**London**: As the food and beverage manufacturing sector confronts urgent workforce shortages and an ageing technician pool, companies are increasingly turning to predictive maintenance strategies and advanced digital tools to optimise operations and extend machinery lifecycles, according to industry experts and recent data reports.
In the ever-evolving landscape of food and beverage manufacturing, executives and plant personnel are increasingly recognising the need for substantial capital investments to extend the lifecycles of machinery and enhance production uptime. This shift in understanding is driven by a combination of workforce limitations and the adoption of digital tools, including machine learning technologies.
Industry data reveals pressing challenges in the sector. A recent report from Deloitte and the Manufacturing Institute predicts that manufacturers may require up to 3.8 million new workers by 2033, with almost half of these positions likely going unfilled if current trends continue. Compounding this issue is the ageing workforce; with the average age of highly skilled workers sitting at 56 years old, nearly a quarter of these professionals are expected to retire in the next decade.
As manufacturing firms battle these workforce challenges, the drive towards predictive maintenance strategies is gaining momentum. Ronak Macwan, senior industry marketing manager for manufacturing at Brightly Software, noted, “Predictive maintenance and related technologies have gained more traction in recent years, but there is a long road ahead as most manufacturers still do not understand this new technology and its potential positive impact on their day-to-day operations.” The need for an evolved maintenance approach is echoed by Lucie Dahuron, global marketing cross-segment leader at Schneider Electric, who remarked on the significant shifts in the roles of maintenance technicians over the past five years — from reactive to proactive strategies.
An illustrative case is that of E.A. Sween, a grab-and-go sandwich producer, which implemented Brightly Software’s Asset Essentials CMMS platform in 2022 to manage over 7,000 parts in its operations. Erik Williams, maintenance planning and purchasing supervisor at E.A. Sween, described the company’s prior cumbersome paper-driven processes and the decline in preventive maintenance completion rates amidst increased production demands. The new CMMS platform enabled the company to identify gaps and extend its workforce through data-driven insights, demonstrating the potential of real-time data for optimising maintenance strategies.
Macwan emphasised the crucial role of historical data in developing preventive maintenance strategies, stating, “Historical data plays a significant role in preventive maintenance strategies by providing detailed information on each asset and piece of equipment…” The sophisticated use of data analytics equips firms to set short maintenance windows and efficiently oversee maintenance tasks, streamlining operations significantly.
The trend is not limited to smaller manufacturers. Larger companies, such as Ajinomoto Health and Nutrition North America, are refining machine data and adopting predictive maintenance strategies. Meg Lashier, a senior production coordinator at Ajinomoto, detailed their use of AVEVA’s PI historian coupled with machine learning solutions to monitor data for anomalies. This proactive approach allows the team to anticipate potential process disruptions, thus facilitating timely maintenance actions during planned downtimes, which are less costly than unplanned interruptions.
Ajinomoto follows a structured method for their predictive maintenance strategy: sorting historian data points hierarchically and contextualising data within their process framework. They also prioritise assets based on sensitivity ratings, ensuring that critical equipment receives timely alerts for even minor deviations from normal operating ranges. This proactive stance is further exemplified by a recent incident involving a fluidised bed dryer, where timely alerts enabled the team to identify and rectify a significant blockage before it could cause broader production issues.
As manufacturers large and small look ahead to 2025, there are opportunities to achieve both quick wins and substantial capital investments in predictive maintenance capabilities. This evolving paradigm not only highlights the need for comprehensive data governance and acquisition but also underscores the vital connection between asset lifespan management and capital planning within the manufacturing sector.
Source: Noah Wire Services
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