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Supply Diagnostic Incentives under Endogenous Information Asymmetry

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  • Mohammad E. Nikoofal
  • Mehmet Gümüş

Abstract

This paper develops a dyadic supply chain model with one buyer who contracts the manufacturing of a new product to a supplier. Due to the lack of experience in manufacturing, the extent of supply risk is unknown to both the buyer and supplier before the time of contract. However, after the contract is accepted, the supplier may invest in a diagnostic test to acquire information about his true reliability, and use this information when deciding on a process improvement effort. Using this setting, we identify both operational and strategic benefits and costs of the diagnostic test. Operationally, it helps the supplier to take the first‐best level of improvement effort, which would increase efficiency of the total supply chain. Strategically, it enables the buyer to reduce the agency costs associated with implementing process improvement on the supplier. Besides these benefits, diagnostic test increases the degree of information asymmetry along the supply chain. This in turn provides the supplier with proprietary information, whose rent would be demanded from the buyer in equilibrium. Benefit‐cost analysis reveals two key factors in determining the value of diagnostic test: (i) degree of endogenous information asymmetry between supply chain firms, and (ii) the relative cost of a diagnostic test with respect to process improvement cost. Our results indicate that when both are high, the mere presence of a diagnostic test can result in less reliable supply chain. This implies that when incentives are not properly aligned, information asymmetry amplified due to diagnostic test neutralizes all its benefits.

Suggested Citation

  • Mohammad E. Nikoofal & Mehmet Gümüş, 2019. "Supply Diagnostic Incentives under Endogenous Information Asymmetry," Production and Operations Management, Production and Operations Management Society, vol. 28(3), pages 588-609, March.
  • Handle: RePEc:bla:popmgt:v:28:y:2019:i:3:p:588-609
    DOI: 10.1111/poms.12935
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    Cited by:

    1. Qi Wang & Kebing Chen & Shengbin Wang & Xiaogang Cao, 2022. "Optimal decisions in a closed-loop supply chain: fairness concerns, corporate social responsibility and information value," Annals of Operations Research, Springer, vol. 309(1), pages 277-304, February.
    2. Jung-Fa Tsai & Chin-Po Wang & Ming-Hua Lin & Shih-Wei Huang, 2021. "Analysis of Key Factors for Supplier Selection in Taiwan’s Thin-Film Transistor Liquid-Crystal Displays Industry," Mathematics, MDPI, vol. 9(4), pages 1-18, February.
    3. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).
    4. Liu, Chao & Chen, Weidong, 2019. "Decision making in green supply chains under the impact of the stochastic and multiple-variable dependent reference point," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 443-469.

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