A Confirmatory Approach To Measuring Risks In Supply Chains

In order to achieve the desired performances and the increased value added to the final consumer, organizations need not only to integrate their core businesses and align them to business strategy but also to handle disturbances in their environment. Existing literature on supply chain management underlines the need for adequate risks management in supply chains. Supply chain risks, if not managed properly do have a negative impact on business performance (Shah, 2009, Florian, 2013, Hendricks and Singhal, 2005). Supply chain risks management (SCRM) emerged as a response to the increasing volatility in today's global supply chain environment. We first review the existing theoretical framework for identification of risks in the supply chains. There is general agreement on the general framework for coping with risks in the context of supply chain. Thus SCRM involves (a) risks identification, (b) assessment, (c) mitigation and (d) responsiveness (Wagner and Neshat, 2012).Also it is generally accepted that supply chain integration and lean management are the main strategy for reducing uncertainty whereas agile supply chains and quantitative modeling are the main solutions to coping uncertainty. The empirical researchfocuses on measuring risks in supply chains. We propose a confirmatory factor analysis of the measurement model of risks in supply chains. Existing literature on confirmatory factor analysis agrees that this technique provides extensive possibilities to analyze the complexity of the relationships among the variables. Results show that all estimated coefficients corresponding to indicator variables are statistically significant and have the desired positive sign. Also all the estimated variances and covariances among latent variables are statistically significant. Our proposed research methodology reveals the advantages of a confirmatory factor analysis over an exploratory principal component analysis in the context of risks management in supply chains. Moreover, as Sodhi and Tang (2012) reveals, more than half of papers in existing SCRM literature are either conceptual or qualitative empirical (case studies). Our proposed quantitative methodology contributes to reducing the above mentioned gap, providing results that can be used for statistical inferences and for enhancing the efficiency of the managerial decisional process.