Abstract
Research on buyer-supplier relationships has debated the advantages and disadvantages of embedded relationships. We join this debate by developing theory on the performance implications of relaxing embedded buyer-supplier relationships for a limited period of time — a previously neglected phenomenon we refer to as temporary de-embedding. To capture this phenomenon’s dynamic and complex nature, we use a combined-method approach. First, we conducted a longitudinal case study of the relationship between Nissan and a strategic first-tier supplier. This case study suggests that temporary de-embedding reinvigorates search and leads to higher performance for both the buyer and supplier. Second, we built a computational simulation model using the search perspective from complexity theory to complement the theory grounded in our case study. Our simulations confirm the case findings while shedding additional light on how frequency, duration, and intensity of de-embedding affect supply chain performance.
Highlights
Chain management is inherently complex and dynamic (Nair, Narasimhan, & Choi, 2009), because decisions made by one member of the supply chain affect subsequent decisions of other actors
We investigate the phenomenon of temporary deembedding of buyer–supplier relationships defined as relaxing embeddedness for a limited period of time
The second triggering event arose on November 18, 2004, when Ghosn at a meeting with suppliers expressed his renewed appreciation of keiretsu. These two triggering events segment our data set into three periods that each indicates different levels of embeddedness: (a) pre-Nissan Revival Plan” (NRP) during which we found the relation between Nissan and its supplier to be closely embedded; (b) 2000–2004 when Nissan effectively deembedded the relationship; and (c) the period from 2004 onward when attempts at reembedding were undertaken
Summary
Chain management is inherently complex and dynamic (Nair, Narasimhan, & Choi, 2009), because decisions made by one member of the supply chain affect subsequent decisions of other actors. Supply chain members engage in coevolutionary search to advance and to innovate (Chandrasekaran, Linderman, Sting, & Benner, 2015; Giannoccaro, 2011; Kim, Choi, & Skilton, 2015; Levinthal, 1997; Sting & Loch, 2016), much like BMW's ongoing “Industrie 4.0” initiative to digitalize manufacturing processes and technologies. As part of this initiative, BMW has scanned its entire Rolls Royce plant in Goodwood, UK, within a two-millimeter tolerance.
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