A Stochastic Optimization Framework for Evaluating Component Versus Finished-Goods Import Strategies Under Tariff Uncertainty
Received: 4 March 2026 | Revised: 5 April 2026 | Accepted: 17 April 2026 | Online: 6 June 2026
Corresponding author: Manikandan Chandran
Abstract
Tariff uncertainty has become an important factor in supply chain decision-making. Firms that import products must often decide whether to import finished goods directly or import components and assemble them domestically. This decision is complicated by fluctuating duty rates, transportation variability, capacity constraints, and service-level requirements. Traditional spreadsheet-based analyses are not sufficient to evaluate the large number of possible configurations that arise when considering component-level restructuring. This paper presents a stochastic optimization framework for evaluating component versus finished-goods import strategies under tariff uncertainty. The proposed approach integrates automated tariff classification, Monte Carlo simulation of landed cost variability, and mixed-integer programming with risk penalization. The objective is to minimize expected landed cost while controlling cost volatility. Experimental evaluation across 200 enterprise-scale synthetic scenarios shows landed cost reductions between 9.5% and 16.8% relative to deterministic baselines, with variance reductions of up to 40.3% under volatile tariff conditions. The proposed framework improves cost efficiency and reduces cost volatility under tariff uncertainty.
Keywords:
stochastic optimization, tariff uncertainty, mixed-integer programming, Monte Carlo simulation, landed cost modeling, supply chain planningReferences
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