Enabling Real-Time Supply Chain Visibility Through Predictive Analytics

Abstract

In today's inter-connected economy, original equipment manufacturers (OEMs) procure parts, many of which have lead times of more than a year, from hundreds of globally distributed suppliers. The OEM suppliers, which are often small and medium-scale enterprises (SMEs), obtain parts themselves from many other dispersed suppliers, leading to the creation of complex supply chain networks with several layers of hierarchical dependencies. The networks frequently include multiple bottlenecks in the form of specialized suppliers who act as sole sources of certain critical parts. These characteristics make it imperative to have a high degree of visibility into the flow of parts through the networks to facilitate production planning and inventory management for OEMs and SMEs alike. However, the critical information pertaining to operation schedules, production capacities, and material procurement decisions are typically not shared among the stakeholders to maintain competitive benefits. Hence, part flows must be predicted based on either supplier promises and historical performances, or demand estimates and buyer preferences.

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Document Details

Document Type
Technical Report
Publication Date
Sep 23, 2018
Accession Number
AD1078503

Entities

People

  • Ashis G. Banerjee

Organizations

  • University of Washington

Tags

Communities of Interest

  • Autonomy
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Computer Programming
  • Computer Programs
  • Computers
  • Data Mining
  • Databases
  • Dimensionality Reduction
  • High Performance Computing
  • Information Science
  • Machine Learning
  • Manufacturing
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Processing Equipment
  • Supervised Machine Learning
  • Supply Chain
  • Supply Chain Management

Readers

  • Defense Acquisition Program Management
  • Distributed Systems and Data Platform Development
  • Industrial Economics