hiccups, bottlenecks, or high-failure points in the process.

Focus groups : Asking open-ended questions to a group of individuals ranging from 6-10 people. Letting you get different perspectives on the same issue.

4. Develop Potential Solutions

The next part is the fun part. You take all the research you’ve gathered in the first three aspects and put them together to come up with a solution to solve the problem. The common way is do Brainstorming.

Harvard Business Review sites that traditional brainstorming, in groups trying to answer the question, isn’t as effective as individuals coming up with ideas on their own first. Working in a big group doesn’t work for many reasons. Working in groups encourages social loafing (coasting on other’s ideas), some members experience social anxiety (introverted members feeling self-conscious of throwing in ideas), and it focuses too much on the solutions over the problem.

The better way to brainstorm is to have everyone work on the main problems and their solutions alone, and then reconvene after twenty minutes. Then everyone shares their top one or two ideas and what features of the problem it tackles.

This method gives everyone time to think about their solutions, present their ideas, and lets all the voices be heard. Plus, all the ideas can then smashed together to come up with a solution based on everyone’s input.

Remember, the solution has to solve the core of the issue and get to the root cause. Plus, it must be feasible in terms of the money, time, and manpower allocated to the project. Use the constraints as a guide to direct the project!

5. Implement a Solution

After running through the potential solutions – pick one and trial run it. Think of the minimum viable product to get to the root cause. You won’t know if you are alleviating the problem until a potential solution is out in the field.

For example , Airbnb founders, Brian Chesky and Joe Gebbia could not afford the rent for their apartment (the problem). They decided to put an air mattress in their living room and turn it into a bed and breakfast (MVP solution). The goal was to make a few bucks, but instead, they discovered the idea the connect Bed and Breakfasts to people looking for renters. They started advertising on Craiglist, then their website, and the story continues.

The point of the story is to illustrate that small tests can be done to see if you are solving the main issue! Their issue was not that someone needed to stay in their apartment for them to make rent – the issue was there was no service that easily let Bed and Breakfasts work with potential clients.

6. Monitor for Success

Once a solution is implemented, that is not the end. You must make sure the solution works. Keeping in mind the below questions

Who is responsible for the solution?
What are the risks of implementing the solution?

Some ways to monitor for success are:

Failure mode and effect analysis: A step-by-step approach for identifying all possible failures in a design, a manufacturing process, product, or service.

Impact analysis: A detailed study of business activities, dependencies, and infrastructure. It reveals how critical products and services are delivered and examines the potential impact of a disruptive (or additive solution) event over time

Kaizen : The Japanese term for “continuous improvement”. It is a business philosophy regarding the process that continuously improves operations and involves all employees.

Illustrated Example

A often find it helpful to see someone do the process as well. Here is a great video of IDEO re-working the shopping cart.

Key Take-Aways

What sets apart okay problem solvers from great problem solvers is the ability to think in repeatable, useful frameworks.

Structured Problem Solving is a general concept used to solve challenging problems, and there are hundreds of different methods that fall underneath it.

Action Item

Think of a tough challenge you are facing at work or in your personal life. Test run your problem through the structured problem-solving process with a few of the above techniques, and see what solution you can generate to get to the root of the issue!

Introduction to Structured Problem Solving

Structured problem solving is an invaluable skill for managers, team leaders, and emerging leaders. It allows for systematic analysis and resolution of complex issues, resulting in more effective decision-making and optimized team performance.

In this article, we will delve into the core principles of structured problem solving, offering real-world examples and practical strategies. Our goal is to equip you with the tools necessary to enhance your managerial dynamics and leadership skills.

What is Structured Problem Solving?

Structured problem solving is a methodical approach to identifying, analyzing, and resolving problems. It involves breaking down a problem into smaller, manageable components and systematically addressing each part to find a solution.

This approach is widely used in various fields, including business, engineering, healthcare, and education, due to its effectiveness in dealing with complex and multifaceted issues.

Systematic Approach

A systematic approach to problem solving involves a step-by-step methodology that ensures all aspects of a problem are addressed. This process begins with a clear understanding of the problem, followed by thorough data collection, analysis, and the development of strategic solutions.

Cross-Disciplinary Applications

Structured problem solving transcends industries and is applicable in any scenario where complex issues arise. Whether it’s a logistical challenge in supply chain management or a critical health diagnosis, the principles of structured problem solving remain consistent and reliable.

