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Managing Supply Chain Execution: Monitoring Timeliness and Correctness via Individualized Trace Data

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  • Jun Shu
  • Russell Barton

Abstract

Improvements in information technologies provide new opportunities to control and improve business processes based on real‐time performance data. A class of data we call individualized trace data (ITD) identifies the real‐time status of individual entities as they move through execution processes, such as an individual product passing through a supply chain or a uniquely identified mortgage application going through an approval process. We develop a mathematical framework which we call the State‐Identity‐Time (SIT) Framework to represent and manipulate ITD at multiple levels of aggregation for different managerial purposes. Using this framework, we design a pair of generic quality measures—timeliness and correctness—for the progress of entities through a supply chain. The timeliness and correctness metrics provide behavioral visibility that can help managers to grasp the dynamics of supply chain behavior that is distinct from asset visibility such as inventory. We develop special quality control methods using this framework to address the issue of overreaction that is common among managers faced with a large volume of fast‐changing data. The SIT structure and its associated methods inform managers on if, when, and where to react. We illustrate our approach using simulations based on real RFID data from a Walmart RFID pilot project.

Suggested Citation

  • Jun Shu & Russell Barton, 2012. "Managing Supply Chain Execution: Monitoring Timeliness and Correctness via Individualized Trace Data," Production and Operations Management, Production and Operations Management Society, vol. 21(4), pages 715-729, July.
    • Handle: RePEc:bla:popmgt:v:21:y:2012:i:4:p:715-729
    DOI: 10.1111/j.1937-5956.2012.01353.x
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    1. Lurie, Nicholas H. & Swaminathan, Jayashankar M., 2009. "Is timely information always better? The effect of feedback frequency on decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 108(2), pages 315-329, March.
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    Cited by:

    1. Alaa Amin Abdalla & Yousif Abdelbagi Abdalla & Akarm M. Haddad & Ganga Bhavani & Eman Zabalawi, 2022. "Connections between Big Data and Smart Cities from the Supply Chain Perspective: Understanding the Impact of Big Data," Sustainability, MDPI, vol. 14(23), pages 1-13, December.
    2. Doetzer, Mathias, 2020. "The role of national culture on supply chain visibility: Lessons from Germany, Japan, and the USA," International Journal of Production Economics, Elsevier, vol. 230(C).
    3. David C. Hall & Tracy D. Johnson-Hall, 2021. "The value of downstream traceability in food safety management systems: an empirical examination of product recalls," Operations Management Research, Springer, vol. 14(1), pages 61-77, June.
    4. Maximilian Klöckner & Christoph G. Schmidt & Stephan M. Wagner, 2022. "When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 46-64, January.

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