Development of Sustainable Inventory Control Models for Green Technology under Trade Credit Policy

Abstract

Businesses must provide customers with desired products or services to meet sales targets. Keeping enough inventory on hand is essential for smooth operations. Predicting demand for new goods and services is challenging due to socioeconomic uncertainty in consumer buying choices but creating an accurate inventory model simplifies the study. Inventory management becomes crucial for perishable goods like food and medicine, as their deterioration over time complicates the optimization problem. An optimization model can reduce spoilage risk and maximize revenues by determining ideal order quantity and storage conditions. Traditional profit maximization and cost-cutting constrain industries, leading to environmental degradation. Inventory management processes such as buying, storing, and transferring goods contribute to carbon emissions. Therefore, the government has implemented regulations focusing on sustainable supply chain management and reducing greenhouse gas emissions to tackle global environmental issues. newlineThe thesis discusses the importance of multiple warehouse models to prevent stockouts and considers trade credit policies for enhancing the relationship between the retailer and the manufacturer. Since degradation is an inevitable phenomenon, preservation investments in the models serve to slow down its rate. Government-implemented carbon tax regulations provide a realistic and sustainable environment, while green investments also lower carbon emissions. The proposed study on supply chain models aim to reduce waste in the textile industry and e-waste reduction in the electronics manufacturing sector by examining reserve logistics and e-waste reduction in the textile industry and electronics manufacturing sectors. The model enhances industry inventory management through creative concepts and realistic scenarios, determining optimal supply chain profit or cost expenditures after computing the total expenses of the model. To illustrate the robustness of the model, its sensitivity is analyzed.