Businesses that handle potatoes and processed potatoes can cut waste, lower supply chain costs, and boost overall organizational effectiveness by collecting, interpreting and using big data. Businesses can achieve this by using a different array of barcodes, RFID tags, and sensors to track the movement of their products.

Potato processing facilities are among the most modern in the processed foods industry in terms of automation technology and the use of Industry 4.0 equipment. Big data enables potato processors to improve production to offer the desired product quality for their potato strips and specialty goods, while also increasing process yield to maximize profitability.

“Product quality data and process data are collected by an array of sensors, both stand-alone and embedded within processing equipment along the line, which is then fed to MES and SCADA systems for analysis and reporting,” Marco Azzaretti, Director of Marketing at Key Technology mentioned.

Big data is best described by three terms: volume, velocity, and variety. According to some experts, veracity—or the truthfulness of the data—is a fourth source as well (i.e., whether the data originates from a respectable, trustworthy, authentic, and accountable source). Big data naturally has a very large volume. It uses enormous amounts of data that are measured in petabytes (thousands of terabytes) and zettabytes (sextillions of bytes). Even though a database this size might appear overwhelming, creating these massive databases isn’t as hard.

Larger data sets are now produced by every system and device, and these data sets are growing exponentially. Information is being produced by every machine on the shop floor, flooding manufacturing facilities and potentially being useful to enterprises.

The development of smart technology has made it possible for a production facility to gather data from nearly any kind of equipment. Temperature, vibration, and changes in operation are examples of variables that can be tracked to predict equipment failure through individual component monitoring.

When data analytics is used to predict when a piece of machinery will break down, costly downtime can be avoided by scheduling any necessary repair or component reordering well in advance.

Blockages and Throughput Approach

A comprehensive understanding of line operation, utilizing big data, makes it possible to detect production problems in real-time and even predict when production is deviating from specifications. These problems could be caused by less-than-ideal equipment performance on the line or, more commonly, by the inherent unpredictability of the raw material that is being received. When problems arise, processors can swiftly identify the reason for the deviation and implement corrective measures by using big data analysis. Big data frequently assists processors in identifying and resolving potential problems before they have an opportunity to impact line output.

Delivering predictable final product quality that meets grade requirements, despite the natural variations in incoming raw products, is a constant challenge for potato processing operations. Through big data analysis, processors can understand earlier in the process how the quality profile of a raw product batch can support achieving the desired final product spec. This allows for better management of raw materials and a more efficient process with less waste.

You can read the rest of this article in your complimentary e-copy of Issue 2 of Potato Business Dossier 2024, which you can access by clicking here.