Abstract
This study develops a stochastic decision-support framework to evaluate manufacturing production relocation alternatives under uncertainty, enabling managers to assess cost-related trade-offs using a total cost of ownership (TCO) perspective. The framework models key cost drivers, including labor, shipping, overhead, inventory, quality, currency fluctuations, as probabilistic variables and integrates them within a Monte Carlo simulation to generate distributional cost outcomes. Considering relocation as a stochastic decision problem, the approach advances traditional methods that rely on point estimates, enabling risk-aware evaluation of alternatives based on expected performance and variability. The framework is applied to a case study involving expanding production relocation from Germany to Bulgaria (EU) and Turkey (non-EU), with alternative sites in Istanbul (Europe) and Ankara (Asia). Empirically fitted distributions, confidence interval estimation, and multi-scenario sensitivity analyses are used to test decision robustness across key cost dimensions. Results indicate that nearshoring production to Bulgaria yields the highest expected cost savings and outcome stability, while relocation to Turkey exhibits greater cost variability driven primarily by uncertainty in overhead and logistics-related costs. Sensitivity analysis shows that labor and overhead costs drive relocation outcomes more than demand and inventory costs and identifies the threshold beyond which relocation should be reconsidered. Beyond its empirical application, the study contributes conceptually by framing production relocation as a stochastic TCO-based decision problem, offering a model that, although derived from a single case, is generalizable to cost-driven relocation under uncertainty across manufacturing firms. The framework offers practical guidance for engineering/operations managers through risk-adjusted decision criteria and implementation steps.