企业使用机器学习技术可以在今时今日实现两位数的增长。这些革命供应链管理的场景包括：预测错误率，按需调节生产力；节省成本指出，及时的交付等等方面。
机器学习的算法和模型基于从大数据集中发现异常，模式乃至预判。许多供应链挑战都离不开时间、成本和资源等要素的制约，这使得机器学习成为解决这些问题的理想技术。
无论是亚马逊机器人系统（仓储自动化机器人）通过机器学习提升准确率，速度和规模；还是DHL依赖AI和机器学习技术赋能其可预测性网络管理系统——一套从内部数据的58个要素中寻找出影响交期延迟首要因素的系统，都通过机器学习定义了下一代供应链管理系统。Gartner预测，到2020年将有95%的SCP（Supply Chain Planning）厂商将在其解决方案中纳入机器学习技术。而2023年，智能算法，AI技术将嵌入超过25%的供应链技术解决方案。
以下是机器学习影响供应链管理的十种场景
1）以机器学习为基础的算法将成为下一代物流技术的基础，通过先进的资源调配系统带来重大收益。
图片来源：MCKINSEY & COMPANY, AUTOMATION IN LOGISTICS: BIG OPPORTUNITY, BIGGER UNCERTAINTY, APRIL 2019. BY ASHUTOSH DEKHNE, GREG HASTINGS, JOHN MURNANE, AND FLORIAN NEUHAUS
2）物联网传感器，新型信息通讯技术，智能运输系统，交通数据将构成宽广的数据集变量，这些内容将通过机器学习技术为供应链改善提供价值。
图片来源：KPMG, SUPPLY CHAIN BIG DATA SERIES PART 1
3）机器学习有机会帮助物流系统节省每年600万美金的成本，这将通过从IoT设备采集的轨迹数据中学习模型来实现
图片来源：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）通过机器学习减少预测错误
通过机器学习技术可以减少因库存不足造成的销售损失，最多可以降低65%。而在库存的准备上也有20%-50%的优化空间。
图片来源：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研究发现，机器学习技术将帮助物流和供应链单元优化库存占用情况，提升用户体验，减少风险和开发新商业模式。
图片来源：SOURCE: DHL TREND RESEARCH, LOGISTICS TREND RADAR, VERSION 2018/2019 (PDF, 55 PP., NO OPT-IN)
6）一家区域制造商正在使用AI技术来检测和应对不一致的供应商质量等级和交付情况
图片来源：MICROSOFT, SUPPLIER QUALITY ANALYSIS SAMPLE FOR POWER BI: TAKE A TOUR, 2018
7）减少欺诈的潜在风险，改善产品和流程质量
图片来源：FORBES, HOW MACHINE LEARNING IMPROVES MANUFACTURING INSPECTIONS, PRODUCT QUALITY & SUPPLY CHAIN VISIBILITY, JANUARY 23, 2019
8）通过增强端对端的供应链透明度，帮助企业更快响应
图片来源：CHAINLINK RESEARCH, HOW INFOR IS HELPING TO REALIZE HUMAN POTENTIAL,
9）减少特权规则的使用来带的安全风险
首席信息官们正在解决供应链中的特权滥用问题，如果机器学习发现活动的环境处于风险当中，将要求更强力的许可来授权活动。
10）通过机器学习技术，结合IoT数据改善设备的维护水平，降低运营成本。
麦肯锡公司发现，通过机器学习赋能的预测式维护技术，将帮助企业更好地避免机器停止运转。设备的生产力将得以提升20%，而整体维护成本将减少10%。
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