大數(shù)據(jù)徹底變革供應(yīng)鏈管理的十大方面

大數(shù)據(jù)可以為供應(yīng)商網(wǎng)絡(luò)(Supplier Networks) 提供更好的數(shù)據(jù)準(zhǔn)確性(Accuracy)、清晰度(Clarity)和洞察力(Insights)，從而在共享的供應(yīng)網(wǎng)絡(luò)中實(shí)現(xiàn)更多的情境智能(Contextual Intelligence)。

Bottom Line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

有前瞻目光的制造商們正在將80%或更大比例的供應(yīng)網(wǎng)絡(luò)經(jīng)營(yíng)活動(dòng)構(gòu)建在其企業(yè)外部，他們利用大數(shù)據(jù)和云計(jì)算技術(shù)來(lái)突破傳統(tǒng)ERP系統(tǒng)和供應(yīng)鏈系統(tǒng)的局限性。對(duì)于商業(yè)模式基于快速產(chǎn)品周期迭代和產(chǎn)品上市速度的制造商，傳統(tǒng)的ERP/SCM系統(tǒng)僅僅是為了完成訂單交付、發(fā)運(yùn)和交易數(shù)據(jù)而設(shè)計(jì)的，這樣的傳統(tǒng)系統(tǒng)的擴(kuò)展性極其有限，根本無(wú)法滿足當(dāng)下供應(yīng)鏈管理所面臨的種種挑戰(zhàn)，已經(jīng)成為企業(yè)供應(yīng)鏈管理的瓶頸。

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today.

如今的制造商都立足于在準(zhǔn)確性(Accuracy)、速度(Speed)和質(zhì)量(Quality)方面開(kāi)展市場(chǎng)競(jìng)爭(zhēng)，這一定位迫使企業(yè)的供應(yīng)商網(wǎng)絡(luò)必須具備一定程度的情景智能的能力，傳統(tǒng)的ERP/SCM系統(tǒng)是無(wú)法幫助企業(yè)達(dá)成這一競(jìng)爭(zhēng)目標(biāo)的。然而當(dāng)今大多數(shù)企業(yè)還沒(méi)有將大數(shù)據(jù)技術(shù)引入其供應(yīng)鏈運(yùn)營(yíng)當(dāng)中，本文介紹的十大要素將成為企業(yè)未來(lái)供應(yīng)鏈戰(zhàn)略變革的重要催化劑。

Choosing to compete on accuracy, speed and quality forces supplier networks to get to a level of contextual intelligence not possible with legacy ERP and SCM systems. While many companies today haven’t yet adopted big data into their supply chain operations, these ten factors taken together will be the catalyst that get many moving on their journey.

舉個(gè)小實(shí)例來(lái)說(shuō)明大數(shù)據(jù)分析(BDA – Big Data Analytics)如何在準(zhǔn)確性、速度和質(zhì)量方面對(duì)供應(yīng)鏈管理提升的作用：

亞馬遜Amazon利用大數(shù)據(jù)來(lái)監(jiān)控、追蹤、確保其15億庫(kù)存商品準(zhǔn)確的存放于全球200個(gè)訂單履行中心（fulfilment centers）當(dāng)中。亞馬遜利用預(yù)測(cè)分析(Predictive Analytics)技術(shù)可以實(shí)現(xiàn)“預(yù)期發(fā)貨(anticipatory shipping)”的情景，即，當(dāng)客戶打算購(gòu)買一件商品的時(shí)候（注意是打算購(gòu)買尚未正式下單），亞馬遜就將貨物提前發(fā)運(yùn)(pre-ship)到離客戶最近的倉(cāng)儲(chǔ)中心。這種對(duì)供應(yīng)鏈管理的優(yōu)化極大的提升了其客戶的體驗(yàn)。

大數(shù)據(jù)變革供應(yīng)鏈

1、情境智能 Contextual Intelligence

目前，由供應(yīng)鏈產(chǎn)生的數(shù)據(jù)的規(guī)模(scale)、廣度(scope)和深度(depth)都在加速增長(zhǎng)，為情景智能(contextual intelligence)驅(qū)動(dòng)的供應(yīng)鏈提供了充足的數(shù)據(jù)基礎(chǔ)。

The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence.

