what parameters inform the problem solving and decision making process

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Appreciate the complexities involved in decision-making & problem solving.

Develop evidence to support views

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A 5-Step Problem-Solving Strategy

Specify the problem – a first step to solving a problem is to identify it as specifically as possible.  It involves evaluating the present state and determining how it differs from the goal state.

Analyze the problem – analyzing the problem involves learning as much as you can about it.  It may be necessary to look beyond the obvious, surface situation, to stretch your imagination and reach for more creative options.

seek other perspectives

be flexible in your analysis

consider various strands of impact

brainstorm about all possibilities and implications

research problems for which you lack complete information. Get help.

Formulate possible solutions – identify a wide range of possible solutions.

try to think of all possible solutions

be creative

consider similar problems and how you have solved them

Evaluate possible solutions – weigh the advantages and disadvantages of each solution.  Think through each solution and consider how, when, and where you could accomplish each.  Consider both immediate and long-term results.  Mapping your solutions can be helpful at this stage.

Choose a solution – consider 3 factors:

compatibility with your priorities

amount of risk

practicality

Keys to Problem Solving

Think aloud – problem solving is a cognitive, mental process.  Thinking aloud or talking yourself through the steps of problem solving is useful.  Hearing yourself think can facilitate the process.

Allow time for ideas to "gel" or consolidate.  If time permits, give yourself time for solutions to develop.  Distance from a problem can allow you to clear your mind and get a new perspective.

Talk about the problem – describing the problem to someone else and talking about it can often make a problem become more clear and defined so that a new solution will surface.

Decision Making Strategies

Decision making is a process of identifying and evaluating choices.  We make numerous decisions every day and our decisions may range from routine, every-day types of decisions to those decisions which will have far reaching impacts.  The types of decisions we make are routine, impulsive, and reasoned.  Deciding what to eat for breakfast is a routine decision; deciding to do or buy something at the last minute is considered an impulsive decision; and choosing your college major is, hopefully, a reasoned decision.  College coursework often requires you to make the latter, or reasoned decisions.

Decision making has much in common with problem solving.  In problem solving you identify and evaluate solution paths; in decision making you make a similar discovery and evaluation of alternatives.  The crux of decision making, then, is the careful identification and evaluation of alternatives.  As you weigh alternatives, use the following suggestions:

Consider the outcome each is likely to produce, in both the short term and the long term.

Compare alternatives based on how easily you can accomplish each.

Evaluate possible negative side effects each may produce.

Consider the risk involved in each.

Be creative, original; don't eliminate alternatives because you have not heard or used them before.

An important part of decision making is to predict both short-term and long-term outcomes for each alternative.  You may find that while an alternative seems most desirable at the present, it may pose problems or complications over a longer time period.

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This is how effective teams navigate the decision-making process

Zero Magic 8 Balls required.

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Flipping a coin. Throwing a dart at a board. Pulling a slip of paper out of a hat.

Sure, they’re all ways to make a choice. But they all hinge on random chance rather than analysis, reflection, and strategy — you know, the things you actually need to make the big, meaty decisions that have major impacts.

So, set down that Magic 8 Ball and back away slowly. Let’s walk through the standard framework for decision-making that will help you and your team pinpoint the problem, consider your options, and make your most informed selection. Here’s a closer look at each of the seven steps of the decision-making process, and how to approach each one. 

Step 1: Identify the decision

Most of us are eager to tie on our superhero capes and jump into problem-solving mode — especially if our team is depending on a solution. But you can’t solve a problem until you have a full grasp on what it actually is .

This first step focuses on getting the lay of the land when it comes to your decision. What specific problem are you trying to solve? What goal are you trying to achieve? 

How to do it: 

• Use the 5 whys analysis to go beyond surface-level symptoms and understand the root cause of a problem.
• Try problem framing to dig deep on the ins and outs of whatever problem your team is fixing. The point is to define the problem, not solve it. 

⚠️ Watch out for: Decision fatigue , which is the tendency to make worse decisions as a result of needing to make too many of them. Making choices is mentally taxing , which is why it’s helpful to pinpoint one decision at a time. 

2. Gather information

Your team probably has a few hunches and best guesses, but those can lead to knee-jerk reactions. Take care to invest adequate time and research into your decision.

This step is when you build your case, so to speak. Collect relevant information — that could be data, customer stories, information about past projects, feedback, or whatever else seems pertinent. You’ll use that to make decisions that are informed, rather than impulsive.

• Host a team mindmapping session to freely explore ideas and make connections between them. It can help you identify what information will best support the process.
• Create a project poster to define your goals and also determine what information you already know and what you still need to find out. 

⚠️ Watch out for: Information bias , or the tendency to seek out information even if it won’t impact your action. We have the tendency to think more information is always better, but pulling together a bunch of facts and insights that aren’t applicable may cloud your judgment rather than offer clarity. 

3. Identify alternatives

Use divergent thinking to generate fresh ideas in your next brainstorm

Blame the popularity of the coin toss, but making a decision often feels like choosing between only two options. Do you want heads or tails? Door number one or door number two? In reality, your options aren’t usually so cut and dried. Take advantage of this opportunity to get creative and brainstorm all sorts of routes or solutions. There’s no need to box yourselves in. 

• Use the Six Thinking Hats technique to explore the problem or goal from all sides: information, emotions and instinct, risks, benefits, and creativity. It can help you and your team break away from your typical roles or mindsets and think more freely.
• Try brainwriting so team members can write down their ideas independently before sharing with the group. Research shows that this quiet, lone thinking time can boost psychological safety and generate more creative suggestions .

⚠️ Watch out for: Groupthink , which is the tendency of a group to make non-optimal decisions in the interest of conformity. People don’t want to rock the boat, so they don’t speak up. 

4. Consider the evidence

Armed with your list of alternatives, it’s time to take a closer look and determine which ones could be worth pursuing. You and your team should ask questions like “How will this solution address the problem or achieve the goal?” and “What are the pros and cons of this option?” 

Be honest with your answers (and back them up with the information you already collected when you can). Remind the team that this isn’t about advocating for their own suggestions to “win” — it’s about whittling your options down to the best decision. 

How to do it:

• Use a SWOT analysis to dig into the strengths, weaknesses, opportunities, and threats of the options you’re seriously considering.
• Run a project trade-off analysis to understand what constraints (such as time, scope, or cost) the team is most willing to compromise on if needed. 

⚠️ Watch out for: Extinction by instinct , which is the urge to make a decision just to get it over with. You didn’t come this far to settle for a “good enough” option! 

5. Choose among the alternatives

This is it — it’s the big moment when you and the team actually make the decision. You’ve identified all possible options, considered the supporting evidence, and are ready to choose how you’ll move forward.

However, bear in mind that there’s still a surprising amount of room for flexibility here. Maybe you’ll modify an alternative or combine a few suggested solutions together to land on the best fit for your problem and your team. 

• Use the DACI framework (that stands for “driver, approver, contributor, informed”) to understand who ultimately has the final say in decisions. The decision-making process can be collaborative, but eventually someone needs to be empowered to make the final call.
• Try a simple voting method for decisions that are more democratized. You’ll simply tally your team’s votes and go with the majority. 

⚠️ Watch out for: Analysis paralysis , which is when you overthink something to such a great degree that you feel overwhelmed and freeze when it’s time to actually make a choice. 

6. Take action

Making a big decision takes a hefty amount of work, but it’s only the first part of the process — now you need to actually implement it. 

It’s tempting to think that decisions will work themselves out once they’re made. But particularly in a team setting, it’s crucial to invest just as much thought and planning into communicating the decision and successfully rolling it out. 

• Create a stakeholder communications plan to determine how you’ll keep various people — direct team members, company leaders, customers, or whoever else has an active interest in your decision — in the loop on your progress.
• Define the goals, signals, and measures of your decision so you’ll have an easier time aligning the team around the next steps and determining whether or not they’re successful. 

⚠️Watch out for: Self-doubt, or the tendency to question whether or not you’re making the right move. While we’re hardwired for doubt , now isn’t the time to be a skeptic about your decision. You and the team have done the work, so trust the process. 

7. Review your decision

9 retrospective techniques that won’t bore your team to tears

As the decision itself starts to shake out, it’s time to take a look in the rearview mirror and reflect on how things went.

Did your decision work out the way you and the team hoped? What happened? Examine both the good and the bad. What should you keep in mind if and when you need to make this sort of decision again? 

• Do a 4 L’s retrospective to talk through what you and the team loved, loathed, learned, and longed for as a result of that decision.
• Celebrate any wins (yes, even the small ones ) related to that decision. It gives morale a good kick in the pants and can also help make future decisions feel a little less intimidating.

⚠️ Watch out for: Hindsight bias , or the tendency to look back on events with the knowledge you have now and beat yourself up for not knowing better at the time. Even with careful thought and planning, some decisions don’t work out — but you can only operate with the information you have at the time. 

Making smart decisions about the decision-making process

You’re probably picking up on the fact that the decision-making process is fairly comprehensive. And the truth is that the model is likely overkill for the small and inconsequential decisions you or your team members need to make.

Deciding whether you should order tacos or sandwiches for your team offsite doesn’t warrant this much discussion and elbow grease. But figuring out which major project to prioritize next? That requires some careful and collaborative thought. 

It all comes back to the concept of satisficing versus maximizing , which are two different perspectives on decision making. Here’s the gist:

• Maximizers aim to get the very best out of every single decision.
• Satisficers are willing to settle for “good enough” rather than obsessing over achieving the best outcome.

One of those isn’t necessarily better than the other — and, in fact, they both have their time and place.

A major decision with far-reaching impacts deserves some fixation and perfectionism. However, hemming and hawing over trivial choices ( “Should we start our team meeting with casual small talk or a structured icebreaker?” ) will only cause added stress, frustration, and slowdowns. 

As with anything else, it’s worth thinking about the potential impacts to determine just how much deliberation and precision a decision actually requires. 

Decision-making is one of those things that’s part art and part science. You’ll likely have some gut feelings and instincts that are worth taking into account. But those should also be complemented with plenty of evidence, evaluation, and collaboration.

The decision-making process is a framework that helps you strike that balance. Follow the seven steps and you and your team can feel confident in the decisions you make — while leaving the darts and coins where they belong.

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In This Article Expand or collapse the "in this article" section Problem Solving and Decision Making

Introduction.

• General Approaches to Problem Solving
• Representational Accounts
• Problem Space and Search
• Working Memory and Problem Solving
• Domain-Specific Problem Solving
• The Rational Approach
• Prospect Theory
• Dual-Process Theory
• Cognitive Heuristics and Biases

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Problem Solving and Decision Making by Emily G. Nielsen , John Paul Minda LAST REVIEWED: 26 June 2019 LAST MODIFIED: 26 June 2019 DOI: 10.1093/obo/9780199828340-0246

Problem solving and decision making are both examples of complex, higher-order thinking. Both involve the assessment of the environment, the involvement of working memory or short-term memory, reliance on long term memory, effects of knowledge, and the application of heuristics to complete a behavior. A problem can be defined as an impasse or gap between a current state and a desired goal state. Problem solving is the set of cognitive operations that a person engages in to change the current state, to go beyond the impasse, and achieve a desired outcome. Problem solving involves the mental representation of the problem state and the manipulation of this representation in order to move closer to the goal. Problems can vary in complexity, abstraction, and how well defined (or not) the initial state and the goal state are. Research has generally approached problem solving by examining the behaviors and cognitive processes involved, and some work has examined problem solving using computational processes as well. Decision making is the process of selecting and choosing one action or behavior out of several alternatives. Like problem solving, decision making involves the coordination of memories and executive resources. Research on decision making has paid particular attention to the cognitive biases that account for suboptimal decisions and decisions that deviate from rationality. The current bibliography first outlines some general resources on the psychology of problem solving and decision making before examining each of these topics in detail. Specifically, this review covers cognitive, neuroscientific, and computational approaches to problem solving, as well as decision making models and cognitive heuristics and biases.

