Abstract
Continuous supplier selection, evaluation and reselection are among buying firms’ key processes to improve their overall performance. This paper aims at increasing knowledge on effective and dynamic supplier evaluation and management by answering the following research question: Do performance decomposition technique and real option theory represent appropriate mechanisms to understand structural change in supply chain and subsequent performance improvements? By taking insights from productivity dynamics in economics literature and real options theory from strategic investment context, we apply a method to measure buying firm’s overall supply chain performance through reallocation of purchases among exiting, surviving, and entering suppliers based on their past performance. The method is tested and illustrated with the case of a Finnish contractor and its 535 suppliers in 269 construction projects during 2013-2016. This study provides new insights into the dynamic business relationships of a large buyer in an uncertain environment where supply chain performance is improved through continuous changes in the supplier network. The novelty of this study lies in adopting concepts from productivity decomposition literature in microeconomics in the context of supplier selection and management to quantify the overall performance development of all suppliers, and break it down to the performance components of exiting, surviving, and entering suppliers as well as reallocation of purchases among survivors. Furthermore, real options theory helps in interpreting the entering, surviving, and exiting supplier groups as real options exercised, maintained, and abandoned, respectively.
Original language | English |
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Article number | 108002 |
Number of pages | 14 |
Journal | International Journal of Production Economics |
Volume | 233 |
Early online date | 14 Dec 2020 |
DOIs | |
Publication status | Published - Mar 2021 |
MoE publication type | A1 Journal article-refereed |
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Supplier evaluation and development in construction: data-driven analyses
03/06/2021
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