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%。

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