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Stochastic Optimization and LSTM-Based Decision Intelligence Framework for Pharmaceutical Supply Chain Control Towers

Book Chapter

Authors: M. Rameswari (Saveetha Institute of Medical and Technical Sciences, India), Vijaya Kittu Manda (PBMEIT, India), and Divya Madanu (Vikrama Simhapuri University, India)

DOI: https://doi.org/10.4018/979-8-3373-8770-3.ch006

Book Name: Digitally-Enabled Control Towers for Pharmaceutical Supply Chains

ISBN: ISBN13: 9798337387703|ISBN13 Softcover: 9798337387710|EISBN13: 9798337387727

Publisher: IGI Global Scientific Publishing, Hershey, PA, USA

Editor(s): Mohammed Majeed, Mustapha Abdul Aziz, Adam Sandow Saani, Mohammed Awal Zignaa, and Hussein Abdul Ganiw (all from Tamale Technical University, Ghana)

Book Chapter Number: 6

Publication Date: August 8, 2026

Pages: Pages 143-186 (44 pages)

Book Chapter Link on Publisher Website

Book Link on Publisher Website

Abstract: Pharmaceutical supply chains face persistent ineficiencies in demand forecasting and inventory control, particularly for temperature-sensitive products under strict regulatory constraints. Existing Supply Chain Control Towers (SCCTs) ofer real- time visibility but lack the predictive and prescriptive intelligence needed to adapt to volatile demand. The study proposes an integrated AI-driven decision intelligence framework combining LSTM-based demand forecasting with stochastic inventory optimization and blockchain-enabled cold-chain traceability. A multi-objective stochastic optimization model is formulated that jointly minimizes total cost and carbon emissions while satisfying regulatory service-level constraints under demand uncertainty. Results show reduced inventory holding costs, improved service levels, and lower cold-chain spoilage than with conventional reorder-point policies. The key contribution is a closed-loop layered SCCT architecture that operationalizes predictive, prescriptive, and adaptive intelligence within a unified platform for use by the industry.

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