Determining the correct process metrics is critical to ensuring that departments within companies are meeting 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 several discussions about what is and is not important to the stakeholders, CTQs are determined.
For example, in a software company, a CTQ might be on-time delivery of software products. In a services company, a CTQ might be customer satisfaction or the average number of customers willing to refer the company’s products and services to someone else. In 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. In an airline, a CTQ might be the average number of on-time departures and arrivals. No matter the CTQ that’s determined, a baseline for it is established, and the CTQs then become key metrics that are actively monitored on a quality metrics dashboard. If negative variance in the CTQ metric occurs relative to its baseline, then some sort of root cause analysis is conducted to determine the cause of the variance so that the group can correct it.
While on the surface, this may appear complicated, it’s really no different from what occurs when driving a car to and from work. For example, let’s assume you’re driving on a road where the speed limit is 35 miles per hour (mph). In this case, 35 mph is the baseline speed. A variance of plus or minus 5 mph is acceptable (i.e., driving 30 mph or 40 mph); however, if your speed reaches 45 mph, then you might adjust to a slower speed to avoid getting a speeding ticket (and to avoid having an accident). Your root cause analysis determined you were speeding because you weren’t paying attention; consequently, you took corrective action by slowing down to match the posted speed limit. Watching your speed and taking corrective action are constantly occurring as you drive to and from work or while you’re just driving your car in general. This is exactly the type of analytical process monitoring that’s needed for organizations. Feedback gained while checking on key metrics in real-time (or near real-time) is what organizations use to determine whether corrective actions are needed.
I once managed a team of software engineering product testers whose main focus was on testing products the software development team handed them. The testers believed they were very good at finding problems in the products they received. Due to their efforts, they positively impacted the quality of our software products. When I asked them whether they knew for sure they were positively affecting the quality of the product, they said they really felt they were. They quantified that feeling by, among other things, measuring the total number of defects (i.e., bugs) found during their test cycles. If they found many bugs in the products, then they felt they were doing well.
The good news is they at least had a metric, but this one metric does not indicate the efficiency of their test process or the total quality of the products they tested. For example, they were not measuring how many of their test cases they had to run manually versus automated. The more automation they had in place, the fewer resources were required to run the tests. At the same time, the more automation they had in place would indicate their process repeatability. They were not monitoring the percentage of test cases that passed versus those that failed, which would indicate the efficacy of the test cases themselves. In addition, they were not measuring how many of the bugs they found were fixed by the time all their product testing cycles were complete and so on.
Therefore, we created a more extensive dashboard to track not only the outcome of the testing effort (i.e., 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 watching not only the speed we were driving (i.e., looking at the speedometer), it also included keeping an eye on the gas and the oil pressure gauges, as well.
In summary, monitoring key process metrics is critical to the success of teams, departments, organizations, and companies alike. Without pertinent information, it’s nearly impossible to make clear business decisions based on anything other than a gut feeling – not a very scientific approach to running a company! Measurement allows organizations to adapt what they do little by little, reducing the amount of wide-reaching changes to processes they might need in the long run if the process metrics were not in place. If organizations do not measure how effectively they’re operating, or if they don’t measure processes in a well-thought-out manner, these processes aren’t quantifiably efficient. Imagine driving your car to and from work each day without a gas gauge. You would have to literally guess when it’s time to refuel your vehicle. This would be even more difficult if you didn’t drive the same route each day due to detours and other road conditions that occasionally occur.
Companies need to define their “measurement dashboard” and ensure they’re constantly monitoring their key critical-to-quality metrics for ongoing success.