For some companies, leveraging the Industrial IoT (IIoT) is simply a strategic "must do". for others, they have to cost justify it. Analysing improvement potential by OEE allows companies to determine potential savings in the context of the plant. Manufacturers can compare availability, performance and quality improvement opportunities to the investment required to achieve them in order to calculate an ROI.
Many manufacturers have reduced cost and improved quality by using Lean, Six Sigma processes to eliminate waste. Digital Transformation using the Industrial IoT extends these improvements, helping manufacturers achieve even higher productivity.
OEE can be used as a way to quantify the potential value by evaluating how IoT analytics can improve each of the the three OEE elements and related "Six Losses of OEE" that undermine productivity. OEE is proven metric to benchmark performance and track progress for a workcell or a plant. While not all companies look at OEE the same way, it offers a valuable framework to identify and evaluate productivity improvements.
Improving IoT Productivity
IoT provides timely, detailed data to create knowledge about production performance. An effective IoT platform can connect a variety of equipment and pull desperate information together in real-time to create dashboards, alerts and triggers to create insights to drive corrective action.
Extending Iot Value with analytics
Analytics amplifies IoT value by pulling together disparate information such as sensor data streams, environmental data, and information from enterprise systems like ERP, MES, EAM, or HRMS. Then algorithms can uncover new and deeper insights by finding hidden trends and data relationships that produce manufacturing operational intelligence. For example analytics may use artificial intelligence (AI) to find correlations between quality defects and factors that might not be obvious, like a specific supplier or adherence to a set up procedure. Together with IoT, analytics uncovers new opportunities to help extend productivity improvements.
Availability is measures as the percent of available run-time compared to the time production equipment is scheduled to operate. This metric is important because increasing equipment uptime drives higher throughput and delivers greater production value.
Reducing Equipment Failure
Downtime from broken equipment is a major source of lost productivity. IoT analytics reduce unplanned stoppage by helping manufacturers monitor equipment to keep it running. The primary source of value comes from identifying issues before they happen so equipment can be services off schedule.
Using algorithms to identify scenarios that commonly preceed a failure helps manufacturers transform to predictive service. Servicing equipment before it breaks down improves Mean Time Between Failures, minimising the disruption of breakdowns that cause cascading effects and escalate into bigger productivity loss.
Not all can be predicted, but faster identification and better knowledge of th situation helps minimise the disruption by aiding with rapid repair. Having the right information helps service personnel prepare the right parts, skills, tools, and knowledge, improving first time fix rates and Mean Time to Repair (MTTR).
Minimisng setups and Adjustments
The second of the OEE losses impacting availability are planned equipment pauses for setups, changeovers, and adjustments. While these may be necessary, they take away from productive time. IoT analytics help companies track actual frequency and duration of these events and identify anomalies or trends that could indicate issues or opportunities for improvement. Better insight may help uncover ways to make them more efficient, or associate pauses with factors that can be adjusted.
Improving Equipment Performance
Performance metric, is also known as the process rate for piece of equipment. It is measured as the speed at which production runs as percentage of its ideal cycle time. This measure is important because it encourages maximum output from the time equipment is operating.
Minimising idling and minor stops
IoT analytics can help reduce idling and minor stops in a number of ways. First, it can help by reducing the need for in-process adjustments by dialing in spacs more precisely based on analysis of past performance. It can also provide insights into which products require more stops and use machine learning to identify root causes that can be corrected. In addition, analytics can preemptively identify situations like material issues that could cause minor delays so they can be addressed before they impact productivity.
Preventing reduced speeds
Reduced speeds is measures slowed cycle times that reduce production volume. IoT analytics can help identify ways to run at full capacity without causing disruptions. It allows faster identification of issues because there is no latency in metrics. IoT monitoring can quickly identify when equipment isn't running at intended speeds to provide greater visibility to the issue, while analytics can help companies monitor and find root causes.
Slow cycle times are often due to equipment failures. These failures can be identified proactively and prevented so equipment remains in the top operating condition. Slow speeds may also be due to a sub-optimal production process that could be identified by analytics. In addition, companies with robust IoT capabilities may be able to correct issues with a remote upgrade of the equipment configuration or controls to improve performance.
IoT analytics can help ensure that produced units are acceptable, improve first pass yield / process yield, and limit scrap and rework.
Reducing process defects
Process defects, also thought of as production rejects, take away from profitable production yield. They also waste time and materials. IoT analytics can play a significant role in improving yield and minimising waste. Monitoring production in real-time via the IoT helps quickly identify issues. for example, IoT can monitor automated measurements devices to quickly detect out of spec production. It can also ensure that equipment settings are correct via monitoring and adjusting with bidirectional communications and remote control.
Analytics extends this by identifying trends or spec slippage while production is still conforming so it can be adjusted to stay within tolerances. In can also help provide valuable insights into conditions leading to quality leaks so they can be identified and mitigated. finally, IoT analytics "close the loop" by feeding actual production information back to process engineers to correct manufacturing design errors and optimise production processes.
Minimising reduced yield
Reduced yield is closely related to process defects. It's typically considered losses that aren't due to steady-state operating issues such as startup yield issues. These could be caused by some of the same reasons as process defects, but could also be sue to startup / warm-up procedures that aren't followed properly. It can also come from set up issue arising from changeovers.
IoT analytics can help reduce these productivity losses by providing visibility to production and identifying issues. Analytics can find trends and anomalies and uncover root causes so that the plant can dial in the production process quickly to get to quality, steady state operation to optimise quality production.
It's time to improve productivity by reducing the 6 Big Losses of OEE and driving improvements in Availability, Performance and Quality. Pick a project and scale up the value by extending the new products, production lines, factories, or locations. It's time for manufacturing to drive meaningful change with IoT analytics and set the stage for future improvements beyond the factory. Remember, a small improvement to OEE can drive significant bottom line results, and the foundation created by manufacturing can open up new business models and revenue opportunities across the business.
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Connected operations offer a diverse range of business benefits. Whether you are most interested in driving efficiencies or increasing flexibility, reducing waste or enhancing asset performance, a connected approach will likely deliver – and more. Here’s how.