Data—produced by shop-floor scanners and other hardware tools—can now be used to more accurately measure and improve the performance of production-line machinery. Such benefits are expected to spur annual spending by global manufacturers on data management and analytics to nearly $20 billion by 2026, up from $5 billion this year.
Advanced data tools will give factories a clearer view of operations and equipment performance, allowing them to speed up production, reduce waste, improve their product quality and avoid downtime by more quickly identifying maintenance issues, among other things. Factories will also be able to identify and extract relevant data sets to feed into artificial intelligence software designed to predict production and supply chain problems. “It’s a case of going from reactive analytics, reporting on what happened, to proactively analyzing what might happen and the suggested actions to take,” said one industry expert.
The pandemic has made manufacturers aware of the need for more sophisticated ways to monitor operations, especially when plants are accessible to only a handful of workers. “We’re working with clients on taking unprecedented amounts of data and deriving insights that can shift decision-making,” said the CIO of NTT Data Services, referring to streams coming from shop-floor sensors, machinery, supply-chain fleets and other systems. Manufacturers are using that data to get a better view of equipment performance and maintenance needs, quality control and workplace safety.
Classroom discussion questions:
- What is the difference between descriptive, predictive, and prescriptive analytics (see Module G in your Heizer/Render/Munson OM text)?
- Which of these methods is discussed in this article? Why?