Continuous Improvement Tools: Avoid Drowning in Data
Continuous improvement tools (CIT) employ analytics and diagnostics to improve and evaluate production, machine performance and more. Many production firms now engineer smart devices in machines to derive filtered data, instead of sifting through mountains of data to find the important, usable information. Providing nearly instant feedback to analytics for evaluation, the process of continuous improvement moves quickly to becoming (internet) edge computing. It is all about competition and meeting the demands of the marketplace. Yet, as CIT grows in acceptance, it becomes more inclusive bringing in a host of experts and services to improve its performance.
The push from competition has excited innovation. When Control Design assembled a team of production experts, the conclusion revealed that by coupling sensors with software systems meeting the current challenges that lay ahead look promising. As one expert said, “To accomplish this, sensors will embed diagnostic tools into their digital payloads. Software systems will offload complex algorithms to embedded devices and pull post-processed values. We can expect the typical bell-curve progression for analytic software tools.” Going right to the source while using algorithms to pull out, or sift, the important data serves to create an almost real-time environment, underscoring the value and meaning of continuous improvement tools (CIT).
The idea is simple.
When a problem arises during production or post-production by the data flowing back to the evaluators, deploying changes can occur swiftly. With the ever-continuous eye on production, feedback from machines etc. managers can compare production quality, conformity and, uniformity to adjust to or adjust production. Analysis of machine performance, maintenance — predictive and prescriptive– for high-intensity industries and, the analysis of process and business process for information-driven concerns represent just some of the areas in which CIT’s now thrive.
The cloud also shares in the evolving world of production innovation through software development. Known as DevOps, the operation steers production toward automation to further maximize the power of CIT’s. Through continuous delivery, testing and integration, those companies that incorporate DevOps with their existing CIT process receive code nearly 30 times faster than their competition. As the process of CIT grow exponentially complex, predictions assume that the tools will grow smarter, migrating to the device level, where once again they will subject managers to the risk of drowning in data if they cannot process and sift the information quickly.
At this point, it sounds like innovation is slowly and gently edging out humans, their contributions to the process blunted by software. Still, the diagnostics that impact the methods of problem resolution will always provide space for people. They too take an important part. Methods for how they perform in a CIT environment vary, as do the theories and recommendations for how they play their part.
The easiest and natural of theories attempts to derive the best from the process, framing the basis for developing CIT’s in the first place. Often referred to as the “5 Whys”, the method of diagnosing the results takes equal importance with analytics. A continuous evaluation of the root problems instead of resolving symptoms relates directly to the overall performance of the process. Using analytics to address a problem proves one thing, but using analytics to get to the problem and why it persists exposes the power of CIT’s. Requiring digging down into the analytics avoids jumping to conclusions and hasty decisions that often keep the real cause hidden.
From sensors and software, to the cloud and humans, they all take an important part in driving the success of CIT’s. Moving closer each day to more intelligent tools drives the need for faster software development that incorporates algorithms for drawing out important data, while imposing ever-better diagnostic demands on humans.