Rapid and reliable distribution support is important for Army forces deployed into theaters of operations. This report describes algorithms developed by the authors that monitor the U.S. Army's logistics distribution system and automatically detect distribution problems (or potential distribution problems) that might affect equipment readiness.
Rapidly Detecting and Correcting Degradation of Military Supply Distribution Performance
Algorithms, Visualizations, and Case Studies
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Research Synopsis
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Research Questions
- What is the best way to monitor the distribution system and automatically detect distribution problems (or potential distribution problems) that might affect equipment readiness?
- 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.
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