Because quality control evaluates whether processes are performing in an acceptable manner, in an operational management environment, it is key to making sure products, designed and manufactured, are produced to meet and exceed the needs of customers with minimal cost derived from unnecessary variability, rejects and bottlenecks.

Statistical process control (SPC) discovers departures from randomness and variation in the process, taking corrective action when output doesn't meet predetermined standards, and is a key technique for process engineers at manufacturing lines. Yet, many SPC methods work under assumptions like data normality and looking at process parameters one at a time. Machine learning pushes the boundaries of traditional SPC methods and can enhance their performance significantly, while at the same time being more robust and less prone to human error.

For automated quality control, large amounts of historical data can greatly contribute to increasing the value of this solution. If you do not have sufficient collection mechanisms in place or if they may benefit from a review, we can advise you on how to kickstart or expand your data-collection systems.

Our established experience in Lean Six Sigma and operationalization of advanced statistical techniques, including machine learning, can take your quality control and thus your production to the next operational level:

• Increase orders that meet quality standards by reducing output variability using machine learning to control input parameters automatically without constant human intervention
• Prescriptively enable fully automated stricter (more controlled) upper and lower limits on final outputs, thereby reducing variation and defects, without sacrificing quality
• Gain insights by automatically inferring how output parameters are influenced by input parameters and thereby predict (and potentially prevent) defects before they occur, without human intervention