Determining the correct process metrics is critical to ensuring that departments within companies meet their desired goals. In Six Sigma, this is done by identifying metrics that are critical to quality (CTQ). Process leaders meet with key process stakeholders and through various discussions about what is and is not important to stakeholders, CTQs are determined.

For example, in a software company, a CTQ might be the on-time delivery of software products. In a service business, a CTQ might be customer satisfaction or the average number of customers willing to recommend the company’s products and services to someone else. At an insurance company, a good CTQ might be the number of claims paid on time. In a restaurant, a CTQ might be the average number of empty tables during peak periods. On an airline, a CTQ might be the average number of on-time departures and arrivals. Regardless of the CTQ that is determined, a baseline is established and the CTQs become key metrics that are actively monitored on a quality metrics dashboard. If there is a negative variance in the CTQ metric relative to its baseline, some form of root cause analysis is performed to determine the cause of the variance so the group can correct it.

While on the surface this may seem complicated, it’s actually no different than driving a car to and from work. For example, suppose you are driving on a highway where the speed limit is 35 miles per hour (mph). In this case, 35 mph is the reference speed. A variance of plus or minus 5 mph is acceptable (ie, driving at 30 or 40 mph); however, if your speed reaches 45 mph, then you may be able to adjust to a slower speed to avoid getting a speeding ticket (and avoid getting into an accident). Your root cause analysis determined that you were speeding up because you were not paying attention; therefore, you took corrective action by reducing your speed to match the posted speed limit. Monitoring your speed and taking corrective action occurs constantly while you are driving to and from work or while driving your car in general. This is exactly the type of analytical process monitoring that organizations need. Feedback obtained by checking key metrics in real time (or near real time) is what organizations use to determine if corrective actions are needed.

I once led a team of software engineering product testers whose main goal was to test the products that the software development team delivered to them. The testers believed that they were very good at finding problems with the products they received. Due to their efforts, they positively impacted the quality of our software products. When I asked them if they knew for sure that they were positively affecting the quality of the product, they said that they really felt that they were. They quantified that sentiment, among other things, by measuring the total number of defects (i.e. bugs) found during their test cycles. If they found a lot of bugs in the products, then they felt like they were doing it right.

The good news is that at least they had a metric, but this metric does not indicate the efficiency of their testing process or the overall quality of the products they tested. For example, they weren’t measuring how many of their test cases they had to run manually versus automated. The more automation they had, the fewer resources were required to run the tests. At the same time, the more automation they had, the more it would indicate the repeatability of their process. They were not monitoring the percentage of test cases that passed vs. failed, which would indicate the effectiveness of the test cases themselves. Also, they weren’t measuring how many of the bugs they found were fixed when all product test cycles were completed, etc.

Therefore, we created a more extensive dashboard to track not only the outcome of the testing effort (ie, the number of bugs found per product), but also the efficiency and effectiveness of the testing effort. Going back to our car analogy, this would be equivalent to not only looking at the speed we were driving (ie looking at the speedometer), but also including keeping an eye on the fuel and oil pressure gauges.

In short, monitoring key process metrics is critical to the success of teams, departments, organizations, and companies alike. Without relevant information, it is almost impossible to make clear business decisions based on anything other than gut feeling, not a very scientific approach to running a business! Measurement allows organizations to adapt what they do over time, reducing the number of far-reaching process changes they might need to make in the long run if process metrics were not in place. If organizations do not measure how effectively they are operating, or if they do not measure processes in a well thought out way, these processes are not quantifiably efficient. Imagine driving your car to and from work every day without a gas gauge. You would have to literally guess when it is time to refuel your vehicle. This would be even more difficult if you were not driving the same route every day due to detours and other road conditions that occasionally occur.

Companies need to define their “measurement dashboard” and ensure they are constantly monitoring their key quality-critical metrics for continued success.