現(xiàn)代社會，新興技術有許多機會來發(fā)展相對陳舊的S2C流程。即便最初的評估可能認為S2C是具有戰(zhàn)略性的，所以需要人為干預，但在某些領域，技術將推動這些變革。

毫無疑問，大量的人工智能(AI)將嵌入到采購戰(zhàn)略的開發(fā)中?，F(xiàn)在，高級的支出分析，機會的識別和外部市場的洞察發(fā)展等都是通過人工執(zhí)行的。在未來，人工智能將展開這些活動，并給品類經(jīng)理帶回這些市場分析，與他們的團隊一同研究分析。

此外，像合同元數(shù)據(jù)提取這樣的普通任務正被光學字符識別(OCR)技術所取代，使得合同搜索變得更加容易。前端自助服務記分卡讓供應商得以提供最新信息，使供應商風險評估和績效管理自動化。這將最大程度上減少甚至消除企業(yè)采用專人做供應商關系管理的需求。

下面的使用實例說明了新興技術如何影響S2C的三個子流程: 供應商風險評估、自動化的合同元數(shù)據(jù)的抓取和合同績效、以及內(nèi)部分析。

使用案例：AI技術支持下的供應商風險評估

通過構(gòu)建供應商風險評估評分功能(如信用評分的Credit Karma)，組織可以自動化其風險評估。供應商風險評估得分可以在不需要人工干預的情況下自動生成，供應商自身也可以通過技術積極監(jiān)控，以更快的速度，更高的準確性和更高的效率，來實時更新風險評估得分。供應商可以查看自己的分數(shù)，以了解應該采取什么行動來提高自己的分數(shù)。人工智能將模擬認知過程，根據(jù)高管信息、對第三方供應商財務信息進行人工智能驅(qū)動分析、積極監(jiān)控來自數(shù)千個互聯(lián)網(wǎng)來源的資訊等綜合因素，生成組織的深入完整的風險檔案。這些人工智能驅(qū)動的供應商風險評估得分就像信用評分算法一樣工作，從而允許組織跟上不斷變化的環(huán)境，并用人工智能驅(qū)動活動取代耗時的人工作業(yè)。

使用案例：自動化的合同元數(shù)據(jù)和合同績效

光學字符識別（OCR）技術將合同照片轉(zhuǎn)換為合同元數(shù)據(jù)，對協(xié)議進行分類和索引，然后將其標記到供應商，從而可以提取和分析元數(shù)據(jù)。然后，人工智能和機器學習可以執(zhí)行供應商支出和趨勢分析，以提出建議，衡量合同績效和合規(guī)性，并根據(jù)里程碑發(fā)送自動警報。這些能力將使采購經(jīng)理能夠把時間分配到更戰(zhàn)略性的工作上。

使用案例：基于自動化和人工智能的內(nèi)部分析

目前，技術成熟的組織利用專用的端到端軟件平臺來滿足其采購需求，覆蓋整個S2P流程流。因此，組織擁有現(xiàn)成的花費數(shù)據(jù)存儲庫。在當前的場景中，系統(tǒng)已經(jīng)能夠分析支出并根據(jù)品類、供應商、地理位置、業(yè)務單元、成本中心等創(chuàng)建支出概要。通過機器學習算法，人工智能將能夠從支出數(shù)據(jù)中提取顆粒度級別的信息，并通過提供趨勢分析、需求預測和歷史基準來幫助支持業(yè)務決策，不斷學習以提高準確性。

 例如，通過對歷史需求和購買模式的分析，系統(tǒng)將能夠進行趨勢分析和預測未來需求。過去必須人工尋找，或者有可能被遺漏的大量數(shù)據(jù)，如今都可以通過系統(tǒng)得到。

附上原文：

Source-to-Contract(S2C): 

Digital Transformation Examples

There are many opportunities for emerging technology to evolve dated S2C processes. While initial assessment may be that S2C activities are strategic, and therefore must require human intervention, there are areas where technology will drive change.

Without a doubt, there will be a high volume of artificial intelligence (AI) embedded in the development of sourcing strategies. Currently, advanced spend analytics, opportunity identification and external / market insight development are executed by humans. In the future, AI will enable these activities and bring insight back to category managers to vet with their teams.

Additionally, mundane tasks like contract metadata extraction are being replaced with optical character recognition (OCR) technology, making contract searches easier. Front-end “self-service” scorecards will allow suppliers to provide up-to-date information that allows supplier risk assessments and performance management to be automated. This will minimize or eliminate the need for dedicated supplier relationship management roles.

The use cases below illustrate how emerging technologies could impact three S2C sub-processes: supplier risk assessment, automated contract metadata capture and contract performance, and internal analysis.

Use Case: Supplier Risk Assessments Using AI

By building a supplier risk assessment scoring capability, like Credit Karma for credit scores, the organization could automate its risk assessment. Supplier risk assessment scores could be automatically generated without manual intervention, and the suppliers themselves could be actively monitored through technology to maintain updated risk assessment scores in real time, with greater speed, accuracy and efficiency. Suppliers could view their own scores to understand what actions to take to improve their scores over time. AI would simulate cognitive processes to generate in-depth risk profiles of organizations based on a combination of factors such as information found on executive officers, AI-driven analysis performed on financial information of third-party suppliers, and actively monitoring news from thousands of internet sources. These AI-driven supplier risk assessment scores work like a credit score algorithm, thus allowing organizations to keep pace with a constantly changing environment, and will reduce time-consuming manual activities to AI-driven activities.

Use Case: Automated Contract Metadata and Contract Performance

OCR technology takes contract photos and converts them to contract metadata, classifies and indexes the agreement, then tags it to a supplier, thus enabling metadata to be extracted and analyzed. Then, AI and machine learning could perform supplier spend and trend analysis to make recommendations, measure contract performance and compliance, and send automated alerts based on milestones. These capabilities would enable procurement managers to spend time on more strategic work.

Use Case: Enhanced Internal Analysis Using Automation and AI

At present, technologically mature organizations utilize dedicated end-to-end software platforms for their procurement needs, covering the entire S2P process flow. As a result, organizations have a ready repository of spend data. In the current scenario, the system is already able to analyze spend and create a spend profile based on categories, suppliers, geographies, business units, cost centers, etc. Through machine learning algorithms, AI will be able to extract granular-level insights from spend data, and help support business decisions by providing trend analysis, demand forecasts, and historical benchmarks, learning continuously to improve accuracy.

For example, through an analysis of historical demand and buying patterns, the system will be able to perform a trend analysis and forecast future demand. It would provide inputs that would previously have to be brought out manually, or otherwise have been missed, given the sheer volume of data that is now available to procurement.