General Overviews

Current research in the area of problem solving and decision making is published in both general and specialized scientific journals. Theoretical and scholarly work is often summarized and developed in full-length books and chapter. These may focus on the subfields of problem solving and decision making or the larger field of thinking and higher-order cognition.

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7 important steps in the decision making process

The decision making process is a method of gathering information, assessing alternatives, and making a final choice with the goal of making the best decision possible. In this article, we detail the step-by-step process on how to make a good decision and explain different decision making methodologies.

We make decisions every day. Take the bus to work or call a car? Chocolate or vanilla ice cream? Whole milk or two percent?

There's an entire process that goes into making those tiny decisions, and while these are simple, easy choices, how do we end up making more challenging decisions? 

At work, decisions aren't as simple as choosing what kind of milk you want in your latte in the morning. That’s why understanding the decision making process is so important. 

What is the decision making process?

The decision making process is the method of gathering information, assessing alternatives, and, ultimately, making a final choice. 

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The 7 steps of the decision making process

Step 1: identify the decision that needs to be made.

When you're identifying the decision, ask yourself a few questions: 

What is the problem that needs to be solved?

What is the goal you plan to achieve by implementing this decision?

How will you measure success?

These questions are all common goal setting techniques that will ultimately help you come up with possible solutions. When the problem is clearly defined, you then have more information to come up with the best decision to solve the problem.

Step 2: Gather relevant information

​Gathering information related to the decision being made is an important step to making an informed decision. Does your team have any historical data as it relates to this issue? Has anybody attempted to solve this problem before?

It's also important to look for information outside of your team or company. Effective decision making requires information from many different sources. Find external resources, whether it’s doing market research, working with a consultant, or talking with colleagues at a different company who have relevant experience. Gathering information helps your team identify different solutions to your problem.

Step 3: Identify alternative solutions

This step requires you to look for many different solutions for the problem at hand. Finding more than one possible alternative is important when it comes to business decision-making, because different stakeholders may have different needs depending on their role. For example, if a company is looking for a work management tool, the design team may have different needs than a development team. Choosing only one solution right off the bat might not be the right course of action. 

Step 4: Weigh the evidence

This is when you take all of the different solutions you’ve come up with and analyze how they would address your initial problem. Your team begins identifying the pros and cons of each option, and eliminating alternatives from those choices.

There are a few common ways your team can analyze and weigh the evidence of options:

Pros and cons list

SWOT analysis

Decision matrix

Step 5: Choose among the alternatives

The next step is to make your final decision. Consider all of the information you've collected and how this decision may affect each stakeholder. 

Sometimes the right decision is not one of the alternatives, but a blend of a few different alternatives. Effective decision-making involves creative problem solving and thinking out of the box, so don't limit you or your teams to clear-cut options.

One of the key values at Asana is to reject false tradeoffs. Choosing just one decision can mean losing benefits in others. If you can, try and find options that go beyond just the alternatives presented.

Step 6: Take action

Once the final decision maker gives the green light, it's time to put the solution into action. Take the time to create an implementation plan so that your team is on the same page for next steps. Then it’s time to put your plan into action and monitor progress to determine whether or not this decision was a good one. 

Step 7: Review your decision and its impact (both good and bad)

Once you’ve made a decision, you can monitor the success metrics you outlined in step 1. This is how you determine whether or not this solution meets your team's criteria of success.

Here are a few questions to consider when reviewing your decision:

Did it solve the problem your team identified in step 1? 

Did this decision impact your team in a positive or negative way?

Which stakeholders benefited from this decision? Which stakeholders were impacted negatively?

If this solution was not the best alternative, your team might benefit from using an iterative form of project management. This enables your team to quickly adapt to changes, and make the best decisions with the resources they have. 

Types of decision making models

While most decision making models revolve around the same seven steps, here are a few different methodologies to help you make a good decision.

​Rational decision making models

This type of decision making model is the most common type that you'll see. It's logical and sequential. The seven steps listed above are an example of the rational decision making model. 

When your decision has a big impact on your team and you need to maximize outcomes, this is the type of decision making process you should use. It requires you to consider a wide range of viewpoints with little bias so you can make the best decision possible. 

Intuitive decision making models

This type of decision making model is dictated not by information or data, but by gut instincts. This form of decision making requires previous experience and pattern recognition to form strong instincts.

This type of decision making is often made by decision makers who have a lot of experience with similar kinds of problems. They have already had proven success with the solution they're looking to implement. 

Creative decision making model

The creative decision making model involves collecting information and insights about a problem and coming up with potential ideas for a solution, similar to the rational decision making model. 

The difference here is that instead of identifying the pros and cons of each alternative, the decision maker enters a period in which they try not to actively think about the solution at all. The goal is to have their subconscious take over and lead them to the right decision, similar to the intuitive decision making model. 

This situation is best used in an iterative process so that teams can test their solutions and adapt as things change.

Track key decisions with a work management tool

Tracking key decisions can be challenging when not documented correctly. Learn more about how a work management tool like Asana can help your team track key decisions, collaborate with teammates, and stay on top of progress all in one place.

Related resources

How to give and take constructive criticism

Data-driven decision making: A step-by-step guide

Listening to understand: How to practice active listening (with examples)

How executives and individual contributors differ when it comes to AI

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Making decisions and solving problems are two key areas in life, whether you are at home or at work. Whatever you’re doing, and wherever you are, you are faced with countless decisions and problems, both small and large, every day.

Many decisions and problems are so small that we may not even notice them. Even small decisions, however, can be overwhelming to some people. They may come to a halt as they consider their dilemma and try to decide what to do.

Small and Large Decisions

In your day-to-day life you're likely to encounter numerous 'small decisions', including, for example:

Tea or coffee?

What shall I have in my sandwich? Or should I have a salad instead today?

What shall I wear today?

Larger decisions may occur less frequently but may include:

Should we repaint the kitchen? If so, what colour?

Should we relocate?

Should I propose to my partner? Do I really want to spend the rest of my life with him/her?

These decisions, and others like them, may take considerable time and effort to make.

The relationship between decision-making and problem-solving is complex. Decision-making is perhaps best thought of as a key part of problem-solving: one part of the overall process.

Our approach at Skills You Need is to set out a framework to help guide you through the decision-making process. You won’t always need to use the whole framework, or even use it at all, but you may find it useful if you are a bit ‘stuck’ and need something to help you make a difficult decision.

Decision Making

Effective Decision-Making

This page provides information about ways of making a decision, including basing it on logic or emotion (‘gut feeling’). It also explains what can stop you making an effective decision, including too much or too little information, and not really caring about the outcome.

A Decision-Making Framework

This page sets out one possible framework for decision-making.

The framework described is quite extensive, and may seem quite formal. But it is also a helpful process to run through in a briefer form, for smaller problems, as it will help you to make sure that you really do have all the information that you need.

Problem Solving

Introduction to Problem-Solving

This page provides a general introduction to the idea of problem-solving. It explores the idea of goals (things that you want to achieve) and barriers (things that may prevent you from achieving your goals), and explains the problem-solving process at a broad level.

The first stage in solving any problem is to identify it, and then break it down into its component parts. Even the biggest, most intractable-seeming problems, can become much more manageable if they are broken down into smaller parts. This page provides some advice about techniques you can use to do so.

Sometimes, the possible options to address your problem are obvious. At other times, you may need to involve others, or think more laterally to find alternatives. This page explains some principles, and some tools and techniques to help you do so.

Having generated solutions, you need to decide which one to take, which is where decision-making meets problem-solving. But once decided, there is another step: to deliver on your decision, and then see if your chosen solution works. This page helps you through this process.

‘Social’ problems are those that we encounter in everyday life, including money trouble, problems with other people, health problems and crime. These problems, like any others, are best solved using a framework to identify the problem, work out the options for addressing it, and then deciding which option to use.

This page provides more information about the key skills needed for practical problem-solving in real life.

Further Reading from Skills You Need

The Skills You Need Guide to Interpersonal Skills eBooks.

Develop your interpersonal skills with our series of eBooks. Learn about and improve your communication skills, tackle conflict resolution, mediate in difficult situations, and develop your emotional intelligence.

Guiding you through the key skills needed in life

As always at Skills You Need, our approach to these key skills is to provide practical ways to manage the process, and to develop your skills.

Neither problem-solving nor decision-making is an intrinsically difficult process and we hope you will find our pages useful in developing your skills.

Start with: Decision Making Problem Solving

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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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7 steps of the decision-making process

Reading time: about 4 min

• Identify the decision.
• Gather relevant info.
• Identify the alternatives.
• Weigh the evidence.
• Choose among the alternatives.
• Take action.
• Review your decision.

Robert Frost wrote, “Two roads diverged in a wood, and I—I took the one less traveled by, and that has made all the difference.” But unfortunately, not every decision is as simple as “Let’s just take this path and see where it goes,” especially when you’re making a decision related to your business.

Whether you manage a small team or are at the head of a large corporation, your success and the success of your company depend on you making the right decisions—and learning from the wrong decisions.

Use these decision-making process steps to help you make more profitable decisions. You'll be able to better prevent hasty decision-making and make more educated decisions.

Defining the business decision-making process

The business decision-making process is a step-by-step process allowing professionals to solve problems by weighing evidence, examining alternatives, and choosing a path from there. This defined process also provides an opportunity, at the end, to review whether the decision was the right one.

7 decision-making process steps

Though there are many slight variations of the decision-making framework floating around on the Internet, in business textbooks, and in leadership presentations, professionals most commonly use these seven steps.

1. Identify the decision

To make a decision, you must first identify the problem you need to solve or the question you need to answer. Clearly define your decision. If you misidentify the problem to solve, or if the problem you’ve chosen is too broad, you’ll knock the decision train off the track before it even leaves the station.

If you need to achieve a specific goal from your decision, make it measurable and timely.

2. Gather relevant information

Once you have identified your decision, it’s time to gather the information relevant to that choice. Do an internal assessment, seeing where your organization has succeeded and failed in areas related to your decision. Also, seek information from external sources, including studies, market research, and, in some cases, evaluation from paid consultants.

Keep in mind, you can become bogged down by too much information and that might only complicate the process.

3. Identify the alternatives

With relevant information now at your fingertips, identify possible solutions to your problem. There is usually more than one option to consider when trying to meet a goal. For example, if your company is trying to gain more engagement on social media, your alternatives could include paid social advertisements, a change in your organic social media strategy, or a combination of the two.

4. Weigh the evidence

Once you have identified multiple alternatives, weigh the evidence for or against said alternatives. See what companies have done in the past to succeed in these areas, and take a good look at your organization’s own wins and losses. Identify potential pitfalls for each of your alternatives, and weigh those against the possible rewards.

5. Choose among alternatives

Here is the part of the decision-making process where you actually make the decision. Hopefully, you’ve identified and clarified what decision needs to be made, gathered all relevant information, and developed and considered the potential paths to take. You should be prepared to choose.

6. Take action

7. review your decision.

After a predetermined amount of time—which you defined in step one of the decision-making process—take an honest look back at your decision. Did you solve the problem? Did you answer the question? Did you meet your goals?

If so, take note of what worked for future reference. If not, learn from your mistakes as you begin the decision-making process again.