Benefits Over Ad-Hoc Methods

Compared to ad-hoc or spontaneous problem solving, structured problem solving offers a more reliable and repeatable path to success. Ad-hoc methods may sometimes result in quick fixes, but they lack the thoroughness and sustainability of a structured approach.

The Importance of Structured Problem Solving

Enhances analytical and critical thinking.

Structured problem solving promotes analytical thinking by encouraging a systematic examination of issues. This fosters a deeper understanding of the problem and its underlying causes, leading to more effective and sustainable solutions.

Cultivating a Methodical Mindset

A methodical mindset is crucial for dissecting complex problems and uncovering the root causes. It involves adopting a disciplined approach to thinking, where one evaluates evidence, recognizes patterns, and deduces conclusions based on logic.

Overcoming Cognitive Biases

Structured problem solving helps in mitigating cognitive biases that can cloud judgment. By following a structured approach, decision-makers are more likely to consider all relevant information and avoid jumping to conclusions based on incomplete data or personal biases.

Long-Term Strategic Thinking

Encouraging long-term strategic thinking, structured problem solving ensures that solutions are not only effective in the short term but also sustainable in the long run. It promotes foresight and anticipation of future challenges, contributing to more resilient and adaptable strategies.

Promotes Data-Driven Decision Making

Structured problem solving often involves collecting and analyzing data to inform decisions. This data-driven approach ensures that decisions are based on objective evidence rather than assumptions or intuition, leading to more accurate and reliable outcomes.

Emphasis on Quantitative Analysis

Quantitative analysis plays a pivotal role in structured problem solving by providing measurable and empirical evidence to support decision making. This can include statistical analysis, key performance indicators, and other numerical data that offer insights into the problem.

Integration of Qualitative Insights

While data-driven decision making often focuses on quantitative data, qualitative insights are equally important. Structured problem solving integrates both quantitative and qualitative data, including stakeholder interviews, surveys, and observational studies, to provide a holistic view of the problem.

Reducing Uncertainty in Decision Making

By basing decisions on data, structured problem solving significantly reduces uncertainty. It provides a clear rationale for chosen solutions and establishes a foundation for predicting outcomes, thus minimizing the risks associated with decision making.

Improves Team Collaboration

By following a structured approach, team members can work together more effectively to identify, analyze, and resolve problems. This collaborative effort promotes a sense of shared responsibility and fosters a more cohesive and productive team environment.

Encouraging Diverse Perspectives

Structured problem solving encourages the inclusion of diverse perspectives, which can lead to more innovative and comprehensive solutions. By valuing the input of all team members, it leverages the collective expertise and creativity within the group.

Streamlining Communication

Clear and streamlined communication is a byproduct of structured problem solving. As team members follow a defined process, it becomes easier to share information, provide updates, and maintain transparency throughout the problem-solving journey.

Building Trust and Accountability

A structured approach to problem solving helps build trust and accountability among team members. As individuals understand their roles and the rationale behind decisions, they are more likely to take ownership of their tasks and work together towards common goals.

Steps in Structured Problem Solving

1. define the problem.

The first step in structured problem solving is to clearly define the problem. This involves identifying the issue, understanding its impact, and determining the desired outcome.

Clarifying the Situation

It’s critical to clarify the situation by asking questions that narrow down the specifics of the problem. Understanding the who, what, when, where, and why provides a comprehensive background to the issue at hand.

Establishing the Scope

Determining the scope of the problem helps to focus efforts on what is truly important. It involves setting boundaries to ensure that the problem-solving process remains manageable and relevant.

Identifying Stakeholders

Identifying the stakeholders affected by the problem and the potential solutions is essential. Stakeholders can provide valuable insights and may be key to implementing the solutions effectively.

2. Gather and Analyze Data

Once the problem is defined, the next step is to gather relevant data and analyze it to identify the root causes of the issue.

Data Collection Methods

Choosing the right data collection methods is crucial for effective analysis. Methods can include surveys, interviews, observation, or data mining, depending on the nature of the problem.

Tools for Data Analysis

Various tools can be used to analyze the collected data, including statistical software, spreadsheets, or specialized analysis software. These tools help to organize, visualize, and interpret data to uncover patterns and insights.

