Traditionally, most maintenance professionals have combined many techniques, both quantitative and qualitative, in an effort to identify failure modes and mitigate downtime in manufacturing facilities. But the rise of new connected technologies can enable machines to do these tasks for them, both maximizing the useful life of machine components while still avoiding machine failure.
Today, poor maintenance strategies can reduce a plant’s overall productive capacity between 5 and 20%. Recent studies also show that unplanned downtime costs industrial manufacturers an estimated $50 billion each year. It can be difficult to determine how often a machine should be taken offline to be serviced, as well as weigh the risks of lost production time against those of a potential breakdown. Traditionally, this dilemma forced most maintenance organizations into a trade-off situation where they had to choose between maximizing the useful life of a part at the risk of machine downtime, attempting to maximize uptime through early replacement of potentially good parts, or, in some cases, using past experience to try to anticipate when breakdowns might occur and addressing them proactively.
Traditional components of a maintenance program often fall into four categories, each with its own series of challenges and benefits (figure 1):
- Reactive maintenance
- Planned maintenance
- Proactive maintenance
- Predictive maintenance
The fourth component, predictive maintenance (PdM), has become possible using smart, connected technologies that unite digital and physical assets. While PdM is not a new concept, the massive investments in technology typically needed to handle the massive volumes of data required often limited deployment to only the largest organizations. Today, the high availability and low cost of digital technologies, coupled with the rise of the digital supply network (DSN), have made it possible for PdM to scale on a broad level across facilities and organizations of all sizes. This combination of operations and information technologies can allow deeper analysis of data from the physical world and drive further intelligent action (see the sidebar “Predictive maintenance and the physical-digital-physical loop” to learn more). In PdM, data gathered from connected, smart machines and equipment can predict when and where failures could occur, potentially maximizing parts' efficiency and minimizing unnecessary downtime. In most cases, this means that PdM is the most efficient maintenance strategy available—a gold standard for which to aim.4 In this way, PdM is often considered a critical capability in the age of the DSN.
In this paper, we examine PdM: its role in the DSN, its impact and potential benefits, the technologies that underpin it, and its typical role in the smart factory. We define strategies for how to incorporate PdM into a wider asset maintenance strategy, explore some of the challenges of PdM implementation, and examine the organizational changes that can make a transition to PdM successful. Finally, we delineate a few ways to get started in implementing PdM as part of the asset maintenance strategy for the smart factory.
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Article written by Deloitte Consulting LLP.