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The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction

Received: 18 December 2018     Accepted: 13 February 2019     Published: 1 March 2019
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Abstract

One of the key elements in supplay cahin management is accurate information. Decision makers are aware of inaccuracies in inventory levels and, therefore, routinely conduct inventory reviews to correct the discrepancies between IT records and actual inventory. Several studies have investigated error sources and the cumulative effect of errors on holding costs, shortage costs, order-up-to levels and time between inventory counts. In most works, the errors were independent of the demand, which is neither realistic nor accurate. Here we use familiar inventory errors and information scenarios already proposed in several previous papers. We offer a model that considers the correlation between inventory errors and demand. The effect of the relationship between the random variables is tested in the context of several different scenarios. Each scenario contains a different level of information about the underlying demand and inventory errors. We then analyze the effect of changes of the covariance on the cost and time between inventory counts in each scenario. Using these results we formulate the value of information and its dependence on the covariance. We use analytical methods to draw conclusions regarding single parameter set cases and a numerical full factorial study for average multiparameter cases. In both settings, we show that the value of information decreases as the covariance increases. Moreover, the reduction is more significant when the information scenario makes less assumptions. The same behavior is observed in stock review frequency. As covariance increases, the optimal number of periods between inventory reviews drops sharply. Finally, we propose several simple methods for proactive error correction. We show that without prior knowledge, these methods perform better than the basic information scenario. Using these results we are able to formulate recommendations for businesses with different profiles of correlation between demand, and demand and errors, e.g., automated warehouses with weak correlation compared to grocery stores.

Published in American Journal of Operations Management and Information Systems (Volume 4, Issue 1)
DOI 10.11648/j.ajomis.20190401.11
Page(s) 1-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Supply Chain Management, Inventory Control, Information Technology

References
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[3] Avrahami, Assaf, Avinoam Tzimerman, Yale T. Herer, and Avraham Shtub. 2012. “The value of inventory accuracy in supply chain management.” Working Paper, Technion.
[4] Lee, Hau, and Özalp Özer. 2007. “Unlocking the value of RFID.” Production and Operations Management 16 (1): 40–64.
[5] Sarac, Aysegul, Nabil Absi, and Stéphane Dauzère-Pérès. 2010. “A literature review on the impact of RFID technologies on supply chain management.” International Journal of Production Economics 128 (1): 77–95.
[6] Ha, Oh-Keun, Yong-Seok Song, Kyung-Yong Chung, Kang-Dae Lee, and Dongjoo Park. 2014. “Relation model describing the effects of introducing RFID in the supply chain: Evidence from the food and beverage industry in South Korea.” Personal and Ubiquitous Computing 18 (3): 553–561.
[7] Huber, Nicholas, and Katina Michael. 2007. “Vendor perceptions of how RFID can minimize product shrinkage in the retail supply chain.” In RFID Eurasia, 2007 1st Annual, 1–6. IEEE.
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[16] Gaukler, Gary M, Ralf W Seifert, and Warren H Hausman. 2007. “Item-level RFID in the retail supply chain.” Production and Operations Management 16 (1): 65–76.
[17] Pelton, Lou E, Madhav Pappu, and Gary M Gaukler. 2010. “Preventing avoidable stockouts: The impact of item-level RFID in retail.” Journal of Business and Industrial Marketing 25 (8): 572–581.
[18] Chuang, Howard Hao-Chun, and Rogelio Oliva. 2015. “Inventory record inaccuracy: Causes and labor effects.” Journal of Operations Management 39: 63–78.
[19] Iglehart, Donald L, and Richard C Morey. 1972. “Inventory systems with imperfect asset information.” Management Science 18 (8): B–388.
[20] DeHoratius, Nicole, Adam J Mersereau, and Linus Schrage. 2008. “Retail inventory management when records are inaccurate.” Manufacturing and Service Operations Management 10 (2): 257–277.
[21] Kök, A Gürhan, and Kevin H Shang. 2014. “Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments.” European Journal of Operational Research 237 (1): 91–105.
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Cite This Article
  • APA Style

    Assaf Avrahami, Evgeni Korchatov. (2019). The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction. American Journal of Operations Management and Information Systems, 4(1), 1-15. https://doi.org/10.11648/j.ajomis.20190401.11