Interpreting Results

Interpreting the results of data analysis involves looking for trends, correlations, and anomalies that point towards the underlying causes of the problem. It’s a critical step that guides the development of potential solutions.

3. Generate Potential Solutions

Based on the data analysis, brainstorm potential solutions to address the root causes of the problem. Consider the feasibility, impact, and potential risks of each solution.

Brainstorming Sessions

Organizing brainstorming sessions with team members can yield a diverse set of potential solutions. Encouraging open and creative thinking during these sessions is key to uncovering innovative ideas.

Assessing Feasibility

After generating ideas, assess the feasibility of each solution in terms of resources, time, and constraints. This step helps to filter out impractical solutions and focus on those that can be realistically implemented.

Considering Risk and Impact

Evaluating the potential risks and impact of each solution is essential. This includes looking at the possible side effects and long-term consequences of each option to ensure sustainable success.

4. Evaluate and Select the Best Solution

Evaluate the proposed solutions and select the one that best addresses the root causes of the problem and aligns with the desired outcome.

Criteria for Selection

Develop criteria for selecting the best solution, which may include effectiveness, cost, time, and alignment with organizational goals. This ensures a balanced and objective decision-making process.

Decision-Making Techniques

Leveraging decision-making techniques such as cost-benefit analysis, decision matrices, or multi-voting can help in choosing the most appropriate solution. These techniques provide a structured way to compare options and make informed choices.

Gaining Consensus

It’s important to gain consensus among team members and stakeholders on the selected solution. This fosters buy-in and commitment to the implementation process.

5. Implement the Solution

Once the best solution is selected, develop an action plan and implement it. Ensure that all team members are aware of their roles and responsibilities in executing the plan.

Developing an Action Plan

An action plan outlines the steps needed to implement the solution, including timelines, resources required, and responsible parties. It serves as a roadmap for execution.

Communicating the Plan

Effective communication of the action plan to all involved parties is crucial for successful implementation. It ensures clarity, aligns expectations, and prepares everyone for their roles.

Providing Resources and Support

Ensure that adequate resources and support are available for team members. This may include training, budget allocation, or access to necessary tools and technology.

6. Monitor and Evaluate Results

After implementing the solution, monitor its progress and evaluate its effectiveness. Make any necessary adjustments to ensure the desired outcome is achieved.

Setting KPIs and Metrics

Setting key performance indicators (KPIs) and metrics helps to objectively measure the success of the solution. These should be aligned with the goals established in the problem definition phase.

Regular Review Meetings

Holding regular review meetings allows the team to discuss progress, address challenges, and make informed decisions about any adjustments needed.

Iterative Improvement

Structured problem solving is an iterative process. Evaluating results may lead to further refinements of the solution or additional problem-solving cycles if the desired outcomes are not met.

Real-World Examples of Structured Problem Solving

Case Study 1: Improving Customer Satisfaction

A retail company faced declining customer satisfaction scores. By following the structured problem-solving approach, the company identified that long wait times at checkout were the primary cause. They implemented a new checkout process and trained staff to improve efficiency, resulting in increased customer satisfaction.

Analyzing Customer Feedback

The retail company analyzed customer feedback to understand the reasons behind the dissatisfaction. This highlighted the issue of long wait times and provided a clear target for improvement.

Process Redesign

The company redesigned the checkout process to streamline operations. This included the introduction of self-service kiosks and additional staff during peak times.

Measuring Results

Post-implementation, the company measured customer satisfaction through surveys and found a marked improvement. This validated the effectiveness of the structured problem-solving approach.

Case Study 2: Reducing Manufacturing Defects

A manufacturing plant experienced a high rate of defects in their products. Through structured problem solving, they identified that poor quality control procedures were to blame. They implemented stricter quality control measures and provided additional training for staff, leading to a significant reduction in defects.

Root Cause Analysis

The manufacturing plant conducted a root cause analysis to identify why defects were occurring. This revealed gaps in quality control and areas for process improvement.

Implementing Quality Standards

The plant established new quality standards and implemented rigorous testing procedures. This ensured that products met the required specifications before leaving the factory.

Tracking Defect Rates

By tracking defect rates before and after the changes, the plant was able to quantify the improvement and demonstrate the success of their structured problem-solving efforts.

