企業(yè)使用機(jī)器學(xué)習(xí)技術(shù)可以在今時(shí)今日實(shí)現(xiàn)兩位數(shù)的增長。這些革命供應(yīng)鏈管理的場景包括：預(yù)測錯(cuò)誤率，按需調(diào)節(jié)生產(chǎn)力；節(jié)省成本指出，及時(shí)的交付等等方面。
機(jī)器學(xué)習(xí)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(yù)判。許多供應(yīng)鏈挑戰(zhàn)都離不開時(shí)間、成本和資源等要素的制約，這使得機(jī)器學(xué)習(xí)成為解決這些問題的理想技術(shù)。
無論是亞馬遜機(jī)器人系統(tǒng)（倉儲自動(dòng)化機(jī)器人）通過機(jī)器學(xué)習(xí)提升準(zhǔn)確率，速度和規(guī)模；還是DHL依賴AI和機(jī)器學(xué)習(xí)技術(shù)賦能其可預(yù)測性網(wǎng)絡(luò)管理系統(tǒng)——一套從內(nèi)部數(shù)據(jù)的58個(gè)要素中尋找出影響交期延遲首要因素的系統(tǒng)，都通過機(jī)器學(xué)習(xí)定義了下一代供應(yīng)鏈管理系統(tǒng)。Gartner預(yù)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(jī)器學(xué)習(xí)技術(shù)。而2023年，智能算法，AI技術(shù)將嵌入超過25%的供應(yīng)鏈技術(shù)解決方案。
以下是機(jī)器學(xué)習(xí)影響供應(yīng)鏈管理的十種場景
1）以機(jī)器學(xué)習(xí)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(jìn)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(yùn)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(jī)器學(xué)習(xí)技術(shù)為供應(yīng)鏈改善提供價(jià)值。
圖片來源：KPMG, SUPPLY CHAIN BIG DATA SERIES PART 1
3）機(jī)器學(xué)習(xí)有機(jī)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(shè)備采集的軌跡數(shù)據(jù)中學(xué)習(xí)模型來實(shí)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(jī)器學(xué)習(xí)減少預(yù)測錯(cuò)誤
通過機(jī)器學(xué)習(xí)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(zhǔn)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(jī)器學(xué)習(xí)技術(shù)將幫助物流和供應(yīng)鏈單元優(yōu)化庫存占用情況，提升用戶體驗(yàn)，減少風(fēng)險(xiǎn)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(yīng)對不一致的供應(yīng)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(fēng)險(xiǎn)，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(qiáng)端對端的供應(yīng)鏈透明度，幫助企業(yè)更快響應(yīng)
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(fēng)險(xiǎn)
首席信息官們正在解決供應(yīng)鏈中的特權(quán)濫用問題，如果機(jī)器學(xué)習(xí)發(fā)現(xiàn)活動(dòng)的環(huán)境處于風(fēng)險(xiǎn)當(dāng)中，將要求更強(qiáng)力的許可來授權(quán)活動(dòng)。
10）通過機(jī)器學(xué)習(xí)技術(shù)，結(jié)合IoT數(shù)據(jù)改善設(shè)備的維護(hù)水平，降低運(yùn)營成本。
麥肯錫公司發(fā)現(xiàn)，通過機(jī)器學(xué)習(xí)賦能的預(yù)測式維護(hù)技術(shù)，將幫助企業(yè)更好地避免機(jī)器停止運(yùn)轉(zhuǎn)。設(shè)備的生產(chǎn)力將得以提升20%，而整體維護(hù)成本將減少10%。
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