Tools for better decision-making

Depending on the decision, you might want to weigh evidence using a decision tree . The example below shows a company trying to determine whether to perform market testing before a product launch. The different branches record the probability of success and estimated payout so the company can see which option will bring in more revenue.

Visual Activities are a perfect choice for quickly synthesizing ideas and gaining consensus. Use these dynamic activities with your team members to turn qualitative feedback into actionable insights and easily make decisions in seconds.

A decision matrix is another tool that can help you evaluate your options and make better decisions. Learn how to make a decision matrix and get started quickly with the template below. 

You can also create a classic pros-and-cons list, and clearly highlight whether your options meet necessary criteria or whether they pose too high of a risk.

With these 7 steps we've outlined, plus some tools to get you started, you will be able to make more informed decisions faster . 

Explore additional strategies to help with your decision-making process.

About Lucidchart

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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Use this guide to learn how to make a decision tree in Microsoft Excel—either directly in Excel using Shapes or using a simple Lucidchart integration.

Definition and examples of the consumer decision-making process

Review these steps of the consumer decision-making process and put yourself in the customer’s shoes to make an impact with your sales or marketing.

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Effective Decision Making Process: 7 Steps with Examples

By Status.net Editorial Team on June 3, 2023 — 9 minutes to read

Making decisions is an inevitable part of life, and knowing how to navigate through the decision-making process can be crucial for both your personal and professional success. In this article, we will explore the seven essential steps to help you make thoughtful and informed choices.

Step 1: Identify the Decision

When you’re faced with a problem or challenge, it’s essential to identify the decision you need to make. Start by defining the objective of what you want to achieve. It’s helpful to take a step back and assess the situation to fully understand the problem at hand.

To get a clear picture of the issue, gather information from multiple angles and examine the factors involved. This will help you gain a better understanding of the context and possible options available. Make sure to evaluate the pros and cons of each scenario.

For example, if your company is facing a dip in sales, you might need to decide whether to launch a new advertising campaign or improve product offerings.

Step 2: Gather Relevant Information

Conduct thorough market research to understand the current state of the market, as well as any expected trends and developments. Make use of both primary and secondary sources, such as interviews with experts and published reports, while remaining mindful of any potential biases. Your objective is to collect accurate, up-to-date data that allows you to make an informed decision.

Consider the various resources at your disposal. These may be online databases, industry reports, or even colleagues with relevant expertise. As you gather information, remember to keep track of your sources to reference them later. Maintaining proper documentation can save time and simplify any further analysis.

Don’t forget to consult the stakeholders involved in the decision. Their opinions, concerns, and suggestions can offer valuable insights and expose any blind spots. Engaging them in the process also fosters a sense of shared responsibility and encourages open communication.

For example:

• If you’re deciding on a new product to launch, gather information on market trends, customer preferences, and competitor offerings. This will provide a comprehensive understanding of the market landscape and opportunities for growth.
• When selecting a new supplier, research their financial stability, environmental performance, and customer reviews. This will help you ensure a long-lasting and beneficial partnership.
• If you’re debating whether to pursue a new marketing campaign, consult your sales team, marketing department, and customer service representatives. Their firsthand experience interacting with customers and interpreting their needs can prove invaluable in shaping the objectives and strategies of the campaign.

Step3: Identify Alternatives

While brainstorming alternatives, keep an open mind and consider all possible options, even if they seem unconventional or unusual at first glance. Don’t limit yourself to the obvious; sometimes, the most effective solution might be the one that is least expected.

As you gather alternatives, it’s helpful to list them down. Organize your list in a way that makes it easy for you to see the various options, their pros, and cons. Summarizing each alternative in a concise manner can help you to better understand their implications.

For example, when deciding on a new marketing strategy, you could list these alternatives:

• Traditional Marketing : Pros: Familiarity, proven results; Cons: High cost, limited audience reach
• Social Media Marketing : Pros: Low cost, broad audience reach; Cons: Time-intensive, potential negative engagement
• Content Marketing : Pros: Engaging, builds trust; Cons: Slow results, resource-intensive

Once you’ve listed your alternatives with their pros and cons, you can start comparing them to one another. Try to objectively assess the advantages and drawbacks of each solution in relation to the decision at hand. It might be helpful to rank them based on their potential effectiveness and feasibility.

As you identify alternatives, always be prepared to revise or expand your list. Be open to new insights and feedback from others.

Step 4: Weigh the Evidence

After gathering all the relevant information and alternatives for a decision, you’ll want to weigh the evidence before making a choice. This step in the 7-step decision-making process is crucial and ensures that you’re evaluating each option fairly.

To weigh the evidence properly, consider starting with a decision matrix. A decision matrix is a tool that helps organize and compare different alternatives based on specific criteria that matter to you. This method helps you quantify each option, making it easier to evaluate and prioritize them.

When using a decision matrix, list your options in rows and your criteria in columns. You’ll then assign a weight to each criterion according to its importance. After that, rate each option based on how well it meets the specific criterion. Multiply the rating by the weight, and then sum the results to get a total score for each option.

While weighing the evidence, it’s essential to trust your intuition. Your gut feeling might provide valuable insights based on your past experiences and knowledge. However, don’t rely solely on intuition, as it may sometimes lead to bias or ignore critical data.

During this stage, it’s crucial to assess the risks of each option. Knowing the potential consequences of each choice allows you to anticipate possible setbacks and challenges, preparing you for better decision-making. Be mindful of the common pitfalls in the process, such as groupthink, lack of diversity in perspectives, or being overly influenced by emotional factors.

Here are some examples to illustrate this step:

• Career: You’re considering a job offer from two companies. You gather information about salary, benefits, company culture, and growth opportunities, then weigh the evidence using a decision matrix. Your intuition tells you that one company aligns better with your values, so you weigh that factor more heavily when making your decision.
• Investment: You’re evaluating two investments with different levels of risk and potential return. By weighing the evidence – historical performance, growth potential, and industry trends – you create a decision matrix that includes your personal risk tolerance and financial goals. This method helps you determine which investment is the better fit for your unique situation.
• Product Launch: You have several ideas for new products, and you need to decide which one to prioritize. By weighing the evidence – potential market demand, production costs, and competition – using a decision matrix, you can quantitatively assess each idea and make an informed decision on which product to develop first.

Weighing the evidence is essential to making well-informed decisions. By considering various factors, relying on both intuition and data, and assessing the risks and challenges, you’ll be better equipped to make choices that benefit both you and your organization.

Step 5: Choose Among Alternatives

Start by prioritizing your options. Analyze each alternative and determine which ones align best with your objectives. This part could be as simple as ranking alternatives from most desirable to least desirable. Prioritize based on factors such as potential benefits, risks, costs, and resources available.

Another approach is using a decision tree, a visual tool that can help clarify and map out your choices. A decision tree charts the various courses of action, outcome probabilities, and expected payoff. By working through a decision tree, you can systematically evaluate your options and find the optimal solution.

As you evaluate each choice, imagine potential outcomes and how they could impact your objectives. Assessing the pros and cons of each alternative will provide insight on the best course of action.

Example 1: Imagine you’re debating between accepting a job offer or staying at your current job. You could prioritize based on factors like salary, career growth potential, work-life balance, and job security. Use a decision tree to visualize the potential payoffs and risks of each choice.

Example 2: If you’re deciding on a marketing strategy for your business, prioritize options like cost-effectiveness, target audience reach, and expected return on investment. Use a decision tree to analyze each strategy, considering factors like potential growth and customer engagement.

Step 6: Take Action

Once you’ve weighed your options and made a decision, it’s time to take action. As a part of an organization, your leadership and management skills will play a crucial role in executing the plan. Follow these steps to bring your decision to life:

• Communicate the decision to all relevant parties: Make sure everyone involved, from team members to stakeholders, knows the chosen course of action. Clear communication will ensure everyone is on the same page.
• Set goals and expectations: Establish both short-term and long-term objectives to monitor progress and determine whether the chosen path is effective. It’s essential to have a clear set of expectations so that your team is aligned with your vision. Examples of goals: – Rolling out a new product within 6 months – Decreasing expenditure by 15% in the next quarter – Increasing overall market share by 10% in the following year
• Create a timeframe: Outline the sequence of tasks and establish deadlines for each step. A well-defined timeline will help in keeping the momentum going, and ensure that the desired results are reached within the allotted time.
• Delegate responsibilities and provide resources: Assign tasks to team members based on their expertise and provide the necessary tools, training, and support to help them succeed.
• Monitor progress and make adjustments as needed: Regularly review your team’s progress and be open to making changes if something isn’t working. Flexibility is crucial for effective decision-making.

Step 7: Review Your Decision

As you go through the decision-making process, it’s essential to review your decision to ensure it’s the best choice for you and your business. This step allows you to reflect on the potential risks and benefits associated with your decision. By reviewing your decision, you position yourself to make better choices and improve your overall decision-making skills.

Example : You decided to implement a new software system in your company. After a few months, review the system’s performance and observe if it increases productivity, reduces errors, and improves customer satisfaction. If needed, make adjustments to maximize its benefits.

Example : After launching a new product, analyze its sales performance, customer feedback, and market response. Identify areas where improvements can be made, or if necessary, consider discontinuing the product.

Don’t be afraid to adjust course if you find that your initial choice isn’t working as you had hoped. Keep in mind that making sound decisions is an ongoing process requiring flexibility and adaptability. As your situation evolves and circumstances change, you must be willing to reassess and revise your decisions to maintain success and growth.

Frequently Asked Questions

Which steps form the decision-making process.

The decision-making process usually consists of seven steps:

• Identify the problem or decision
• Gather information and resources
• Identify possible options or solutions
• Evaluate the options and their outcomes
• Choose the best option for the situation
• Implement your chosen decision
• Review the results and learn from them

What variations exist in the decision-making process?

While the decision-making process is typically broken down into seven steps, there may be variations depending on factors like individual preferences, the complexity of the decision, and time constraints. Some variations include:

• Relying more on intuition or gut feelings
• Skipping certain steps due to urgency
• Using decision-making tools or models like SWOT analysis or decision trees

How can I apply decision-making steps in real life?

You can apply the seven-step decision-making process in real life by:

• Clearly defining the problem or decision
• Gathering relevant information and resources
• Identifying possible options, solutions or alternatives
• Evaluating each option and predicting their outcomes
• Selecting the best option based on your criteria
• Implementing your chosen decision
• Reviewing the results and learning from the experience

Related: Personal SWOT Analysis: Unlock Your Potential in 4 Steps

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• Media Center

Decision-making process

The basic idea.

Which do you prefer, hamburgers or pizza? 

You probably came up with the answer to that question quickly — unless you’re really torn between those two delicious meals. Yet, whenever we are making a choice between two or more things, our brains go through a decision-making process.

When we are presented with a choice, we first have to identify the decision. For the hamburger vs. pizza question, that’s an easy step — you immediately know that the decision is between two food items. Next, we have to gather the relevant information. You think of hamburgers and pizzas and their respective tastes. Then, you identify alternatives. Are there any other options? Maybe a secret taco stash laying around? No.

