RISK QUANTIFICATION AND MANAGEMENT IN RETAIL WAREHOUSE LOGISTICS THROUGH EVIDENCE FROM TRANSPORT SYSTEM DATA

Abstract

In today’s crowded and speedy retail environment, a dependable way of moving goods is essential to satisfy customers and run the business well. Many uncertainties like delays from traffic jams, broken-down vehicles, bad weather and workers’ mistakes can interrupt and slow down the movement of transportation networks. If a warehouse sends items to various stores, small problems may negatively affect the entire supply chain and harm the business.. The thesis shows how to spot, measure, cut down and oversee risks in the Transport Management System (TMS) of a retail warehouse. Thanks to real transportation data from a retail company, the study finds that the performance of deliveries is largely determined by features like the chosen delivery vehicle, how much experience the driver has, the shipment value, traffic situations, the sensitivity of cargo and any problems during delivery. Since these factors are uncertain, a Monte Carlo simulation model was built by using Microsoft Excel. It examines various scenarios to find out every potential risk and its likelihood. With the study, it is easier to understand what leads to major delays and reveals how often such issues as late deliveries, breakdowns or financial losses will happen—which are all helpful for improving the reliability of our transportation systems. As a result of the simulation, using tools for fleet assignment, scheduling and route planning are encouraged to guard against unforeseen risks. The conclusions of this study provide a cheap tool for practitioners built in Excel which contributes both academically and in practice to the area of logistics risk management. In summary, using simulation along with specially designed risk management principles in retail logistics, the findings of this thesis can easily be implemented in real life. The study gives decision-makers suggestions for making the retail supply chain more reliable, certain and able to withstand tough situations.