What do you do if you want to optimize engineering problem-solving with technology and data analysis?

One thing I have found is that there is a tremendous amount of data that can be collected with today’s technology. If you don’t have a plan up front as to ‘why’ you are collecting data and ‘how’ you are going to use it! You’ll find that you end up being buried alive in data and you spending more time trying to make sense of it all versus just solving the problem the traditional way. I would not recommend trying this without a plan!

I have found the most important part of the optimization goals includes the measured impact and if possible non-customer feedback. The gap with customer feedback is it comes after the change for optimization and there is often a significant amount of time between the change for optimization and if (or when) feedback arrives. By defining how you measure success before you implement you can have faster knowledge on direction (positive, negative, or neutral) to minimize impact to the customer as well as effectiveness. In design and development having that measure of success allows for faster evolution. Implementing a project because the result "is obvious" or "a home run" in my experience has been to show effort without showing results.

There are different problem solving tools that are used for optimization. However, most tools can be applicable to different kinds of sectors or fields. Gather all the possible and available data for analysis. In that data, review the problem-solving tools that were used previously. After that, analyze if the technologies proposed to used will be applicable and compatible in the problem-solving process. One technique can be good for one method but bad for another methods, so this step is very important. Optimization of problem solving tools requires extensive research and accurate information, because it can be make or break for the organization. Once everything is in place, analyse the results and make further improvement if needed.

Design for 6 sigma gives a very good set of tools to indentify correlations, influencing factors with their relative impact, and even the influence of some combination of factors.

Many a times, 8D process is followed to a closure but the problem is not killed by its root cause. Only it is suppressed for the time being. It is important to plan and spend considerable time in validating the solution. There can be many possible scenarios where the issue still lives.

One must understand how the data was collected or be the data collector yourself. Garbage data gives garbage results. Data without context will provide meaningless answers.