The Real-Time Value of Predictive Maintenance
What’s the cost of machine downtime in the factory environment? It depends on how you calculate it. Lost production, labor costs for idled workers, inventory expenses — these are all direct costs. But there are indirect costs, too, that can be even more devastating… including loss of customer confidence.
Your costs will depend on your company’s unique circumstances, but most manufacturing professionals would agree that machine downtime should be kept to an absolute minimum. And for decades now, preventative maintenance (PM) has been the go-to strategy to avoid costly downtime.
Preventative maintenance is regularly scheduled service based on time and usage. For example: Parts ABC will be replaced on the XYZ machine after it has been in service for 1,000 hours or every six months. The PM strategy is typically more effective than replacing parts when they break and disrupt production.
At least, that’s the assumption. But there are costs associated with PM. It takes machines offline and involves labor expenses for the necessary maintenance work—even when it is scheduled to minimize disruption. The cost incurred by these issues begs the question: does a schedule built around time and usage make sense?
It did once, when time and usage statistics were all we had to work with in scheduling maintenance. But thanks to the increasing availability of data, we have more options today. Now maintenance managers have access to real-time information on the health and performance of their machines.
This higher data availability has given rise to a more advanced strategy: predictive maintenance, or PdM. With PdM, factories can detect and anticipate potential machine failures, helping to protect worker safety, maintain uptime and contribute to production goals.
A predictive maintenance strategy is made possible by machine-embedded sensors and diagnostic tools like ultrasound, thermography and fluid analysis capabilities. These technologies can let us know how machines are performing — and accurately predict problems before they occur.
So, how can predictive maintenance improve operations over a traditional preventive maintenance strategy? Predictive maintenance can help factories save in two ways.
- PdM typically reduces the amount of time machines are offline and prevents unnecessary replacement of still-serviceable parts.
- Factories can redirect resources formerly allotted to performing PM maintenance to more strategic tasks, and use that time saved to engage in continuous improvement activities to increase overall efficiency.
There are a host of other advantages to adopting a predictive maintenance strategy over a preventive maintenance approach. They include the ability to manage work orders more effectively, improve safety, reduce liability exposure and plan equipment replacement more efficiently.
Companies have become more data-driven across the board, and the benefits of a data-centered approach have improved virtually all facet of operations, from marketing and sales, to product development, to supply chain management. Doesn’t it make sense to use data to modernize maintenance operations too?
The technology to make the leap from PM to PdM already exists. If you’re interested in exploring a transition to predictive maintenance, a good first step is to use the data you have on hand to estimate what equipment downtime is costing you, and how much money you could save with a predictive maintenance strategy.
To learn more about the benefits of predictive maintenance and find out how to adopt a PdM program at your company, download our white paper, “Transforming from PM to PdM: Why Factories Are Making the Switch.”
Jeremy Wright is Director of Product Management for Advanced Technology Services, Inc.