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@article{180039,
        author = {Shilpi Singh},
        title = {A Comparative Study of Inventory Models with and without Innovation Diffusion: An Analytical and Numerical Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {10},
        number = {2},
        pages = {1052-1062},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180039},
        abstract = {Traditional inventory models like the Economic Order Quantity (EOQ) are built on the assumption of static, predictable demand. However, in the era of fast-paced technological change and aggressive marketing, many products—especially those in consumer electronics, pharmaceuticals, and FMCG—experience time-varying demand influenced by innovation and social adoption. This paper presents a comparative analysis between two inventory modeling approaches: one based on classical EOQ with constant demand, and another extended EOQ framework incorporating demand dynamics modeled through the Bass innovation diffusion model.
In the first model, demand is assumed to be constant, and the total cost is minimized by balancing ordering and holding costs. In the second model, demand is a function of innovation and imitation, reflecting realistic product adoption behavior over time. We analytically derive the cost functions and optimal policies for both models and then conduct numerical simulations to examine the behavior of total cost, order quantity, and cycle length under varying parameter conditions.
The results reveal that innovation-adjusted models yield more adaptive inventory strategies and better cost efficiency, particularly during product launch and growth phases. Classical models are found to underestimate peak demand and overestimate inventory needs during saturation, leading to inefficiencies. The paper concludes with a discussion on the applicability of each model in real-world inventory management and offers recommendations for firms operating in volatile, innovation-driven markets.},
        keywords = {EOQ Model, Innovation Diffusion, Bass Model, Inventory Optimization, Time-Dependent Demand, Comparative Analysis, Order Quantity, Cycle Time, Total Cost, Product Adoption},
        month = {May},
        }