A holistic model for understanding the dynamics of outsourcing
Name
A holistic model for understanding the dynamics of outsourcing.pdf
Description
Published version
Size
6.12 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Uygun, Yilmaz
Gotsadze, Nikoloz
Schupp, Florian
Gzirishvili, Lizi
Tindjou Nana, Brigitte Stephanie
Date Issued
February 14, 2022
Journal
International Journal of Production Research
Publisher
Taylor & Francis
Citation
Uygun, Y., Gotsadze, N., Schupp, F., Gzirishvili, L., & Tindjou Nana, B. S. (2022). A holistic model for understanding the dynamics of outsourcing. International Journal of Production Research, 61(4), 1202–1232.
Version
Final published version
Abstract
Outsourcing is a complex process as many external and internal factors that look convincing in the first place might, however, lead to a failure in the long run. Motivated by this, we wanted to get a holistic understanding of such outsourcing decisions. Thus, we created a comprehensive System Dynamics simulation model including all relevant variables to examine the dynamic nature of outsourcing in a holistic manner and over time that consists of more than 200 interrelated variables. Our results show, amongst others, that higher process specialisation that requires substantial investments by the supplier appears to be favourable for an outsourcing company and shifting a larger quantity to such a supplier achieves better cost savings and thus accounts for a better overall outsourcing result. On an operational level, we identified an innovation trap, a bargaining power shift, a plagiarism trap, and a knowledge trap. Based on that, we give specific managerial recommendations to tackles these aspects. We conclude that, amongst others, it is important for innovative companies with rather complex processes and parts to carefully plan which and how many employees to release so as not to lose the knowledge on those outsourced processes and parts.
MIT Department
MIT Industrial Performance Center
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00207543.2022.2031330
