OEMs who track process data may already have all the information required for significant operational improvements. The challenge is to implement systems that analyze the data for patterns to create actionable insights. This requires long-term focus and a top-down approach. Even incremental investments surrounding particular important manufacturing steps can yield strong results.
In the case of one top pharmaceutical company, more than 200 variables required monitoring for compliance and purity standards. With product yields varying from 50-100%, it became imperative to analyze which factors most influenced the variation. By applying advanced analytics practices to their existing data and locating the parameters with the highest impact on yield, the manufacturer was able to increase production of a single vaccine by 50%. This one improvement resulted in an annual savings of between $5M to $10M.
Any manufacturing facility with highly complex processes, process variability, and capacity limits may benefit from such analysis which can serve to separate them from the competition. Applying advanced analytics to existing data can reduce process variability and waste while also saving time and money. This is particularly critical in the industries of pharmaceuticals, chemicals, and mining where lean manufacturing and Six Sigma techniques may only scratch the surface of addressing process variability.