Extending the EOQ Model: A Mathematical Framework for Incorporating Real-World Constraints (Published)
This paper aims to seek ways on how EOQ model can be improved through integration of realistic constraints like stochastic demand and variable lead times. The conventional EOQ models, as they are developed, do not fully capture the dynamics of managing inventories in contemporary businesses, resulting in higher costs and reduced effectiveness of operations. The objective of this research is to design a mathematical model that will incorporate all these constraints so as to enhance inventory efficiency. From this study, we use data obtained from a mid-sized retail company to examine performance indicators such as inventory turnover, stockout rates, and total inventory costs before and after the application of the extended EOQ model. A direct percentage rise of 37.78% in inventory turnover and 46.67% drop-in stock rate are some of the findings highlighted by the research. In addition, the importance of utilizing data analysis and future technologies is stressed for real decisions improving the organization’s reaction to market fluctuations. However, the study also outlines several disadvantages tied to implementation of the practice, including financial investments and training of the employees. These issues highlight the need for implementing more sophisticated approach to inventory management that also addresses sustainability, making the extended EOQ model an ideal solution for organizations that aim at operating efficiently and being competitive within the contemporary business context. The expansion of the proposed model to various industries for further validation of the effectiveness of the implementation of the model together with the improvement of the methods incorporated into the model will form the basis of the future research studies.
Keywords: Inventory Management, data analytics, economic order quantity (EOQ), real-world constraints, stochastic demand, supply chain optimization, variable lead times