Now that you know the parameters of the decision, it’s time to weigh the evidence. Since it’s based on personal preference, you only have to conjure up your own experience eating both. Which one have you preferred in the past? And finally: you’re ready to make the choice. 1

If the question was to require an action, such as “Do you want to go get hamburgers or pizza?”, after making the choice, you would implement the action and order one or the other. The last step is to reflect on that decision. As you sit thedown with your slice of pizza, are you content? You evaluate your outcome so that in the future, you can improve your choices. 2

A decision-making process is the cognitive process where you weigh alternatives to achieve a desired result. 3

It doesn’t matter which side of the fence you get off on sometimes. What matters most is getting off. You cannot make progress without making decisions. - American entrepreneur and motivational speaker Jim Rohn. 4

Problem Solving: while problem solving and decision making are related, they are not the same. Problem solving is an analytical process used to identify the possible solutions to a situation at hand and is sometimes a part of the decision-making process. However, sometimes you make a decision without identifying all possible solutions to the problem. 5

Analysis Paralysis: the inability to decide because of over-thinking. People usually get stuck in the stage where they consider all the alternatives and can be overwhelmed by choice (known as the choice overload bias ), preventing them from picking a choice. 6

Boundary conditions: constraints necessary for the solution of a decision. 7 The boundary conditions determine what the objective of the decision is and therefore what solutions will be suitable. 8

Maximizing: a decision-making process in which an individual employs optimization, where they gain all the necessary data on alternatives to make the best decision. 9

Bounded rationality : decision-making processes where individuals satisfice instead of optimize. 

Decision-making processes are one of the facets that make humans unique. There are definitely similarities between the ways that humans and animals make decisions (in fact, we wrote a whole article about it: “ Decision-Making Parallels Between Humans and Animals ”). But humans are thought to engage in more complex decision-making processes - our actions are less governed by instinct than animals.  

Since decision-making processes are an integral part of humanity, it’s impossible to pinpoint a time where they were first an object of research, as we have been interested in how humans make decisions as long as we have existed! In research, decision-making processes are usually examined as either group decision-making, or individual decision-making. 

In group decision-making, multiple individuals work together to analyze a question or problem, consider alternative choices, and make a choice. An obvious difference between group decision-making and individual decision-making is that the former process is usually more formal and occurs outside of internal thought processes. Being in a group also has quite impactful effects on the types of decisions we come to. For example, groupthink is a phenomenon borne from our desire to achieve consensus - individuals ignore evidence or neglect to speak out if their thoughts contradict the majority opinion. It is a form of group conformity that hinders critical thinking and can lead to suboptimal decisions. Additionally, groups tend to make riskier decisions than individuals. 10

Yet, sometimes group decision-making processes are advantageous because there are a greater number of perspectives which can reduce the personal biases that come into play with individual decision-making. Groups can usually come up with a greater number of alternatives, because they have more time and resources. Oftentimes, individuals have to employ the satisficing technique, where they make a choice that is satisfactory rather than optimal. When satisficing, individuals don’t engage in problem-solving, but pick the first choice they come across that adheres to the boundary conditions, because it requires too much time, effort, and resources to gather all the necessary evidence and alternative options. 

Various research has gone into trying to outline the steps involved in the decision-making process. One of the earlier processes,developed by Australian psychologist Leon Mann in the 1980s, is known as the GOFER process. It was based on a theory Mann had previously proposed alongside psychologist Irvin Janis, the conflict theory of decision making, which suggests decision-makers must choose from a set of alternatives that each have positive and negative outcomes. 11 GOFER represents an acronym for five decision-making steps:

• G oal clarification. In this step, the decision-maker must determine what their goal is and what they are trying to achieve with their decision.
• O ptions generation. The decision-maker conjures up different options that are available to them and that would help them achieve their goals.
• F acts-finding. During this stage, the decision-maker examines what evidence they have on each alternative and what information they are missing that could help them make their decision. 
• E ffects. The decision-maker considers the positive and negative outcomes of each alternative.
• R eview. Now that a decision has been arrived at, the decision-maker considers how it will be implemented. 12

Another popular decision-making process model is the DECIDE model, developed in 2008 by Hawaiian educator, Kristina Guo. It has similar steps as the GOFER model and Guo developed it to help healthcare managers make better decisions. The acronym stands for:

• D efine the problem.
• E stablish the criteria.
• C onsider the alternatives.
• I dentify the best alternative.
• D evelop a plan and implement the plan of action.
• E valuate and monitor the solution and feedback. 10

While the first five steps are similar to the GOFER model, the DECIDE model has an additional step, evaluate, which is crucial for improving decision-making processes in the long run, as evaluating your choice will help you learn how successful it was and whether you should make the same decision in the future.

Consequences

Although we usually make decisions quickly, following decision-making models can help us make thoughtful decisions. By outlining the decision-making process, you can ensure that you are going through each step, considering the alternatives, and making an informed decision, which should lead to better outcomes. When it comes to group settings, being transparent about the decision-making process can also help other people understand why you’re making the choices you are. After all, wasn’t it irritating when your parents told you that the answer was no ‘because they said so’? 

Controversies

Oftentimes, people assume that decision-making processes are rational. Many models assume that individuals will make choices that maximize utility while minimizing costs. They also assume people have all of the information required to make the optimal choice — all of the models include a stage where the decision-maker analyzes various alternatives. 

But consider how many decisions you have made today. Is it really possible to go through each stage of the decision-making process, weigh out all the advantages and disadvantages of each alternative, and then come to a clear decision? Humans are thought to make around 35,000 choices per day, which makes it impossible for us to have the time, resources, or brain capacity to have all the information necessary to make a perfectly rational decision. 13

That’s why many people criticize decision-making models for being unrealistic, and instead suggest that decision-making processes actually adhere to bounded rationality , where decision-makers satisfice instead of optimize. 

Effectiveness of the GOFER Decision-Making Process

Giving teens the tools to make better, more thought-out decisions can help improve outcomes. In 1988, Leon Mann and his colleagues from The Flinders University of South Australia conducted a study to determine how effective teaching students the GOFER decision-making process was. 14  

Mann and his colleagues developed and ran a GOFER course to teach teens generalized decision-making skills that would enable the students to apply the decision skills to a range of problems even beyond the school context. They delivered the course over the span of a year with the aid of two principal texts. The first book outlined decision-making processes and the GOFER technique, while the second applied the principles of GOFER to five areas: decision-making in groups, friendships and decision-making, subject choices, money, and finally, the GOFER technique employed in various different professions. 14  

Mann et al. compared the students in South Australia who had taken the GOFER course with a control group of high school students who had not taken the course. They gave all students a questionnaire that asked them to reflect on their decision self-esteem , their vigilance with decisions, panic measures they might be prone to, cop out tendencies they use to avoid making decisions, and complacency. They filled out another questionnaire that examined their performance on decision-making across the five areas taught by the course, and a third questionnaire that asked them general information about decision-making, with questions like, “What makes a good decision maker?” 14

Mann found that the students who had been trained in the GOFER method during the course had much higher reports of self-esteem as a decision-maker, and better habits when it came to making decisions (fewer panic, cop-out, and complacency tendencies). Mann et al. concluded that the GOFER decision-making process helped improve student knowledge on decision-making, raise their confidence, and improved their habits. 14 

Decision-making styles

Just as there are various different decision-making models, there are different decision-making styles. Each style has different advantages and disadvantages, and can be categorized into four main types: directive, conceptual, analytical, and behavioral. 15

Directive: Individuals with directive decision-making styles rely on rationality, but the decision-maker usually relies solely on their own knowledge without taking into account other opinions. 15

Conceptual: The conceptual decision-maker likes to approach problems from every angle. They like to brainstorm potential alternatives, gather insights from other people, and try to come up with creative solutions to problems. This style of decision-making can be time-consuming as a result. 15

Analytical: Similar to the conceptual decision makers, analytical decision-makers like to gather a lot of information before they make a decision. While the conceptual decision-maker is keen to come up with creative solutions, the analytical decision-maker wants to find data and facts that will support their decision. At times, this can prevent innovative choices, but it means that choices are well-informed and objective. 16

Behavioral: Individuals with a behavioral decision-making style are group-oriented. However, instead of leaving the process open-ended, they will provide groups with potential options and alternatives and use the group sessions to discuss potential pros and cons. To be effective, this style needs a decisive leader who can listen to the pros and cons and execute a decision, or else discussion could go on forever. 16 

Related Content

Group Decision Making: How to Be Effective and Efficient

Groupthink can be an obstacle to group decision-making processes, but so can it’s opposite. You’ve probably heard of the phrase ‘too many cooks in the kitchen.’ When many people are involved in the decision-making process, too much time can be spent debating between alternatives which can lead to a standstill or a delay in making a decision. While opposite issues, both of these phenomena are common challenges in group decision-making. To discover some principles that can help groups make more effective and efficient decisions, read this article by our contributor, Yasmine Kalkstein.

The Power of Narratives in Decision Making

Decision-making processes are usually outlined as a series of steps or stages. Humans like to categorize things in that way, because we tend to process the world around us as narratives that have a sequence: a beginning (a cause), a middle, and an end (an effect). That is known as the theory of narrative thought, which this article explores. In the article, contributor Constantin Huet explores why it is that we think in terms of stories, and what effects stories have on our consumer decisions. 

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Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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Master the 7-Step Problem-Solving Process for Better Decision-Making

Discover the powerful 7-Step Problem-Solving Process to make better decisions and achieve better outcomes. Master the art of problem-solving in this comprehensive guide. Download the Free PowerPoint and PDF Template.

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Introduction.

The 7-Step Problem-Solving Process involves steps that guide you through the problem-solving process. The first step is to define the problem, followed by disaggregating the problem into smaller, more manageable parts. Next, you prioritize the features and create a work plan to address each. Then, you analyze each piece, synthesize the information, and communicate your findings to others.

In this article, we'll explore each step of the 7-Step Problem-Solving Process in detail so you can start mastering this valuable skill. At the end of the blog post, you can download the process's free PowerPoint and PDF templates .

Step 1: Define the Problem

One way to define the problem is to ask the right questions. Questions like "What is the problem?" and "What are the causes of the problem?" can help. Gathering data and information about the issue to assist in the definition process is also essential.

Step 2: Disaggregate

After defining the problem, the next step in the 7-step problem-solving process is to disaggregate the problem into smaller, more manageable parts. Disaggregation helps break down the problem into smaller pieces that can be analyzed individually. This step is crucial in understanding the root cause of the problem and identifying the most effective solutions.

Disaggregation helps in breaking down complex problems into smaller, more manageable parts. It helps understand the relationships between different factors contributing to the problem and identify the most critical factors that must be addressed. By disaggregating the problem, decision-makers can focus on the most vital areas, leading to more effective solutions.

Step 3: Prioritize

Once the issues have been prioritized, developing a plan of action to address them is essential. This involves identifying the resources required, setting timelines, and assigning responsibilities.

Step 4: Workplan

The work plan should include a list of tasks, deadlines, and responsibilities for each team member involved in the problem-solving process. Assigning tasks based on each team member's strengths and expertise ensures the work is completed efficiently and effectively.

Developing a work plan is a critical step in the problem-solving process. It provides a clear roadmap for solving the problem and ensures everyone involved is aligned and working towards the same goal.

Step 5: Analysis

Pareto analysis is another method that can be used during the analysis phase. This method involves identifying the 20% of causes responsible for 80% of the problems. By focusing on these critical causes, organizations can make significant improvements.

Step 6: Synthesize

Once the analysis phase is complete, it is time to synthesize the information gathered to arrive at a solution. During this step, the focus is on identifying the most viable solution that addresses the problem. This involves examining and combining the analysis results for a clear and concise conclusion.

During the synthesis phase, it is vital to remain open-minded and consider all potential solutions. Involving all stakeholders in the decision-making process is essential to ensure everyone's perspectives are considered.

Step 7: Communicate

In addition to the report, a presentation explaining the findings is essential. The presentation should be tailored to the audience and highlight the report's key points. Visual aids such as tables, graphs, and charts can make the presentation more engaging.

