Predictive Maintenance Taking pro-active measures based on advanced data analytics to predict and avoid machine failure

"In the environment of Industry 4.0, maintenance should do much more than merely preventing downtimes of individual assets. Predicting failures via advanced analytics can increase equipment uptime by up to 20%."

Knowing well ahead of time when an asset will fail avoids unplanned downtimes and broken assets. On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%. It is based on advanced analytics and marks a new way of organizing and implementing maintenance on an industrial scale. Deloitte has developed an approach to smoothly introduce predictive maintenance into business processes in a customized and structured manner.

Industry 4.0 – the proclaimed fourth industrial revolution – is unfolding at the moment. It is characterized by interconnectedness and vast amounts of available information. Productivity has risen continuously due to modern machines. They are highly complex and often represent substantial investments. In 2015, German automotive OEMs alone have invested over 14 billion Euros in tangible assets1 . Despite all efforts to prolong lifecycles, wear, erosion and depletion will eventually lead to machine failure. The Value of Predictive Maintenance In the environment of Industry 4.0, maintenance does much more than merely preventing downtimes of individual assets. Machines are increasingly interconnected along the production chain. One failing machine might halt the whole production process. Today, poor maintenance strategies can reduce the overall productive capacity of a plant by 5 to 20 percent2 . Long and continuous runtimes of capital-intensive, highly-integrated assets can represent a significant competitive advantage. So can efficient and well-orchestrated maintenance. Deloitte has identified six core dimensions of maintenance and failure to be managed.