A Methodology for Assessing the Value of Knowledge in a Service Parts Supply Chain

In this paper, we investigate the value of knowledge [V(K)] in a service organization where knowledge is strictly uncodified and used as a supplement to data and information in the decision logic in a service parts replenishment problem. An earlier case study by the authors has revealed that automotive service parts managers often have considerable knowledge about special supply chain events that alter demand-such as a recall for a failing component, production rate changes, and discontinuation notices. Likewise, service parts managers know about local conditions that will influence service part sales, including weather conditions and changes in the service and body shop schedules. In this paper, we develop a methodology for assessing the value of this knowledge, including a service parts demand model with parameters that are estimated and updated using a Bayesian approach. We demonstrate how the V(K) can be quantified via a simulation of the effect of knowledge in the replenishment process for six selected service part categories. As the V(K) is an important input to the economic justification of information technologies, the development of a methodology that quantifies its value is an important contribution to the management of information systems.