A study on project risk analysis model for outsourcing based on Bayesian networks: Application to small company OEM project of mold industry

Youn Sook Kim, Tomohiro Murata, Jaesug Ki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As a basic industry, mold industry is critical to the competitiveness of manufacturing industry. Due to its OEM operational traits, it is difficult to manufacture standardized products in mold industry and market demands require short production lead times. Today, small- and medium-sized companies (SMEs) in mold industry are seeking more efficient production processes where automated production facilities are better used to reduce lead times and costs. Globally, mold industry is becoming a specialized field compared to other industries and manufacturing processes of global SMEs require close data sharing and management of correlation among each process. As various processes are being operated simultaneously all over the world with different molding techniques under uncertain conditions in mold industry, project risk management is all the more important. This paper surveyed on Korean small- and medium-sized mold producers that outsource more than 50% of manufacturing processes. Companies can swiftly respond to the problems inside the company but when they outsource or toll manufacturing processes, the “critical path” of each process where there are issues of product quality and delayed delivery can also negatively affect product quality and delivery schedule of the entire process, so the paper suggests a new integrated management model for the activities in each process of outsourcing. The new model is an instrument to evaluate and monitor the progress of an outsourcing project and is used as a predictive tool based on probability to find the dangers and fundamental risk factors of project delay.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018
EditorsOscar Castillo, David Dagan Feng, A.M. Korsunsky, Craig Douglas, S. I. Ao
PublisherNewswood Limited
ISBN (Electronic)9789881404886
Publication statusPublished - 2018 Jan 1
Event2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, Hong Kong
Duration: 2018 Mar 142018 Mar 16

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2
ISSN (Print)2078-0958

Other

Other2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018
CountryHong Kong
CityHong Kong
Period18/3/1418/3/16

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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  • Cite this

    Kim, Y. S., Murata, T., & Ki, J. (2018). A study on project risk analysis model for outsourcing based on Bayesian networks: Application to small company OEM project of mold industry. In O. Castillo, D. D. Feng, A. M. Korsunsky, C. Douglas, & S. I. Ao (Eds.), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018 (Lecture Notes in Engineering and Computer Science; Vol. 2). Newswood Limited.