An Inventory Model Incorporating Interval-Valued Generalized Trapezoidal Bipolar Fuzzy Numbers in EOQ and JIT Frameworks

In today’s dynamic business environment, inventory management plays a pivotal role in maintaining a company’s competitiveness and profitability. Decision-makers frequently face complex real-world challenges, where uncertainty arises from both optimistic and pessimistic outlooks. This dual nature reflects the different perspectives a decision-maker may hold during the decision-making process. To address this, the present study introduces concepts within a bipolar fuzzy environment. Specifically, the interval-valued generalized trapezoidal bipolar fuzzy number (IVGnTrBpFN) is introduced, and its defuzzification is formulated using the $$\left(s,t\right)$$ s , t -cut method. This approach is implemented in an effective inventory model that analyzes the vendor’s optimal cost under the just-in-time (JIT) purchasing system and the economic order quantity (EOQ) technique. Several studies have compared EOQ and JIT inventory management methods, generally showing that JIT is more suitable for dynamic and cost-sensitive environments. While EOQ focuses on minimizing inventory costs through optimal ordering, this study addresses such challenges by incorporating IVGnTrBpFN into a multi-product inventory model, providing a more flexible and realistic analysis under uncertainty. The vendor’s inventory model is examined, where the vendor procures multiple products from various suppliers to meet retailer demand. The total cost for the vendor using the economic order quantity restocking method and the just-in-time purchasing system is formulated under a bipolar fuzzy environment by considering the demand quantity, holding cost, and transportation cost as IVGnTrBpFNs. In the JIT purchasing system, only the purchasing cost and transportation cost are computed, whereas the EOQ model incorporates these costs along with carrying and ordering costs. This study emphasizes the ever-changing aspect of inventory management and provides valuable insights for businesses to make decisions based on market demand fluctuations. When aiming to minimize costs, it is preferable to implement the JIT technique in situations with low demand and the EOQ technique in high-demand scenarios. The effectiveness of the proposed model is demonstrated through comprehensive numerical examples, sensitivity analyses, and comparative studies.