Strategic decisions in supply‐chain intelligence using knowledge management: an analytic‐network‐process framework

Abstract

Purpose

To investigate the linkage between organization performance criteria and the dimensions of agility, e‐supply‐chain drivers and knowledge management.

Design/methodology/approach

The analytic network process is applied as the research methodology in the context of executive decisions that include qualitative and quantitative attributes. The decision model is presented, along with a case study with an e‐supply chain of a global telecommunications company.

Findings

The study develops a framework for measuring the relative importance of a particular dimension based on the application of theoretical concepts from the information systems and management science literature to the digital, knowledge economy. Since contextual factors play a critical role in the design of effective knowledge‐management (KM) systems, technical and process solutions need to be customized to fit the organization performance criteria, dimensions of agility and supply chain drivers.

Research limitations/implications

The model presented is dependent on the perceptual weightings provided by the decision‐maker and the generalizability of findings based on our model to other organizations may be limited.

Practical implications

This paper addresses the need for a strategic decision‐making tool to assist management in determining which knowledge management construct is most beneficial in the development of an agile supply chain.

Originality/value

This paper fulfils an identified information need and offers practical help in a dynamic and competitive environment by providing a decision model that assists in determining which construct of KM is most important based on an organization's performance criteria, dimensions of agility and supply‐chain drivers.

Citation

Raisinghani, M.S. and Meade, L.L. (2005), "Strategic decisions in supply‐chain intelligence using knowledge management: an analytic‐network‐process framework", Supply Chain Management, Vol. 10 No. 2, pp. 114-121. https://doi.org/10.1108/13598540510589188