Modeling On-Demand Warehousing Strategies: A Comparative Static Framework for Supply Chain Decision-Making

Details

Supervisors

Agrell, Per Joakim

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

On-demand warehousing (ODW) offers a flexible, cost-effective solution for managing fluctuating demand and capacity constraints in supply chains. Despite its potential, the strategic integration of ODW into decision-making frameworks remains underexplored, particularly in balancing cost, capacity, and service levels under uncertain conditions. This research addresses this gap by developing a comprehensive framework to optimize capacity allocation, profitability, and service levels in ODW environments. A systematic literature review and a quantitative modeling approach expose critical gaps in understanding ODW’s operational dynamics. To bridge these gaps, a rigorous Comparative Static Model is formulated, capturing four distinct demand scenarios—low, high, extreme, and mixed. The model systematically evaluates how private warehousing capacity, ODW usage, and service levels interplay under varying cost structures and demand variability. Key findings underscore the diminishing returns of private capacity investments beyond a certain threshold, emphasizing the need for cost-efficient resource allocation. In highly volatile scenarios, ODW emerges as an effective contingency mechanism that stabilizes service levels and mitigates unmet demand penalties. Complementing this, universal metric-based activation rules—such as service-level thresholds and profitability deltas—offer near-real-time operational triggers. This dual approach—combining scenario classification with metric-based activation—underlies a robust, adaptable decision framework for balancing resilience, profitability, and equity. By offering a robust framework for optimizing ODW and clear guidelines for deploying ODW alongside private capacity, the study contributes to both academic discourse and industry practice. Future research directions include extending the model to multi-period settings, incorporating risk preferences, and validating these findings through industry collaborations. Overall, this research lays a data-driven foundation for scenario-specific capacity planning to navigate the complexities of ODW and advance supply chain resilience and efficiency.