OEE as the Data Collection and Utilization

PMMI Business Intelligence’s White Paper “Data Acquisition, Sharing, and Utilization” explores the obstacles and benefits of integrating digital connectivity and data utilization into manufacturing operations to enhance operational efficiency.

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PMMI Business Intelligence: Data Acquisition, Sharing, and Utilization

PMMI Business Intelligence’s White Paper “Data Acquisition, Sharing, and Utilization” reveals the results of the Vision 2030 panel session at PACK EXPO International. It highlighted the significance of Overall Equipment Effectiveness (OEE) as a key metric for tracking efficiency and data utilization in manufacturing operations. OEE, which focuses on availability, performance, and quality, is often the starting point for companies integrating data capture into their processes. The panelists emphasized the importance of real-time data collection for predictive maintenance and efficiency improvements while acknowledging the challenges posed by legacy equipment and cost constraints.

How to Achieve Effective Data Utilization?

The collection of real-time data can guide predictive maintenance, as well as yield management and efficiency improvements. Quality data plays a crucial role in OEE, especially for end users; however, legacy equipment, cost constraints, and variable data sources often complicate scaling, making collaboration with OEMs essential for seamless data capture and interoperability. Panelists underscored the need for gradual implementation, ensuring projects secure management buy-in and focus on capturing quality data that genuinely influences performance. They also highlighted the importance of stakeholder support to ensure the most effective use of data, citing the provision of suitable modern PLCs to legacy machinery as an example. “Data is the new oil,” said one A data-centric mindset must flow from the top down to effect change and encourage the use of analytics. All stakeholders must understand the significance of the data they receive and record. Measuring output was seen as the most effective data capture process, as it provides information that can be extracted from most machines, even older models. It is also a necessary step before implementing data processes such as predictive or prescriptive maintenance.

One speaker recommended cultivating a culture of Statistical Process Control (SPC) at both the macro and micro level, explaining: “At the end of the day, operators are going to know what’s going on, but using technology, specifically SPC, at that micro level to engage leadership to say, ‘Hey, there’s something going on in this area. Go look at it.’ And then at the macro level, again, using SPC on top of a data set to say this is noise or this is actually something that we need to pay attention to.”

At the micro level, operators should be able to assess data and alert managers to any issues. Analyzing a data set using an SPC method allows tactical leadership and management to identify issues, enhancing maintenance and efficiency. Panelists stated that, where possible, dedicated teams should be created for data capture and analysis.