Case Study 3: Enhancing Employee Engagement

A tech company struggled with low employee engagement. By using structured problem solving, they discovered that a lack of career development opportunities was the main issue. They introduced new training programs and career development plans, resulting in higher employee engagement and retention.

Employee Surveys and Interviews

The tech company conducted surveys and interviews to gauge employee sentiments. This data revealed that employees desired more opportunities for professional growth.

Creating Professional Development Programs

In response, the company developed a range of professional development programs tailored to different career paths within the organization.

In conclusion, structured problem-solving approaches, such as leveraging decision-making techniques, gaining consensus, implementing solutions, developing action plans, communicating effectively, providing resources and support, monitoring and evaluating results, setting KPIs and metrics, holding regular review meetings, and embracing iterative improvement, are essential for addressing complex issues in various scenarios. Real-world examples demonstrate how organizations can successfully apply these methods to improve customer satisfaction, reduce manufacturing defects, and enhance employee engagement. By following a systematic problem-solving process, businesses can identify root causes, implement effective solutions, and measure outcomes to achieve their desired goals. Embracing structured problem-solving not only resolves current challenges but also fosters a culture of continuous improvement and innovation within an organization.

Case Studies on Structured Problem Solving
Steps to Implement Structured Problem Solving
Tools and Techniques for Structured Problem Solving
The Art of Problem Solving
5 Effective Strategies to Find Solutions

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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Structured Approach to Problem Solving

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To succeed in this course, you need to have a curious and logical mind. No programming skills are needed.

What you'll learn

Explain the lifecycle of DS project and role of structured thinking in DS project.

Define a problem statement using the SMART framework. Explain the activities, best practices, and pitfalls in the implementation phase.

Construct MECE issue tree to break-down business problems into parts. Create a problem statement worksheet to scope business problems.

Explain the role of human centered design during solutioning of business problems.

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Critical Thinking
structured thinking
Problem Solving
Human Centric Design
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There are 3 modules in this course

This course is an introductory course that equips you with the concepts and tools of problem thinking. By the end of this course, you will be able to:

1. Explain the different stages of a data science project 2. Discuss some of the tools and techniques used in data science. 3. Apply structured thinking to solving problems and avoid the common traps while doing so 4. Apply human-centric design in problem-solving. This course also acts as the first stepping-stone for aspiring data scientists. This course is a part of the program titled Fractal Data Science Professional Certificate. This course is tailored for learners seeking to enhance their analytical and critical thinking abilities. Ideal for students, professionals, and anyone intrigued by systematic approaches, this course empowers you with the foundational skills needed to approach problems with clarity and confidence.

Structured Thinking

This module teaches you how to use structured thinking to solve problems at work and in everyday life. You will also learn about the different stages and roles in data science, as well as the tools used in data science.

What's included

15 videos 8 readings 5 assignments 1 peer review 2 discussion prompts

15 videos • Total 69 minutes

What Is Data Science? • 3 minutes • Preview module
Course Introduction • 3 minutes
A day in the life of a Data Scientist • 2 minutes
Stages of a Data Science Project • 6 minutes
Roles in a Data Science Project • 5 minutes
Tools of the Trade • 5 minutes
Applications of Data Science • 4 minutes
Introduction to Structured Thinking • 5 minutes
Frameworks of Structured Thinking • 4 minutes
Structured Thinking and Problem Solving • 3 minutes
Structured Thinking in Solving Business Problems • 7 minutes
Framing the Problem Statement • 4 minutes
The Common Pitfalls — The Loop Trap • 3 minutes
The Common Pitfalls — The Complexity Trap • 4 minutes
The Common Pitfalls — The Obvious Trap • 3 minutes

8 readings • Total 112 minutes

The Future of Data Science • 10 minutes
Course Syllabus • 10 minutes
Skills and Competencies for the Future Workforce • 30 minutes
The Global Problem Statement • 30 minutes
Disclaimer • 2 minutes
Story of a Data Scientist • 10 minutes
Solving Problems Like a Pro • 10 minutes
Tell us what you think! • 10 minutes

5 assignments • Total 95 minutes

End of Module Graded Quiz • 30 minutes
Introduction to Data Science • 15 minutes
Structured Thinking & Efficient Project Management • 30 minutes
Structured Approach - An Introduction • 10 minutes
Problem Statements • 10 minutes

1 peer review • Total 60 minutes

Defining a Problem Statement • 60 minutes

2 discussion prompts • Total 20 minutes

Meet and Greet • 10 minutes
Collaboration & problem-solving. • 10 minutes

Solving Problems with Structured Thinking

This module teaches structured thinking for solving business problems and covers tools and frameworks used in the process. It also highlights common mistakes to avoid.

