OEE

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While OEE is a must-have for troubleshooting line problems, contextualized OEE data with its time signatures is a key input in analyzing when and what went wrong with a plant’s total production process, including wasted product and utilities. And it can even aid in the track-and-trace process.

Success in operational readiness is all about data collection. When predictive maintenance systems are integrated with actionable alert-enabled HMIs, OEE increases.

Food giants are closing legacy plants and building greenfield facilities to achieve higher volumes, greater flexibility and increased margins. So how are plant managers and corporate leadership executing, managing and delivering data to help operators find accurate KPIs and OEE metrics?

Overall equipment effectiveness (OEE) remains the go-to benchmark for gauging performance, but raising OEE requires more than tracking metrics. It calls for a holistic approach that combines robust planning, skilled people and seamless digital integration.

Achieving a high OEE score has been elusive in the past as processors grapple with where to begin in troubleshooting OEE issues. The future of OEE, with the help of AI technology, will help manufacturers find the most miniscule of line problems and be on top of their game.

With manufacturing barely expanding and costs tightening, companies are looking to squeeze every bit of efficiency from their operations. AI-driven optimization of OEE metrics is becoming an essential lever for doing exactly that.

Because boiler systems are inherently dangerous, safety must be factored into the design of not just the boiler but also the burner, combustion control, and overall operation of the system.

Data collection has become a major part of food manufacturing, but without data standardization, measuring overall equipment effectiveness (OEE) poses a challenge.

Creating efficient conveying systems begins with knowing what you want to accomplish, then enlisting the help of system integrators and suppliers — you may find that you can do things you hadn’t thought possible.

Artificial intelligence-powered PLM can make accurate projections about planning new products, introducing them to the market and looking at all the factors a processor might miss in execution.