A Neutrosophic Eoq Model With Green Technology Under Inflation and Trade- Credit Facilities
This paper proposes a neutrosophic two-warehouse inventory model that integrates green technology, preservation investment, and trade-credit facilities under inflation to minimize total average costs, providing optimal solutions and managerial insights through numerical examples and sensitivity analysis.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the complex world of modern commerce, businesses constantly walk a tightrope between keeping shelves stocked and avoiding the waste of unsold goods. This balancing act is made even more difficult by the reality that many products, from fresh produce to medicine, naturally degrade over time, losing value or becoming unusable. To manage this, companies often rely on mathematical models to decide exactly how much to order and when. However, the real world is rarely as predictable as a simple equation. Prices fluctuate with inflation, customers behave unpredictably, and environmental concerns now demand that businesses account for the carbon emissions generated by their storage and transport. Furthermore, the financial landscape is shifting, with suppliers and retailers increasingly using delayed payment terms, known as trade credit, to keep cash flowing. When all these factors—deterioration, environmental impact, inflation, and financial delays—collide, the traditional tools for planning inventory often fall short, leaving managers to guess rather than calculate with precision.
A team of researchers from Siksha 'O' Anusandhan University in India has developed a new approach to this problem, creating a sophisticated inventory model designed to navigate these uncertainties. Their work focuses on a scenario where a business operates two warehouses: a primary one and an additional storage facility to handle high demand. The researchers built a system that accounts for the cost of investing in green technology to reduce carbon emissions and the use of preservation technology to slow down the spoilage of goods. Crucially, they recognized that real-world data is often vague or imprecise. Instead of forcing uncertain values like future inflation rates or customer demand into rigid, fixed numbers, they used a mathematical framework called a neutrosophic environment. This method allows the model to handle three layers of information simultaneously: what is known to be true, what is false, and what is simply uncertain or indeterminate. By doing so, the model can find the most cost-effective strategy even when the future is foggy.
The researchers applied this model to a specific business case involving time-dependent demand, where customer interest changes throughout the sales cycle, and partial shortages, where some customers are willing to wait for out-of-stock items while others leave. They simulated two different financial scenarios regarding credit periods: one where the supplier gives the retailer time to pay, and the retailer gives the customer time to pay, and another where these periods vary. The goal was to find the perfect balance of ordering quantities, investment in green and preservation technologies, and the timing of sales to minimize the total average cost. The study revealed that by using this neutrosophic approach, businesses can achieve a lower total cost compared to traditional models that rely on fixed, "crisp" numbers. In their simulations, the optimized cost for the neutrosophic model was approximately 32,043, slightly lower than the 32,229 found in the traditional crisp model for one of the scenarios, suggesting that accounting for uncertainty leads to more efficient financial planning.
The study also uncovered how specific business decisions ripple through the entire system. For instance, the researchers found that investing more in preservation technology effectively extends the time goods remain sellable, which in turn reduces the need for frequent reordering and lowers the overall cost. Similarly, the distance goods must travel plays a significant role; longer distances increase fuel consumption and carbon emissions, which forces the model to adjust the order size and timing to compensate for these higher costs. Interestingly, the study showed that the amount a business invests in green technology remains relatively stable regardless of changes in other parameters, except for transportation distance. This suggests that a consistent commitment to green investment is a reliable strategy, whereas other variables like the length of the credit period offered to customers have a more nuanced effect. When the credit period for customers is extended, the total cost rises slightly, but the researchers note this can be a worthwhile trade-off to make products more marketable and attract more buyers.
Ultimately, this research provides a clearer path for managers navigating a volatile market. It demonstrates that by embracing uncertainty rather than ignoring it, and by integrating environmental and financial factors into a single planning tool, businesses can make smarter decisions. The model confirms that while inflation and the natural decay of goods are unavoidable challenges, they can be managed more effectively when the planning process acknowledges the "maybe" and the "unknown" alongside the "definitely." The findings suggest that the most efficient strategy involves a careful mix of holding enough stock to meet demand without overstocking, investing in technology that slows spoilage, and maintaining a steady commitment to reducing environmental impact. While the current model assumes certain fixed conditions, such as a constant rate of customer waiting during shortages, it lays a strong foundation for future studies that could incorporate even more complex, real-world variables like changing prices and seasonal demand shifts.
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