Dynamic Economic Order Quantity (EOQ) Model Integrating Sustainability and Industry 4.0 for Inventory Cost Efficiency

Abstract

Efficient inventory management has become a critical challenge for the manufacturing industry due to market demand fluctuations, digital transformation, and sustainability pressures. This research proposes a dynamic EOQ framework that integrates Monte Carlo simulation, sustainability principles, and Industry 4.0 technologies to support adaptive inventory decision-making.The research employs a quantitative exploratory approach utilizing operational data from a corrugated box manufacturing company in Surabaya, Indonesia. A Monte Carlo simulation with 1,000 iterations is used to model stochastic demand uncertainty and determine the optimal order quantity.The results show that the proposed dynamic EOQ model can reduce inventory costs by approximately 8–12 percent compared to the conventional EOQ approach. The optimal order quantity is identified as 70,890 kg per cycle, while the integration of sustainability principles successfully reduces material waste by around 5 percent per production cycle. This research contributes theoretically by integrating sustainability concepts and Industry 4.0, including the Internet of Things (IoT) and closed-loop supply chain concepts, into inventory management optimization. This integration has proven to improve resource use efficiency and reduce production waste. The findings confirm that implementing a sustainability- and digital technology-based EOQ model can be an effective strategy to enhance economic efficiency while supporting environmental responsibility in the manufacturing industry in the Industry 4.0 era. Practically, the research offers a more adaptive inventory control framework for the manufacturing industry