Maintenance 4.0 — Minimising Food Recalls, And Maximising Trust
Thursday, April 28th, 2022
A spate of recent headlines have indicated that food safety recalls are on the rise in Singapore. To address this problem, asset management and maintenance are key areas that can be improved.
By Fabio Tiviti, Senior Vice President & General Manager, ASEAN-India, Infor
To say the past two years have left food and beverage manufacturers in disarray would be an understatement. The ongoing pandemic has sparked delays and disruptions across the food manufacturing pipeline, all of which have been further aggravated by a confluence of factors — fluctuating consumer demand, bottlenecks across the global supply chain, and rising costs — converging to create a perfect storm. Safety recalls, in particular, continue to represent one of the biggest, costliest, and riskiest challenges facing manufacturers today.
A spate of recent headlines have indicated that food safety recalls are on the rise in Singapore — from juices * and cooking pastes * to more worryingly, infant formula *. Rather than pointing to a dip in standards and safety, however, reasons cited typically centered around new labelling requirements, improved capacity for identifying and detecting anomalies, and growth in the “free from” sector which is particularly vulnerable to cross-contamination. Additionally, the pandemic has also prompted a renewed focus on the risks of contamination from people and pathogens within food production and processing plants.
In a world where food safety is paramount, and brand reputation is king, even one safety recall is one too many. To address this problem, asset management and maintenance remain, for many, areas that hold huge potential for improvement.
Maximising automation, minimising risk
It’s no secret that the key to addressing these challenges resides in leveraging smart software to digitally transform one’s processes. Despite the increased focus on technology today, many plants continue to adopt manual, time and labor-intensive spreadsheet-centric processes to support their equipment maintenance programs. This bears huge risks as it leaves food companies vulnerable to variables such as human error, unnecessary downtime and costs, and potentially lengthy compliance processes. Furthermore, the resources required to support such a model can be more strategically deployed elsewhere.
While the level of automation associated with food production facilities varies immensely, one thing rings clear. The more limited the automation, the greater the risks of human error, which can jeopardize food safety and result in operational downtime, as well as waste. On the flipside, implementing high levels of automation through machine learning and artificial intelligence (AI), and adopting a predictive or prescriptive approach to asset management and maintenance can substantially reduce this risk. This also minimizes product recalls, while enabling organizations to protect their reputation, manage resources, and maximize profits at the same time — a triple win for manufacturers.
The big data challenge
It’s not uncommon to hear about recalls involving the risk of metal contaminants in products. Typically, this type of recall is synonymous with machine failure and a theoretical risk that batches of food products have been compromised. The problem is that machine insights are often only collected at the end of the day, rather than in real-time, resulting in a lag which quickly translates into a bottleneck, and reputational and logistical risk.
This is critical, as real-time data and insights enables manufacturers to spot anomalies and disruptions before they become big, debilitating problems, rather than after the damage has already been done. For starters, sensors across machines and production lines can provide organizations with real-time data for live insights and updates.
This provides an opportunity to map live data with historical and third-party information, empowering companies with the insights necessary to make informed decisions quickly. The level of granularity of insight which can be obtained using such sensors is invaluable. For example, vibrations on machines can be monitored to detect the extent to which bearings morph from round to oval shapes, thus impacting the reliability of the piece of equipment and predicting the point at which it is likely to fail or require repair.
These insights provide data on operatives displaying signs of wear-and-tear and indications of contamination, providing an opportunity for manufacturers to reduce this risk. Predictive maintenance software can also help identify the appropriate cleaning intervals to minimise contamination risk, and simultaneously avoid unnecessary cleaning, which may impact a machine’s lifespan.
This is also where Maintenance 4.0 comes into the picture. Similar to the Industry 4.0 evolution, Maintenance 4.0 captures this sensor data in a ‘data lake’ repository, and applies intelligent algorithms and analytics to better interpret why assets fail, when a given asset will fail, and how to correct the problem — all within a live environment. These insights adviseand equip organizations with the necessary precautions to avoid asset malfunction, which can result in waste, contamination, and operational downtime.
Enabling real-time data and insight is crucial. It allows manufacturers to adopt a predictive and proactive, rather than reactive, approach to disruptions or equipment failures. This shift from reactive maintenance, which is often considered the easiest yet costliest option in the long term, to a condition-based, predictive strategy is increasingly imperative for success and safety in the food and beverage industry, enabling companies to effectively detect and forecast risks and potential disruptions before, rather than after, the damage is done.
Ultimately, this helps in reducing wastage and improving standards overall, paving the way for companies to expand their market share. The time for transformation is now If the past two years of rampant disruption have taught us anything, it’s that transformation is no longer a question of ‘what’ or ‘why’ but ‘when’. As it is, we are seeing a convergence of IT, operations, and business across the sector, to scope out manufacturing and processing models which adopt an agile and predictive approach to mitigate potential disruptions.
This empowers food manufacturers to leverage smart software and Maintenance 4.0 technologies to drive intelligible, in-depth analysis and insights that refine reliability and performance across their operations, raise the safety standards of their processes and products, and minimize the risks of product recall overall.
At the end of the day, manufacturers only stand to benefit from transforming their operations, especially as they seek to protect the customers they serve and propel their businesses forward in a time of widespread uncertainty.
*References available upon request
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