A multi-horizon analytics framework for disruption response decisions in manufacturing supply chains

Bicycle manufacturers face frequent supply disruptions due to long lead times, global component sourcing and complex product structures. When components are shared across product lines, a single delayed shipment can affect dozens of production orders. Practitioners typically respond with ad hoc expediting, yet have little guidance on which intervention is most effective. This paper develops and evaluates a multi-horizon framework for disruption management in bicycle manufacturing comprising three modules: (i) a planning module that adjusts already-open purchase orders 2–8 weeks ahead via mixed-integer programming, (ii) a scheduling module that swaps at-risk and safe production orders within 1–7 days and (iii) a real-time module that decides on expediting or delaying orders within 24 h using reinforcement learning. All modules share a common risk metric based on component buffer times and are coordinated through a data-driven decision table that recommends which module to apply based on urgency, exposure and schedule flexibility. Using seven months of production data from a single large European bicycle manufacturer, we evaluate the framework through an offline simulation that compares it against a reconstructed no-intervention baseline, a reactive expediting baseline and fixed single-module strategies. In this case-based simulation, the framework reduces cumulative production delay by 38.9% relative to no intervention and improves on-time completion from 67.3% to 79.6%, with a 4.7% increase in inventory holding cost and 41.2% lower intervention spending than the reactive expediting baseline. The results show that a shared risk metric and simple module-selection logic can support disruption response in a bill-of-materials (BOM)-driven manufacturing setting. Much of the benefit in this case-based simulation comes from matching responses to disruption characteristics rather than applying a single fixed intervention. The framework remains beneficial when lead-time assumptions are tightened and when scheduling soft costs are introduced, although the magnitude of the benefit narrows under stricter assumptions. Realising it in practice depends on data readiness, cross-functional governance and recalibration to firm-specific characteristics. Within these boundaries, the results provide evidence of the value of coordinating planning, scheduling and real-time responses through a shared risk signal.