Often competition requires to substantially increase quality level of actual business processes or to reach highest quality level immediately for new business processes.

However business processes are often very complex. They consist of cross-linked equipment, logistic network, big, distributed data, data gaps, diverse specialists, and their respective organizations. Process key performance indicators (KPI) often vary in a wide range.

In case of current business processes we reach highest quality by reducing quality variation through Statistical Engineering fast, efficiently, and sustainably.

This we do in steps. First we set a measurable quality target. This can be a quality level or – even better – a limiting value of costs and failure rate, that is not allowed to be crossed. Then we identify top problems, which we solve in projects one-by-one. For each top problem we measure quality variation of the process. Then we analyze fast and efficiently the root cause of quality variation, first by qualitatively identifying the clue (the presumable root cause), then by empirically, iteratively excluding small process quality variations top-down. For this we need the combined know-how of diverse specialists – for instance production, R&D, sales, quality management, and IT – as well as several simple methods and procedures of Statistical Engineering. Expensive recordings of actual processes, which are time consuming and capacity binding, are dispensable. Then we verify the so uncovered root cause and validate a sustainable technical or organizational solution. Afterwards we implement the standardized solution in all comparable and future processes.

In case of innovative business processes we reach highest quality level at low quality variation and at high robustness by Innovation Engineering.

In both cases you get a controlled, capable, and robust business process at target level. This reduces for instance quality escapes, field returns, and warranty costs and improves your image in the eyes of your customer. An additional IT-based monitoring of root causes in the logistic network makes an early detection of failures happen, giving you more reaction time in case of failures, and reducing your non-conformity costs.