How big data can improve manufacturing
In the past 20 years or so, manufacturers have been able to reduce waste and variability in their production processes and dramatically improve product quality and yield (the amount of output per unit of input) by implementing lean and Six Sigma programs. However, in certain processing environments—pharmaceuticals, chemicals, and mining, for instance—extreme variability is a fact of life, sometimes even after lean techniques have been applied. Given the sheer number and complexity of production activities that influence yield in these and other industries, manufacturers need a more granular approach to diagnosing and correcting process flaws. Advanced analytics provides just such an approach.
Advanced analytics refers to the application of statistics and other mathematical tools to business data in order to assess and improve practices. In manufacturing, operations managers can use advanced analytics to take a deep dive into historical process data, identify patterns and relationships among discrete process steps and inputs, and then optimize the factors that prove to have the greatest effect on yield. Many global manufacturers in a range of industries and geographies now have an abundance of real-time shop-floor data and the capability to conduct such sophisticated statistical assessments. They are taking previously isolated data sets, aggregating them, and analyzing them to reveal important insights.
Consider the production of biopharmaceuticals, a category of healthcare products that includes vaccines, hormones, and blood components. They are manufactured using live, genetically engineered cells, and production teams must often monitor more than 200 variables within the production flow to ensure the purity of the ingredients as well as the substances being made. Two batches of a particular substance, produced using an identical process, can still exhibit a variation in yield of between 50 and 100 percent. This huge unexplained variability can create issues with capacity and product quality and can draw increased regulatory scrutiny.
One top-five biopharmaceuticals maker used advanced analytics to significantly increase its yield in vaccine production while incurring no additional capital expenditures. The company segmented its entire process into clusters of closely related production activities; for each cluster, it took far-flung data about process steps and the materials used and gathered them in a central database.
A project team then applied various forms of statistical analysis to the data to determine interdependencies among the different process parameters (upstream and downstream) and their impact on yield. Nine parameters proved to be most influential, especially time to inoculate cells and conductivity measures associated with one of the chromatography steps. The manufacturer made targeted process changes to account for these nine parameters and was able to increase its vaccine yield by more than 50 percent—worth between $5 million and $10 million in yearly savings for a single substance, one of hundreds it produces.