The 7-step problem-solving process is a powerful tool for helping individuals and organizations make better decisions. By following these steps, individuals can identify the root cause of a problem, prioritize potential solutions, and develop a clear plan of action. This process can be applied to various scenarios, from personal challenges to complex business problems.

By mastering the 7-step problem-solving process, individuals can become more effective decision-makers and problem-solvers. This process can help individuals and organizations save time and resources while improving outcomes. With practice, individuals can develop the skills to apply this process to a wide range of scenarios and make better decisions in all areas of life.

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Team Dynamics: Problem-Solving and Decision Making

• Teamwork and Team Leadership Table of Contents
• Fostering Communication & Promoting Cooperation
• Problem-Solving and Decision Making
• Handling Conflict
• Dealing with Power and Influence

1. Overview

• Different stages of team development call for different problem solving methods
• Problem solving requires the use of a systematic process
• The appropriate decision making method is determined by the amount of time available for the decision and the impact of the decision
• Effective decision making requires the use of smart techniques

2. Problem Solving in Team Development Stages

3. General Problem Solving Steps

• Defining the problem : phrase problem as probing questions to encourage explorative thinking; make explicit goal statement
• Establish criteria for evaluating the solution : identify characteristics of a satisfactory solution; distinguish requirements from desires
• Analyzing the problem : discover the root cause and extent of the problem
• Considering alternate solutions : brainstorm to generate many ideas before judging any of them
• Evaluate alternate solutions : use ranking-weighting matrix; check for issues/disagreement
• Deciding on a solution :  choose best answer to the problem from among all possible solutions
• Develop action plan : make team assignments with milestones(don’t underestimate time)
• Implementing the action plan : check for consistency with requirements identified in step 2
• Following up on the solution :  check up on the implementation and make necessary adjustments
• Evaluate outcomes and process :  review performance, process, and personal aspects of the solution

4. Decision Making Method Based on Time and Impact

5. Smart Decision Making is Enabled By. . .

• Modeling an open mind and asking for candid opinions
• What elements would you choose to change?
• What changes would you make to solve …?  
• Aligning rewards to team successes to ensure that individuals share what they know
• Ensuring that team members are aware of relevant roles and unique information required for team success
• Charging some team members to assume a position that opposes the team’s preference
• Creating an alternate team that attempts to find errors and weaknesses in the solution
• Using successive rounds of blind voting interspersed with discussions

6. Additional Readings

• Hartnett, T. (n.d). Consensus decision making. Retrieved from http://www.consensusdecisionmaking.org/
• UMass|Dartmouth (n.d.) 7 steps to effective decision making . Retrieved from https://www.umassd.edu/media/u massdartmouth/fycm/decision_ma king_process.pdf
• Sunstein, C.R. (2014).  Making dumb groups smarter.  Harvard Business Review, 92(12), 90-98. 
• << Previous: Fostering Communication & Promoting Cooperation
• Next: Handling Conflict >>

• Last Updated: Jul 15, 2024 1:09 PM
• URL: https://guides.himmelfarb.gwu.edu/teamdynamics

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Decision Making: a Theoretical Review

• Regular Article
• Published: 15 November 2021
• Volume 56 , pages 609–629, ( 2022 )

Cite this article

• Matteo Morelli 1 ,
• Maria Casagrande   ORCID: orcid.org/0000-0002-4430-3367 2 &
• Giuseppe Forte 1 , 3  

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Decision-making is a crucial skill that has a central role in everyday life and is necessary for adaptation to the environment and autonomy. It is the ability to choose between two or more options, and it has been studied through several theoretical approaches and by different disciplines. In this overview article, we contend a theoretical review regarding most theorizing and research on decision-making. Specifically, we focused on different levels of analyses, including different theoretical approaches and neuropsychological aspects. Moreover, common methodological measures adopted to study decision-making were reported. This theoretical review emphasizes multiple levels of analysis and aims to summarize evidence regarding this fundamental human process. Although several aspects of the field are reported, more features of decision-making process remain uncertain and need to be clarified. Further experimental studies are necessary for understanding this process better and for integrating and refining the existing theories.

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Morelli, M., Casagrande, M. & Forte, G. Decision Making: a Theoretical Review. Integr. psych. behav. 56 , 609–629 (2022). https://doi.org/10.1007/s12124-021-09669-x

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Decision-Making

Eight key steps for effective decision making, cognitive neuroscience and behavioral economics show how to make wise decisions..

Posted August 27, 2019

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Effective decision making to avoid failures and maximize success is your key role as a leader: that’s why “decision-makers” is synonymous with leaders. Yet how can you ensure that your decision making results in the right calls as opposed to decisions disasters?

Let’s say you make a poor choice on a hire for a major position. General Electric’s Board of Directors hired John Flannery as CEO in 2017 to restructure the company, but he didn’t work out. Shares fell by over 35% in the next year and the Board forced him out in 2018; immediately afterward, GE’s shares rose by 14%.

Or perhaps you make a major decision-making error in strategy. Research shows that 46% percent of companies go bankrupt due to wrong-headed strategic moves by their leaders. For instance, Toys ‘R’ Us went bankrupt due to a number of lousy strategic decisions by the company’s leadership , such as taking on too much debt and failing to compete effectively in online retailing.

Another type of problematic decision making is ignoring a looming problem: deciding not to decide. Kodak helped invent the digital camera but dragged its feet on ramping up its investment into the digital camera market because its film business made more money. By the time it recognized the future was digital, more nimble competitors seized the market. Kodak proved unable to catch up, eventually filing for bankruptcy.

All of these examples from large companies have their equivalent in mid-size and small businesses. Bad leadership decision making helps explain why about half of all new businesses fail within 5 years.

Leaders commit serious decision-making mistakes largely due to the many dangerous judgment errors that result from how our brains are wired. Scholars in cognitive neuroscience and behavioral economics call such mental blindspots cognitive biases .

Fortunately, recent research in these fields has discovered strategies to realize when you’re falling into cognitive biases , as well as ways to defeat these dangerous judgment errors . These techniques are applicable in your work life , in your professional and personal relationships , and in other aspects of your life .

Doing so will not only help you make the best decisions, whether quick decisions on a day-to-day basis or in more important cases, but also prevent failure and amplify success in implementing these choices . Furthermore, they can empower you to minimize threats and maximize opportunities when you make and enact your long-term strategic plans .

Separately from these structured techniques, you’ll also need to gain mental skills and habits to notice and quickly overcome cognitive biases.

8 Steps to Effective Decision Making for Leaders

“Avoiding Disastrous Decisions” is a pragmatic and battle-tested strategy that helps you choose the best option among several that each have strengths and weaknesses. I developed this technique based on research on the multi-attribute utility theory .

Then, I used this model extensively during my consulting and coaching engagements for the last 20 years helping leaders in large and mid-size companies and nonprofits avoid business disasters. After perfecting it based on these engagements, I am sharing it with you. It will help you to make the right calls even if you don’t hire me.

Use the technique in cases where it’s worthwhile to spend serious time and energy on a decision, meaning where the decision is really significant. These might include:

• Making a substantial strategy shift
• Pursuing a merger or acquisition
• Making a key employee hire
• Choosing which new product to launch
• Deciding on a critical supplier
• Moving your headquarters
• Making a major career move

Evaluating whether and how your systems and processes need to be adjusted to match changing market needs

“Avoiding Disastrous Decisions” can be used by yourself or with a team. This web app , designed specifically for use with the technique, helps make the decision-making process and the math involved easy and simple. Moreover, the app ensures that the decision making is transparent to and inclusive of all stakeholders.

My strong suggestion is to use this method together with the “ Making the Best Decisions ” technique. That’s because the “Avoiding Disastrous Decisions” strategy focuses only on trade-offs between different options rather than all the other aspects of making the best decisions. I make sure that all of my consulting and coaching clients use these two techniques together, and I am giving you the same advice I give them.

Step 1: List Decision-Making Criteria

Write out all the relevant and important attributes for your decision, meaning the key criteria you will use to make your choice. Don’t get stuck in analysis paralysis by listing all possible criteria: try to limit yourself to 10, unless it’s a truly complex decision. For a key hire, you can use criteria such as “salary requirements,” “fit into organizational culture,” “ability to perform the job,” “contribution to diversity,” and so on.

If you’re going through this process as a team, brainstorm the categories and then vote on which should make it into the top 10. Then put them into the web app for easing your calculations and guiding you through the process.

Step 2: Weigh the Attributes

Give weights to each of your attributes, from 1-10 on their importance to you (1 lowest importance, 10 highest). Make sure to use this step to evaluate honestly which of these criteria is more important to you. For example, you can weight “salary requirements” at 4, meaning you have a good budget, and weigh “fit into organizational culture” at 9, meaning it’s a critical factor for success in your firm.

If you’re doing this as a team, come up with weights independently and anonymously. Then, average out your weights.

Step 3: Rank It!

Rank each option that you are considering choosing on all the attributes in a decision matrix table, from 1-10 on how good they are (1-poor, 10-great).

Similar to above, if you’re doing this as a team, come up with rankings independently and anonymously. Then, average out your rankings.

Step 4: Math It!

Using the table, multiply weights by rankings – the web app makes it easy.

Step 5: Check with Your Gut

Your gut can give you some useful information, as long as you make sure to use your head to evaluate the data provided by your gut. Your gut is particularly valuable on questions that have to do with your values, and major decisions often relate to values questions.

Does the answer you got feel aligned with your intuitions? Would you be surprised if you looked back and wished you made a different decision? Experiment with adjusting weights and rankings to address gut feelings , but be cautious about trying to get the numbers to fit some predetermined choice.

Step 6: Check with Your Head

Check for potential dangerous judgment errors, especially ones resulting from paying too much attention to the gut. Look out for the 30 most dangerous judgment errors for decision making in the workplace.

Pay particular attention to cognitive biases to which you might be prone personally. Play around with adjusting weights and rankings to address such errors.

Step 7: Red Flags

Decide what kind of red flags you would use to reconsider the decision if relevant new evidence emerges that would influence your rankings and/or weights. It’s best to decide in advance what you would consider to constitute important evidence. By doing so, you’ll reduce the chance of being swayed by short-term emotions as an individual or simmering tensions and disagreements as a team.

Step 8: Choose and Commit

Make your choice and stick with it. This precommitment will help reduce feelings of anxiety and doubt, help you be happier , and reduce conflict in team settings.

The “Avoiding Disastrous Decisions” strategy should be used every time you need to make a critical decision, by yourself or as part of a team. Using this technique will allow you and your team to be confident about the quality of your decision making and maximize the chance that you’ll make the right call. If you’d like case studies with in-depth guidelines of how you can apply this strategy as an individual or a team, see the Manual on Avoiding Disastrous Decisions .

Key Takeaway

Effective leadership decision making on critical decisions involves: 1) Deciding the decision criteria; 2) Weighing the importance of criteria; 3) Grading your options using the criteria; 4) Checking with your head and gut; 5) Sticking to your choice.

Questions to Consider

• Do you have any questions about where and how to apply this technique?

How do you think using this technique might benefit your organization?

What steps can you take to most effectively bring it to your team and integrate it into your organization’s processes?

Originally published at Disaster Avoidance Experts on July 1, 2019.

Gleb Tsipursky, Ph.D. , is on the editorial board of the journal Behavior and Social Issues. He is in private practice.

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Organization and Self-Management

22 Effective Problem Solving and Decision Making

Types of decision makers.

Problem solving and decision making belong together. You cannot solve a problem without making a decision. There are two main types of decision makers. Some people use a systematic, rational approach. Others are more intuitive. They go with their emotions or a gut feeling about the right approach. They may have highly creative ways to address the problem, but cannot explain why they have chosen this approach.

