Analyzing the Risks in Supply Chain Information System Implementations

Analyzing the Risks in Supply Chain Information System Implementations

Kunal Ganguly (Indian Institute of Management Kashipur, Kashipur, India) and R. K. Padhy (Indian Institute of Management Kashipur, Kashipur, India)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/IRMJ.2018040101


Organizations make considerable efforts when implementing Supply Chain Information systems (SCIS) to increase their competitiveness. This chapter attempts to identify the Risk Factors (RFs) in SCIS implementation and evaluate them. Sixteen risk factors were identified based on an extensive literature survey. A comprehensive framework is presented with three major phases to select an adequate SCIS. The risk assessment for SCIS implementation is then empirically investigated. The RFs are formulated as hierarchy structures and Fuzzy AHPs as a Multi Attribute Decision Making (MADM) tool applied to judge the viable candidates. Based on a fuzzy AHP approach, a revised risk matrix with a continuous scale is proposed to assess the RFs' classes. The result classifies the risk factors in different categories (Extreme, High, Medium and Low). The revised risk matrix with continuous scale for risk assessment in SCIS implementation is a novel approach.
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2. Supply Chain Information System

Christopher and Juttner (2000) argue that to develop more effective and longer-term relationships among the members of the supply chain, it is important to have better collaboration and integration among them. SCM systems have been categorized by Talluri (2000) into three domains, i.e., strategic, tactical, and operational planning systems. He applied mathematical goal programing for optimizing the solution. But the big debate remains that all the attributes may not be quantifiable. This forces the attributes to be restricted mainly to the quantitative factors, such as costs, benefits, and time factors as shown by Sarkis and Sundarraj (2000). Beach and Muhlemann (2000), Sohal et al. (2002) have adopted techniques like exploratory method, case studies, and meta-methodology to avoid such problems. A SCIS selection and implementation is a group multiple attribute decision-making (MADM) problem where some measures may not be easily quantifiable with exact numerical values. In such cases the decision makers may be more comfortable to express their ratings in natural language rather than in numbers (Chen and Hwang, 1992).

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