Abstract

The Fast-Moving Consumer Goods (FMCG) industry in Bangladesh was not ready to resist the repercussions of the worldwide pandemic recently. The pandemic caused the supply chain to become disorganized. Companies must make both micro and macro adjustments to their strategy in order to adapt to the new norm and be ready for future occurrences of this nature. This research seeks to give a paradigm for creating new pandemic defense tactics. This study also examines the facets of imminent supply chain collapse and alters the existing characteristics to overcome the shortcomings. The objective is to build comprehensive guidelines that an FMCG can use to avoid the possibility of such a scenario. The study incorporates short and long-term supply chain restoration policies for material sourcing, manufacturing, and distribution. The process entails identifying the fundamental drivers that are typically affected by the disruption. The short-term proposals serve as corrective agents committed to fighting the interruption as soon as it arises. Long-term ideas, on the other hand, are a type of preventive measure that require more time to implement. However, they focus on building barriers around the brand so that external disruptions cannot impact it in the long run. Among other advantages, these recommendations will result in improved forecasting, responsiveness, real-time monitoring, and cost-effectiveness. In addition, the proposed philosophies were formulated into a Mixed-Integer Linear Programming (MILP) model. The focus of the paper is the formulation of supply chain equations involving ordering cost, holding cost, inbound and outbound transportation cost, downtime, inventory level, response time, and other pertinent aspects. It also houses pandemic and non-pandemic coordination initiatives across the organization. The incurred costs were determined using a formulated objective function and constraints. The presented model demonstrated a 36% cost reduction while maintaining supply chain integrity during disruptions using a deterministic data set. Finally, 25 industry experts were interviewed in two rounds utilizing both structured and unstructured patterns to determine the KPI scores achieved due to the execution of the proposals. The Key Performance Indicators (KPI) were evaluated following a comparison of the costs of conventional and proposed methods. According to the study, the KPI scores for procurement, manufacturing, and distribution were 90%, 86.67%, and 80.30% correspondingly. The models are 85.66% viable overall.

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