3 Big Data Use Cases for Manufacturing Analytics
Penned by Bala Deshpande, Managing Partner at SimaFore, an NCMS partner, “3 big data use cases for manufacturing analytics” states the case for Manufacturing Overhead and Labor cost tracking, Fault prediction and preventive maintenance, and Cost modeling and cost forecasting.
We have executed a multitude of data science projects in the manufacturing vertical. While most of them were done without the help of big data tools, we think most of them will require such capabilities as we expand the scope of available data. Below we run through a series of use cases when viewed from a big data perspective.
Use Case: Manufacturing Overhead and Labor cost tracking
One factor that impacts profitability in manufacturing is overhead. Tracking and understanding the root causes behind what drives manufacturing overhead costs can be very challenging. Setting up a data driven process to do this is therefore key. Labor costs form roughly 30-40% of overall manufacturing costs, particularly for small and medium businesses. Understanding which are productive activities and which are overhead raising ones will impact the overall profitability of the business.
What big data would imply: Collecting and integrating data from disparate sources is one of the requirements for developing a tracking process for manufacturing overhead costs. However the data flow does not exactly cry out for big data tools. Capturing accurate labor costs on the other hand would be the impossible without any big data capabilities. This gets into the realm of IoT, where tracking devices located at various workstations through out the factory floor would communicate with employee badges (for example) to identify which activity a given employee is currently involved in. Collecting, aggregating and developing visualizations of this data would certainly require distributed processing and computing.