2756. Mitigating Demand Uncertainty: Resilient, Green Supply Chain Design for Multi-Product, Multi-Period Operations in a Changing Trade Landscape
Invited abstract in session WC-39: Sustainable Supply Chains II, stream Sustainable & Resilient Systems and Infrastructures.
Wednesday, 12:30-14:00Room: Newlyn LG.01
Authors (first author is the speaker)
| 1. | Surya Prakash
|
| Operations Management, Great Lakes Institute of Management Gurgaon | |
| 2. | Sachin Kumar Mangla
|
| Jindal Global Business School | |
| 3. | Harish Kumar
|
| Marketing, Great lakes institute of management, Gurgaon |
Abstract
Tariffs and geopolitical uncertainties create significant demand uncertainty for companies by introducing price volatility, supply chain disruptions, and sustainability goals. This uncertainty can be categorized as both external (market-driven) and strategic (policy-driven). To manage this demand uncertainty, firms must adopt resilient supply chain strategies and attempt to design or redesign their supply chains under this uncertain environment. This study proposes designing resilient, robust, and sustainable (by including emissions) multi-period multi-product supply chains design under demand uncertainty. A multi-objective mixed-integer linear programming model is implemented to capture network design requirements and mathematically modeled in the AIMMS environment and solved using CPLEX 12.0. The demand uncertainty is handled using a robust optimization approach. A comparative analysis of supply chain performance is performed through supply chain cost, shipments or product flow, transportation cost, network configuration, service level, etc. for the current (before adopting resilient and robust optimization modifications) and for the proposed modified network. The results from numerical experiments and sensitivity analysis show efficient use of the proposed improved network to address the demand uncertainty. The proposed resilient sustainable supply chain configuration model outperforms the basic deterministic model if both are subjected to a certain level of uncertainty.
Keywords
- Robust Optimization
- Location
- Supply Chain Management
Status: accepted
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