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How To Improve Supply Chains With Machine Learning: 10 Proven Ways

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

References

Accenture, Reinventing The Supply Chain With AI, 20 pp., PDF, no opt-in.

Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics. Journal Of Business Logistics37(1), 6-17.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

Capgemini, Supply Chain Management – The Quiet Revolution. April 24, 2019

CB Insights, Stocked Up: 150+ Companies Attacking The Supply Chain & Logistics Space, November 30, 2016

Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal Of Management Information Systems32(4), 4-39.

Council of Supply Chain Management Professionals (CSCMP) Supply Chain Quarterly, Machine learning: A new tool for better forecasting, Joseph Shamir, Q4, 2014
Council of Supply Chain Management Professionals (CSCMP) Supply Chain Quarterly, Paving the way for AI in the warehouse, Luke Waltz   Quarter 1 2018 issue

DHL, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in)

DHL, Cracking Logistics with the help of Machine Learning, Harvard Business School, November 13, 2018

DHL, Logistics Trend Radar, 2016 (PDF, 55 pp., no opt-in)

DHL Trend Research, Artificial Intelligence in Logistics, 2018 (PDF, 45 pp., no opt-in)

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

Feizabadi, J., & Shrivastava, A. (2018). Does AI-enabled demand forecasting improve supply chain efficiency? Supply Chain Management Review, 22(6), 8-10.
Gartner, Supply Chain Trends in the Digital Age, February 17, 2016 (PDF, 9 pp., no opt-in)

Govindan, K., Cheng, T., Mishra, N., & Shukla, N. (2018). Big data analytics and application for logistics and supply chain management. Transportation Research: Part E114343-349.

Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems7645-52.

IDC, Digital Transformation Drives Supply Chain Restructuring Imperative, July 2017

IDC, Overcoming Supply Chain Complexity with Predictive Logistics, August 2017 (PDF, 8 pp., no opt-in)

Jayant, A. (2013). Evaluation of 3PL Service Provider in Supply Chain Management: An Analytic Network Process Approach. International Journal Of Business Insights & Transformation6(2), 78-82.

KPMG, Supply Chain Big Data Series, Part 2. June 2018 (PDF, 14 pp., no opt-in)

Lai, Y., Sun, H., & Ren, J. (2018). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management. International Journal Of Logistics Management29(2), 676-703.
Mackelprang, A. W., Robinson, J. L., Bernardes, E., & Webb, G. S. (2014). The Relationship Between Strategic Supply Chain Integration and Performance: A Meta-Analytic Evaluation and Implications for Supply Chain Management Research. Journal Of Business Logistics35(1), 71-96.

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Digital supply chains: Do you have the skills to run them?, July 2017.

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control28(11/12), 873-876.

Schoenherr, T., & Speier-Pero, C. (2015). Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential. Journal Of Business Logistics36(1), 120-132.

Sharma, V. (2018). Demand forecasting in the cloud: Modern computing meets the forecasting discipline. The Journal of Business Forecasting, 37(3), 4-7,9-10.
Stanford University, Department of Management Science and Engineering, Lectures in Supply-Chain Optimization (PDF, 261 pp., no opt-in)

Tata Consulting Services, Using Machine Learning to Transform Supply Chain Management

The Hackett Group, Analytics: Laying the Foundation for Supply Chain Digital Transformation (PDF, 10 pp., no opt-in)

Tiwari, S., Wee, H., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering115319-330.

World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22 pgs., no opt-in)

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

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