"Predictive analytics in the manufacturing industry: Niche topic or mainstream?" This is the question that Pierre Audoin Consultants (PAC) asks in a current study. The analysts at PAC come to the conclusion that the technology has grown out of its infancy and is now a driver of Industry 4.0. Blue Yonder was the main sponsor of the study.
Processes in manufacturing enterprises grow quickly, but the values that enterprises are measured by remain constant. Reliability is the central requirement of customers and their suppliers. Commitment to deadline as well as high quality are a part of that, and that includes the processes. About 65 percent of the participants in the PAC study believe that manufacturing enterprises can increase their ability to meet deadlines using predictive analytics. In about every third enterprise, the future-focused analysis of big data is already viewed as a key technology.
An additional figure makes clear that predictive analytics solutions are increasingly a key factor for enterprises: About 70 percent of those asked expect of this technology that it will boost Germany's innovative power, and about 40 percent of the enterprises are already implementing these types of solutions. On the other hand, about 60 percent of production managers view it as very time-intensive to implement the software and to integrate it properly into their business processes. As a SaaS provider of predictive analytics, we at Blue Yonder take that cost very seriously. Not just our reference model, but also the individual forecast solutions are designed to release their full power in daily business life.
We help manufacturing companies realize Industry 4.0, step by step. 60 percent of the PAC study participants view predictive analytics as a driver of network-enabled manufacturing. We are convinced that today, big data analytics is a prerequisite for the intelligent factory. This is because data that is generated in manufacturing only support the enterprise in making the right decisions when they are analyzed in real time and when decision-making is automated using them. When that happens, enterprises manage their full productive resource use across their locations precisely and plan maintenance as well as their spare parts service optimally. The result: a boost of between one and five percent in increased productivity as well as significant cost savings.