Business Intelligence Architecture for Process Quality Monitoring with BDD

Abstract

The Industry 4.0 vision aims for high-quality and flexible production processes that are automated with Cyber-Physical Production Systems (CPPSs) to address changes in demand and the environment. Process Quality Monitoring (PQM) shall ensure the desired process quality and low delay in reacting to deviations towards undesired process outcomes. Digital Twin (DT) functions mend CPPS limitations to monitor conditions in a multi-domain environment, including the physical system. However, it remains unclear how to elicit the tacit and scattered knowledge required to specify conditions for effective PQM under uncertainty. This paper introduces the approach Process Quality Monitoring with Behavior-Driven Development (PQM+BDD) to (i) represent the business intelligence architecture, i.e., cause-effect knowledge and data, required for PQM of a valuable process outcome and (ii) leverage capabilities of Behavior-Driven Development scenarios to specify key conditions as input to design a PQM information system with DT functions. We evaluated PQM+BDD on the design of a CPPS to explore its feasibility, effectiveness, and efficiency. The results indicate PQM+BDD to be feasible, and effective in comparison to a best-practice approach.