Rapidly Detecting and Correcting Degradation of Military Supply Distribution Performance

Algorithms, Visualizations, and Case Studies

by James R. Broyles, Kenneth J. Girardini, Jason Mastbaum, Marc Robbins, Patricia Boren

Download eBook for Free

Full Document

FormatFile SizeNotes
PDF file 3.8 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

Research Synopsis

FormatFile SizeNotes
PDF file 0.1 MB

Use Adobe Acrobat Reader version 10 or higher for the best experience.

Research Questions

  1. What is the best way to monitor the distribution system and automatically detect distribution problems (or potential distribution problems) that might affect equipment readiness?
  2. What data visualizations could assist Army managers and analysts to determine the root causes and potential corrective actions related to the detections?

Army units worldwide depend on a complex network of distribution centers, managed primarily by the Defense Logistics Agency, to support equipment readiness and sustainability. Rapid and reliable logistics distribution support is especially important for U.S. Army forces deployed into theaters of operations. There are many factors that can cause performance changes affecting the distribution timeliness to the Army. Currently, distribution problems are detected manually and reactively by Army units once these problems start to affect equipment readiness. This report describes (1) algorithms developed by the authors that monitor the logistics distribution system and automatically detect distribution problems (or potential distribution problems) that might affect equipment readiness, and (2) data visualizations developed by the authors that assist Army managers and analysts to determine the root causes and potential corrective actions related to the detections. The report also provides several case studies illustrating the algorithms' effectiveness.

Key Findings

  • Current Army distribution metrics are lagging indicators of problems because they focus on requisition wait time, which requires the receipt of the shipment, and require manual monitoring to detect problems.
  • In several historical case studies of distribution performance degradation, the detection algorithms and metrics could have automatically detected actual or potential distribution problems several months prior to when they were realized by the Army units.

Recommendations

  • Implement the detection algorithms and visualizations in an Army analytics platform for continued use and to inform corrective actions.
  • Expand the metrics beyond requisition wait time and provide open shipment data in the analytics platform so that the algorithms can identify potential distribution problems earlier.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    Automatic Detection Algorithms

  • Chapter Three

    Overview of the OCONUS Distribution Data Visualization Dashboards

  • Chapter Four

    Case Studies of Distribution Degradation Detections, Visualizations, and Corrective Actions

  • Chapter Five

    Conclusion and Way Forward

  • Appendix A

    OCONUS Distribution Visualization Dashboards Details

  • Appendix B

    Documentation of Calculations in Tableau

  • Appendix C

    Geographic Regions

  • Appendix D

    Data Fields and Sources

Research conducted by

The research described in this report was sponsored by the Office of the Deputy Chief of Staff, G-4 (Logistics), U.S. Army and conducted by the Forces and Logistics Program with the RAND Arroyo Center.

This report is part of the RAND Corporation Research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.