Abstract

The aftereffects of the COVID-19 epidemic highlighted the need for efficient data-driven approaches to tracking, comprehending, and mitigating its effects. This study examines the application of statistical forecasting model and inventory management strategies to revitalize business operations during the 2023 economic downturn. Before that, the SCOR Model and Failure Mode and Effect Analysis (FMEA) method are used in a consumer electronic and home appliance company to identify the key supply chain risk components those need to be addressed immediately. Despite such an economic fall, which never happened in previous years, achieving forecasting accuracy and supply assurance with optimized inventory is a long-standing issue. The company achieved a 54% oversell in the first half of 2023 by utilizing regression analysis with a statistical forecasting model, resulting in an additional revenue of $1.7M. Improvements in inventory management, including the deployment of a reorder model and inventory profiling, led to an 85% reduction in slow-paced inventory, a 65% reduction in air shipment costs, less CO2 emission, and optimized inventory levels across over 430 retail outlets. These strategies optimized resources and facilitated the successful and timely launch of new products. The case highlights how data-driven supply chain management can significantly enhance business performance in challenging economic environments.

Joytun Nisa Joti

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Leveraging Forecasting Model with Machine Learning Approach and Deploying Supply Chain Strategies to Revitalize Business Performance During The 2023 Economic Fall