insideAI News Guide to Big Data for Manufacturing

Manufacturing concerns consistently have sought ways to reduce waste and variability in their production processes to dramatically improve product quality and yield (e.g. the amount of output per unit of input). Further, these companies need a granular approach toward recognizing and correcting manufacturing process flaws. Big data technology provides just such an approach and many hightier manufacturers possess a significant degree of interest and motivation in adopting the big data technology stack.  Big data analytics refers to the application of tools based on principles of computer science, statistics, data mining algorithms and mathematics to enterprise data assets with the fundamental goal to assess and improve business practices.  In manufacturing, operations managers can use big data to drill down into historical process data, discover previously unidentified knowledge among discrete process steps and inputs, and then optimize the factors that are shown to have the greatest effect on yield. Many manufacturers across a broad range of industries now have an abundance of real-time shop floor data and the capability to conduct sophisticated statistical learning assessments. They are taking previously siloed data sets, aggregating and joining before analyzing them to reveal key insights.  Even when considering manufacturing operations that are thought to be best in class, the use of big data may reveal further opportunities to increase yield above industry benchmarks. In addition, companies can reduce their waste of raw materials, reduce energy costs and thus increase profitability by rigorously assessing production data, all without having to make additional capital investments or implementing major change initiatives.  The essential first step for manufacturers that want to use big data to increase yield is to consider how much data the enterprise has at its disposal.  Most manufacturers collect vast troves of process data but typically use them only for tracking purposes, not as a basis for improving operations.  The challenge is for these players to invest in the systems and skill sets that will allow them to enhance their use of existing process statistics. For example, it might be prudent to index information from multiple sources so it can be analyzed more easily and hire data scientists who are trained in identifying patterns and drawing actionable business insights from the data.