Lead Time Estimation Using Artificial Intelligence

Abstract

DLA asked LMI to identify artificial intelligence (AI) methods that improve accuracy of total lead time (TLT) estimation and its component parts. AI methods can improve the accuracy of lead time estimates for ALT, PLT, and TLT by 19 to 40 percent. On average, random forest (RF) models improve the accuracy of lead time predictions by 38 days.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 2020
Accession Number
AD1108203

Entities

People

  • Hasan Khan
  • Russell S. Salley
  • Stephanie D. Brown
  • Wei Zhu

Organizations

  • LMI

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Aircraft Engines
  • Algorithms
  • Artificial Intelligence
  • Artificial Neural Networks
  • Contracts
  • Correlation Analysis
  • Data Processing
  • Data Set
  • Data Sets
  • Databases
  • Engineering
  • Industrial Production
  • Information Science
  • Lead Time
  • Machine Learning
  • Materials
  • Neural Networks
  • Predictive Modeling
  • Procurement
  • Supervised Machine Learning
  • Supply Chain

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Logistics and Supply Chain Management.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy