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

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.