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With this delineation between the layers of applications in the smart factory, there is then a clear flow of data: business requirements flow down from the enterprise applications to the site applications. The site applications translate the requirements into concrete manufacturing plans, which flow down to the process applications. The process applications gather event data to send back up to the site applications. The site applications aggregate and summarize the relevant event data to be sent finally up to the enterprise applications.
Factory Intelligence Application
Depending on the point of view, there are many different, yet valid, perspectives on the performance of the factory. For instance, the fact the factory is on shutdown may be significant to a planner looking at overall factory capacity, but it is less significant to the production manager who simply wants to know if the machines are running efficiently when they are scheduled to run. With a mix of different customers, products, factories, lines, and machines, there may be hundreds of different KPIs to consider. Some of these measurements may be complex requiring data from multiple processes, for example overall equipment effectiveness (OEE) calculations, where we consider not only the performance of the factory resources but also the quality of the products being made.
With all this complexity, it is often the case that a bottleneck is caused by some external force that is not being measured. A machine may not be operating because of an actual malfunction in the equipment, or it may be waiting for some upstream or downstream process. Perhaps the operator is on break, or there is a shortage of materials causing the downtime. To identify the root cause of a problem and provide for an actionable response, these external forces must be considered.
A site-level factory intelligence application would need to consider information coming from the both the enterprise applications and the process applications. To begin, the process-specific applications would provide performance data regarding the status of the equipment being managed. Next, the site-based constraints will be used to qualify any process status based on constraints such as the overall factory schedule, material availability, or the upstream/downstream bottleneck.
With information about process performance and external the constraints influencing production, many optimization opportunities are possible. The process-specific layer can optimize based on external knowledge from other processes and higher-level applications, while the site application layer benefits from detailed process information from each individual equipment.
To read the full article, which appeared in the September 2018 issue of SMT007 Magazine, click here.
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