Predictive analytics for food production - could you afford to lose £180 billion?

equipment of this type. Combining this information with the typical operating hours of the factory and the production line speed, there is enough information to predict how, why and when the equipment is likely to fail or deteriorate. In this case, failure to identify a

potential breakdown could cause significant downtime. However, even the slightest reduction in efficiency could also skew the accuracy of depositing, causing faulty batches in production. This method of condition monitoring is

often referred to as predictive analytics. However, an influx of AI developments is making this technique even more effective and advanced, coining the term, ‘prescriptive maintenance’.

According to a study by Oneserve, unplanned downtime costs British manufacturers over £180 billion a year, and businesses operating in the food and beverage industry are no exception. Here, Alan Binning, regional sales manager at Copa-Data UK, explains how food and beverage manufacturers can reduce downtime using predictive analytics to enable prescriptive maintenance


echnology has evolved to provide manufacturers with constant insight

into the health of their equipment. Rather than inspecting machinery on a scheduled basis, because a timetable advises them to, manufacturers are using technology to repair items that actually need attention — and leaving those that are in good working order to continue operating. There are various forms of condition

monitoring, many of which are enabled by sensors. Sensors allow manufacturers to monitor vibration, pressure and temperature readings, identifying when a machine or part is beginning to show signs of wear. Advances in industrial automation software are also enabling better visualisation of this information, allowing manufacturers to react quicker, and with better insight — even across multiple sites. By amalgamating operating information

from the various islands of data on the factory floor, manufacturers are provided with a factory-wide view of performance.


Using this information, manufacturers can identify when a piece of equipment is showing signs of wear and act accordingly.

PREDICTING THE FUTURE Recent software developments are enabling manufacturers to identify future faults in machinery, before any signs of failure occur. Using machine learning technologies, data models can inform why and when a machine might fail based upon trends in machine data. Consider a depositor used in food

processing as an example. Depositors are often used in conjunction with belt conveying systems, to automatically fill product cases with an ingredient or mixture. Due to the combined effort of these processes, ensuring the equipment maintains a speed synchronised with the conveyor is essential. Monitoring software could pinpoint the

typical lifespan of the depositor’s motor by examining historical data from other

Unexpected downtime is one of the biggest threats to revenue, especially to those in the food & beverage sector who have a reliance on perishable goods

INCORPORATING AI Copa-Data, developer of industrial software zenon, has collaborated with specialists in the fields of data science and artificial intelligence to ensure its software can deliver the highest level of insight for manufacturers. With partner, Resolto Informatik, Copa-

Data’s software uses algorithms from the field of machine learning that can interpret readings from sensors and actuators to detect correlating patterns in equipment failure. When combined with historical and real-time data streams, it is possible that this wealth of data could grant manufacturers with enough knowledge to completely eliminate unexpected breakdowns. In a food manufacturing setting,

unplanned downtime due to breakdowns can cause staggering financial losses. This is due to the high volume of products being produced continuously, plus the risk of spoilage of unused ingredients. While traditional, planned maintenance

Copa-Data UK www.copadata. com T: +44 (0) 2920 329 175

does have its place in food manufacturing, technology has evolved to better inform the maintenance of industrial equipment. Looking to the future, manufacturers have the opportunity to deploy technologies to inform as well as act when maintenance conditions arise. This is thanks to automation platforms such as Copa- Data’s zenon, which simplifies the process of integration with ERP systems. With companies reporting up to 80 per cent reduction in downtime using these platforms, can you afford not to use of predictive analytics?


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