Supply Chain Management and Risk Management in an Environment of Stochastic Uncertainty (Retail)

Abstract

In the context of stochastic uncertainty and the increasing complexity of logistics processes in the retail sector, managers face a problem in obtaining accurate forecasts for the dynamics of changes in key business performance indicators. The purpose of the present work is to assess the impact of risk events and unstable conditions on the level of quality of supply chain services and economic indicators of the retail trade network. Using the anyLogistix software tool, a simulation model was constructed that allows assessing operational risks and their impact on key indicators of the supply chain using the bullwhip effect. Besides, a statistical model of the impact of the ripple effect in the event of failures caused by the occurrence of a man-made risk event and the shutdown of production of one of the suppliers on the financial, customer, and operational performance indicators of the supply chain of grocery retail. The results obtained show that the main factors of changes in the supply chain are operational risks associated with fluctuations in demand and order execution time by the distribution center. With a sufficiently high level of occurrence, their impact on productivity and quality of service is low because they can be eliminated in a short time. The simulation results show that the most tangible risks for the food retail supply chain are supply chain failures, whose consequences require significant coordinating efforts and longer recovery times, as well as additional investments. For example, events, such as a fire in one distribution center and the shutdown of production for 1 week of one of the suppliers of dairy products will lead to the loss of USD 181.75 million by the grocery retailer, which is 3% of the expected revenue. We believe that risk management in supply chains is becoming increasingly complex, and to make effective managerial decisions, it is necessary to constantly improve the tools that combine analytical and optimization methods, as well as simulation modeling.

Keywords

• risk management
• supply chain modeling
• stochastic uncertainty
• risk event
• simulation model
• threats