Six Problem-Solving Steps

The most effective method uses both rational and intuitive or creative approaches. There are six steps in the process:

Identify the problem

Search for alternatives, weigh the alternatives, make a choice.

• Implement the choice
• Evaluate the results and, if necessary, start the process again

To solve a problem, you must first determine what the problem actually is. You may think you know, but you need to check it out. Sometimes, it is easy to focus on symptoms, not causes. You use a rational approach to determine what the problem is. The questions you might ask include:

• What have I (or others) observed?
• What was I (or others) doing at the time the problem occurred?
• Is this a problem in itself or a symptom of a deeper, underlying problem?
• What information do I need?
• What have we already tried to address this problem?

For example, the apprentice you supervise comes to you saying that the electric warming oven is not working properly. Before you call a repair technician, you may want to ask a few questions. You may want to find out what the apprentice means by “not working properly.” Does he or she know how to operate the equipment? Did he or she check that the equipment was plugged in? Was the fuse or circuit breaker checked? When did it last work?

You may be able to avoid an expensive service call. At the very least, you will be able to provide valuable information to the repair technician that aids in the troubleshooting process.

Of course, many of the problems that you will face in the kitchen are much more complex than a malfunctioning oven. You may have to deal with problems such as:

• Discrepancies between actual and expected food costs
• Labour costs that have to be reduced
• Lack of budget to complete needed renovations in the kitchen
• Disputes between staff

However, the basic problem-solving process remains the same even if the problems identified differ. In fact, the more complex the problem is, the more important it is to be methodical in your problem-solving approach.

It may seem obvious what you have to do to address the problem. Occasionally, this is true, but most times, it is important to identify possible alternatives. This is where the creative side of problem solving really comes in.

Brainstorming with a group can be an excellent tool for identifying potential alternatives. Think of as many possibilities as possible. Write down these ideas, even if they seem somewhat zany or offbeat on first impression. Sometimes really silly ideas can contain the germ of a superb solution. Too often, people move too quickly into making a choice without really considering all of the options. Spending more time searching for alternatives and weighing their consequences can really pay off.

Once a number of ideas have been generated, you need to assess each of them to see how effective they might be in addressing the problem. Consider the following factors:

• Impact on the organization
• Effect on public relations
• Impact on employees and organizational climate
• Ethics of actions
• Whether this course is permitted under collective agreements
• Whether this idea can be used to build on another idea

Some individuals and groups avoid making decisions. Not making a decision is in itself a decision. By postponing a decision, you may eliminate a number of options and alternatives. You lose control over the situation. In some cases, a problem can escalate if it is not dealt with promptly. For example, if you do not handle customer complaints promptly, the customer is likely to become even more annoyed. You will have to work much harder to get a satisfactory solution.

Implement the decision

Once you have made a decision, it must be implemented. With major decisions, this may involve detailed planning to ensure that all parts of the operation are informed of their part in the change. The kitchen may need a redesign and new equipment. Employees may need additional training. You may have to plan for a short-term closure while the necessary changes are being made. You will have to inform your customers of the closure.

Evaluate the outcome

Whenever you have implemented a decision, you need to evaluate the results. The outcomes may give valuable advice about the decision-making process, the appropriateness of the choice, and the implementation process itself. This information will be useful in improving the company’s response the next time a similar decision has to be made.

Creative Thinking

Your creative side is most useful in identifying new or unusual alternatives. Too often, you can get stuck in a pattern of thinking that has been successful in the past. You think of ways that you have handled similar problems in the past. Sometimes this is successful, but when you are faced with a new problem or when your solutions have failed, you may find it difficult to generate new ideas.

If you have a problem that seems to have no solution, try these ideas to “unfreeze” your mind:

• Relax before trying to identify alternatives.
• Play “what if” games with the problem. For example, What if money was no object? What if we could organize a festival? What if we could change winter into summer?
• Borrow ideas from other places and companies. Trade magazines might be useful in identifying approaches used by other companies.
• Give yourself permission to think of ideas that seem foolish or that appear to break the rules. For example, new recipes may come about because someone thought of new ways to combine foods. Sometimes these new combinations appear to break rules about complementary tastes or break boundaries between cuisines from different parts of the world. The results of such thinking include the combined bar and laundromat and the coffee places with Internet access for customers.
• Use random inputs to generate new ideas. For example, walk through the local shopping mall trying to find ways to apply everything you see to the problem.
• Turn the problem upside down. Can the problem be seen as an opportunity? For example, the road outside your restaurant that is the only means of accessing your parking lot is being closed due to a bicycle race. Perhaps you could see the bicycle race as an opportunity for business rather than as a problem.

Working in the Food Service Industry Copyright © 2015 by The BC Cook Articulation Committee is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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How to Make Great Decisions, Quickly

by Martin G. Moore

Summary .   

As a new leader, learning to make good decisions without hesitation and procrastination is a capability that can set you apart from your peers. While others vacillate on tricky choices, your team could be hitting deadlines and producing the type of results that deliver true value. That’s something that will get you — and them — noticed. Here are a few of a great decision:

• Great decisions are shaped by consideration of many different viewpoints. This doesn’t mean you should seek out everyone’s opinion. The right people with the relevant expertise need to clearly articulate their views to help you broaden your perspective and make the best choice.
• Great decisions are made as close as possible to the action. Remember that the most powerful people at your company are rarely on the ground doing the hands-on work. Seek input and guidance from team members who are closest to the action.
• Great decisions address the root cause, not just the symptoms. Although you may need to urgently address the symptoms, once this is done you should always develop a plan to fix the root cause, or else the problem is likely to repeat itself.
• Great decisions balance short-term and long-term value. Finding the right balance between short-term and long-term risks and considerations is key to unlocking true value.
• Great decisions are timely. If you consider all of the elements listed above, then it’s simply a matter of addressing each one with a heightened sense of urgency.

Like many young leaders, early in my career, I thought a great decision was one that attracted widespread approval. When my colleagues smiled and nodded their collective heads, it reinforced (in my mind, at least) that I was an excellent decision maker.

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5 Key Decision-Making Techniques for Managers

• 31 Mar 2020

Decision-making is an essential business skill that drives organizational performance. A survey of more than 750 companies by management consulting firm Bain found a 95 percent correlation between decision-making effectiveness and financial results. The data also showed companies that excel at making and executing strategic decisions generate returns nearly six percent higher than those of their competitors.

At many organizations, it’s up to managers to make the key decisions that influence business strategy. Research by consulting firm McKinsey , however, shows that 61 percent of them believe at least half the time they spend doing so is ineffective.

If you want to avoid falling into this demographic, here are five decision-making techniques you can employ to improve your management skills and help your organization succeed.

Access your free e-book today.

Decision-Making Techniques for Managers

1. take a process-oriented approach.

One of your primary responsibilities as a manager is to get things done with and through others, which involves leveraging organizational processes to accomplish goals and produce results. According to Harvard Business School Professor Len Schlesinger, who’s featured in the online course Management Essentials , decision-making is one of the processes you can use to your advantage.

“The majority of people think about making decisions as an event,” Schlesinger says. “It’s very rare to find a single point in time where a ‘decision of significance’ is made and things go forward from there. What we’re really talking about is a process. The role of the manager in overseeing that process is straightforward, yet, at the same time, extraordinarily complex.”

When establishing your decision-making process , first frame the issue at hand to ensure you ask the right questions and everyone agrees on what needs to be decided. From there, build your team and manage group dynamics to analyze the problem and craft a viable solution. By following a structured, multi-step process, you can make informed decisions and achieve the desired outcome.

2. Involve Your Team in the Process

Decision-making doesn’t have to be done in a vacuum. To avoid relying on managerial decisions alone, involve your team in the process to bring multiple viewpoints into the conversation and stimulate creative problem-solving .

Research in the journal Royal Society Open Science shows team decision-making is highly effective because it pools individuals’ collective knowledge and experience, leading to more innovative solutions and helping to surface and overcome hidden biases among groups.

Considering others’ perspectives on how to approach and surmount a specific challenge is an ideal alternative because it helps you become more aware of your implicit biases and manage your team with greater emotional intelligence .

Related: Emotional Intelligence Skills: What They Are & How to Develop Them

3. Foster a Collaborative Mindset

Fostering the right mindset early in the decision-making process is critical to ensuring your team works collaboratively—not contentiously.

When facing a decision, there are two key mindsets to consider:

• Advocacy: A mindset that regards decision-making as a contest. In a group with an advocacy mindset, individuals try to persuade others, defend their positions, and downplay their weaknesses.
• Inquiry: A mindset that navigates decision-making with collaborative problem-solving. An inquiry mindset centers on individuals testing and evaluating assumptions by presenting balanced arguments, considering alternatives, and being open to constructive criticism.

“On the surface, advocacy and inquiry approaches look deceptively similar,” HBS Professor David Garvin says in Management Essentials . “Both involve individuals engaged in debates, drawing on data, developing alternatives, and deciding on future directions. But, despite these similarities, inquiry and advocacy produce very different results.”

A study by software company Cloverpop found that decisions made and executed by diverse teams deliver 60 percent better results. Strive to instill your team members with an inquiry mindset so they’re empowered to think critically and feel their perspectives are welcomed and valued rather than discouraged and dismissed.

4. Create and Uphold Psychological Safety

For your team members to feel comfortable sharing their diverse perspectives and working collaboratively, it’s crucial to create and maintain a psychologically safe environment. According to research by technology company Google , psychological safety is the most important dynamic found among high-performing teams.

“Psychological safety is essential—first and foremost—for getting the information and perspectives out,” HBS Professor Amy Edmondson says in Management Essentials . “It’s helpful to be able to talk about what we know and think in an effective and thoughtful way before coming to a final conclusion.”

To help your team feel psychologically safe, be respectful and give fair consideration when listening to everyone’s opinions. When voicing your own point of view, be open and transparent, and adapt your communication style to meet the group’s needs. By actively listening and being attuned to your colleagues’ emotions and attitudes, you can forge a stronger bond of trust, make them feel more engaged and foster an environment that allows for more effective decisions.

Related: 5 Tips for Managing Change in the Workplace

5. Reiterate the Goals and Purpose of the Decision

Throughout the decision-making process, it’s vital to avoid common management pitfalls and lose sight of the goals and purpose of the decision on the table.

The goals you’re working toward need to be clearly articulated at the outset of the decision-making process—and constantly reiterated throughout—to ensure they’re ultimately achieved.

“It’s easy, as you get into these conversations, to get so immersed in one substantive part of the equation that you lose track of what the actual purpose is,” Schlesinger says.

Revisiting purpose is especially important when making decisions related to complex initiatives—such as organizational change —to ensure your team feels motivated and aligned and understands how their contributions tie into larger objectives.

Why Are Decision-Making Skills Important?

Effective decision-making can immensely impact organizational performance. By developing your decision-making skills, you can exercise sound judgment and guide your team through the appropriate frameworks and processes—resulting in more data-driven decisions .

You can also anticipate and navigate organizational challenges while analyzing the outcomes of previous efforts, which can have lasting effects on your firm’s success.

Improve Your Decision-Making Skills

Enhancing your decision-making capabilities can be an integral part of your journey to becoming a better manager , reaching your business goals, and advancing your career. In addition to real-world experience, furthering your education by taking a management training course can equip you with a wide range of skills and knowledge that enable both your team and organization to thrive.

Do you want to design, direct, and shape organizational processes to your advantage? Explore Management Essentials , one of our online leadership and management courses , and discover how you can influence the context and environment in which decisions get made.

