Advanced analytics in manufacturing can pinpoint ways to lower costs and improve business results. Manufacturing costs arise in many areas, and they tend to be interrelated. Unfortunately, typical approaches to continuous improvement (CI) cannot always characterize these complex dependencies, making it harder to get to root causes and—most importantly—corrective actions. Thus, moving from traditional to advanced analytics can accelerate and boost CI projects’ effectiveness at lowering operating costs.
While manufacturing is full of traditional analytics, advanced analytics are not yet commonplace. Part of the reason may be that they start from a fundamentally different vantage point than conventional analytics. So, while they may use the same data, the methods and mindset are quite different.
Advanced analytics includes a range of analytical methods designed to help uncover patterns in data, identify trends, find hidden insights, accurately forecast events, and drive improvement. It goes beyond Business Intelligence by using sophisticated mathematical algorithms and analytical techniques such as artificial intelligence and machine learning. These enable a more automated approach to easily correlate larger and more complex data sets, explore problems that may not have complete data sets, and drive more refined insights, all of which enable more educated decisions.
In manufacturing, machines, controls, people, and IoT create many continuous data streams. Problems that generate extra costs may come in nearly every production area and involve any or all of the resources: machines, employees, processes, methods, materials, tools, energy, and safety.
There is a set of manufacturing products that, together, enable advanced analytics (See figure). Reading this stack from the bottom up:
Even within a single company, every plant or factory is different, and in each, there are many possible ways for advanced analytics to show how to lower costs. Despite the infinite possibilities, there are some common types of insights that can lead to lower operating costs.
Manufacturing facilities are full of equipment, machines, and tools. Advanced analytics can help clarify interdependencies and understand subtle trends and patterns. Long before an asset goes out of calibration, machine monitoring can often raise an alarm. Insight into that type of trend enables predictive maintenance. Prediction lowers costs by minimizing the chances of out-of-spec processes and costly downtime on lines. It can also reduce equipment maintenance expenses by pointing to what people and materials to have ready rather than last-minute expediting or adding overtime.
Energy is a significant component of operating costs for nearly any production operation. With energy prices regularly fluctuating, ensuring that high-energy processes run at off-peak energy cost times can be extremely challenging. Advanced analytics in manufacturing can lower costs by delivering insights into energy consumption and its price at every moment of use and suggesting what processes to run when.
Nothing is more costly to a company than safety or environmental problems. The costs go far beyond any injury or damage to individuals, equipment, or facilities. They even go beyond non-compliance fines. Safety and regulatory incidents can damage the trust of the workforce, their families, and the surrounding community—not to mention customers. Advanced analytics supports safety by tracking, monitoring, and predicting hazards and emissions levels. Insights and early warnings of possible problems can go a long way to keeping workers, facilities, and communities safe—while reinforcing the company’s reputation as a good corporate citizen.
For those who know where to look, justifying an investment in advanced analytics can be straightforward. Advanced analytics in manufacturing have a wide range of potential benefits. Manufacturing leaders have built a proven track record of success. Reference their experiences to gain insights into your own advanced analytics strategy.
Julie Fraser is the Vice President of Research for Operations and Manufacturing for research firm Tech-Clarity. She covers Industry 4.0, Smart Manufacturing, MES/MOM, QMS, APS, APM/CMS, IIoT, AR/VR, other technologies and solutions for manufacturing.
Julie has over 25 years as an industry analyst in addition to experience in marketing and strategy (Berclain/Baan, now Infor) and editorial roles for computer and technology publications. She worked as an assembler over college summers and that got her hooked on manufacturing. She has a BA in German and French, Magna cum laude, Phi Beta Kappa, from Lawrence University in Wisconsin. She is also a certified business change agent and conscious business ambassador.
Julie’s current areas of research include the realities of moving Industry 4.0 from vision to reality; the role of MES/MOM in the new landscape; incremental vs. transformational change in manufacturing; approaches to empower plant workers and their leaders; IT/OT convergence; personalized and local manufacturing; and more. She is fascinated by the organizational, cultural and personal transformations required to drive success with new technology and approaches to manufacturing.
Julie is a certified yoga and meditation teacher. When she’s in love with life, good things like the opportunity to work for Tech-Clarity come at the right time.