Three tactics to improve OEE

Filling_Packing_StockCalculating OEE seems relatively simple because the metric includes three variables: availability, performance and quality. But we often see that production managers have insufficient data to measure these, or that OEE calculations are too subjective to be accurate.

Meanwhile,  simply knowing your OEE is only the start; understanding OEE is critical because it can provide more than just a measure of factory floor stability. OEE helps benchmark current and future performance; identify and eliminate bottlenecks; and overcome constraints that limit production. Indeed, optimizing OEE is beneficial for all critical stakeholders:

  • Workers gain peace of mind from knowing that equipment and processes are running smoothly.

  • Managers can plan more reliably when they know that downtimes are less likely to occur.

  • C-suite leaders are confident that reduced downtime translates into an improved bottom line.

Furthermore focusing on OEE can contribute to creating business value because it provides insights that help improve quality, capacity and productivity while decreasing costs. Thus the most successful OEE-based initiatives actually drive the organization’s greater strategic goals. These three tactics will help your organization use the right technology and strategies to improve OEE and maximize its impact across the organization.

#1. Automate accurate, reliable measurement.

Most production managers could tell you their OEE, but how accurate is that number? The conventional method for determining OEE relies on workers recording downtime data and production losses the old-fashioned way, on a clipboard. This approach usually results in inaccurate OEE calculations due to data-entry errors and failure to collect information on short stops. Furthermore, the data collected on a clipboard lacks real-time visibility and provides no context about the root cause of unexpected downtime.

If your organization has not yet embraced digitalization, focusing on using simple technologies to enable accurate OEE calculations is often a great place to start. For example,  the right machine data collection application will automatically collect comprehensive  data accurately and reliably for any piece of machinery. Alternatively, manual work logging terminals could also be a first step toward better capturing OEE because they require workers to record reasons for downtime. Both of these technologies can help improve OEE by providing the information necessary to establish benchmarks for different lines, gain better understanding of downtime and identify best practices.

#2. Strive for real-time visibility.

Even a seemingly inconsequential dip in OEE can translate into a major output reduction. Yet in many production environments, these dips occur all the time--completely undetected--because operators, factory managers and business leaders lack access to real-time OEE data. This means it is virtually impossible to detect and correct problems in time to meet production goals for the current shift. And from an organizational perspective, it also means that managers have insufficient master data to set realistic production and OEE goals.

Text_AlertsReal-time reporting can be accomplished through web-based applications and even automated SMS notifications. For example, maintenance staff might receive a text message directly from a machine when it goes down unexpectedly, cutting the time spent on notification. One Actyx client found that they lost 17 hours per day on notifying maintenance staff of unplanned downtimes. Sending automated text messages whenever a machine went down reduced that time by 90%, resulting in a 15 hour increase in potential uptime per day (cf. this calculation).

#3. Scale up and drill down.

OEE can serve to benchmark current and future performance, so it’s an apt foundation for a continuous improvement program (CIP). Building your CIP around OEE ensures that it gets the necessary attention and keeps the focus on the relationship between OEE and the organization’s strategic goals. This approach essentially scales OEE across the entire organization. That said, it’s possible to get hyper-focused on OEE, which is often detrimental to maintaining a customer-centric focus. Avoid making OEE the sole indicator of success or tying employee rewards or incentives to OEE.

Meanwhile it’s also important that relevant stakeholders at every level of the organization can drill down to understand the intricacies of OEE. After all, two machines can have identical OEE but have differences in availability, performance or quality. One machine might have limited availability and higher quality output, while a neighboring machine has greater availability but inferior performance. Understanding those variations is critical to making meaningful improvements in OEE. Additionally, OEE can be affected by other issues like operator training, scheduling or even product mix.

Differences in factors like product lifecycle, production process and supply chain mean that every manufacturer’s path to improving OEE will be unique. However, focusing on automating data collection, enabling real-time data access and scaling up (while drilling down) enable better insights that drive OEE.

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Topics: Digitization & IIoT, Data-driven production, Industry 4.0

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