Strategic fit in global production networks – A decision support model for strategic configuration of global production networks

Abstract

The configuration of global production networks is a highly complex management task. It involves decisions like site selection, site specialization, and long-term investments. Entering new markets, securing resource access and realizing cost advantages are only examples for the vast variety of strategic motives overlapping in these decisions. However, current material bottlenecks, geopolitical risks, and restrictions due to COVID-19 are increasingly bringing risk mitigation into the focus of decision-makers. In practice, an optimal configuration of the network in line with the production strategy, which is referred to as strategic fit, is rarely considered systematically. Approaches from academia do provide a variety of decision support models. However, these either consider the production network very aggregated and qualitative and are thus limited in their explanatory power, or they only focus on partial decisions, which are only evaluated based on costs. The aim of this paper is, therefore, to provide a decision support model for the configuration of the production network in line with the production strategy. The model derives an optimal, company-specific configuration based on strategic capabilities that constitute both the production strategy and the internal and external company environment. The procedure comprises six steps and is divided into an as-is analysis and a target derivation. In the as-is analysis, the current configuration is first recorded with description models. Subsequently, current strategic capabilities as well as the company environment are captured. Based on this data, a fuzzy inference system assesses the actual strategic capabilities of individual plants and the entire network. The strategic capability gap forms the starting point for the target derivation. First, a strategically fitting network phenotype is selected to provide guidelines for further configuration. Then, capability-oriented design principles are used to further elaborate the phenotypes. Subsequently, the resulting alternatives are evaluated with a fuzzy-TOPSIS evaluation model.