• Shilpi Singh
• Dr. Alpna Mishra

In contemporary supply chain environments, inventory planning must navigate increasing levels of uncertainty in both demand and product lifecycles. Traditional deterministic models, while useful in stable conditions, are inadequate for managing products that experience dynamic adoption patterns and innovation-driven market responses. This research develops a novel inventory optimization model that incorporates stochastic diffusion-based demand, integrating uncertainty through probabilistic modeling of innovation adoption across different phases of a product’s lifecycle. Using a probabilistic extension of the Bass Diffusion Model, demand is treated as a stochastic process influenced by both external innovation effects and internal imitation, compounded by market randomness. This framework enables simulation of adoption under uncertainty, addressing real-world variability due to consumer behavior, competitor activity, and marketing inconsistency. The paper compares inventory performance under deterministic and probabilistic policies using Monte Carlo simulation and risk metrics such as expected cost, service level reliability, and replenishment variance. Furthermore, it models the probabilistic market size evolution using log-normal and triangular distributions to reflect realistic growth trajectories. Key findings reveal that under high uncertainty, deterministic policies tend to either overstock or understock, while probabilistic models, despite requiring more data and computational effort, yield significantly improved outcomes in terms of cost minimization and service stability. This work provides a scalable and adaptive approach to inventory optimization, particularly suited for technology products, consumer electronics, pharmaceuticals, and other innovation-driven markets.

Inventory Optimization, Stochastic Demand, Innovation Diffusion, Bass Model, Monte Carlo Simulation, Probabilistic EOQ, Product Lifecycle, Uncertainty, Risk Analysis, Supply Chain Modeling

"Inventory Optimization under Uncertain Demand: A Diffusion-Driven Probabilistic Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.i13-i23, May-2025, Available :http://www.jetir.org/papers/JETIR2505902.pdf

"Inventory Optimization under Uncertain Demand: A Diffusion-Driven Probabilistic Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppi13-i23, May-2025, Available at : http://www.jetir.org/papers/JETIR2505902.pdf