Performance Measurement in Global Production Networks to Identify Knowledge Transfer Needs Using Statistical Process Control

Abstract

Due to globally distributed value chains, manufacturing companies are increasingly operating in production networks. The management of these often historically grown networks requires the coordination of many tangible and intangible flows, including the transfer of knowledge between sites. In order to remain competitive in a dynamic market environment, the transfer of in-house production knowledge is essential. In practice, the systematic identification of knowledge transfer needs poses a major challenge for industrial managers, one reason being the high complexity of production networks and thus the large number of knowledge transfer possibilities in the network. However, the transfer of knowledge is always time-consuming and costly. An obstacle is to identify if there is a significant need to initiate a knowledge transfer. For this purpose, it should be examined weather the performance within the production network differs significantly or not. This paper presents an approach to measure performance differences in global production networks to identify knowledge transfer needs. Therefore, a hierarchical performance measurement system, consisting of key performance indicators (KPIs) and focusing on four performance dimensions (quality, time, flexibility, and efficiency), is introduced. Depending on the hierarchical level of the production network (workstation, production system or factory segment), a specific set of KPIs is selected and calculated based on production data acquired by different information systems. In a next step, statistically significant deviations are identified using statistical process control (SPC). Control charts are used to examine the stability and thus to identify knowledge transfer needs. The analytical approach is validated with data from a real industrial case study.