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Developing Key Performance Indicators

Key performance indicators (KPIs) are critical to ensuring a project team has the performance data it needs to sustain improvements. With KPIs, a team can evaluate the success of a project against its established goals.

Types of Metrics

There are two types of metrics to consider when selecting KPIs for a project: outcome metrics and process metrics.

Outcome metrics provide insight into the output, or end result, of a process. Outcome metrics typically have an associated data-lag due to time passing before the outcome of a process is known. The primary outcome metric for a project is typically identified by project teams early on in their project work. This metric for most projects can be found by answering the question, “What are you trying to accomplish?”

Process metrics provide feedback on the performance of elements of the process as it happens. It is common for process metrics to focus on the identified drivers of process performance. Process metrics can provide a preview of process performance for project teams and allow them to work proactively to address performance concerns.

Example of Selected KPIs

Consider an example of KPIs for a healthcare-focused improvement project:

  • Project: optimizing hospital patient length of stay
  • Outcome metric: hospital patient length of stay (days)
  • Process metrics: discharge time of day (hh:mm); time discharge orders signed (hh:mm); time patient education completed (hh:mm); discussion of patient at daily discharge huddle (percentage of patients)

In the example above the project has one primary outcome metric and four process metrics that compose the KPIs the team is monitoring. Well-crafted improvement project KPIs will include both outcome metrics and process metrics. Having a mix of both provides the balance of information that the team needs to successfully monitor performance and progress towards goals.

Teams should develop no more than three to six KPIs for a project. Moving beyond six metrics can dilute the effects of the data and make it more challenging to effectively communicate the progress of a project.

Questions to Help Select KPIs

Common questions coaches can use with teams to generate conversation about potential KPIs include:

  • What does success look like?
  • How will it be known if performance is trending away from goals?
  • What data would the stakeholders and sponsors be most interested in?
  • What data is available to the team?

The 3Ms: Meaningful, Measurable and Manageable

Coaches should keep the three Ms of crafting KPIs in mind when working with teams.

  1. Meaningful: KPIs should be meaningful to project stakeholders. Developing metrics that those closest to the project team find useful without getting feedback from a broader group of stakeholders can be a recipe for stakeholder disengagement. The KPIs a team selects need to resonate with the stakeholders closest to the process and the problem. The team will know it is on the right track when it has KPIs that stakeholders want to know the current status of and are discussing progress toward the project goals with their colleagues. Meaningful KPIs make excellent additions to departmental data walls for use in daily huddles and to support the efforts of leaders to get out on the floor and speak directly with employees. leader rounding.
  2. Measurable: KPIs should be easily measurable. Sometimes teams can get stuck trying to identify the “perfect” metric for measuring progress toward their project goals. In this pursuit, the team may lose sight of metric options that are already available or automatically reported. Sustainable KPIs should be relatively easy to obtain updates for. If a metric requires time-consuming auditing, or is not readily available to the project team, groups should think twice before selecting it as a KPI. Data that is challenging or time-consuming to obtain is not likely to be regularly updated and reported to stakeholders. Providing timely and accurate updates on KPI performance is an excellent way to support the sustainability of improvements and spark conversations about additional opportunities to enhance processes and reach the team’s goals.
  3. Manageable: KPIs should include metrics that are within the sphere of management control and influence for the project team. If the team selects metrics that include measuring process elements that the team has no control over, then they are not going to be measuring what matters. Teams should select KPIs that are within the scope of their project, are reflective of a successful outcome and are performance drivers for their work. Sometimes nice-to-have or might-be-interesting metrics can sneak onto the KPI list for project teams. These additional metrics are not needed; the team should focus in on the metrics that will provide accurate feedback on its performance.


Remember that successful KPIs:

  • Include a balance of outcome metrics and process metrics.
  • Total three to six metrics.
  • Are developed with the 3Ms in mind.

Crafting KPIs is an important step to guide teams through a continuous improvement process. A coach needs to keep the team focused on what success looks like and how best to measure it.


5 Tips to Make Process Improvements Stick!

For a process improvement practitioner, finishing the Control Phase of the DMAIC process is your ticket to move on to your next project. You’ve done an excellent job leading the project team because they identified root causes, developed and implemented solutions to resolve those root causes, put a control plan in place and transitioned the process back to the Process Owner. Soon, however, you learn that the process has reverted to its original state.

I’ve often heard project leaders lament, “We worked so hard to identify and implement these solutions—why won’t they stick?”

So let’s talk about fishing for a moment, because it offers some great lessons for making process change. Remember the quote, “Give a man a fish, and you feed him for a day. Teach a man to fish, and you feed him for a lifetime?” Seems simple enough, right?  But what is involved and how long does it take to teach people to fish so they could eat for a lifetime?