This post was updated on December 21, 2022. It was originally published on March 31, 2020.

About the Author

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• Published: 13 September 2024

The conceptualisation of cardiometabolic disease policy model in the UK

• Septiara Putri 1 , 2 ,
• Giorgio Ciminata 1 ,
• Jim Lewsey 1 ,
• Bhautesh Jani 3 ,
• Nicola McMeekin 1 &
• Claudia Geue 1  

BMC Health Services Research volume  24 , Article number:  1060 ( 2024 ) Cite this article

Metrics details

Decision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting.

This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2.

First, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models’ parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed.

This conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.

Peer Review reports

Cardiometabolic disease (CMD) is an umbrella term encompassing a range of chronic, co-occurring conditions, including cardiovascular disease (CVD), diabetes mellitus, chronic renal failure, hypertension, and stroke [ 1 , 2 , 3 ]. The aetiology of CMD is often attributed to shared and frequently co-occurring risk factors, such as dyslipidaemia, obesity, and hypertension [ 2 ].

CMDs are a leading cause of mortality and morbidity globally, and they impose a significant economic burden on healthcare systems [ 4 , 5 ]. In the United Kingdom, approximately 4.7 million people are currently living with diabetes and is projected to rise to over 5.5 million by 2030 [ 6 ]. Additionally, an estimated 850,000 individuals remain undiagnosed, further complicating efforts to manage the disease effectively [ 7 ]. CVD also poses a substantial public health challenge in the UK, affecting approximately 7.6 million people, including around 4 million men and 3.6 million women who are living with heart and circulatory diseases [ 8 ].

Policies for early and timely prevention of CMD are crucial for improving cardiometabolic health. Over several years, modelling techniques have been increasingly applied to assist decision-makers in considering and evaluating public health strategies. A model, particularly in health economics terms, is defined as a structured approach that typically involves the use of mathematical and statistical techniques to simulate the natural history of disease and the impact of particular interventions, leading to the estimation of health outcomes, cost, and cost-effectiveness [ 9 ]. The decision analytic model plays a vital role in evaluating these strategies by predicting health and economic outcomes, facilitating scenario analysis, prognosis, and generalisability concerns [ 10 , 11 , 12 ]. We therefore propose the development of a CMD policy model, tailored specifically to the UK context.

While decision models are increasingly used to inform the policy-making processes, there is a need to improve their credibility [ 13 ]. The development process itself influences the estimation of health and economic outcomes generated from the decision models. An appropriate decision model requires more than mathematical operationalisation alone; it demands understanding complex real-world systems and translating these complexities into credible conceptual structures [ 14 ]. This understanding is supported by model structuring, which is pivotal in the decision analytic model. Model structuring involves defining the model’s framework and components, and ensuring all relevant elements such as disease pathways, risk factors, and potential interventions are considered and accurately represented. Once the structure is established, the model can be populated with data and parameters. A conceptual model can illustrate these overall processes [ 13 , 15 ].

In the health economic model, the transition from conceptual model frameworks to practical model structuring is also crucial for producing reliable and actionable insight. Within the conceptual process, focusing on model structure is important. This involves choosing the appropriate type of model, integrating data, and dealing with uncertainties. It is where abstract ideas from the conceptual model are translated into a quantifiable model. This ensures that the policy model is not only theoretically sound but also practical and relevant for guiding healthcare decision-making [ 16 ].

Developing an appropriate conceptual model and specifying key model structure offers several advantages, including enhancing understanding of the decision problem, ensuring alignment with policy objectives, and supporting stakeholder engagement. Conceptual models are instrumental in fostering consensus on problem definition and guiding the development of the model structure [ 15 , 17 , 18 ].

As an initial stage, the conceptual model will be used to guide our CMD policy model development. The CMD policy model is intended to be applied further to estimate costs (e.g., healthcare costs) and outcomes (e.g., life years and quality-adjusted life years) as well as the cost-effectiveness of CMD prevention strategies. This conceptual model is also aimed to enhance the transparency of the model development process, providing clear documentation and justification for the considerations made during its construction.

This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2 [ 13 ]. Two main components of modelling processes are provided in this report. First, is the conceptualisation of the problem, which covers the translation of the healthcare process knowledge into a representation of the problem. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem as well as data and parameters used, followed by transparency and validation of the model. The general stage of the conceptual model process is illustrated in Fig.  1 .

Schematic flow diagram of conceptual model development

Conceptualising the problem

The conceptualisation of the problem requires an understanding of CMD progression and prevention based on clinical and public health guidelines available in the UK. This was followed by conducting a systematic literature review (SLR) of published CMD policy models.

Systematic literature review (SLR)

Before developing a model, it is crucial to define and elaborate on the decision problem and the significance of modelling within that context. In this study, the scoping of CMD policy model development was initially discussed within a small workgroup (SP, CG, GC, and JL) [ 19 ]. This group formulated an initial proposal based on a literature review and potential data sources. Following this, the SLR was conducted to summarise and critically appraise published CMD policy and decision models, with a particular focus on models addressing CVD and T2DM.

The search strategy was applied in multiple databases including MEDLINE (Ovid), EMBASE (Ovid), CINAHL, Google Scholar, and Open Grey, with publication dates restricted from January 1, 2000, to 1st May 2024. A hand search of reference lists from a previous SLR was also conducted using the snowball technique. The review included models that evaluated both long-term health and economic outcomes, with a focus on primordial prevention targeting the entire population or population-based prevention strategies [ 20 ].

The review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 21 ]. Details of the inclusion and exclusion criteria, as well as the search strategy, were published in the protocol registered on PROSPERO (CRD42022354399) [ 22 ]. Data from fully eligible studies were extracted using a standardised table in Excel spreadsheets. The quality of reporting on decision models and economic evaluation studies was assessed by independent reviewers using the Philips et al. checklist [ 23 ]. The critical assessment results are presented in a narrative format.

Conceptualising the Model

Expert opinion.

The objective of the model development is not to reproduce, but to represent a simplified reality [ 14 ]. The model development process should reflect the reality that represents the decision problem. To accommodate this, the role of clinical experts is important to meet contextual relevance [ 13 ].

This stage highlights the significance of collaboration between clinical experts and experienced modellers to achieve consensus on model structure. The expert group, consisting of a clinician, two health economists, and a medical statistician, provided diverse and valuable perspectives that contributed to the refinement of the model. The clinician’s input was particularly crucial in ensuring that the model remained aligned with clinical practice and relevant to real-world applications. The proposed conceptual model draft (Additional information 1 ) was presented to the group, and informal feedback was gathered during the presentation.

After engaging in informal consultations with these experts (2–3 meetings), the conceptual model and model structure were revised to better reflect clinical realities and to enhance its overall validity.

Understanding disease progression

The complexity of CMD arises from the interconnection of numerous risk factors (cardiometabolic syndrome (CMS)), including insulin resistance, dyslipidaemia, hypertension, and obesity, as well as comorbidities such as infections, gastrointestinal disease, and kidney disease [ 1 , 2 , 3 ]. Without intervention, this can further develop into more severe conditions such as CVD and T2DM [ 24 ].

The progression of CMD typically begins with insulin resistance, which may lead to metabolic syndrome or ‘pre-diabetes’. As CMS progresses, the body’s ability to respond to insulin diminishes, compelling the pancreas to compensate by producing higher levels of insulin. However, over time, this compensatory mechanism becomes insufficient, leading to impaired glucose tolerance (IGT) and, ultimately, the onset of T2DM. CMS also doubles the risk of CVD contributing to the rising incidence of heart attacks, strokes, and coronary artery disease. The interplay of insulin resistance, dyslipidaemia, hypertension, and chronic inflammation in CMS accelerates atherosclerosis by promoting endothelial dysfunction, oxidative stress, and plaque formation. This process narrows the arteries, increasing the likelihood of myocardial infarction (MI) and stroke [ 2 , 24 , 25 , 26 ].

The identification and diagnosis of cardiometabolic syndrome (CMS), which encompasses a cluster of these risk factors, are based on guidelines from several authoritative sources. These include the World Health Organization (WHO) [ 27 ], the European Group for the Study of Insulin Resistance (EGIR) [ 28 ], the International Diabetes Federation (IDF) [ 29 ], the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) [ 30 ], the National Heart, Lung, and Blood Institute/American Heart Association (NHLBI/AHA) [ 31 ]. Several recent recommendations and guidelines for CMD staging have also been introduced [ 3 , 32 , 33 ]. Based on those guidelines, the general CMD staging system is summarised in Table  1 .

The CMD prevention and treatment guidelines are still in the development stage in the UK [ 34 ]. A recent screening strategy has also recently been proposed [ 35 ]. However, the diagnosis, prevention, and treatment guidelines for CVD and T2DM have been published by The National Institute of Care and Excellence (NICE) as well as the Scottish Intercollegiate Guidelines Network (SIGN) [ 36 ]. Physical activity, dietary recommendations, behavioural changes, and other primordial preventive policies are covered in the guidelines and recommendations. Risk assessment tools such as QRISK3 are used for people without CVD aged between 25–84 years old. Further, lifestyle advice and drug treatment for HbA1c management level for people with T2DM are elaborated in these guidelines. It is evident that these guidelines are continuously evolving based on the latest evidence and are aimed at addressing the prevention and management of CMD in the population [ 37 , 38 , 39 ].

Decision modelling which accommodates cost-effectiveness analysis is also available for guidelines in the UK, for instance, existing CVD models simulate several CVD prevention strategies targeting the whole population [ 38 ]. Similarly, diabetes models have been used to simulate the prevention and treatment strategies for diagnosed T2DM patients [ 37 ]. From these available models and an understanding of cardiometabolic disease staging, a model can be constructed that represents cardiometabolic events (i.e., including both CVD and T2DM).

Systematic literature review

Model types and structure

A total of 44 articles were retrieved for this review. Detailed results and discussions are available in our systematic review published elsewhere [ 20 ]. One of the clear advantages of modelling is the capability to estimate and simulate long-term disease progression and the impact of an intervention, which complements RCTs [ 9 , 40 ]. Our review established that models were either state transition cohort approach (Markov-based cohort) or individual level (microsimulation) approaches, with different perspectives chosen, costs incurred, and sensitivity analysis performed (Additional information 2 ). The choice between these approaches depends on the study objectives, policy questions, and data availability.

Cohort simulations are advantageous for their efficiency and generalisability but are limited by their inability to account for individual variability, lack of precision, potential for ecological fallacy, and challenges in modelling complex interactions. In contrast, individual-level simulations offer greater granularity and personalised insights, capturing heterogeneity and specific outcomes, but they require extensive data, are resource-intensive, may involve significant uncertainty, and can be less interpretable and generalisable [ 41 , 42 , 43 ].

In state-transition models, disease states are defined by clinical guidelines and the natural progression of the disease. The structure of models represented either T2DM or CVD disease progression, and some studies include both of those conditions either as an additional state or as a comorbidity [ 20 ]. Although the states represent the final stages in CMD staging, there is a need to include states that are likely to be clinically important in the future, for example, specify complication state for T2DM, or MI and stroke states to represent CVD events.

Risk factors

Risk factors are attributes, characteristics, or exposures that increase the likelihood of developing a disease or health condition [ 44 ]. The relationship between risk factors and outcomes is essential for predicting the impact of interventions, understanding disease progression, and estimating cost-effectiveness analysis. In a health economics model, the use of risk factors is to demonstrate the causal pathways (e.g., unhealthy diet increases T2DM), transition probabilities (influence probability between health states; e.g., from healthy to disease), and outcome (e.g., affecting mortality and morbidity incidence) [ 12 , 23 , 43 ].