13 videos 3 readings 6 assignments 2 discussion prompts

13 videos • Total 64 minutes

Defining a Problem Statement Worksheet (PSW) • 7 minutes • Preview module
How the PSW Helps in Defining a Problem Statement • 6 minutes
Mutually Exclusive and Collectively Exhaustive (MECE) • 4 minutes
What is an Issue Tree? • 6 minutes
Creating an Issue Tree • 4 minutes
Documenting the Implementation Phase • 7 minutes
The Adoption Plan • 3 minutes
Metrics for Evaluation-1 • 6 minutes
Metrics for Evaluation-2 • 1 minute
Creating User Research for Insights • 4 minutes
Common Design Pitfalls • 3 minutes
Common Strategic Pitfalls • 3 minutes
Phases of Testing a DS Project • 4 minutes

3 readings • Total 22 minutes

Best Practices and Pitfalls of a PSW • 8 minutes
What is a Hypothesis Tree? • 8 minutes
Other Frameworks for Problem-Solving • 6 minutes

6 assignments • Total 85 minutes

Creating a PSW • 15 minutes
Solving Problems • 10 minutes
Frameworks of Problem Solving • 10 minutes
Finalizing Solutions • 10 minutes
Implementing a Solution - Getting ready • 10 minutes

2 discussion prompts • Total 25 minutes

AI and Automation • 10 minutes
User Centricity in Design and Strategy • 15 minutes

Human-Centered Design

Human-Centered Design is an approach that prioritizes the end-users' needs by understanding them and creating products that cater to their preferences and behaviors. The goal is to design products that are user-friendly, effective, and enjoyable to use. This module would help you understand why and how you should also keep your users/humans at the center of solving your business problems.

6 videos 3 readings 1 assignment 1 peer review 1 discussion prompt

6 videos • Total 56 minutes

Human-Centered Design (HCD) and Empathy • 8 minutes • Preview module
What is HCD? • 4 minutes
The Discovery Phase • 4 minutes
The Solution Phase • 8 minutes
Fireside Chat with Biju Dominic • 26 minutes
Empathy in Design — Three Strategies to Build Empathy • 4 minutes

3 readings • Total 38 minutes

Optional Reading - User Centered Design in a Product Development Lifecycle Process • 15 minutes
Empathy Map — Why and How • 8 minutes
Empathy Map, User Persona, and User Flow • 15 minutes

1 assignment • Total 180 minutes

Keeping User at the Center • 180 minutes

1 peer review • Total 90 minutes

Case Study: Traffic Signals — How to Design Better Traffic Signals for Colorblind Individuals (Especially Red and Green Colorblind Individuals) • 90 minutes

1 discussion prompt • Total 10 minutes

Human-Centric Designs in Daily Life • 10 minutes

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Reviewed on May 5, 2024

A decently informative course, though the guest videos were mostly pointless. The pace was good.

Reviewed on Nov 1, 2023

The structured approach to problem-solving course exceeded my expectations. It provided clear strategies and practical tools that improved my problem-solving skills. Highly recommended!

Reviewed on Feb 19, 2024

This course is the best course to develop structured thinking approach towards problem solving. It has changed the way I look at problem solving.

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McKinsey Problem Solving: Six steps to solve any problem and tell a persuasive story

The McKinsey problem solving process is a series of mindset shifts and structured approaches to thinking about and solving challenging problems. It is a useful approach for anyone working in the knowledge and information economy and needs to communicate ideas to other people.

Over the past several years of creating StrategyU, advising an undergraduates consulting group and running workshops for clients, I have found over and over again that the principles taught on this site and in this guide are a powerful way to improve the type of work and communication you do in a business setting.

When I first set out to teach these skills to the undergraduate consulting group at my alma mater, I was still working at BCG. I was spending my day building compelling presentations, yet was at a loss for how to teach these principles to the students I would talk with at night.

Through many rounds of iteration, I was able to land on a structured process and way of framing some of these principles such that people could immediately apply them to their work.

