Determining the correct process metrics is critical to ensuring that departments within companies meet 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 can be customer satisfaction or the average number of customers willing to recommend the company’s products and services to someone else. In an insurance company, a good CTQ could be the number of claims paid on time. In a restaurant, a CTQ could be the average number of empty tables during peak periods. In an airline, a CTQ could be the average number of departures and arrivals on time. Regardless of the CTQ that is determined, a baseline is established and the CTQs become key metrics that are actively monitored in a dashboard of quality metrics. If a negative variance occurs in the CTQ metric relative to its baseline, some type of root cause analysis is performed to determine the cause of the variance so that the group can correct it.
While on the surface this may seem complicated, it is actually no different than what happens when driving a car to and from work. For example, let’s say 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, drive 30 mph or 40 mph); however, if your speed reaches 45 mph, then you can adjust to a slower speed to avoid getting a speeding ticket (and avoid getting into an accident). His root cause analysis determined that he was speeding because he was not paying attention; Consequently, he took corrective action by slowing down to match the posted speed limit. Watching your speed and taking corrective action happen constantly while 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 from verifying key metrics in real time (or near real time) is what organizations use to determine if corrective action is 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 in the products they received. Due to their efforts, they had a positive impact on the quality of our software products. When I asked them if they knew for sure that they were positively impacting product quality, they said they really felt that they were. They quantified that feeling, among other things, by measuring the total number of defects (that is, errors) found during their test cycles. If they found a lot of bugs in the products, they felt like they were doing well.
The good news is that they at least had one 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 compared to automated ones. The more automation they had, the fewer resources they needed to run the tests. At the same time, the more automation they had in place would indicate the repeatability of their process. They weren’t monitoring the percentage of test cases that passed versus those that failed, which would indicate the effectiveness of the test cases themselves. Also, they weren’t measuring how many of the bugs they found were corrected when all the product testing cycles were completed, etc.
Therefore, we created a more extensive dashboard to track not only the result of the testing effort (that is, the number of errors found per product), but also the efficiency and effectiveness of the testing effort. Going back to our car analogy, this would be equivalent to looking at not only how fast we were driving (i.e. looking at the speedometer), but it also included keeping an eye on the throttle and oil gauges.
In short, monitoring key process metrics is critical to the success of teams, departments, organizations, and businesses alike. Without relevant information, it is almost impossible to make clear business decisions based on something other than a hunch – not a very scientific approach to running a business! Measurement allows organizations to adapt what they do little by little, reducing the amount of far-reaching changes to processes that might be required in the long term 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 meter. You would have to literally guess when it is time to refuel your vehicle. This would be even more difficult if you weren’t driving the same route every day due to detours and other road conditions that occasionally occur.
Companies must define their “dashboard” and ensure that they are constantly monitoring their key quality-critical metrics for continued success.
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