A decision algorithm for evaluating supply chain learning in small and medium-scale enterprises Online publication date: Mon, 14-Jul-2008
by Jitesh Thakkar, Arun Kanda, S.G. Deshmukh
International Journal of Value Chain Management (IJVCM), Vol. 2, No. 3, 2008
Abstract: This paper proposes a novel quantitative approach for diagnosing supply chain learning potential, specifically, for the context of Small and Medium-scale Enterprises (SMEs). The proposed algorithm is based on Multi-Criteria Decision-Making (MCDM) techniques like Analytic Hierarchy Process (AHP) and Technique for Order Preference and Similarity to Ideal Solution (TOPSIS) and inferences derived from the diverse field of knowledge, like mechanics and mechanisms. Initially, 25 learning factors were extracted from the literature, which were related to the various learning characteristics of SMEs. The AHP model is run in two cycles. The first cycle prioritises the various supply chain planning areas, while the second cycle deals with the prioritisation of the various supply chain learning links. An application of TOPSIS is employed to prioritise various supply chain ethics for the case organisation. It is expected that the decision algorithm will help SME managers understand their supply chain learning orientation and planning needs.
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