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Code · Zenodo · July 22, 2026

A predictive prescriptive AI framework for sustainable inventory optimization of perishable dairy products: A multi-model forecasting and reinforcement learning approach

Archana Sasi, Chin‐Shiuh Shieh, Mong-Fong Horng, Arulmurugan Ramu, S Ravichandran

Arulmurugan Ramu
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Abstract

This repository contains the implementation of a predictive-prescriptive AI framework for sustainable inventory optimization of perishable dairy products, combining multi-model demand forecasting with a PPO-based reinforcement learning agent. Key components in this version: Stochastic delivery lead time environment (L ~ DiscreteUniform{0,1,2} weeks), replacing an earlier zero-lead-time formulation N-BEATS and PatchTST forecasting benchmarks, compared against the existing ensemble forecaster Empirical sample-adequacy analysis (learning curves and training-size ablation) Policy sensitivity analysis under stochastic lead time, verified across 5 independent random seeds Consolidated statistical verification (Wilcoxon signed-rank and Diebold-Mariano tests, with confidence intervals and effect sizes) See README.md for the full repository structure and instructions to reproduce all results.

Supply Chain and Inventory ManagementForecasting Techniques and ApplicationsImpact of AI and Big Data on Business and Societyinventory optimizationsupply chain sustainabilitydemand forecasting

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