A Hybrid Markov–Simulation Approach for the Design of Flexible Multi-Product Manufacturing Systems

High-mix manufacturing systems are vulnerable to stochastic equipment failures, changeovers and bottlenecks that deterministic capacity planning cannot adequately represent. This study develops a hybrid Markov discrete-event methodology for selecting bottleneck capacities in an unreliable serial–parallel multi-product foundry producing aircraft-engine components. A five-state discrete-time Markov chain of a bottleneck machine, derived from model inputs, estimates the stationary shares of processing, changeover, idle time, and two-phase repair and bounds the feasible configurations, whereas an AnyLogic model represents eleven operations, eight finite buffers, three product types, changeovers, and equipment failures. Replacing green-sand molding with cold-hardening mixtures and increasing the bottleneck from one to two parallel machines raised the bottleneck capacity from 15.1 to 26.6 workpieces per hour and reduced the shift-order makespan from 487 to 414 min, whereas a third machine added 5% capacity. Two machines maximized the constrained criterion of productive utilization, makespan, and resource intensity at every loading time, and the monthly plan was met for a cold-hardening yield of at least 0.63; a deterministic calculation overestimated the capacity by 7–49% and accepted a single machine, which, in simulation, completed only 20% of the shift orders within the shift. The chain reproduced the simulated processing and idle shares within 1.5% and, when excluding four of the seven machine numbers, reduced the simulation runs by 57% without changing the selection. Practitioners should validate the unit times, criterion weights, and yield against independent workshop data.