Keeping key performance indicators in context
Treating key performance indicators as isolated values is a common mistake.
Real-time monitoring and analytical decision support for process management and improvement are essential to world-class manufacturing. Key performance indicators (KPIs) can help, but manufacturers too often use them as isolated values without analytics, setting themselves up for bad decisions.
KPIs are chosen to help give a more complete picture of process performance. When properly designed, they simplify and accelerate understanding of process status by integrating multiple pieces of data into one value. Best-in-class companies have a 40 percent higher rate of standardized metrics than do laggards. Performance gains from KPIs further benefit from standard industry reference models such as ISA 95.
The primary goal of KPIs is to convert quantities of manufacturing data into actionable knowledge, called manufacturing intelligence (MI) that can drive business results. A new MESA report concludes that use of MI derived from KPIs yields larger increases in profit and quality. The study’s authors say aligning metrics across the organization improves management productivity at all levels, and clearly defining source metrics and understanding source data ensure accuracy and buy-in from all departments.
According to MESA, a major benefit of MI is that it enables a process-based view of manufacturing. Statistical analysis is the most important tool in understanding and managing those processes. MI and a process-based view of manufacturing convert the snapshot of a single metric into a defensible prediction of future behavior, creating truly useful manufacturing decision support.
MI incorporates statistical process control (SPC) and process capability analysis, two of the most effective methods of identifying and managing process behavior. When put into context with SPC, a single metric delivers much more useful process management information.
Therefore, MI is able to positively impact corporate performance through the synthesis of quality data, process-based analytics and timely delivery of role-specific information. MI’s impact on the management process is most obvious when the operational value of metrics is increased with corresponding analytics.
OEE as a process-based metric
MESA’s report suggests companies would benefit from treating OEE as a process-based KPI with variations and trends rather than an isolated value. When OEE is combined with analytics such as SPC, the report says, it can provide excellent insight into manufacturing performance and opportunities for improvement without requiring new assets. MESA found the most profitable companies were also most successful in improving OEE values in their plants.
Utilizing SPC and OEE like any other process parameter extracts greater value from the monitoring process and increases the quality of operational decision support. By better utilizing predictive trends in operational data, companies can easily identify problems with quality, throughput or availability and perform useful statistical analysis on automated data collection systems.
You can read the full article here.