Maintaining revenue growth and controlling costs is the fuel that keeps a manufacturer strong. Knowing how effective each machine is in meeting production goals by tracking availability, performance, and quality is the essence of overall equipment effectiveness (OEE).
Here are some of the lessons learned by manufacturers who have successfully integrated OEE into their daily operations.
- Use OEE to reduce downtime losses by stabilizing all machines on the shop floor during the pilot phase. Tracking and analyzing, in real-time, if a given machine on the shop floor is about to break, factoring in equipment setup times and unplanned and planned downtimes, are just a few of the ways OEE makes an immediate impact on manufacturing performance. Consider how an initial OEE pilot can capture and provide data regarding production machines that need immediate attention to reduce and eliminate downtime losses.
- Set the goal of creating trusted, scalable datasets that accurately reflect 100% of all machine activity and states of operation. Be sure to guard against OEE measurements becoming biased or too politicized. OEE has started to be included in manufacturing, quality and production teams’ compensation and bonus plans. With annual reviews, quarterly and year-end compensation, and bonuses riding on OEE levels, manufacturers are practically asking for the data to be skewed. While achieving high OEE scores is important, it is far more important to have a trustworthy, credible process for arriving at OEE that scales across the company. Consider de-linking OEE from salary increases and bonuses and redefining how it is measured to ensure the data produced is accurate and trustworthy.
- When comparing aggregate OEE metrics between one production line and another, factor in the individual machine first-pass yields, scrap rates and run times. There’s an exponential increase in the number of manufacturers who are using OEE to compare production line performance. Many are using aggregate product line metrics at the top of their dashboards and scorecards, and also have drill-down metrics to the machine level all on one screen. Enabling production and quality management teams to drill down into OEE calculations and seeing the mix of availability, efficiency and quality metrics is invaluable. Having drill-down data available helps to troubleshoot individual problems quickly that may be hidden behind a single aggregate OEE metric. Comparing machines with two identical OEEs doesn’t ensure accuracy. One could have 70% x 90% x 80%, and the second could have 90% x 70% x 80%. Both have the same OEE, yet one machine has limited availability (70%) while the second is not as efficient (70%). The same logic holds for comparing production lines and entire plants.
- Factor out equipment setup times from OEE measurements. Reducing overall equipment setup times has a direct impact on availability, further artificially inflating OEE performance. Equipment setup times often skew value stream–based production scenario calculations, so it’s a good idea to also factor them into any process re-engineering projects across the shop floor. Initiating a time series analysis of setup times in conjunction with OEE measurements provides insights into how availability can be improved quickly. One extrusion molding manufacturer is tracking setup times for its largest machinery and working to reduce them, indexing gains in OEE as a result. It’s working, and OEE is improving, also, to yield rates for its largest, most expensive machines to operate.
- Consolidate OEE measurements on a single dashboard, enabling real-time monitoring to accelerate machine, production line and plant performance gains. Every machine operates at a unique cadence with variations in the key components of availability, performance and quality. By having real-time data from every machine in a production line, heads-up displays like the one shown below are being used across production floors today. This specific dashboard reflects OEE performance across work centers, breaking out availability, performance and quality components. This specific view of the dashboard also reflects time series results by calendar year and month, which is invaluable for finding trends in each OEE component’s value over time.
Stabilizing machinery performance is the first factor that drives the majority of manufacturers to adopt OEE. As individual machines and production lines stabilize, OEE reflects manufacturing reliability.
For all of its contributions to improving manufacturing performance, OEE is not meant to be used as a single, end-all metric of manufacturing performance. Instead, it’s best to group OEE into a dashboard of metrics that expand visibility into availability, performance and quality at a deeper dimension than the structure of the OEE metrics allows on its own. OEE has the potential to revolutionize production operations taken in the context of overall manufacturing performance.
Bottom line: OEE delivers insights at the machine, production line and plant level that have not been available before to many manufacturers. Knowing how availability, performance and quality impact the most financially important areas of their business, including time-to-customer, order cycle times, perfect order performance and meeting customer ship dates, can revolutionize how manufacturers meet and exceed customer-driven goals for their business.
This three-part series was adapted from an e-book published by IQMS, "Lessons Learned on How to Increase OEE Performance," which is available as a free download.
About the author
Louis Columbus is currently serving as Principal at manufacturing software company IQMS. Previous positions include Director Product Management at Ingram Cloud; Vice President Marketing at iBASEt, Plex Systems; Senior Analyst at AMR Research (now Gartner); and marketing and business development at SaaS start-ups. Columbus holds an MBA from Pepperdine University and the Strategic Marketing Management and Digital Marketing Programs at Stanford University Graduate School of Business. He also teaches MBA courses in international business, global competitive strategies, international market research, strategic planning and market research. Columbus currently is a member of the faculty at Webster University and has taught at California State University, Fullerton; University of California, Irvine; and Marymount University.
IQMS (Paso Robles, CA) is a manufacturing software provider that uniquely combines ERP and MES functionality to give manufacturers a comprehensive end-to-end suite for running the business, backed by the real-time performance and scalability that companies demand. Developed specifically for mid-market repetitive, discrete and batch process manufacturers, IQMS provides robust capabilities for addressing strict customer and regulatory certification and compliance. IQMS achieves this by delivering traditional ERP functionality for accounting, sales orders, material requirements, inventory and purchasing, plus extended native features for CRM, human resources, production scheduling, shop floor control, warehouse and quality modules. With offices across North America, Europe and Asia, IQMS serves manufacturers around the world.