" The role of predictive maintenance in enhancing performance: an empirical investigation in a manufacturing company. "

Historically, the reputation of product companies has been created on the foundation of offering premium goods at reasonable costs. The businesses attempted to satisfy clients' requirements as effectively and efficiently as they could by developing “Improvements in equipment maintenance, inventory optimization, and worker health and safety are also sources of value in factories." (McKinsey&Company, 2015). Many industries need effective maintenance process management in order to be profitable and survive over the long run. In fact, most sectors require an effective and efficient maintenance process to ensure asset performance, which is frequently measured in terms of high asset availability, high degree of safety, and good quality. The emergence of the internet of things (IoT) and Industry 4.0 have made possible the interconnectedness between machines and products, and the collection and analysis of data with the use of sensors. A real case study was presented to quantify the savings that result from transitioning from a standard maintenance approach (corrective and preventative) to a predictive maintenance solution. Specifically, the core of the thesis would be a real demonstration of how predictive maintenance contributes to help the companies to improve maintenance performances, especially focusing on the financial metrics. Implemented sensors, analytics and real-time data to help identify existing maintenance issues before an actual malfunction or accident happens. Efficiency is not only cost saving, but it also is waste reduction. The only way to compete effectively for established companies is to start cooperating in an unprecedented way and start thinking in terms of ecosystems (ICT4Executive, 2014).