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How Do You Know It Needs Fixing? August 22, 2014

Posted by Tim Rodgers in Management & leadership, Process engineering, Quality.
Tags: , , , ,

We’ve always been told that “if it isn’t broken, don’t fix it.” In the world of statistical process control, making changes to a production process that’s already stable and in-control is considered tampering. There’s a very real chance that you’re going to make it worse, and at the least it’s a bad use of resources that should be committed elsewhere to solve real problems. That’s great advice if you have the historical data and a control chart, but how do you know if a business process needs fixing if you don’t have the data to perform a statistical analysis? How can you tell the difference between a bad business process that can’t consistently deliver good results, and an outlier from a good business process? How do you know if it’s broken?

We often judge a process from its results, an “the ends justify the means” effectiveness standard. If we get what we want from the process, we’re happy. However, we’re also often concerned with how the ends were achieved, which means looking at the cost of the process. This can be literally in terms of the time required and the labor cost, and also in terms of the organizational overhead required and the non-value, or waste, that a process can generate. A process that isn’t giving the desired results is obviously a problem that needs fixing, but so is a process that’s inefficient. You have to spend time with the people who are routinely using the process to understand the costs.

Sometimes we’re not clear about what results we expect from a process. Simply getting outputs from inputs is not enough; we also usually care about how much time is required, or if rework is required (the “quality” of the outputs). We have to look at the process as part of a larger operational system, and how the process helps or hinders the business achieve greater objectives. This is often our starting point for identifying processes that need fixing because these processes create bigger problems that are highly-visible.

Sometimes we jump to the conclusion that a process is broken because we get one bad result. This is where a little root cause analysis is needed to determine if there are any differences or extenuating circumstances that may explain the undesirable outcome. In statistical process control we refer to these as special causes. Finding and eliminating special causes is the recommended approach here, not demolishing the process and starting all over again.

If the process does appear to be broken, it’s important to distinguish between a problem with the design of the process and a problem with how the process is executed. A process can look great on paper, but if the assigned people are poorly trained or lack motivation or don’t understand why the process is even necessary, then they’re unlikely to follow the process. People are usually the biggest source of variability in any process. They also tend to blame the process as the problem, even if they’ve never actually used it as intended. You might think this can be solved by simply applying positional power to compel people, but it’s often possible to make small changes to the process design to make compliance easier, essentially reducing the influence of “operator variation.” Once again, you have to experience the process through the eyes of the people who are using it, what we in quality call a Gemba Walk.

It’s easy to blame “the process” as some kind of inanimate enemy, and there are surely processes in every business that can be improved. However it’s worth spending a little time to determine exactly what the process is supposed to accomplish, how it fits into the larger business context, and whether there are special causes before launching a major overhaul. Sometimes it’s not really broken at all.



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