While the “official” McKinsey problem solving process is seven steps, I have outline my own spin on things – from experience at McKinsey and Boston Consulting Group. Here are six steps that will help you solve problems like a McKinsey Consultant:

Step #1: School is over, stop worrying about “what” to make and worry about the process, or the “how”

When I reflect back on my first role at McKinsey, I realize that my biggest challenge was unlearning everything I had learned over the previous 23 years. Throughout school you are asked to do specific things. For example, you are asked to write a 5 page paper on Benjamin Franklin — double spaced, 12 font and answering two or three specific questions.

In school, to be successful you follow these rules as close as you can. However, in consulting there are no rules on the “what.” Typically the problem you are asked to solve is ambiguous and complex — exactly why they hire you. In consulting, you are taught the rules around the “how” and have to then fill in the what.

The “how” can be taught and this entire site is founded on that belief. Here are some principles to get started:

Step #2: Thinking like a consultant requires a mindset shift

There are two pre-requisites to thinking like a consultant. Without these two traits you will struggle:

A healthy obsession looking for a “better way” to do things
Being open minded to shifting ideas and other approaches

In business school, I was sitting in one class when I noticed that all my classmates were doing the same thing — everyone was coming up with reasons why something should should not be done.

As I’ve spent more time working, I’ve realized this is a common phenomenon. The more you learn, the easier it becomes to come up with reasons to support the current state of affairs — likely driven by the status quo bias — an emotional state that favors not changing things. Even the best consultants will experience this emotion, but they are good at identifying it and pushing forward.

Key point : Creating an effective and persuasive consulting like presentation requires a comfort with uncertainty combined with a slightly delusional belief that you can figure anything out.

Step #3: Define the problem and make sure you are not solving a symptom

Before doing the work, time should be spent on defining the actual problem. Too often, people are solutions focused when they think about fixing something. Let’s say a company is struggling with profitability. Someone might define the problem as “we do not have enough growth.” This is jumping ahead to solutions — the goal may be to drive more growth, but this is not the actual issue. It is a symptom of a deeper problem.

Consider the following information:

Costs have remained relatively constant and are actually below industry average so revenue must be the issue
Revenue has been increasing, but at a slowing rate
This company sells widgets and have had no slowdown on the number of units it has sold over the last five years
However, the price per widget is actually below where it was five years ago
There have been new entrants in the market in the last three years that have been backed by Venture Capital money and are aggressively pricing their products below costs

In a real-life project there will definitely be much more information and a team may take a full week coming up with a problem statement . Given the information above, we may come up with the following problem statement:

Problem Statement : The company is struggling to increase profitability due to decreasing prices driven by new entrants in the market. The company does not have a clear strategy to respond to the price pressure from competitors and lacks an overall product strategy to compete in this market.

Step 4: Dive in, make hypotheses and try to figure out how to “solve” the problem

Now the fun starts!

There are generally two approaches to thinking about information in a structured way and going back and forth between the two modes is what the consulting process is founded on.

First is top-down . This is what you should start with, especially for a newer “consultant.” This involves taking the problem statement and structuring an approach. This means developing multiple hypotheses — key questions you can either prove or disprove.

Given our problem statement, you may develop the following three hypotheses:

Company X has room to improve its pricing strategy to increase profitability
Company X can explore new market opportunities unlocked by new entrants
Company X can explore new business models or operating models due to advances in technology

As you can see, these three statements identify different areas you can research and either prove or disprove. In a consulting team, you may have a “workstream leader” for each statement.

Once you establish the structure you you may shift to the second type of analysis: a bottom-up approach . This involves doing deep research around your problem statement, testing your hypotheses, running different analysis and continuing to ask more questions. As you do the analysis, you will begin to see different patterns that may unlock new questions, change your thinking or even confirm your existing hypotheses. You may need to tweak your hypotheses and structure as you learn new information.

A project vacillates many times between these two approaches. Here is a hypothetical timeline of a project:

Step 5: Make a slides like a consultant

The next step is taking the structure and research and turning it into a slide. When people see slides from McKinsey and BCG, they see something that is compelling and unique, but don’t really understand all the work that goes into those slides. Both companies have a healthy obsession (maybe not to some people!) with how things look, how things are structured and how they are presented.

