The moderating role of master production scheduling method on throughput in job shop systems

Abstract

Accurately predicting throughput is a challenging task for managers of manufacturing companies and analytical models for this purpose have been limited to small and simple production systems. Motivated by a complex and real-world case from the automotive industry, this paper first examines how realistically the throughput of complex job shop systems can be predicted based on problem characteristics and different master production scheduling (MPS) approaches. Next, it investigates how different MPS approaches moderate the relationship between problem characteristics and throughput. To achieve these aims, we develop a mixed-effects model based on operational characteristics and the MPS development method to predict the system's throughput. The analyses are based on data from a real-world case in the automotive industry and two complex job shop systems in the literature. The experimental results indicate that the throughput of job shop systems can be predicted with a high level of accuracy (R2=0.756) based on the problem's characteristics and that the predictive model cross-validates well on a holdout sample. Moreover, we observed that the MPS method can moderate the impact of problem characteristics reflecting complexity, capacity shortages, and setup requirements on throughput, with moderation especially pronounced in the presence of capacity shortages.