Machine Learning 101 for Supply Chain Leaders Part 2: Talent, Organization and Technology Requirements


Published: 02 February 2018 ID: G00349011

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Summary

Successful adoption of machine learning hinges on available talent, technology and organizational enablers. This research provides supply chain leaders responsible for machine learning and artificial intelligence with clarity on the requisite skills, organization structure and technology approaches.

Table of Contents

  • Introduction
  • Analysis
    • Assess Requisite Staffing and Organization Structure
      • How Do We Get Started With ML Without Hiring Additional Staff?
      • How Do We Staff for ML?
      • What Is the Best Organizational Model to Support ML?
    • Make an Informed Technology Selection, and Understand the Pitfalls
      • How Do We Verify Vendors' Claims of Offering ML Capabilities?
      • Should We Build, Buy or Partner for Our ML Solutions?
    • Continue to Educate Yourself on ML Advances and Examples
      • What Newsletters Are There to Stay Up to Date?
      • What Are the Major Conferences and Summits?
      • How Can I Learn More About ML?
  • Gartner Recommended Reading
© 2018 Gartner, Inc. and/or its Affiliates. All Rights Reserved. Reproduction and distribution of this publication in any form without prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Although Gartners research may discuss legal issues related to the information technology business, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice.

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