They also don’t understand how much work is spent on telling a compelling “story.” The biggest mistake people make in the business world is mistaking showing a lot of information versus telling a compelling story. This is an easy mistake to make — especially if you are the one that did hours of analysis. It may seem important, but when it comes down to making a slide and a presentation, you end up deleting more information rather than adding. You really need to remember the following:

Data matters, but stories change hearts and minds

Here are four quick ways to improve your presentations:

Tip #1 — Format, format, format

Both McKinsey and BCG had style templates that were obsessively followed. Some key rules I like to follow:

Make sure all text within your slide body is the same font size (harder than you would think)
Do not go outside of the margins into the white space on the side
All titles throughout the presentation should be 2 lines or less and stay the same font size
Each slide should typically only make one strong point

Tip #2 — Titles are the takeaway

The title of the slide should be the key insight or takeaway and the slide area should prove the point. The below slide is an oversimplification of this:

Even in consulting, I found that people struggled with simplifying a message to one key theme per slide. If something is going to be presented live, the simpler the better. In reality, you are often giving someone presentations that they will read in depth and more information may make sense.

To go deeper, check out these 20 presentation and powerpoint tips .

Tip #3 — Have “MECE” Ideas for max persuasion

“MECE” means mutually exclusive, collectively exhaustive — meaning all points listed cover the entire range of ideas while also being unique and differentiated from each other.

An extreme example would be this:

Slide title: There are seven continents
Slide content: The seven continents are North America, South America, Europe, Africa Asia, Antarctica, Australia

The list of continents provides seven distinct points that when taken together are mutually exclusive and collectively exhaustive . The MECE principle is not perfect — it is more of an ideal to push your logic in the right direction. Use it to continually improve and refine your story.

Applying this to a profitability problem at the highest level would look like this:

Goal: Increase profitability

2nd level: We can increase revenue or decrease costs

3rd level: We can increase revenue by selling more or increasing prices

Each level is MECE. It is almost impossible to argue against any of this (unless you are willing to commit accounting fraud!).

Tip #4 — Leveraging the Pyramid Principle

The pyramid principle is an approach popularized by Barbara Minto and essential to the structured problem solving approach I learned at McKinsey. Learning this approach has changed the way I look at any presentation since.

Here is a rough outline of how you can think about the pyramid principle as a way to structure a presentation:

As you build a presentation, you may have three sections for each hypothesis. As you think about the overall story, the three hypothesis (and the supporting evidence) will build on each other as a “story” to answer the defined problem. There are two ways to think about doing this — using inductive or deductive reasoning:

deductive versus inductive reasoning in powerpoint arguments

If we go back to our profitability example from above, you would say that increasing profitability was the core issue we developed. Lets assume that through research we found that our three hypotheses were true. Given this, you may start to build a high level presentation around the following three points:

example of hypotheses confirmed as part of consulting problem solving

These three ideas not only are distinct but they also build on each other. Combined, they tell a story of what the company should do and how they should react. Each of these three “points” may be a separate section in the presentation followed by several pages of detailed analysis. There may also be a shorter executive summary version of 5–10 pages that gives the high level story without as much data and analysis.

Step 6: The only way to improve is to get feedback and continue to practice

Ultimately, this process is not something you will master overnight. I’ve been consulting, either working for a firm or on my own for more than 10 years and am still looking for ways to make better presentations, become more persuasive and get feedback on individual slides.

The process never ends.

The best way to improve fast is to be working on a great team . Look for people around you that do this well and ask them for feedback. The more feedback, the more iterations and more presentations you make, the better you will become. Good luck!

If you enjoyed this post, you’ll get a kick out of all the free lessons I’ve shared that go a bit deeper. Check them out here .

Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now.

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Six Steps to Structured Problem Solving

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What happens when a big problem pops up? For most of us, our first reaction is, “Quick! Let’s fix it and make this problem go away.” However, if we rush to fix the problem too quickly, we may end up implementing a “solution” or “quick fix” that doesn’t solve anything because we didn’t take the time to truly identify and understand the root cause of the problem itself.

One way we can keep ourselves from falling into this trap is by having a formal structured problem-solving (SPS) process in place. Then, when problems do occur, we know exactly what steps to take to help ensure that our solution really will “make it go away.” While there are different variations to an SPS ( 8D , 5Why , DMAIC , etc.), they all follow the same basic steps.

