企業(yè)使用機器學習技術(shù)可以在今時今日實現(xiàn)兩位數(shù)的增長。這些革命供應鏈管理的場景包括：預測錯誤率，按需調(diào)節(jié)生產(chǎn)力；節(jié)省成本指出，及時的交付等等方面。
機器學習的算法和模型基于從大數(shù)據(jù)集中發(fā)現(xiàn)異常，模式乃至預判。許多供應鏈挑戰(zhàn)都離不開時間、成本和資源等要素的制約，這使得機器學習成為解決這些問題的理想技術(shù)。
無論是亞馬遜機器人系統(tǒng)（倉儲自動化機器人）通過機器學習提升準確率，速度和規(guī)模；還是DHL依賴AI和機器學習技術(shù)賦能其可預測性網(wǎng)絡管理系統(tǒng)——一套從內(nèi)部數(shù)據(jù)的58個要素中尋找出影響交期延遲首要因素的系統(tǒng)，都通過機器學習定義了下一代供應鏈管理系統(tǒng)。Gartner預測，到2020年將有95%的SCP（Supply Chain Planning）廠商將在其解決方案中納入機器學習技術(shù)。而2023年，智能算法，AI技術(shù)將嵌入超過25%的供應鏈技術(shù)解決方案。
以下是機器學習影響供應鏈管理的十種場景
1）以機器學習為基礎(chǔ)的算法將成為下一代物流技術(shù)的基礎(chǔ)，通過先進的資源調(diào)配系統(tǒng)帶來重大收益。
圖片來源：MCKINSEY & COMPANY, AUTOMATION IN LOGISTICS: BIG OPPORTUNITY, BIGGER UNCERTAINTY, APRIL 2019. BY ASHUTOSH DEKHNE, GREG HASTINGS, JOHN MURNANE, AND FLORIAN NEUHAUS
2）物聯(lián)網(wǎng)傳感器，新型信息通訊技術(shù)，智能運輸系統(tǒng)，交通數(shù)據(jù)將構(gòu)成寬廣的數(shù)據(jù)集變量，這些內(nèi)容將通過機器學習技術(shù)為供應鏈改善提供價值。
圖片來源：KPMG, SUPPLY CHAIN BIG DATA SERIES PART 1
3）機器學習有機會幫助物流系統(tǒng)節(jié)省每年600萬美金的成本，這將通過從IoT設備采集的軌跡數(shù)據(jù)中學習模型來實現(xiàn)
圖片來源：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
4）通過機器學習減少預測錯誤
通過機器學習技術(shù)可以減少因庫存不足造成的銷售損失，最多可以降低65%。而在庫存的準備上也有20%-50%的優(yōu)化空間。
圖片來源：DIGITAL/MCKINSEY, SMARTENING UP WITH ARTIFICIAL INTELLIGENCE (AI) - WHAT’S IN IT FOR GERMANY AND ITS INDUSTRIAL SECTOR? (PDF, 52 PP., NO OPT-IN).
5）DHL研究發(fā)現(xiàn)，機器學習技術(shù)將幫助物流和供應鏈單元優(yōu)化庫存占用情況，提升用戶體驗，減少風險和開發(fā)新商業(yè)模式。
圖片來源：SOURCE: DHL TREND RESEARCH, LOGISTICS TREND RADAR, VERSION 2018/2019 (PDF, 55 PP., NO OPT-IN)
6）一家區(qū)域制造商正在使用AI技術(shù)來檢測和應對不一致的供應商質(zhì)量等級和交付情況
圖片來源：MICROSOFT, SUPPLIER QUALITY ANALYSIS SAMPLE FOR POWER BI: TAKE A TOUR, 2018
7）減少欺詐的潛在風險，改善產(chǎn)品和流程質(zhì)量
圖片來源：FORBES, HOW MACHINE LEARNING IMPROVES MANUFACTURING INSPECTIONS, PRODUCT QUALITY & SUPPLY CHAIN VISIBILITY, JANUARY 23, 2019
8）通過增強端對端的供應鏈透明度，幫助企業(yè)更快響應
圖片來源：CHAINLINK RESEARCH, HOW INFOR IS HELPING TO REALIZE HUMAN POTENTIAL,
9）減少特權(quán)規(guī)則的使用來帶的安全風險
首席信息官們正在解決供應鏈中的特權(quán)濫用問題，如果機器學習發(fā)現(xiàn)活動的環(huán)境處于風險當中，將要求更強力的許可來授權(quán)活動。
10）通過機器學習技術(shù)，結(jié)合IoT數(shù)據(jù)改善設備的維護水平，降低運營成本。
麥肯錫公司發(fā)現(xiàn)，通過機器學習賦能的預測式維護技術(shù)，將幫助企業(yè)更好地避免機器停止運轉(zhuǎn)。設備的生產(chǎn)力將得以提升20%，而整體維護成本將減少10%。
原文參考資料包括：
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 Logistics, 37(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 Systems, 32(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 E, 114343-349.
Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 7645-52.
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 & Transformation, 6(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 Management, 29(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 Logistics, 35(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 & Control, 28(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 Logistics, 36(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 Engineering, 115319-330.
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)
