Operational complexity and supplier-customer integration: case study insights and complexity rebound

The main contribution of this paper is the demonstration that, contrary to conventional thinking, a measurable increase in the operational complexity of the production scheduling function between two companies can occur following closer supply chain integration. The paper presents the practical application of previous work carried out and validated by the authors in terms of (a) methodology formeasuring operational complexity, (b) predicted implications of Supplier-Customer integration and (c) derivation of an operational complexity measure applied to before and after Supplier-Customer integration. This application is illustrated via a longitudinal case study. The analysis is based on information theory, whereby operational complexity of a Supplier-Customer system is defined as the amount of information required to describe the state of this system. The results show that operational complexity can increase when companies decide to integrate more closely, which is a fact likely to be overlooked when making decisions to pursue closer Supply-Chain integration. In this study, operational complexity increases due to reduced buffering arising from reduction in the Supplier's inventory capacity. The Customer did not change their operational practices to improve their schedule adherence post-integration, and, consequently, suffered an increase in complexity due to complexity rebound. Both the Supplier's and Customer's decision-making processes after the case study reported in this paper were enhanced by being able to quantify the complex areas to prioritise and direct managerial efforts towards them, through the use of the operational complexity measure. Future work could extend this study (in the 'low product customisation' and 'low product value impact' quadrant) to investigate Supplier-Customer integration in other quadrants resulting from further combinations between 'product customisation' and 'product value impact' levels. Journal of the Operational Research Society (2010) 61, 1709-1718. doi:10.1057/jors.2009.138 Published online 25 November 2009