Overall Equipment Effectiveness (OEE) is one aspect of manufacturing that has consistently challenged industries, especially in terms of utilization and efficiency. The quality (scrap) aspect of OEE is known to have more limitations when it comes to improvement. It has constraints like complex part design, machinery process capability, non-availability of advanced metrology tools, and availability of skilled labor. On the other hand, machine utilization and efficiency – the scope of improvement largely rests on internal and controllable factors. They are also considered the low hanging fruits of OEE. IoT powered devices can help gather information like machine downtime due to waiting for setup even after SMED implementation, non-availability of materials, and operators. Data from auxiliaries like collecting bin and scrap bin can also be easily collected continuously and monitored in real-time with the IoT sensors and data analytics. While SMED can handle Quicker setups, machines waiting for setup are purely data driven. Hence, these platforms play a vital role in lean implementation for sustained improvement.
Usually when it comes to lean manufacturing, process improvements are short lived because of the lack of continuous data for monitoring and sustaining the improvement. Capturing of big data allows the ML platform to run applied data analytics for various scenarios and thereby providing insights for lost production hours along with units. In other words, these applied analytics provides manufacturers with opportunity hours for more production (opening up existing / new additional capacity) along with increased efficiency for peak performance.