The role of equipment flexibility in Overall Equipment Effectiveness (OEE)-driven process improvement

In manufacturing and assembly operations, Overall Equipment Effectiveness (OEE) is a frequently used quantitative metric for measuring the overall productivity of a single machine, cell or an integrated manufacturing system. However, it does neglect and typically even penalizes flexibility capabilities. Today’s customer needs for highly customized products put these productivity-based measurements more and more under pressure. Frequent product changes on assembly workstations typically result in lower availability through more set-up, more performance losses due to slower cycles and the learning-forgetting effect of operators, and start-up defects resulting in more frequent quality issues. A contradiction arises: in modern production and assembly this flexibility becomes more and more important as an enabler for the mass customization paradigm, but is difficult to incorporate in (or put in relation to) an OEE figure or trend and conflicts with the OEE-driven process improvement strategies. Consequently, it can be argued that flexibility capabilities should be embedded in the equipment effectiveness calculation. Modern manufacturing and assembly cells should have a high equipment effectiveness through a high product mobility with a stable and uniform productivity across the complete range of products. This paper first highlights the importance of flexibility in the measurement of equipment effectiveness to facilitate the mass customization paradigm and to try to continuously improve towards a resilient manufacturing system. Next, the heuristic measurement framework for the Flexibility-included Overall Equipment Effectiveness (OEEFlex) metric is introduced, based on three core indicators: mobility, uniformity and range. The three factors are introduced and described. Links to current OEE measurement frameworks are made. The approach towards the new metric starts from a long list of losses and variables and possible calculation methods for the indicator values. Future research describes illustrative simulation scenarios to filter towards a short list of relevant and valuable calculation options for the overall metric. Followed by an expert based approach towards final selection of the metric and a case based in-company validation of the result