Abstract
Within the Assembly to Order (ATO) production strategy, the common approach is to produce the parts to assemble with a Push-Make to Stock policy.In recent decades, the effects of the modern Just in Time (JIT) moved to a Pull-Make to Order policy. Assembled parts characterized by wide variety and huge storage space utilization are critical, and a proper Push/Pull production policy definition is required. An appropriate balance of storage space utilization and setup times leads to the optimization of the production policy. The aim of this paper is to define a bi-objective mathematical optimization model to assign the most suitable production policy to the parts within the production mix in an ATO industrial context. A numerical simulation and an operative case study showcases the model application, proving the industrial relevance of this research.
Highlights
Offering differentiated products is essential for many manufacturing companies.To reach high levels of efficiency and throughput, they developed product platforms, implementing product standardization and modularization
Within the Assembly to Order (ATO) production strategy, the common approach is to produce the parts to assemble with a Push-Make to Stock policy.In recent decades, the effects of the modern Just in Time (JIT) moved to a Pull-Make to Order policy
Assembled parts characterized by wide variety and huge storage space utilization are critical, and a proper Push/Pull production policy definition is required
Summary
Offering differentiated products is essential for many manufacturing companies. To reach high levels of efficiency and throughput, they developed product platforms, implementing product standardization and modularization. The result will be a hybrid Push/Pull policy applied to the internally manufactured parts used in assembly (Figure 1). A proper combination of (Make To Order/Make To Stock) MTO/MTS policies can exploit the advantages of both lower inventory and short delivery time [11] It needs to be optimized for each part as function of its attributes and in accordance with the production system constraints. The novelty of the paper is represented by the proposed bi-objective mathematical optimization model to properly set for each part the Push or Pull policy to minimize setup time and the inventory used space by using a multicriteria approach.
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