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Operations Management Research

, Volume 11, Issue 1–2, pp 14–31 | Cite as

Determining an optimal margin of error for supply chain audits

  • Scott M. Shafer
Article
  • 120 Downloads

Abstract

Unsaleables are products that need to be removed from the primary distribution channel for a variety of reasons including being damaged in the supply chain or reaching the end of their useful life before being sold. One approach for processing unsaleables is the development of Adjustable Rate Policies (ARP) whereby manufacturers establish reimbursement rates for damaged and expired product based on data collected from supply chain audits. A prerequisite for developing equitable reimbursement rates is the need for representative audits of the supply chain which in turn depend on the development of proper sampling plans. Given the need to obtain a representative sample in order to establish accurate reimbursement rates and critical flaws in the conventional sampling methodology, the purpose of this research is to propose a new methodology for determining the sample size for conducting supply chain audits and compare the proposed method to other methods. The results of the study consistently support the value of optimally determining the margin of error based on contextual factors such as sales volume, unit cost, and unit sampling costs.

Keywords

Reverse logistics Unsaleables Supply chain management Simulation Sampling 

References

  1. Agresti A, Coull BA (1998) Approximate is better than “exact” for interval estimation of binomial proportions. Am Stat 52:119–126Google Scholar
  2. Baskak A, Ford A (2002) Adjustable rate policies – an Unsaleables white paper. Joint Industry Unsaleables Steering Committee, www.gmaonline.org/downloads/research-and-reports/adjust.pdf
  3. Brown LD, Cai TT, DasGupta A (2001) Interval estimation for a binomial proportion. Statistical Sciences 16:101–133Google Scholar
  4. Deloitte, Grocery Manufacturers Association (GMA), Food Marketing Institute (FMI) (2008) 2008 Joint Industry Unsaleables Report: The Real Causes and Actionable Solutions. https://www.gmaonline.org/downloads/research-and-reports/UnsaleablesFINAL091108.pdf
  5. GENCO (2013a) Unsaleables Management. www.genco.com/unsaleables/unsaleables-management.php
  6. GENCO (2013b) Unsaleables 101 – A Primer on Unsaleables & Damage Research, www.genco.com/unsaleables/unsaleables-101.php
  7. Montgomery DC (2001) Design and analysis of experiments, 5th edn. Wiley, HobokenGoogle Scholar
  8. Ng S, Rockoff JD (2013) Amazon and J&J clash over third-party sales. The Wall Street Journal, November 11, 2013, A1, A9Google Scholar
  9. Petrinovich LF, Hardyck CD (1969) Error rates for multiple comparison methods: some evidence concerning the frequency of erroneous conclusions. Psychol Bull 71(1):43–54.  https://doi.org/10.1037/h0026861 CrossRefGoogle Scholar
  10. Santner TJ (1998) Teaching large-sample binomial confidence intervals. Teach Stat 20(1):20–23.  https://doi.org/10.1111/j.1467-9639.1998.tb00753.x CrossRefGoogle Scholar
  11. Terreri A (2009) Unsaleables Rx: look to your supply chain. Food Logistics, www.foodlogistics.com/article/10255731/unsaleables-rx-look-to-your-supply-chain
  12. Terreri A (2011) How green is your Returnables process? Food Logistics, June, 16—22Google Scholar
  13. Vollset SE (1993) Confidence intervals for a binomial proportion. Stat Med 12(9):809–824.  https://doi.org/10.1002/sim.4780120902 CrossRefGoogle Scholar
  14. Wipro, Grocery Manufacturers Association (GMA), Food Marketing Institute (FMI) (2010) The impact of sales and procurement on reverse logistics management. www.wipro.com/documents/insights/impact_of_sales_and_procurement_on_reverse_logistics.pdf

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of BusinessWake Forest UniversityWinston SalemUSA

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