A Monte Carlo-Based Approach to Demand Forecasting and Stochastic Optimization in Supply Chains

Economic shipment planning under demand uncertainty is still the major problem in today’s supply chains, in which transportation cost, inventory holding cost, and stockout cost are considered simultaneously. This work contributes by comparing demand forecasting-based planning with stochastic optimization, where the latter is enhanced with Monte Carlo simulation, and closes with the introduction of a Monte Carlo-Enhanced Route and Inventory Optimization (MC-RIO) approach as a suitable planning option in uncertain logistics environments. The probabilistic MC-RIO framework superimposes by routing and inventory decisions the probabilistic generation of demand, allowing for considering concurrently shipment routing and inventory decisions in the face of a finite set of demand possibilities. Numerical tests are based on 1, 000 Monte Carlo scenarios and consider perishable products, capacity-limited vehicles and penalty-oriented stockouts. Results show that while MC-RIO achieves a total expected logistics cost of 350 in the baseline case, demand forecasting with deterministic approximation and classical stochastic programming delivers 330 and 340, respectively. We also observe that the performance of forecasting-based planning degrades dramatically as demand becomes more variable, resulting in penalties 36% higher for stockouts. Instead, in high-variability scenarios, the MC-RIO approach is more robust, achieving a reduction in stockout costs on the order of 40–55%, at the cost of moderate increment in holding stocks. Sensitivity analysis also shows that variability in demand and the cost of holding inventory are the most significant factors, and the total expected cost rises from 350 to 450 for high demand volatility and from 350 to 470 for doubled holding cost scenarios. The results corroborate that our proposed MC-RIO framework can achieve a balanced and risk-aware shipment policy that is better than the conventional ones in the presence of volatility. In summary, this work suggests Monte Carlo-based optimization as a robust and scalable decision support tool for cost-effective shipment planning in the face of uncertainty. • Demonstrate how demand forecasting supports proactive shipment planning and inventory control. • Present Monte Carlo simulations to evaluate shipment costs under uncertain demand conditions. • Compare demand forecasting and stochastic optimization for supply chain adaptability. • Show how transportation costs shape inventory and routing strategies in logistics. • Provide a hybrid analytics framework that balances efficiency, accuracy, and responsiveness.