下面“圖1”很有意思，它收集了整個(gè)供應(yīng)鏈中的52種不同的數(shù)據(jù)源（包括結(jié)構(gòu)化/半結(jié)構(gòu)化/非結(jié)構(gòu)化數(shù)據(jù)），并從大數(shù)據(jù)的三個(gè)維度(3Vs)進(jìn)行了統(tǒng)計(jì)分析，數(shù)據(jù)量(Volume)/數(shù)據(jù)速度(Velocity)和數(shù)據(jù)多樣性(Variety)。其中很明顯絕大部分?jǐn)?shù)據(jù)都是從企業(yè)外部產(chǎn)生的。有前瞻性的制造商已經(jīng)開(kāi)始將大數(shù)據(jù)作為更廣泛供應(yīng)鏈協(xié)作的催化劑。

The following graphic provides an overview of 52 different sources of big data that are generated in supply chains Plotting the data sources by variety, volume and velocity by the relative level of structured/unstructured data, it’s clear that the majority of supply chain data is generated outside an enterprise. Forward-thinking manufacturers are looking at big data as a catalyst for greater collaboration.

圖 1：點(diǎn)擊查看高清大圖

值得注意的是，在核心交易系統(tǒng)范疇內(nèi)，傳統(tǒng)的ERP, SRM和CRM系統(tǒng)通常在企業(yè)內(nèi)部的數(shù)據(jù)量（Volume）是很高的，但是這些數(shù)據(jù)放在整個(gè)52中數(shù)據(jù)源框架下只占了很小的比例，這就是為什么圖1中的“核心交易系統(tǒng)數(shù)據(jù)”處于縱向較低的位置。如果你看右上角可以發(fā)現(xiàn)，高數(shù)據(jù)量和速度的非結(jié)構(gòu)化數(shù)據(jù)大都是與“客戶”交互的數(shù)據(jù)：社交數(shù)據(jù)、在線調(diào)研、移動(dòng)位置傳感設(shè)備等。

大數(shù)據(jù)分析（BDA – Big Data Analytics）技術(shù)在供應(yīng)鏈管理領(lǐng)域的應(yīng)用通常被稱為：供應(yīng)鏈大數(shù)據(jù)分析技術(shù) SCM Big Data Analytics，它可以被定義為一個(gè)流程，即，將高級(jí)數(shù)據(jù)分析(Advanced Analytics)技術(shù)與供應(yīng)鏈管理理論相結(jié)合并應(yīng)用于更大的數(shù)據(jù)集合當(dāng)中，這個(gè)數(shù)據(jù)集合的體量、速度和多樣性需要借助于大數(shù)據(jù)技術(shù)工具來(lái)分析；同時(shí)，需要借助供應(yīng)鏈管理專業(yè)人士的技能通過(guò)提供精準(zhǔn)實(shí)時(shí)的商業(yè)洞察來(lái)持續(xù)感知和反饋解決SCM相關(guān)的問(wèn)題。

SCM Big Data Analytics is the process of applying advanced analytics techniques in combination with SCM theory to datasets whose volume, velocity or variety require information technology tools from the Big Data technology stack; leveraging supply chain professionals with the ability to continually sense and respond to SCM relevant problems by providing accurate and timely business insights.

大數(shù)據(jù)驅(qū)動(dòng)的供應(yīng)鏈管理（Big Data Driven SCM) 需要首先理解供應(yīng)鏈中的四種行為：買(buy)、賣(sell)、移動(dòng)(move)和存儲(chǔ)(store)；這四種行為對(duì)應(yīng)四種SCM杠桿(SCM levers)：采購(gòu)(procurement)、市場(chǎng)(marketing)、運(yùn)輸(transportation)和倉(cāng)庫(kù)(warehouse)。根據(jù)52種SCM數(shù)據(jù)源與這四種行為杠桿的關(guān)系，可以繪制出如下關(guān)系網(wǎng)絡(luò)圖（圖2），從而幫助我們更好的理解不同數(shù)據(jù)源在整個(gè)供應(yīng)鏈網(wǎng)絡(luò)中的位置。

