Structured Problem Solving – the Missing Link in Labs

Structured Problem Solving has been one of the foundations of Lean transformation, and of almost any high performing company over the past 50 years. However, many labs reject Structured Problem Solving techniques outright, or use them as a ‘box – ticking’ exercise to satisfy management that they are adhering to the latest directive. Why is it, when successful organisations pride themselves on a culture of continuous improvement and problem-solving, that in Labs, it is often the missing link to true transformative improvements…?

One of the main arguments against Structured Problem Solving is that the particular methodology (A3, 7-Step, Model-Based) that has been employed across the organisation either comes with a specific set of tools that are poorly designed and difficult to use, or is a ‘blanket’ approach that must be closely followed for all problems, regardless of severity and complexity. While it is true that there is often undue reverence for a specific methodology, and that taking a ‘one-size fits all’ approach to problem solving is a serious mis-judgement, the fact remains that this argument does not refute the need for Structured Problem Solving, but calls for improved tools and implementation. For Structured Problem Solving to be effective, it must provide tools that are easy to use, and a hierarchy of methods and techniques to use for problems of varying degrees of complexity, ranging from simple 5 Whys? Root Cause Analysis for simple problems, through to 7-Step and Model-Based problem solving for more complex, technical problems.

The second, more common, argument questions the need for Structured Problem Solving. If the scientific luminaries of the past, such as Albert Einstein, had no need of Structured Problem Solving, then experienced and skilled scientists today similarly have no such need. Leaving aside the questionable idea that Albert Einstein did not use some form of structured approach to problem solving, the fact remains that, unfortunately, most of us are not Einstein! Most of us benefit greatly from these techniques, because going through that process to structure the data allows us to analyse it appropriately.

Rather than hindering, structure actually facilitates greater problem-solving creativity by allowing the concise summation of the problem and potential solutions, and by striving to avoid over-reliance on experience in problem solving. While experience is key to understanding and identifying a potential cause of a problem, too often we rely on familiar crutches to identify a root cause. Consider this (all too common) situation: seeing a deviation similar to one we’ve experienced before, we skip several important investigative steps and decide what the root cause is before we’ve even defined the problem. We expend effort coming up with a corrective action, but before long we see the same problem arising again. After several iterations of this process the problem is written-off as a ‘known issue’, and accepted as an unpleasant fact of life in the lab.

The most effective problem-solving organizations avoid this vicious cycle by having tools and methodologies that allow them to address the real root cause within a very short time frame of the incident. This not only makes solving problems simpler, it makes the effort involved in problem solving much more productive.