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    ACS Style

    Assaf Avrahami; Evgeni Korchatov. The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction. Am. J. Oper. Manag. Inf. Syst. 2019, 4(1), 1-15. doi: 10.11648/j.ajomis.20190401.11

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    AMA Style

    Assaf Avrahami, Evgeni Korchatov. The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction. Am J Oper Manag Inf Syst. 2019;4(1):1-15. doi: 10.11648/j.ajomis.20190401.11

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  • @article{10.11648/j.ajomis.20190401.11,
      author = {Assaf Avrahami and Evgeni Korchatov},
      title = {The Value of Inventory Accuracy in Supply Chain Management: Correlation Between Error Sources and Proactive Error Correction},
      journal = {American Journal of Operations Management and Information Systems},
      volume = {4},
      number = {1},
      pages = {1-15},
      doi = {10.11648/j.ajomis.20190401.11},
      url = {https://doi.org/10.11648/j.ajomis.20190401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajomis.20190401.11},
      abstract = {One of the key elements in supplay cahin management is accurate information. Decision makers are aware of inaccuracies in inventory levels and, therefore, routinely conduct inventory reviews to correct the discrepancies between IT records and actual inventory. Several studies have investigated error sources and the cumulative effect of errors on holding costs, shortage costs, order-up-to levels and time between inventory counts. In most works, the errors were independent of the demand, which is neither realistic nor accurate. Here we use familiar inventory errors and information scenarios already proposed in several previous papers. We offer a model that considers the correlation between inventory errors and demand. The effect of the relationship between the random variables is tested in the context of several different scenarios. Each scenario contains a different level of information about the underlying demand and inventory errors. We then analyze the effect of changes of the covariance on the cost and time between inventory counts in each scenario. Using these results we formulate the value of information and its dependence on the covariance. We use analytical methods to draw conclusions regarding single parameter set cases and a numerical full factorial study for average multiparameter cases. In both settings, we show that the value of information decreases as the covariance increases. Moreover, the reduction is more significant when the information scenario makes less assumptions. The same behavior is observed in stock review frequency. As covariance increases, the optimal number of periods between inventory reviews drops sharply. Finally, we propose several simple methods for proactive error correction. We show that without prior knowledge, these methods perform better than the basic information scenario. Using these results we are able to formulate recommendations for businesses with different profiles of correlation between demand, and demand and errors, e.g., automated warehouses with weak correlation compared to grocery stores.},
     year = {2019}
    }
    

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  • TY  - JOUR
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    AU  - Assaf Avrahami
    AU  - Evgeni Korchatov
    Y1  - 2019/03/01
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    AB  - One of the key elements in supplay cahin management is accurate information. Decision makers are aware of inaccuracies in inventory levels and, therefore, routinely conduct inventory reviews to correct the discrepancies between IT records and actual inventory. Several studies have investigated error sources and the cumulative effect of errors on holding costs, shortage costs, order-up-to levels and time between inventory counts. In most works, the errors were independent of the demand, which is neither realistic nor accurate. Here we use familiar inventory errors and information scenarios already proposed in several previous papers. We offer a model that considers the correlation between inventory errors and demand. The effect of the relationship between the random variables is tested in the context of several different scenarios. Each scenario contains a different level of information about the underlying demand and inventory errors. We then analyze the effect of changes of the covariance on the cost and time between inventory counts in each scenario. Using these results we formulate the value of information and its dependence on the covariance. We use analytical methods to draw conclusions regarding single parameter set cases and a numerical full factorial study for average multiparameter cases. In both settings, we show that the value of information decreases as the covariance increases. Moreover, the reduction is more significant when the information scenario makes less assumptions. The same behavior is observed in stock review frequency. As covariance increases, the optimal number of periods between inventory reviews drops sharply. Finally, we propose several simple methods for proactive error correction. We show that without prior knowledge, these methods perform better than the basic information scenario. Using these results we are able to formulate recommendations for businesses with different profiles of correlation between demand, and demand and errors, e.g., automated warehouses with weak correlation compared to grocery stores.
    VL  - 4
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Author Information
  • Faculty of Industrial Engineering, Israel Institute of Technology, Haifa, Israel

  • Faculty of Industrial Engineering, Israel Institute of Technology, Haifa, Israel

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