Managerial risk data analytics applications using grey influence analysis (GINA)

Abstract

We observe and analyze the causal relations among risk factors in a system, considering the manufacturing supply chains. Seven major categories of risks were identified and scrutinized and the detailed analysis of causal relations using the grey influence analysis (GINA) methodology is outlined. With expert response based survey, we conduct an initial analysis of the risks using risk matrix analysis (RMA) and the risks under high priority are identified. Later, the GINA is implemented to understand the causal relations among various categories of risks, which is particularly useful in group decision-making environments. The results from RMA concludes that the capacity risks (CR) and delays (DL) are in the category of very high priority risks. GINA results also ratify the conclusions from RMA and observes that managers need to control and manage capacity risks (CR) and delays (DL) with high priorities. Additionally from the results of GINA, the causal factors disruptions (DS) and forecast risks (FR) appear to be primary importance and if unattended can lead to the initiation of several other risks in supply chains. Managers are recommended to identify disruptions at an early stage in supply chains and reduce the forecast errors to avoid bullwhips in supply chains.