Generally, risk factors/covariates can be classified into two main types: modifiable and non-modifiable. Modifiable are further categorised into clinical (BMI, blood pressure), behavioural (diet, smoking) and socio-economic (economic status) (Fig.  2 ). On the other hand, age, gender, genetic factors, race/ethnicity, and family/medical history are considered non-modifiable risk factors. We included common CMD risk factors in the first draft of our conceptual model.

Risk factors included in policy model

Other model features (costs, outcomes, validation, sensitivity analysis)

Most policy models adopt a healthcare provider perspective, however, incorporating productivity loss from patient and caregiver perspectives can be beneficial to inform decision making. Furthermore, validation tests can test model consistency (e.g., face and internal validation) but need to be transparent in terms of reporting the results and the impact on the modelling. All studies reported that they conducted sensitivity analysis as part of the modelling. These uncertainties are often explored through sensitivity analyses (SA), deterministically and/or probabilistically [ 20 ].

Few studies assess the equality and equity assessment, addressing these can help to design mode holistic interventions that balance efficiency with fairness, leading to more socially acceptable and sustainable health policies [ 20 ].

The reliability of model appraisals is contingent upon the quality of underlying data. From this SLR, a consistent challenge is the scarcity of representative, locally derived data for model development. Consequently, researchers often rely on assumptions or data from external sources, introducing uncertainty and compromising data validity. While secondary data can be valuable, inconsistencies in data transferability standards and inadequate justifications for data application hinder their effective use. The use of survey and observational data, susceptible to biases, further undermines data quality [ 45 ].

To enhance model accuracy and generalisability, the incorporation of appropriate real-world data (RWD) is essential [ 46 ]. RWD offers a more representative patient population and treatment outcomes. Nevertheless, RWD presents complexities that should be carefully addressed, including confounding variables, missing data, and lead-time bias, necessitating rigorous methodological considerations to optimise its utility in model development [ 46 , 47 ].

Inputs from a clinician and experts are mostly focused on the proposed model structure and key features of the model since this model is planned to accommodate any further early prevention strategies that can improve cardiometabolic health.

Initially, the risk factors included following the SLR result – see Fig.  2 . However, aligned with the clinical guidelines reviewed above, metabolic conditions such as obesity, hypertension and hyperlipidemia/dyslipidaemia are also considered covariates due to their strong correlation with metabolic syndrome [ 1 , 48 ]. Experts proposed atrial fibrillation (AF) to be included in the health state in the conceptual model, as cardiometabolic risk factors can increase the risk of AF, while AF itself can contribute to the progression of cardiometabolic conditions [ 49 ]. There was also an input to re-evaluate the T2DM progression, whether it is adding states with/without diabetes complications before the final state.

The onset of diabetes accelerates the development of atherosclerosis and other CVD risk factors, showing that people with diabetes also have a risk of having CVD [ 50 ]. This progression was recommended to be added to the final conceptual model since the first conceptual model did not draw this relationship.

For CVD, we must include the second event (e.g., MI and stroke), and post-CVD event, since there may be differences in terms of utility assessment that could influence the cost-effectiveness results if we plan to conduct further cost-utility analysis (CUA) [ 51 , 52 ].

Finally, we discussed further with the health economist team that the model should be representative but not too complex, AF remains considered a potential covariate when analysing data. In addition, the structure was presented in an internal meeting with a broad research audience, and several points were discussed. First, the simplicity but representativeness of the model state, considering the feasibility and time needed for analysis and modelling exercise. Second, the possibility to put productivity loss parameters in analysis or sensitivity analyses, therefore the proposed outcome not only healthcare costs but also indirect costs of patients/caregivers perspective. Third, aligned with the findings from SLR, addressing the equality and equity concerns in the model will enrich the further analysis.

Final conceptual model

The final conceptual model is illustrated in influence diagram form (Fig.  3 ). This proposed model will facilitate the improvement of clinical and economic representation of CMD, where metabolic dysfunction conditions could lead to various events including both CVD and T2DM.

Final conceptual model. T2DM: type 2 diabetes, BMI: body mass index, BP: blood pressure, CVD: cardiovascular disease, QALE: quality-adjusted life expectancy, QALY: quality-adjusted life years

To translate this conceptual framework we present a more detailed model structuring stage [ 16 ]. A state transition model (STM) will be applied. STM (i.e.: Markov) is well-suited for population health assessment due to several reasons. First, the ability to capture disease progression across different stages within a population by representative health states. Second, this approach is particularly useful when the timing of transitions between health states is important. These models allow for the analysis of long-term outcomes by simulating multiple cycles (e.g., years), capturing the dynamic nature of disease progression (e.g., recurring events such as MI). Third, it can incorporate uncertainty and variability in inputs, allowing for more robust predictions and planning [ 43 , 53 , 54 ]. Specifically, the semi-Markov approach will be applied with several assumptions, such as non-exponential holding time, accounting for time-varying covariates, competing risks, and the ability to integrate with statistical methods such as survival analysis or joint modelling [ 55 , 56 ].

From the proposed model, the patient population of interest is all adult patients (≥18 years) with no confirmed CMD. The first part specifies patient characteristics when entering the model and specifies them by modifiable and non-modifiable risk factors, as well as deprivation groups. Disease states include disease-free, T2DM, CVD (MI, Stroke), post-CVD (post-MI and post-stroke), and death (Fig.  4 ).

State transition model structure

The Cox multi-state model, as an extension of traditional Cox model will be applied to analyse time to event data where individuals can transition through multiple states over time [ 57 ]. This method aligned with the model structured in conceptual model. Transition probabilities between the states are derived and converted from survival rates. The values of the modifiable risk factors will change ‘naturally’ over time and will change the risks of developing CMD when they do so. We will estimate the outcomes including all-cause mortality, disease-related death, life expectancy, QALE/QALY, and lifetime healthcare costs associated with the disease. Sensitivity analysis will be performed both deterministic and probabilistic sensitivity analyses, to handle the uncertainty in the model [ 43 ].

Following this conceptual model, for further data analysis, we will utilise Clinical Practice Research Datalink (CPRD) data. CPRD contains data that encompasses 60 million patients, including 16 million currently registered patients [ 58 ]. The data records patients anonymously in the electronic health record (EHR) system from general practices (GPs) in England, Scotland, Wales, and the Northern Island on a monthly basis. CPRD includes routine clinical practice information on patients’ demographics, behavioural factors, signs and symptoms, diagnoses, prescriptions, immunisations, referrals, and lifestyle [ 59 ]. As well as the strength of a large representative sample size within the UK, CPRD collects data over extended periods, allowing researchers to study the development and progression of diseases over time. In addition, the feasibility of data linkage for hospital and death registries will be beneficial to capturing comprehensive patient’ journeys [ 58 ].

It is well-understood that models should be clearly defined and conceptualised before analysis. This paper provides a conceptual model that serves as a foundational framework for developing a policy model that is both appropriate and fit for purpose, by carefully outlining the key components, relationships, and underlying assumptions [ 13 , 14 ]. It is instrumental in ensuring that the resulting policy model is both theoretically sound and practically effective.

Given adequate reporting quality concluded in the SLR, the findings from our review were deemed reliable and served as a valuable resource for informing the development of the conceptual model. The incorporation of clinical guidelines, the systematic review, and expert input significantly enhanced the model development process. Moreover, particular attention was devoted to the stage of model structuring [ 16 ], resulting in a more technically precise and detailed conceptual framework.

Areas requiring further attention include carefully selecting and incorporating relevant parameters, particularly using high-quality routine data to enhance the generalisability of the model’s conclusions [ 20 ], which we have tried to address in our final conceptual model. The proposed model structure aligns with established stages of CMD and existing economic evaluation models, demonstrating its consistency with current practices. Based on these findings, no major modifications seem necessary for our conceptual model.

Once the CMD policy model has been developed, it potentially be applied to assess early prevention such as dietary intervention, screening programmes, and preventive medication. Within the analysis, structural sensitivity analysis and model performance evaluation will be conducted following good practice in modelling [ 23 ].

To date, the published conceptual models are relatively limited [ 60 , 61 , 62 , 63 , 64 ], and our conceptual model represents a novel contribution to CMD particularly in the health economic modelling area. It extends and enriches existing research by providing a comprehensive and systematic conceptualisation process, by following the good practice for modelling transparency.

We acknowledge several limitations of this conceptual model. First, we asked an experienced clinician and experts to ensure the disease state relevance at a practice level. It is done by gathering input informally during the presentation of the modelling plan. A Delphi process panel with a structured questionnaire potentially improves the process and minimises subjectivity [ 65 ]. Second, the structure is trying to cover both T2DM and CVD states that represent major CMD events. Unlike the second event such as the post-CVD event, we did not consider T2DM complications as a second state in the model. Third, we plan to use a healthcare perspective for the model in terms of facilitating further economic analysis. Considering societal perspectives in the model may optimise societal decisions [ 66 ]. If any sufficient data is available, we may incorporate this economic perspective in the sensitivity analysis. Furthermore, we are still considering the use of utility value to generate QALY, such as EQ-5D. However, the EQ-5D-5L valuation study remains ongoing for the UK general population [ 67 ]. Our solution is to potentially use published EQ-5D-5L for each state (if we conducted a hypothetical public health intervention), or consider QALE as one of the outcomes (without incorporating utility value).

Conclusions

This paper serves as a first step in representing the systematic process for structuring a CMD policy model in the UK setting. It will be beneficial to enhance our model’s transparency and credibility and also provide insight to a broad audience who are scoping and planning policy models to inform decision-making.

Data availability

We do not have any research data outside the submitted manuscript file. All information supporting the findings of this study are available within the paper and its supplementary material.

Abbreviations

Atrial Fibrillation

• Cardiometabolic disease

Cardiometabolic syndrome

Clinical Practice Research Datalink

Cardiovascular disease

Myocardial infarction

Quality-adjusted life expectancy

Quality-adjusted life years

State transition model

Type 2 diabetes mellitus

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Acknowledgements

We would like to thank to the LPDP (Indonesian Endowment Fund for Education) with BUDI LN scheme under Ministry of Finance Republic of Indonesia, for granting full scholarship to SP for PhD programme in Health Economics and Health Technology Assessment (HEHTA), University of Glasgow, UK.

The authors received no financial support for this research.

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Health Economics and Health Technology Assessment (HEHTA), School of Health and Wellbeing, University of Glasgow, Clarice Pears Building, 90 Byres Road, Glasgow, G12 8TB, UK

Septiara Putri, Giorgio Ciminata, Jim Lewsey, Nicola McMeekin & Claudia Geue

Health Policy and Administration Department, Faculty of Public Health, University of Indonesia, Depok, Indonesia

Septiara Putri

General Practice and Primary Care, School of Health and Wellbeing, University of Glasgow, Glasgow, UK

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Concept and design: SP, GC, JL, NM, CG; Acquisition of data: CG; Analysis and interpretation: SP, GC, JL, CG; Drafting in the manuscript: SP, NM, CG; Critical revision of paper for important intellectual context: GC, JL, BJ, NM, CG; Supervision: GC, JL, CG.

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Putri, S., Ciminata, G., Lewsey, J. et al. The conceptualisation of cardiometabolic disease policy model in the UK. BMC Health Serv Res 24 , 1060 (2024). https://doi.org/10.1186/s12913-024-11559-y

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Received : 09 February 2024

Accepted : 09 September 2024

Published : 13 September 2024

DOI : https://doi.org/10.1186/s12913-024-11559-y

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