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Testing Benford's Law for improving supply chain decision-making: A field experiment

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  • Hales, Douglas N.
  • Chakravorty, Satya S.
  • Sridharan, V.

Abstract

Supply chain managers must often trust data reported from suppliers to make decisions about sourcing and product reliability due to the costs or complexity of implementing traditional monitoring systems. Without some form of monitoring, these types of data are vulnerable to manipulation, thus making their suitability for decision-making ambiguous and creating an opportunity for 'supplier opportunism'. Recent practitioner literature suggests one solution to this problem they refer to as 'trust-but-verify'. The purpose of this empirical study is to scientifically examine the feasibility and cost of implementing one 'trust-but-verify' method in a real-world supply chain using a principle called Benford's Law. The results of this two-year study suggest that the technique is feasible and cost effective in identifying supply chain data that have been intentionally manipulated. This finding can allow supply chain managers to segregate suspect data from decision-making until they can be validated and thus mitigate supplier opportunism.

Suggested Citation

  • Hales, Douglas N. & Chakravorty, Satya S. & Sridharan, V., 2009. "Testing Benford's Law for improving supply chain decision-making: A field experiment," International Journal of Production Economics, Elsevier, vol. 122(2), pages 606-618, December.
    • Handle: RePEc:eee:proeco:v:122:y:2009:i:2:p:606-618
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    References listed on IDEAS

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    1. Li, Wenli & Humphreys, Paul K. & Yeung, Andy C.L. & Edwin Cheng, T.C., 2007. "The impact of specific supplier development efforts on buyer competitive advantage: an empirical model," International Journal of Production Economics, Elsevier, vol. 106(1), pages 230-247, March.
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    3. Glenn Hoetker & Anand Swaminathan & Will Mitchell, 2007. "Modularity and the Impact of Buyer-Supplier Relationships on the Survival of Suppliers," Management Science, INFORMS, vol. 53(2), pages 178-191, February.
    4. Hales, Douglas N. & Sridharan, V. & Radhakrishnan, Abirami & Chakravorty, Satya S. & Siha, Samia M., 2008. "Testing the accuracy of employee-reported data: An inexpensive alternative approach to traditional methods," European Journal of Operational Research, Elsevier, vol. 189(3), pages 583-593, September.
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    Cited by:

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    2. Mateja Gorenc, 2019. "Benford’s Law As a Useful Tool to Determine Fraud in Financial Statements," Management, University of Primorska, Faculty of Management Koper, vol. 14(1), pages 19-31.
    3. Horton, Joanne & Krishna Kumar, Dhanya & Wood, Anthony, 2020. "Detecting academic fraud using Benford law: The case of Professor James Hunton," Research Policy, Elsevier, vol. 49(8).
    4. Asmus Olsen, 2013. "The politics of digits: evidence of odd taxation," Public Choice, Springer, vol. 154(1), pages 59-73, January.

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