1. Define the Problem

It is important to write a problem statement that is easily understood and is stated purely in terms of measurable or observable symptoms. At this point, there should be no mention of suspected causes or possible solutions. We just need to know what the problem is. A good problem statement might look something like this: “High (greater than 8 ppb) trace metals in one of our hardmask products are causing unacceptably high defect counts with our most important customer. We will lose this business if we cannot correct the problem by the end of the year.”

2. Describe the Current Situation

We do this by examining data that is readily available. In our hardmask example, we would collect batch data to identify the specific batches with the elevated defects and then study this data to see what they have in common. Were they all made in a particular manufacturing location or on the same equipment set? Did they all use the same lots of the raw materials? By identifying where the problem is occurring, and where it is not occurring, we begin to zero in on what is causing our problem.

3. Identify Possible Causes

To identify possible causes, we should gather the team and brainstorm all potential causes that come to mind. Once we’ve exhausted all possibilities, we can then start systematically ruling items out. This is the most time-consuming, but one of the most vital steps in the process as we take each potential cause and work to rule it out. We must keep working until we have eliminated everything but the root cause.

4. Verify Root Cause

When we think that we have identified the true root cause, we need verify that it is the root cause by testing out our theory. For our hardmask example, let’s say that we believe that the root cause is due to a bad batch of one of the raw materials. We can test our theory by making a new batch with everything the same except for a new batch of the suspect ingredient. If the defect levels are back to normal, that’s a good indication that we have confirmed our root cause.

5. Implement Solution

After we’ve identified the root cause, we implement a solution to remedy the issue, standardizing our solution and making the change permanent. This could mean updating specifications, writing new training materials, updating training packages or updating the FMEA. In the case of our hardmask example, we would need to create specifications and appropriate testing methods that will alert us that a batch of raw material is bad before we use it.

6. Monitor for Success

After the solution has been implemented, test data can then be created to find which solutions offer the best improvements. In the case of our hardmask example, we would run test batches to make sure that we have the correct solution in place. Measurements should also be taken on a scheduled basis to continue to confirm that the solution is still valid, making updates if and when needed.

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About Author

Karen Brown, Director of Organizational Development & Relationships, has been employed with Brewer Science for over a decade. Her background in employee development and training has made her an expert for the Brewer Science team in crafting successful, problem-solving employees

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The Basics of Structured Problem-Solving Methodologies: DMAIC & 8D

WHAT IS STRUCTURED PROBLEM-SOLVING?

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3. Gather Data

4. Develop Potential Solutions

5. Implement a Solution

6. Monitor for Success

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Introduction to Structured Problem Solving

What is Structured Problem Solving?

Systematic Approach

Cross-Disciplinary Applications

Benefits Over Ad-Hoc Methods

The Importance of Structured Problem Solving

Cultivating a Methodical Mindset

Overcoming Cognitive Biases

Long-Term Strategic Thinking

Promotes Data-Driven Decision Making

Emphasis on Quantitative Analysis

Integration of Qualitative Insights

Reducing Uncertainty in Decision Making

Improves Team Collaboration

Encouraging Diverse Perspectives

Streamlining Communication

Building Trust and Accountability

Steps in Structured Problem Solving

Clarifying the Situation

Establishing the Scope

Identifying Stakeholders

2. Gather and Analyze Data

Data Collection Methods

Tools for Data Analysis

Interpreting Results

3. Generate Potential Solutions

Brainstorming Sessions

Assessing Feasibility

Considering Risk and Impact

4. Evaluate and Select the Best Solution

Criteria for Selection

Decision-Making Techniques

Gaining Consensus

5. Implement the Solution

Developing an Action Plan

Communicating the Plan

Providing Resources and Support

6. Monitor and Evaluate Results

Setting KPIs and Metrics

Regular Review Meetings

Iterative Improvement

Real-World Examples of Structured Problem Solving

Case Study 1: Improving Customer Satisfaction

Analyzing Customer Feedback

Process Redesign

Measuring Results

Case Study 2: Reducing Manufacturing Defects

Root Cause Analysis

Implementing Quality Standards

Tracking Defect Rates

Case Study 3: Enhancing Employee Engagement

Employee Surveys and Interviews

Creating Professional Development Programs

How to master the seven-step problem-solving process

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