Impact Analysis of KPI Scenarios, Automated Best Practices Identification, and Deviations on Manufacturing Processes

Abstract

Data-driven applications are becoming more and more ubiquitous throughout the manufacturing industry. The decision of which projects to start often comes from the input of process experts who identify a concrete potential for improvement in a certain area. However, a different approach may be taken when it is not entirely clear where to start looking for patterns or potential information which can trigger continuous improvement activities. This is specially relevant in manufacturing processes with a high level of maturity and stability. In this article, the authors propose a generic approach for conducting impact analysis with a use case which aims to deconstruct the Overall Equipment Effectiveness (OEE), a quite known Key Performance Indicator (KPI), in a manufacturing production line from a Bosch plant located in Portugal. This methodology is focused on identifying the best and worst scenarios by creating a ranking and subsequently pinpointing possible causes, identifying best practices and devising strategies to deal with these non-optimal scenarios. The methodology can be seen as an alternative to complex statistical hypothesis testing by relying on measuring several distribution differences.