Analytics has completely revolutionized manufacturing processes from the factory floor to the boardroom where decisions are made. Business intelligence insights impact everything from logistics to product management and more.
Manufacturing analytics helps to improve operational efficiency, cut costs, and develop in-demand products. Thanks to artificial intelligence, IoT, machine learning, and automation, factories are smarter than ever. Continue reading to learn about some of the many manufacturing analytics use cases.
Supply Chain Management
Manufacturers rely on their suppliers to enhance product quality, meet market demands, inventory management, and much more. Supply chains are notoriously volatile, often leaving manufacturing companies searching for ways to find alternative solutions to continue shop floor operations.
By using real-time and predictive analytics, manufacturers can predict changes in their supply chains and develop contingency plans to mitigate the effects. Furthermore, real-time data also allows you to see up-to-the-moment prices for raw materials from a myriad of suppliers, enabling you to cut costs and pass the savings on to customers.
When manufacturing machinery is on the fritz, it can bring a halt to the entire process and create unplanned downtimes on the shop floor. The worst part about these stops on the production line is that you often have to keep paying the people on the shop floor to remain idle. That’s why many manufacturing companies are using the power of IoT and machine learning to prevent manufacturing equipment malfunctions.
One of the greatest business cases for manufacturing analytics is predictive analytics for preventive maintenance. With the ability to predict equipment failures before they occur, manufacturers are able to provide predictive maintenance and keep the production line rolling.
Manufacturing Process Efficiency
Manufacturers today collect massive amounts of data for various purposes, and one of the greatest uses for big data in manufacturing is enhancing operational efficiency. Product makers spend millions of dollars every year on quality assurance, but with real-time data, you can find correlations between product quality and different manufacturing processes.
Using big data to fine-tune manufacturing processes also has a positive impact on your company’s bottom line. One of the best ways to increase business value is to trim the fat by eliminating ineffective and inefficient processes. Furthermore, companies use big data to identify more effective ways to deliver products.
Many manufacturers use operational data to create competition between departments. Some companies post real-time data to their dashboards and post dashboards in breakrooms and production areas so team members can see their production data for themselves. There’s nothing better than some friendly, inter-departmental competition to optimize production and operational efficiency.
Changes in demand can have dramatic effects on manufacturing operations. Predictive analytics solutions are used in many cases within manufacturing settings. By using predictive analytics to forecast changes in product demand, you can tailor your production volumes to meet the realities of the market. Data analytics can also help manufacturers to identify new consumer markets and develop plans to deal with product surpluses and demand shortages.
Product Optimization and Development
Of the many big data use cases in manufacturing, product optimization and development is among the most exciting. By collecting data from different data sources, you can get an idea of what consumers are looking for in products and alter your manufacturing processes to meet their expectations.
Today, we live in a world driven by big data insights, and nowhere is this more evident than in the manufacturing industry. Business intelligence enables manufacturing companies to optimize their supply chain, prevent downtimes, optimize production lines, and use predictive models to forecast demand changes. As you can see, there are plenty of use cases for big data analytics in the manufacturing industry.