An effective optimization process is a key component to remaining competitive as a manufacturer. As turnarounds tighten, product complexity increases, and the skilled labor pool shrinks, achieving more with less—and faster—is a growing necessity. At the same time, the dramatic amplification of the optimization process has emerged as a key, early use case for industry 4.0 technologies.
Broadly, process optimization is minimizing the resources used, and/or maximizing throughput, of a set of parameters, within given constraints. Less technically, it is re-evaluating processes to make them more efficient: producing more with less, without negatively impacting other processes or parts of the business.
The optimization process can be divided into five broad steps.
Identify an area that requires optimization. This could come from simple observation, statistics, or an active process mining exercise. Perhaps output from a particular process is down. Or maybe reducing spend in one area is necessary to meet a wider business objective.
Why is the process less than optimal?
Typically, there are three areas for analysis:
Each of these should be analyzed to understand exactly what is causing increased spending, decreased throughput, or sub-optimal performance.
How can the process be reformulated to solve the problem? Staff may require extra training. The procedure may need re-designing. Machinery may need servicing, upgrading or replacing.
Roll out the solution. Design and deliver the training program. Distribute new work instructions for the updated procedure. Service or replace machinery.
Measure performance before and following the implementation to evaluate success. Be careful to measure other production lines and related areas of the organization to ensure the intervention hasn’t had a negative impact elsewhere.
‘Smart operations’ refers to the amplification of Lean and Six Sigma methods with digital technology: the predominant use of Industry 4.0 technology so far. The two key areas in which smart operations can amplify the optimization process are in the use of connected assets for industrial production, and digital supply chain management.
The combination of real-time industrial internet of things (IIoT) sensors, big data, and machine learning makes vast amounts of data available for analysis. The biggest initial impact is on the connected machines themselves. Aside from a granular understanding of when and how a machine may be operating even slightly out of spec, big data analysis enables predictive maintenance—preventing problems before they even occur.
However, the same data—and analysis techniques—can also be used to evaluate procedural and control parameters in more detail, significantly boosting the identify, analyze, and evaluate steps of the optimization process.
Manufacturers are increasingly using software such as Product Lifecycle Management (PLM) to run a digital thread through their entire supply chain—including partners and suppliers. Gaining such visibility and control principally enables manufacturers to better manage the buy and sell sides in reference to sales and production. Organizations can operate—as close as possible—with only the resources and inventory they need.
Digital supply chains also open the entire organization to holistic optimization, refining every process in the context of its place in the supply chain, rather than merely improving upon procedures towards an absolute efficiency goal.
In keeping with existing Lean and Six Sigma methods, the smart optimization process is continual and iterative. The difference is that smart operations enable much finer improvements at a lower cost—and across the organization, rather than as siloed refinements. Taken across the enterprise, even small advances can add up to a significant impact on the bottom line.