The same is true for process improvements. Seems simple enough to make a change and expect it to stick. So why is it so hard?

catch a fish

The fishing analogy hits home with me. I love to go fishing and have been an avid angler since I was young. And though it’s been a while since I taught my kids how to fish, I do remember it was a complicated process. There is a lot to learn about fishing—such as what type of equipment to use, rigging the rod, baiting the hook, deciding where to fish, and learning how to cast the line.

One of the most important fishing tips I can offer a beginner is that it’s better to go fishing five times in a few weeks as opposed to five times in an entire year. Skills improve quickly with a focused effort and frequent feedback. People who spread those introductory fishing experiences out over a year wind up always starting over, and that can be frustrating. While there are people who are naturally good at fishing and catch on (pun intended) right away, they are rare. My kids needed repeated demonstrations and lots of practice, feedback and positive reinforcement before they were able to fish successfully. Once they started catching fish, their enthusiasm for fishing went through the roof!

Tips for Making Process Improvements Stick

Working with teams to implement process change is similar. Most workers require repeated demonstrations, lots of practice, written instructions, feedback and positive reinforcement before the new process changes take hold.

Here are several tips you can use to help team members be successful and implement process change more quickly. Take the time to design your solution implementation strategy and control plan with these tips in mind. Also, Companion by Minitab® contains several forms that can make implementing these tips easy.

Tip #1: Pilot the Solution in the Field

A pilot is a test of a proposed solution and is usually performed on a small scale. It’s like learning to fish from the shore before you go out on a boat in the ocean with a 4-foot swell. It is used to evaluate both the solution and the implementation of the solution to ensure the full-scale implementation is more effective. A pilot provides data about expected results and exposes issues with the implementation plan. The pilot should test both if the process meets your specifications and the customer expectations. First impressions can make or break your process improvement solution. Test the solution with a small group to work out any kinks. A smooth implementation will help the workers accept the solution at the formal rollout.   Use a form like the Pilot Scale-Up Form (Figure 1) to capture issues that need resolution prior to full implementation.

Figure 1. Pilot Scale-Up Form

Tip #2: Implement Standard Work

Standard work is one of the most powerful but least used lean tools to maintain improved process performance. By documenting the current best practice, standardized work forms the baseline for further continuous improvement. As the standard is improved, the new standard becomes the baseline for further improvements, and so on.

Use a Standard Work Combination Chart (Figure 2) to show the manual, machine, and walking time associated with each work element. The output graphically displays the cumulative time as manual (operator controlled) time, machine time, and walk time. Looking at the combined data helps to identify the waste of excess motion and the waste of waiting.

Standard Work
Figure 2. Standard Work Combination Chart

Tip #3: Update the Procedures

A Standard Operation Procedure (SOP) is a set of instructions detailing the tasks or activities that need to take place each time the action is performed. Following the procedure ensures the task is done the same way each time. The SOP details activities so that a person new to the position will perform the task the same way as someone who has been on the job for a longer time.

When a process has changed, don’t just tell someone of the change: legitimize the change by updating the process documentation. Make sure to update any memory-jogger posters hanging on the walls, and the cheat sheets in people’s desk drawers, too. Including a document revision form such as Figure 3 in your control plan will ensure you capture a list of procedures that require updating.

Document Revision
Figure 3. Document Revision Form

Tip #4: Feedback on New Behaviors Ensures Adoption

New processes involve new behaviors on the part of the workers. Without regular feedback and positive reinforcement, new process behaviors will fade away or revert to the older, more familiar ways of doing the work. Providing periodic feedback and positive reinforcement to those using the new process is a sure-fire way to keep employees doing things right. Unfortunately, it’s easy for managers to forget to provide this feedback. Using a Process Behavior Feedback Schedule like Figure 4 below increases the chance of success for both providing the feedback and maintaining the gains.

Process BehaviorFigure 4. Process Behavior Feedback Schedule

Tip #5: Display Metrics to Reinforce the Process Improvements

Metrics play an integral and critical role in process improvement efforts by providing signs of the effectiveness and the efficiency of the process improvement itself. Posting “before and after” metrics in the work area to highlight improvements can be very motivating to the team.   Workers see their hard work paying off, as in Figure 5. It is important to keep the metric current because it will be one of the first indicators if your process starts reverting.

Before After ChartFigure 5. Before and After Analysis

When it comes to fishing and actually catching fish, practice, effective feedback, and positive reinforcement makes perfect.

The same goes for implementing process change. If you want to get past the learning curve quickly, use these tips and enjoy the benefits of an excellent process!

