Preventing Supplier-Auditor Collusion in Ethical Sourcing
SSRN Electronic Journal
2022
- 483Usage
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We consider a supply chain in which a buyer sources from a supplier for the production of a product. The supplier’s production process is subject to probable violations of either government regulations or ethical requirements. The sourcing contract offered by the buyer demands that the supplier exerts an effort to improve its production process, thus reducing the probability of the occurrence of supplier violation. Because the supplier’s effort is hardly observable, the buyer has to send an auditor to perform an audit of the supplier’s facility. The auditor in turn reports whether the supplier has exerted the effort or not. If the supplier fails to pass the audit, she may offer a side contract to the auditor, thus engaging in a supplier-auditor collusion. We study how such supplier-auditor collusion may affect the buyer’s outsourcing strategy. We find that the buyer should reduce the order quantity to deter the supplier-auditor collusion when the collusion penalty is relatively low. We further examine reaudit and dual sourcing, two mechanisms which could probably prevent supplier-auditor collusion. We find that a reaudit may complement the collusion penalty under certain conditions; using dual sourcing to deter collusion is viable only if supplier-improvement techniques or procedures are effective. Finally, we extend our model by considering false positive audit error and linear penalty function. Our analytical and numerical results show that our insights are robust.
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