Automating computer simulation and statistical analysis in production planning and control research

• Main Authors: Chin, J.F., Prakash, J., Kamaruddin, S., Tan, M.C.L.
• Format: Article
• Published: Taylor and Francis Ltd. 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045111284&doi=10.1080%2f1206212X.2017.1395104&partnerID=40&md5=76cdf77dbd570e92b4ec908b2c0e3687; http://eprints.utp.edu.my/22020/

Computer simulation is commonly used to study production planning and control prior to actual shop floor implementation. The majority of simulations are discrete events and involve modeling of elements and interactions. A complete simulation analysis requires multiple runs to infer stochastic behaviors of the system under different combination of factors. The analysis takes in results obtained from all the runs and confirms a hypothesis statistically. The resources required greatly rely upon the number of simulation models, simulation run length, technical knowledge, and computer resource available. Although latest commercial production simulation software allows some forms of automation, the analysis functions included are considered rudimentary. Integrating computer simulation and advanced statistical methods can result in substantial time and resource savings. In this paper, computer simulation and statistical analysis have been integrated and automated to cater for a large combination of simulation runs. With the system named as ProSA (production simulation and analysis), the work has been completed in 2011 and was demonstrated in a recent case study. The evidence provides concrete proof of such a possibility and provides an invitation to others to explore application research into technical knowledge and tasks transfer to computer. © 2017 Informa UK Limited, trading as Taylor & Francis Group.