Moving from a reactive to a proactive, and ultimately predictive maintenance and reliability model is not something that can happen overnight. It takes a focused approach with strong leadership to drive the necessary cultural shifts required to achieve full business value.
In a world where productivity increases are increasingly harder to achieve, who wouldn’t want to reduce machine downtime by 30-50% and increase machine life by 20-40%? According to research from McKinsey, an effective predictive maintenance program can efficiently make these goals possible. Why, then, isn’t every manufacturing CEO clamoring for this capability? More importantly, why is it so difficult to achieve this level of maturity even when we make it a priority?
The answer is simple; the solution is much more complicated. In short, predictive analytics require volumes of high quality, reliable data, and most companies struggle to reach the process maturity required to provide it. Outdated technology can be a limitation. For older facilities, adding sensors to previously “dumb” equipment is a massive and costly effort. But even if the technology is available, often the business processes around maintenance and reliability are not sufficiently disciplined to reach the point where data can be trusted.
Even though the concept of data as an asset is largely accepted, that doesn’t necessarily translate to the behavior changes needed to truly leverage it. Here, we’ll explore the journey from reactive to proactive, as we start the journey to the “holy grail” of predictive maintenance. And we’ll outline some practical steps that can be taken to navigate it.
