Demand uncertainty and long capacity lead times make supply chain capacity planning particularly challenging for integrated circuit (IC) design houses that manage multi-stage and multi-site outsourcing networks. In this study, we developed a long-term rolling-horizon supply chain capacity planning (RHSCCP) framework to support production planning decisions under evolving demand forecasts. For the analysis, we used simulation-generated scenarios based on additive and multiplicative martingale models of forecast evolution (MMFE) processes, incorporating different demand scenarios, covariance levels, and capacity regret strategies. Four RHSCCP models were developed in this study: minimax regret (RHSCCP-MMR), upper bound and lower bound (RHSCCP-ULB), expected value (RHSCCP-EV), and stochastic programming (RHSCCP-SP). Their performance was evaluated in a rolling-horizon simulation environment using total cost, capacity utilization, and order fulfillment rate. The results showed distinct performance characteristics across the four strategies. RHSCCP-ULB adopted a more conservative capacity-planning policy, resulting in relatively high fulfillment rates but lower capacity utilization as demand variability increased. In contrast, RHSCCP-EV maintains relatively stable capacity utilization but shows lower fulfillment performance. RHSCCP-MMR and RHSCCP-SP provide a better balance between fulfillment and capacity utilization. Under additive MMFE, particularly at higher demand fluctuations, RHSCCP-MMR is the preferred strategy because of its stronger fulfillment performance and robustness to demand uncertainty. Under multiplicative MMFE, RHSCCP-SP is preferred because it achieves minimum total cost while maintaining relatively high fulfillment and capacity utilization rates.
Citation: James C. Chen, Tzu-Li Chen, Yin-Yann Chen, Chen-Yu Wang, Dewanti Anggrahini. Rolling-horizon supply chain capacity planning for IC design house considering forecast evolution and capacity regret[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 5308-5350. doi: 10.3934/jimo.2026183
Demand uncertainty and long capacity lead times make supply chain capacity planning particularly challenging for integrated circuit (IC) design houses that manage multi-stage and multi-site outsourcing networks. In this study, we developed a long-term rolling-horizon supply chain capacity planning (RHSCCP) framework to support production planning decisions under evolving demand forecasts. For the analysis, we used simulation-generated scenarios based on additive and multiplicative martingale models of forecast evolution (MMFE) processes, incorporating different demand scenarios, covariance levels, and capacity regret strategies. Four RHSCCP models were developed in this study: minimax regret (RHSCCP-MMR), upper bound and lower bound (RHSCCP-ULB), expected value (RHSCCP-EV), and stochastic programming (RHSCCP-SP). Their performance was evaluated in a rolling-horizon simulation environment using total cost, capacity utilization, and order fulfillment rate. The results showed distinct performance characteristics across the four strategies. RHSCCP-ULB adopted a more conservative capacity-planning policy, resulting in relatively high fulfillment rates but lower capacity utilization as demand variability increased. In contrast, RHSCCP-EV maintains relatively stable capacity utilization but shows lower fulfillment performance. RHSCCP-MMR and RHSCCP-SP provide a better balance between fulfillment and capacity utilization. Under additive MMFE, particularly at higher demand fluctuations, RHSCCP-MMR is the preferred strategy because of its stronger fulfillment performance and robustness to demand uncertainty. Under multiplicative MMFE, RHSCCP-SP is preferred because it achieves minimum total cost while maintaining relatively high fulfillment and capacity utilization rates.
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James C. Chen, Tzu-Li Chen, Yin-Yann Chen, Chen-Yu Wang, Dewanti Anggrahini. Rolling-horizon supply chain capacity planning for IC design house considering forecast evolution and capacity regret[J]. Journal of Industrial and Management Optimization, 2026, 22(10): 5308-5350. doi: 10.3934/jimo.2026183
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