Maintenance Turns Proactive

Predictive maintenance takes preventive programs a step further by using digital tools to monitor, analyze, and predict asset behavior. © Alexey Stiop –

Preventive and Predictive Practices Solve Problems Before Downtime Occurs

By Tom Egan, Vice President of Industry Services for PMMI, the Association for Packaging and Processing Technologies 

A part fails or shifts out of adjustment, the line stops, and the downtime clock starts ticking. The longer it ticks, the higher the cost because overhead expenses continue but no product is being produced, potentially causing lost sales and impacting revenue, profits, and customer satisfaction. Unplanned downtime also can result in product loss and waste.

Proactive practices can prevent many unplanned stops and related downtime. A preventive maintenance program sets a schedule so failure-preventing tasks are performed regularly. This includes lubrication, component tightening, and replacement of wear parts, which can mitigate some common causes of downtime (see graphic).

Predictive maintenance

Predictive maintenance takes preventive programs a step further by using digital tools to monitor, analyze, and predict asset behavior. With this information, repairs can be performed before unplanned downtime occurs, according to Packaging and Predictive Maintenance, a report published in January 2021 by PMMI, The Association for Packaging and Processing Technologies.

Other benefits of predictive maintenance include longer machine life and a reduction in parts requirements because replacements are installed when needed rather than on an arbitrary schedule.

As a result, a growing number of end users have adopted predictive maintenance programs or are planning to do so. A new report from PMMI, Trends in Remote Services and Monitoring, indicates remote monitoring currently is being used by 37.5% of the end users surveyed, and 18.5% are using the remote monitoring data to support predictive maintenance efforts.

This number is expected to experience strong growth with 50% of the end-user respondents planning to implement predictive maintenance in their plants by 2026. This will mean installing new machines or retrofitting machines, which can self-monitor by collecting performance data such as run time, voltage (especially for motors), speed, pressure, temperature, and vibration and generating an alert if attention is needed.

Predictive maintenance tools

Predictive maintenance relies primarily on three tools: thermography, full equipment monitoring, and computerized maintenance management system software. Other areas of interest noted in another PMMI report, Challenges and Opportunities for Packaging and Processing Operations, include vibration analysis, machine health monitoring sensors, parts room setup and organization, oil monitoring analysis, risk and reliability software, machine-level fault codes, tracking hours of use until downtime, outage planning and scheduled total productive maintenance, risk minimization, best practice, and mean time between failures data.

Predictive maintenance relies on hardware, typically smart sensors, and software/analytics (often hosted in the cloud). The Packaging and Predictive Maintenance report notes new business models are needed to ensure that predictive maintenance technology will optimize equipment performance for end users while generating revenue for original equipment manufacturers (OEMs). The Machine as a Service, or MaaS, model is one option. Instead of buying the machine, the end user makes payments based on output, e.g., the number of cases palletized. With maintenance integral to this business model, it’s possible to minimize downtime and maximize machine life to the benefit of both end user and OEM.

Maintenance organization tools

Another maintenance organizing tool, the Asset Reliability Roadmap for the Consumer Products Industry, developed by PMMI’s OpX Leadership Network, helps end users and OEMs understand common definitions; outlines key performance indicators related to people, operations, and finances; provides useful calculations; and delivers the leadership guidance needed to develop an asset reliability initiative.

With this roadmap, both end users and OEMs gain a better understanding of the need to provide a solid business case for calculating maintenance program costs. This cooperative effort can yield significant improvements in overall equipment effectiveness by identifying actions most likely to minimize the consequences of planned and unplanned downtime.

Prescriptive maintenance

Predictive maintenance is not the last step in the maintenance hierarchy. Its successor, prescriptive maintenance, relies on machine learning. Instead of monitoring machine status and recommending when to perform maintenance, prescriptive maintenance relies on this nascent technology to determine not only when an asset will fail but also how to fix it. After the work has been completed, monitoring continues to confirm the action taken solved the problem, and the tool begins identifying where the next improvement should be.

A range of downtime-mitigating technologies also will be on display at PACK EXPO East (March 18-20, Pennsylvania Convention Center, Philadelphia). The largest PACK EXPO East to date will feature exhibitors offering crossover solutions to many of today’s biggest manufacturing needs for more than 40 vertical markets. Companies will find the convenient and easy-to-access location ideal for teams to attend, assess the latest technologies, learn from leading industry experts, and make valuable connections to meet current or upcoming projects. For more information, visit

About the Author

Tom Egan serves as the vice president of Industry Services for PMMI, the Association for Packaging and Processing Technologies. He joined the PMMI staff in 2003 following more than 20 years in the packaging industry during which he was also an active PMMI member. Opx Leadership Network tools, including the Asset Reliability Roadmap, are available for free download at Learn more at

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