Supply Chain Strategies for Mobile Food Services: Extending Traditional Models

Abstract

This study addresses the unique challenges faced by food trucks in managing inventory and supply chains, including demand uncertainty, perishability, and mobility constraints. By extending the classical Newsvendor Problem, this research develops an optimized inventory model tailored to the operational dynamics of food trucks. The model incorporates stochastic demand modeling, salvage value integration, and real-time decision-making to balance overstock and understock risks while maximizing profitability.
A numerical example demonstrates the application of the model. Using a scenario with demand following a normal distribution (mean: 100 units, standard deviation: 20 units), the critical ratio and z-score determine an optimal inventory level of 115 units. Simulation over 10 days reveals consistent profitability, with revenue ranging from $1,200 to $1,725 and profits between $870 and $1,360. Unsold inventory was effectively mitigated through salvage value, highlighting the model’s ability to reduce waste while maintaining operational efficiency.
The results validate the extended Newsvendor Problem framework as a robust tool for optimizing inventory management in mobile food services. The findings emphasize the importance of integrating predictive analytics and adaptive decision-making to meet customer demand, reduce waste, and enhance profitability. This research provides actionable insights for food truck operators and contributes to the broader field of supply chain optimization in dynamic and uncertain environments.