如此復(fù)雜的數(shù)據(jù)關(guān)系，如果不借助大數(shù)據(jù)分析的技術(shù)是無(wú)法將其轉(zhuǎn)化為企業(yè)供應(yīng)鏈可利用的價(jià)值的。現(xiàn)在的企業(yè)往往收集大量的數(shù)據(jù)卻不知道如何利用(business collect more data than they know what to do with), 所以企業(yè)必須將數(shù)據(jù)不再看成信息資產(chǎn)而是戰(zhàn)略資產(chǎn)，也就是說(shuō)在所有企業(yè)都在努力收集這些供應(yīng)鏈數(shù)據(jù)的大環(huán)境下，擁有大量數(shù)據(jù)已經(jīng)不能成為企業(yè)絕對(duì)的競(jìng)爭(zhēng)優(yōu)勢(shì)了；企業(yè)如何通過(guò)其獨(dú)特的信息使用戰(zhàn)略（大數(shù)據(jù)驅(qū)動(dòng)的供應(yīng)鏈管理）才是建立更有力的供應(yīng)鏈競(jìng)爭(zhēng)優(yōu)勢(shì)的途徑。

圖 2：點(diǎn)擊查看大圖

2、進(jìn)化為知識(shí)共享型供應(yīng)鏈價(jià)值網(wǎng)絡(luò)

驅(qū)動(dòng)更為復(fù)雜的專注于知識(shí)分享和協(xié)作的供應(yīng)商網(wǎng)路，從而讓供應(yīng)商網(wǎng)絡(luò)不僅僅是完成交易而是帶來(lái)增值。

Enabling more complex supplier networks that focus on knowledge sharing and collaboration as the value-add over just completing transactions.

大數(shù)據(jù)正在變革供應(yīng)商網(wǎng)絡(luò)在新市場(chǎng)和成熟市場(chǎng)中形成、增長(zhǎng)、擴(kuò)張的方式。交易不再是唯一的目標(biāo)，創(chuàng)建知識(shí)共享型的網(wǎng)絡(luò)更為重要。從下圖（圖3）中可以看到供應(yīng)鏈價(jià)值網(wǎng)絡(luò)如何逐步向知識(shí)共享型進(jìn)化。

Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren’t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. The following graphic from Business Ecosystems Come Of Age (Deloitte University Press) (free, no opt-in) illustrates the progression of supply chains from networks or webs, where knowledge sharing becomes a priority.

圖 3：點(diǎn)擊查看大圖

3、供應(yīng)鏈能力的提升

大數(shù)據(jù)和高級(jí)分析技術(shù)正更快速的集成到供應(yīng)鏈能力(Supply Chain Capabilities)當(dāng)中。

Big data and advanced analytics are being integrated into optimization tools, demand forecasting, integrated business planning and supplier collaboration & risk analytics at a quickening pace.

德勤的調(diào)研顯示，當(dāng)前使用最多的前四種供應(yīng)鏈能力為：優(yōu)化工具，需求預(yù)測(cè)，集成業(yè)務(wù)預(yù)測(cè)、供應(yīng)商協(xié)作和風(fēng)險(xiǎn)分析。更多見(jiàn)圖4.

These are the top four supply chain capabilities that Delotte found are currently in use form their recent study, Supply Chain Talent of the Future Findings from the 3rdAnnual Supply Chain Survey (free, no opt-in). Control tower analytics and visualization are also on the roadmaps of supply chain teams currently running big data pilots.

圖 4：點(diǎn)擊查看大圖

4、供應(yīng)鏈領(lǐng)域的顛覆性技術(shù)

64%的供應(yīng)鏈高管將大數(shù)據(jù)分析看成顛覆性的重要技術(shù)，這是企業(yè)長(zhǎng)期變革管理的重要基礎(chǔ)。

64% of supply chain executives consider big data analytics a disruptive and important technology, setting the foundation for long-term change management in their organizations.

圖 5：點(diǎn)擊查看大圖

5、優(yōu)化整合供應(yīng)鏈配送網(wǎng)絡(luò)

利用基于大數(shù)據(jù)的地理分析技術(shù) (Geoanalytics) 來(lái)整合優(yōu)化供應(yīng)鏈配送網(wǎng)絡(luò)。

Using geoanalytics based on big data to merge and optimize delivery networks.

