How Manufacturers Can Optimize Overall Equipment Effectiveness with IoT

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How Manufacturers Can Optimize Overall Equipment Effectiveness with IoT

IoT renders unprecedented visibility into machine operations to help manufacturers spotlight ongoing productivity losses and improve Overall Equipment Effectiveness.

Overall Equipment Effectiveness (OEE) is the universal gold standard for measuring production efficiency across industries. Rooted in lean manufacturing practices, OEE is the percentage of total operating time a machine runs at its full capacity and produces the optimal yield. OEE is calculated as the product of Availability, Performance and Quality rates.

While manufacturers strive for nearly 100% OEE, there are numerous reasons where production could fail. Seiichi Nakajima – the father of OEE and Total Productive Maintenance methodology – effectively captured these inefficiencies in his “Six Big Losses” framework. The six big losses are well aligned with the three core elements of OEE mentioned above.

Overall Equipment Effectiveness with IoT

Effective maintenance and equipment handling are key to minimizing OEE losses; and so is asset visibility. To determine root causes and plan solutions, manufacturers must first be able to accurately and consistently monitor existing bottlenecks; and the Internet of Things (IoT) is the most powerful tool to do just that. Below we deep dive into how manufacturers can leverage IoT to reduce each OEE loss.

1. Availability Losses

Unplanned downtime resulting from equipment or part breakdowns and material shortages is the number one enemy of operational efficiency. This type of production stop lasts longest and incurs the most expenses for manufacturers.

IoT-driven predictive maintenance is ideal for tackling with unplanned downtime. Using machine learning algorithms, different operational parameters like temperature, motor vibration and currents can be analyzed to pinpoint common symptoms preceding historical failures. This knowledge enables operators to predict future breakdowns and service equipment before they happen, thereby reducing Mean Time to Repair and production downtime. Besides predictive maintenance, IoT sensors also help operators track real-time levels of supply materials to better align upstream and downstream production. Ordering and replenishment can be executed in a timely and efficient manner to avoid out-of-stock conditions conducive to disruptions.

Planned downtime, as part of equipment setup, changeovers or planned inspections and maintenance, is the other main contributor to availability losses. While planned downtime seems inevitable, IoT can help manufacturers control and minimize it.

As opposed to its preventive counterpart, predictive maintenance is performed based on actual asset conditions rather than a speculated recurrent schedule. This eliminates redundant servicing activities that increase planned downtime. Automated data captured by IoT sensors additionally reduces the need for manual inspections that may entail stopping the production line. They also provide better visibility into time variability among downtime events to help spotlight inefficiencies and their sources during setup and changeover. For example, operators may realize that it takes longer for one team than the others to execute changeovers, indicating a need for training improvement.

2. Performance Losses

Minor stops are events in which machines stop for a short period due to quickly resolvable issues such as misfeeds, material jams or misaligned parts. Because of the moderate impact of each event, operators often overlook their snowball effect in the long run. In this context, IoT sensors can help accurately record the frequency and length of these small stops for a more valid assessment of their total impact. Operators can also identify where exactly in the production line minor stops often happen to pinpoint chronic issues that can be eliminated with corrective measures.

Reduced speed of equipment slows down production cycles and hampers total output. It may also indicate machine wear and tear, poor lubrication or poor environmental conditions like high dust and/or humidity levels, which potentially trigger a serious breakdown. With vibration measurements, operators can be informed when assets are running at sub-optimal speed. Combined with other data sources like environmental sensors, SCADA and MES, it enables a drill-down analysis to determine underlying causes and the following course of action.

3. Quality Losses

Process defects occur when products fail to meet required quality standards during steady-state production, resulting in scraps and rework. Besides equipment-related issues, changes in environmental conditions on the shop floor can significantly influence the consistency and quality of raw materials and end products – leading to process defects. For example, temperature fluctuation can cause the liquid viscosity, flow rate and filling amount to vary greatly.

IoT allows managers to monitor a wide range of equipment and environmental variables along the manufacturing line. Mapping this data with recorded defects as they occur can shed light on the root causes for remedial action like adjustments in equipment or HVAC system settings.

Startup losses, different from production defects, occur between the warmup and stabilization state of equipment. Like planned downtime, start-up loss is part of equipment operations, but there’s certainly room for improvement. For example, leveraging sensor data, manufacturers can identify which start-up conditions or changeover cycles generate more defects, and how this can be addressed accordingly.

Final Thoughts

OEE is a well-rounded productivity benchmark as it encompasses all efficiency losses including stop times, speed loss and quality loss. To improve OEE, IoT renders actionable insights into the major productivity losses and where they occur on the shop floor for counteractive measures. An IoT solution can be implemented to extract essential equipment and production data from existing PLC systems while capturing new operational parameters from a smart sensor network. Combining these two sets of data allows for problem diagnosis and analysis with unprecedented granularity.

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