Automotive Procurement Under Opaque Prices: Theory with Evidence from the BMW Supply Chain

Danko Turcic (), Panos Markou (), Panos Kouvelis () and Daniel Corsten ()
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Danko Turcic: A. Gary Anderson Graduate School of Management, University of California Riverside, Riverside, California 92507
Panos Markou: Darden School of Business, University of Virginia, Charlottesville, Virginia 22903
Panos Kouvelis: Olin Business School, Washington University in St. Louis, St. Louis, Missouri 63130
Daniel Corsten: IE Business School, IE University, Madrid 38006, Spain

Management Science, 2024, vol. 70, issue 6, 3664-3683

Abstract: Several features of automotive procurement distinguish it from the prototypical supply chain in the academic literature: pass-through pricing that reimburses suppliers for raw material costs, market frictions that prohibit cost transparency and imbue suppliers with pricing power, and contractual commitments that span multiple production periods. In this context, we formalize a procurement model by considering an automaker that buys components from an upstream supplier to assemble cars over several production periods in an environment where period demands and raw material costs are both stochastic. Our paper clarifies how information asymmetry and market factors that amplify or weaken this asymmetry affect the firms’ procurement protocol preferences. Then, using proprietary contract and supplier data from BMW, we empirically validate this model and show that it reflects BMW’s reality: the factors that should theoretically go into automotive procurement decisions do so. Our analysis also reveals that existing contracting protocols in this context are not optimal for procurement under asymmetric information, and so we propose an alternative contracting method. We calibrate our model and estimate an automaker’s performance improvement from this optimal contract over the status quo.

Keywords: asymmetric information; risk management; supply chain; automotive; empirical (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:6:p:3664-3683

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