Overall equipment effectiveness (OEE) continues to gain in popularity as manufacturers seek to quantify plant, manufacturing line, and machine-level performance and find ways each area can be improved.
The metric plays a central role in lean manufacturing by providing valuable data that manufacturers use to produce the highest quality products at the lowest cost within the challenging constraints of short lead times. OEE and comparable manufacturing metrics also are fueling the development of advanced analytics and business intelligence-based software that includes the next generation of manufacturing intelligence applications.
At the same time, it is important to step back from all the intensity, hype and urgency surrounding OEE and level-set expectations of what this metric can and can’t do. Many times, OEE slows down production and causes companies to be less customer-centric and lean. As useful as OEE is as a metric, it can mask bigger and more potentially more challenging manufacturing problems if not used in the right context. In particular, too much reliance on OEE can hide manufacturing performance gaps at the machine, production line, and plant or factory level.
Defining OEE
OEE is an aggregated metric that multiplies machine availability by performance by quality to provide a clear picture of how effectively machines on the factory floor are being utilized. OEE is very effective for stabilizing production and quality levels, setting the foundation for making production lines and plants more reliable over time.
Finding Performance Gaps
Nearly 70% of the manufacturers I’ve spoken within the last year have adopted OEE and are using it today to measure, analyze and report data from the shop floor to the top floor. Adoption typically starts at the machine level and then progresses to production lines. Only 15% of these companies scale their use of the metric across production centers or plants in less than a year.
Manufacturers in the 15% group say OEE becomes more valuable as a way to measure on-plant performance gains over time. They observe that, while OEE can highlight noticeable differences across production locations, results need context with each production center’s unique role in the broader manufacturing operations. One manufacturing VP observed that OEE is best used to compare individual production center performance taking into account different constraints, customer, fulfillment and supply chain requirements.
For the majority of manufacturers who are comparing relative levels of OEE by machine on the same production floor, it’s startling how wide variations are between OEE measurements. When OEE levels are sampled across a production line, they often show wide differences because availability, efficiency and quality levels by production machine vary widely. There is a benefit to tracking how OEE fluctuates with the volume and velocity of work through the adoption of real-time monitoring. To that end, there is a growing potential for Industrial Internet of Things technology to provide trusted, up-to-the-moment OEE data, enabling additional insights into performance fluctuations based on equipment effectiveness and efficiency.
Despite the continual improvements in measuring and monitoring technologies, however, the issue of how much OEE can be trusted as a metric that scales from machine, to production line and on to entire production plants is open to debate. That is why it’s important to think of OEE as one of many metrics and key performance indicators (KPIs) that need to be used in managing to excellent manufacturing performance. Because symptoms and signs of manufacturing performance gaps are hidden behind aggregated of data used to calculate OEE, using OEE alone is not the answer to increasing lean manufacturing performance in any plant or with any production line or machine.
Lessons Learned in Using OEE Effectively
Following are lessons learned from manufacturers who have found and fixed performance gaps in production operations using OEE.
Be wary of the hype surrounding OEE as a multi-plant measurement metric, and use it only as a baseline for each plant’s performance when product lines, suppliers and product runs vary. For OEE to deliver the most value from a multi-plant perspective, each facility needs to produce the same products, use comparable suppliers, rely on similar scheduling constraints, and have comparable quality management and compliance systems in place. When any of these factors vary, there will be differences in the OEE levels attained, making the comparison across plants meaningless. When each plant is producing a completely different product, it’s best to use OEE to measure in-plant performance against a previous benchmark for future objectives.
Aim to create a trusted, scalable dataset of manufacturing performance that isn’t inflated or 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 running up high OEE scores is important, it is 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 can be trusted.
Don’t just trust the aggregated number. Get into the practice of drilling down into OEE calculations and seeing the mix of availability, efficiency and quality metrics, since gaps in these three areas are hidden in aggregated OEE measurements. Comparing machines with two identical OEEs doesn’t ensure accuracy. One could have 70%x90%x80% and the second could have 90%x70%x80%. Both have the same OEE, yet one has limited availability (70%) while the second is not as efficient (70%) compared to the second machine. The same logic holds for comparing production lines and entire plants.
Factor in equipment setup times to get the true OEE levels a machine is delivering. Reducing overall equipment setup times has a very positive, direct impact on availability, especially in value stream-based production scenarios. Another reason to factor in equipment setup times is that it helps manufacturers to break down organizational silos that get in the way of excelling at production floor operations. Factoring in equipment setup times keeps OEE more consistent, removing any potential for measurement bias and variation, leading to more optimized production workflows.
Conclusion
OEE needs to be considered as one tool in a toolbox of many manufacturing effectiveness metrics and KPIs. Much of the buzz surrounding OEE is creating unrealistic expectations, including the much-hyped ability to compare plants or production centers. It can be used for that task, but the specifics of each plant need to be taken into account.
OEE is most effective when used as a strategic or overarching metric, coupled with drill downs into each of its components of availability, efficiency, and quality. In seeking to optimize each of these areas, manufacturing operations need to balance production schedules with time, cost and machine-availability constraints. The surest way to increase OEE is to find new ways to permanently improve product quality, gaining a great reputation with customers in the process.
Louis Columbus is a principal at manufacturing ERP provider IQMS. He is also a contributing writer and analyst for Forbes.
