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Predictive Analytics in Food Processing : Testimony BÜHLER AG

In the future, countless networked sensors will continuously monitor parameters such as temperature, humidity, vibrations, or the composition of products in production lines. Enormous amounts of data will be analyzed in the cloud or on edge in close to real-time. These technical advances open the way to an unprecedented process control with respect to transparency, product quality and yield optimization. In
order to create the underlying insights, data science is vital.

Our vision is to enable the food industry to derive maximum benefit from all these technological developments. One possible use is predictive maintenance for machines. Thanks to continuous monitoring of vital operating parameters, it will be possible to predict the failure of a component or wearing part in advance and trigger the delivery of a spare part in a timely manner.

Bühler is laying the foundations for this today. We are creating added value for our customers with innovative, data-driven services. For example, we offer already sensors that continuously check for color and specks during flour production. Other sensors measure moisture content together with ash or protein content and enable optimization of production in real time.

In this presentation I will highlight key factors to close the data science loop successfully : Data availability at scale, data quality, data relevance, advanced analytics and the active involvement of domain experts across different disciplines.