波士頓咨詢公司在文章“大數(shù)據(jù)如何在供應(yīng)鏈管理中有效的應(yīng)用”中解釋了大數(shù)據(jù)是如何在供應(yīng)鏈管理中應(yīng)用的。其中一個(gè)案例解釋了如何利用地理分析技術(shù)(Geoanalytics)規(guī)劃兩個(gè)供應(yīng)鏈網(wǎng)絡(luò)的優(yōu)化和合并，見(jiàn)下圖6：通過(guò)結(jié)合地理分析技術(shù)和大數(shù)據(jù)技術(shù)解決了這一領(lǐng)域中最大的服務(wù)問(wèn)題 – 極大的減少了有線TV技術(shù)的等待時(shí)間同時(shí)提升了服務(wù)精準(zhǔn)性。

The Boston Consulting Group provides insights into how big data is being put to use in supply chain management in the article Making Big Data Work: Supply Chain Management (free, opt-in). One of the examples provided is how the merger of two delivery networks was orchestrated and optimized using geoanalytics. The following graphic is from the article. Combining geoanalytics and big data sets could drastically reduce cable TV tech wait times and driving up service accuracy, fixing one of the most well-known service challenges of companies in that business.

圖 6：點(diǎn)擊查看大圖

6、供應(yīng)鏈問(wèn)題的優(yōu)化

對(duì)供應(yīng)鏈問(wèn)題的優(yōu)化。大數(shù)據(jù)可以幫助企業(yè)將對(duì)供應(yīng)鏈問(wèn)題的反應(yīng)時(shí)間提升41%，將供應(yīng)鏈效率提升10%甚至超過(guò)36%，跨供應(yīng)鏈的整合提升至36%。

Big data is having an impact on organizations’ reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%).The Big Data Analytics in Supply Chain: Hype or Here to Stay? Accenture Global Operations Megatrends Study found that companies are achieving significant results using big data analytics to improve supply chain performance and gain greater contextual intelligence.

圖 7：點(diǎn)擊查看大圖

7、供應(yīng)鏈運(yùn)營(yíng)的整合

將大數(shù)據(jù)分析集成到供應(yīng)鏈運(yùn)營(yíng)中可以將訂單滿足周期提升4.25倍、將供應(yīng)鏈效率的提升2.6倍

Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater. Accenture found that embedding big data into supply chain operations accelerates supply chain processes a minimum of 1.3x over using big data on an ad hoc basis.

Source: Big Data Analytics in Supply Chain: Hype or Here to Stay? Accenture Global Operations Megatrends Study

圖 8：點(diǎn)擊查看大圖

8、供應(yīng)鏈財(cái)務(wù)指標(biāo)的追蹤

對(duì)供應(yīng)鏈戰(zhàn)略、戰(zhàn)術(shù)、運(yùn)營(yíng)更深入的情境智能應(yīng)用，正在影響公司的財(cái)務(wù)指標(biāo)。

Greater contextual intelligence of how supply chain tactics, strategies and operations are influencing financial objectives.

供應(yīng)鏈可視化，通常是指能夠清晰的看到供應(yīng)鏈網(wǎng)絡(luò)中供應(yīng)商的多層次結(jié)構(gòu)。作者的經(jīng)驗(yàn)告訴我們，通過(guò)供應(yīng)鏈決策的財(cái)務(wù)結(jié)果追蹤回財(cái)務(wù)指標(biāo)是可行的；而且通過(guò)將大數(shù)據(jù)應(yīng)用與財(cái)務(wù)系統(tǒng)集成，提升行業(yè)快速的庫(kù)存周轉(zhuǎn)率是非常有效的。

Supply chain visibility often refers to being able to see multiple supplier layers deep into a supply network. It’s been my experience that being able to track financial outcomes of supply chain decisions back to financial objectives is attainable, and with big data app integration to financial systems, very effective in industries with rapid inventory turns. Source: Turn Big Data Into Big Visibility.

圖 9：點(diǎn)擊查看大圖

9、產(chǎn)品質(zhì)量追蹤

產(chǎn)品追蹤和召回本質(zhì)上都是數(shù)據(jù)密集型的，大數(shù)據(jù)在這方面的潛在貢獻(xiàn)是顯著的。

Traceability and recalls are by nature data-intensive, making big data’s contribution potentially significant. Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted.

10、供應(yīng)商質(zhì)量提升

通過(guò)基于大數(shù)據(jù)的質(zhì)量控制可以提升供應(yīng)質(zhì)量。

Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. IBM has developed a quality early-warning system that detects and then defines a prioritization framework that isolates quality problem faster than more traditional methods, including Statistical Process Control (SPC). The early-warning system is deployed upstream of suppliers and extends out to products in the field.