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Applied Probability-Driven General Linear Models for Adaptive Pricing Algorithms in Perishable Goods Supply Chains under Demand Uncertainty
Department of Mathematics, Illinois State University, USA.
Research Article
International Journal of Science and Research Archive, 2022, 06(02), 213-232
Article DOI: 10.30574/ijsra.2022.6.2.0292
Publication history:
Received on 26 July 2022; revised on 28 August 2022; accepted on 30 August 2022
Abstract:
Perishable goods supply chains operate under acute time constraints and volatile demand patterns, making pricing decisions both critical and complex. Applied Probability-Driven General Linear Models (GLMs) offer a mathematically rigorous framework for adapting pricing strategies in real time, integrating stochastic representations of demand uncertainty with the structural flexibility of GLMs. By embedding applied probability components such as Poisson processes, Bayesian updating, and Markov decision frameworks into the pricing model, decision-makers can simulate and anticipate fluctuations in both demand and supply chain conditions. This hybrid modelling approach enables the dynamic adjustment of prices based on probabilistic forecasts of consumer purchasing behaviour, shelf-life decay rates, and replenishment cycles. The framework can incorporate multi-level data, including store-level sales, regional demand trends, and seasonality factors, while accounting for covariates such as promotional effects, competitor pricing, and inventory holding costs. By modelling demand as a stochastic process, the system can proactively recommend optimal price points that maximise revenue while minimising waste due to spoilage. At the strategic level, the model facilitates scenario testing for supply chain resilience, helping operators evaluate the impact of various market conditions on profitability. At the operational level, it enables granular decision-making through real-time price updates, leveraging continuous inflows of point-of-sale and inventory data. The probabilistic integration ensures robustness in decision-making under incomplete or rapidly changing information, allowing for adaptive strategies that outperform static pricing rules. By uniting applied probability with the analytical power of GLMs, this approach advances adaptive pricing algorithms tailored to the unique challenges of perishable goods supply chains, offering stakeholders a competitive advantage in efficiency, profitability, and sustainability.
Keywords:
Applied Probability; General Linear Models; Adaptive Pricing; Perishable Goods; Demand Uncertainty; Supply Chain Optimisation
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