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Improving productivity and profitability are two key areas manufacturers work to perfect. This isn’t always an easy task. Creating preventive maintenance plans has long been an industrial best practice and it ensures that scheduled equipment maintenance and routine cleanings are completed. This practice alone can help reduce downtime, but there is more work to be done. Implementing a predictive maintenance plan may be a good long-term approach when working towards productivity and profitability in your facility.

Preventive maintenance aims to keep you up and running by maintaining equipment at regular intervals. Unfortunately, “regular” may not equal “optimal,” as prevention intervals are typically based on average maintenance needs for a category of equipment, not the exact needs of a particular piece of machinery. Predictive maintenance, however, uses sensors to monitor the precise condition of a specific machine, calling for attention exactly (and only) when needed. This prevents wasting time and money by performing maintenance sooner than necessary, or risking costly breakdowns by performing it too late.

By feeding real-time data from sensors into advanced analytics programs, predictive maintenance helps optimize mechanical function, reduce downtime, extend machine life — and deliver significant savings. Management consultancy McKinsey estimates that the savings to U.S. industries (as a whole) may add up to as much as $630 billion dollars by 2025.

Realizing those benefits will require a combination of new technology, training — and even a new organizational mindset — here are four key considerations that can help your company make the most of predictive maintenance.

1. Don’t assume you have to start over completely.

There’s a temptation to think that taking full advantage of new technologies means getting rid of old systems. While some existing business intelligence solutions may not be capable of integrating predictive capabilities, many can be upgraded and adapted to the task. Challenge your current system provider to demonstrate if and how your existing technology infrastructure can incorporate predictive abilities.

2. Let history be your guide.

Start by reviewing your most common issues, to predict what maintenance will need to be performed. Use historical data and pair it with the real time data from sensor technology. By using both historical and real-time inputs, it will be easier to determine what steps must be taken to keep equipment in tip-top shape.

3. Integrate new predictive capabilities with traditional preventive measures.

The move to predictive maintenance should be evolutionary and still incorporate traditional efforts such as basic scheduled maintenance. Start by creating a clear plan for integrating existing practices with new methods and the transition will have a high probability of success.

4. Make HR as important as IT during the change to predictive.

The shift from preventive to predictive maintenance is as much about a new operational mindset as it is about new technology. Be sure to involve your human resource team from the very beginning, to plan for the training and communication needed to get your workforce to embrace and effectively use these new capabilities.

Improving productivity and profitability is never easy — but it can become more predictable, thanks to operational innovations like predictive maintenance.

SOURCES
Anodot: Predictive Maintenance: What’s the Economic Value?
Enterprise Insights: Three predictive maintenance case studies
EY: What Damage Could Predictive Consulting Prevent?
McKinsey: The Internet of Things: Mapping the Value Beyond the Hype