This paper explores the challenges and the complexity associated with the concurrent use of mixed production policies, specifically focusing on the Make-to-Stock (MTS) and Make-to-Order (MTO) approaches. 

In the real-world, the application of either pure MTS or MTO policies is becoming less common, and a compromise between the two based on industry, product specifications, and demand characteristics is usually considered.

To address the complexity introduced by mixed production policies, Simulation modeling can be leveraged as a versatile tool capable of addressing various supply chain problems associated with JIT, such as investment optimization, resource planning, daily operations management, stock coverage identification, predictive maintenance, and other complex what-if analysis.

The paper introduces a case study involving a prominent B2B cotton yarn and fabric producer in the Italian textile market. The company’s decision to invest in a new plant for high-value, high-quality items prompts the use of simulation modeling to optimize machine sizing, plant design, and daily operational scheduling. 

The simulation model helps identify potential bottlenecks, predict plant throughput under different scenarios and optimize the production mix to achieve both efficiency and efficacy. Additionally, the paper emphasize the importance of a simulative digital twin – a low-level simulation model — to achieve operational excellence by dynamically iterating production schedules and empower the collaboration and coordination of various business units silos.