To access these and other continuous improvement forms, download the 30-day free trial of Companion from the Minitab website at

Understanding Qualitative, Quantitative, Attribute, Discrete, and Continuous Data Types

“Data! Data! Data! I can’t make bricks without clay.”
— Sherlock Holmes, in Arthur Conan Doyle’s The Adventure of the Copper Beeches

Whether you’re the world’s greatest detective trying to crack a case or a person trying to solve a problem at work, you’re going to need information. Facts. Data, as Sherlock Holmes says.


But not all data is created equal, especially if you plan to analyze as part of a quality improvement project.

If you’re using Minitab Statistical Software, you can access the Assistant to guide you through your analysis step-by-step, and help identify the type of data you have.

But it’s still important to have at least a basic understanding of the different types of data, and the kinds of questions you can use them to answer.

In this post, I’ll provide a basic overview of the types of data you’re likely to encounter, and we’ll use a box of my favorite candy—Jujubes—to illustrate how we can gather these different kinds of data, and what types of analysis we might use it for.

The Two Main Flavors of Data: Qualitative and Quantitative

At the highest level, two kinds of data exist: quantitative and qualitative.

Quantitative data deals with numbers and things you can measure objectively: dimensions such as height, width, and length. Temperature and humidity. Prices. Area and volume.

Qualitative data deals with characteristics and descriptors that can’t be easily measured, but can be observed subjectively—such as smells, tastes, textures, attractiveness, and color.

Broadly speaking, when you measure something and give it a number value, you create quantitative data. When you classify or judge something, you create qualitative data. So far, so good. But this is just the highest level of data: there are also different types of quantitative and qualitative data.

Quantitative Flavors: Continuous Data and Discrete Data

There are two types of quantitative data, which is also referred to as numeric data: continuous and discreteAs a general rule, counts are discrete and measurements are continuous.

Discrete data is a count that can’t be made more precise. Typically it involves integers. For instance, the number of children (or adults, or pets) in your family is discrete data, because you are counting whole, indivisible entities: you can’t have 2.5 kids, or 1.3 pets.

Continuous data, on the other hand, could be divided and reduced to finer and finer levels. For example, you can measure the height of your kids at progressively more precise scales—meters, centimeters, millimeters, and beyond—so height is continuous data.

If I tally the number of individual Jujubes in a box, that number is a piece of discrete data.

a count of jujubes is discrete data

If I use a scale to measure the weight of each Jujube, or the weight of the entire box, that’s continuous data.

Continuous data can be used in many different kinds of hypothesis tests. For example, to assess the accuracy of the weight printed on the Jujubes box, we could measure 30 boxes and perform a 1-sample t-test.

Some analyses use continuous and discrete quantitative data at the same time. For instance, we could perform a regression analysis to see if the weight of Jujube boxes (continuous data) is correlated with the number of Jujubes inside (discrete data).

Qualitative Flavors: Binomial Data, Nominal Data, and Ordinal Data

When you classify or categorize something, you create Qualitative or attribute data. There are three main kinds of qualitative data.

Binary data place things in one of two mutually exclusive categories: right/wrong, true/false, or accept/reject.

Occasionally, I’ll get a box of Jujubes that contains a couple of individual pieces that are either too hard or too dry. If I went through the box and classified each piece as “Good” or “Bad,” that would be binary data. I could use this kind of data to develop a statistical model to predict how frequently I can expect to get a bad Jujube.

When collecting unordered or nominal data, we assign individual items to named categories that do not have an implicit or natural value or rank. If I went through a box of Jujubes and recorded the color of each in my worksheet, that would be nominal data.

This kind of data can be used in many different ways—for instance, I could use chi-square analysis to see if there are statistically significant differences in the amounts of each color in a box.

We also can have ordered or ordinal data, in which items are assigned to categories that do have some kind of implicit or natural order, such as “Short, Medium, or Tall.”  Another example is a survey question that asks us to rate an item on a 1 to 10 scale, with 10 being the best. This implies that 10 is better than 9, which is better than 8, and so on.

The uses for ordered data is a matter of some debate among statisticians. Everyone agrees its appropriate for creating bar charts, but beyond that the answer to the question “What should I do with my ordinal data?” is “It depends.”  Here’s a post from another blog that offers an excellent summary of the considerations involved.

Additional Resources about Data and Distributions

For more fun statistics you can do with candy, check out this article (PDF format): Statistical Concepts: What M&M’s Can Teach Us.

For a deeper exploration of the probability distributions that apply to different types of data, check out my colleague Jim Frost’s posts about understanding and using discrete distributions and how to identify the distribution of your data.

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