The margin for error in food processing and packaging is slim to none, with a breakdown in the production line potentially resulting in a whole batch of products being discarded. The Open IIoT Group explains how machinery data and predictive maintenance tools can reduce those risks.
To avoid food waste and costly production disruptions, manufacturers are looking to machinery data and predictive maintenance tools. These provide greater insights into what is happening on the factory floor, perform essential maintenance, and anticipate and prevent breakages.
“Once you give manufacturers involved in food and beverage manufacturing the ability to visualise data, everything changes,” says Jim Wallace, sales manager at Balluff Australia and member of Industry 4.0 advocacy group Open IIoT.
“It gives them greater control over the production process, and once that data visualisation is paired with predictive maintenance, efficiency and revenue gains are realised.”
Predictive benefits
Predictive maintenance is a proactive approach that uses innovative diagnostic and sensing technologies to monitor the condition of equipment and predict when maintenance should be performed.
Predictive maintenance tools such as infrared thermography (detecting high temperatures), acoustic monitoring (detecting leaks), vibration analysis and oil analysis, alert manufacturers of potential failures.
“Essentially, predictive maintenance uses data to estimate when a machine might fail so maintenance can be scheduled before the point of failure,” says Wallace.
“Another benefit is that it gives manufacturers the ability to schedule maintenance when it is most cost-effective and does not interfere with production, as well as helping to extend the equipment’s lifespan.”
As food and beverage manufacturing is a tightly regulated industry, the strictest hygiene and sanitation standards must be upheld. The need for heightened cleanliness can create a wet environment, which can easily damage important equipment.
Wallace explains, “Add on the fact that machines deployed in the food processing industry are highly complex and challenging to maintain due to the connected system of conveyors, electronic and electrical equipment, and the heightened risk of machinery breakdown becomes abundantly clear.”
Impact of breakages
Poor maintenance results in unexpected breakages, and even worse – if a machine has missed multiple maintenance cycles due to a lack of monitoring – it may be broken beyond the point of repair.
The Wall Street Journal estimates that unexpected downtime costs manufacturers around $50 billion annually and reduces plant productivity by five to 20 per cent.
In the food industry, these consequences are magnified. Food processing equipment is working with delicate products which have a variety of time requirements to ensure health and safety standards are met. Any delays in the production process may result in spoiled goods.
Broken machines are unsafe and carry the threat of contaminating food and beverages or damaging food packaging.
If any contamination or damage occurs, manufacturers will need to dispose of the goods and restart the production process from scratch leading to food waste, missed deadlines and additional costs incurred.
“While predictive maintenance is key to predicting and ultimately avoiding these obstacles, manufactures in this industry will realise additional benefits when these technologies are combined with data visualisation tools,” says Wallace.
Data visualisation
Data visualisation refers to presenting data in a visual context such as a chart or graph so that it can be more easily understood. In food and beverage production, this is made possible by adding sensors to machinery to monitor what is happening on the factory floor.
By using IoT connectivity, this information is shared as data that manufacturers can access in real-time and use to make decisions.
“Using sensors to transmit real-time data can alert employees when equipment malfunctions so that they can make the necessary adjustments to avoid goods from becoming contaminated or destroyed.
“Data insights allow employees to adjust equipment in real-time to get it back to normal functionality, reducing the need to shut down production completely,” says Richard Roberts, Industry 4.0 operations manager at ZI-Argus and fellow member of Open IIoT.
In the food and beverage industry, where contamination is always a risk, data-driven insights have further advantages. If there are reports of consumers getting sick from products, manufacturers can check machinery data to trace back the food production line and determine the source of the contaminants. This gives them the facts necessary to decide whether a product should be recalled.
Combining predictive maintenance with data visualisation helps to boost equipment reliability, quality standards and production.
Why so slow?
With all these benefits, why is predictive maintenance not more widely adopted by food and beverage manufacturers?
“Compared to other manufacturing industries, the food and beverage sector has historically been a late adopter of digital trends. This is often because of the complex manufacturing processes needed to comply with the strict safety and hygiene standards of this industry, which may result in manufacturers being more hesitant to adopt new solutions,” explains Roberts.
The initial cost of implementing predictive maintenance and related Industry 4.0 technologies on the factory floor is a factor, but Roberts reassures manufacturers that these tools have not only become much more affordable in recent years but will soon pay for themselves in gains realised.
“Predictive maintenance is a cost-effective strategy as it reduces downtime and helps prevent food waste.
“Smart connected systems give food and beverage manufacturers a competitive advantage, boost product quality and safety, increase efficiencies and increase productivity – there is very little to lose by implementing them,” Roberts says.
The Open IIoT Group is an initiative of some of Australia’s most prominent automation brands – SMC Corporation ANZ, Beckhoff Automation, NORD DRIVESYSTEMS, Balluff, ZI-Argus and KUKA Robot Automation.
