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 automot...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2008-05, Vol.38 (3), p.446-460
Main Authors: Meixell, M.J., Shaw, N.C., Tuggle, F.D.
Format: Article
Language:English
Subjects:
Applied sciences
Automotive components
Automotive engineering
Bayesian forecasting
Bayesian methods
Categories
Computer science
control theory
systems
Economics
Exact sciences and technology
Information systems
Information systems. Data bases
inventory management
Job shop scheduling
Knowledge management
knowledge management (KM)
Logic
Logistics
Marketing and sales
Memory organisation. Data processing
Methodology
Operational research and scientific management
Operational research. Management science
Parameter estimation
Production
Replenishment
service parts management
Software
Studies
Supply chain management
Supply chains
value of knowledge [V(K)]
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Summary: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.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2008.919175