sap s4hana1709expert session overview of the machine learning and predictive function机器学习和预测功能概述

1、Overview of the Machine Learning and the Predictive FunctionalitySAP S/4HANA 1709 Expert SessionBenjamin Köhler, SAP September 25th 2017INTERNALDisclaimerThe information in this presentation isand proprietary to SAP and may not be disclosed without the permission of SAP.Except for yourobligatio

2、n to protectinformation, this presentation is not subject to your license agreement or any other service or subscription agreementwith SAP. SAP has no obligation to pursue any course of business outlined in this presentation or any related document, or to develop or release any functionality mention

3、ed therein.This presentation, or any related document and SAP's strategy and possible future developments, products and or platforms directions and functionalityare all subject to change and may be changed by SAP at any time for any reason without notice. The information in this presentation is

4、not acommitment, promise or legal obligation tiver any material, code or functionality. This presentation is provided without a warranty of any kind, eitherexpress or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringeme

5、nt. This presentation is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this presentation, except if such damages were caused by SAPs intentional or gross negligence.Allforward-lookingstatementsaresubjecttovariousrisks

6、anduncertaintiesthat expectations.Readers are cautioned not to place undue reliance on these forward-looking should not be relied upon in making purchasing decisions.couldcauseactualresultstodiffermateriallyfrom statements, which speak only as of their dates, and they2© 2017 SAP SE or an SAP af

7、filiate company.s. INTERNAL“We have to build anintelligent S/4HANA by ML.”Wieland Schreiner, S/4HANA P&I - June 20173© 2017 SAP SE or an SAP affiliate company.s. INTERNALDigitizationEvery company to become a software-driven company byConnected DataCollaborationBusiness Process InnovationInt

8、egrationMobileBig DataIntelligently connecting People, Things, and BusinessesIoTNetworksNatural LanguageMachine LearningMicroservicesAPIsReal-time Analytics4© 2017 SAP SE or an SAP affiliate company.s. NTERNALSAP Leonardo Machine Learning enables the intelligent enterpriseBusiness OutcomesIncre

9、ase revenue with superior sales targeting and executionRe-imagine business processes with digital intelligenceImproving quality time at work for employeesIncreased customer satisfaction with superior serviceEnabling product, process & businessminnovations5© 2017 SAP SE or an SAP affiliate c

10、ompany.s. NTERNALIntelligentServicesIn-DatabaseMLData Science PlatformIntelligent Apps76% of the worlds transaction revenue25 industries12 lines of businessThe worlds largest business networkSmart Enterprise Intelligence by ML in SAP S/4HANA with SAP LeonardoSAP LeonardoIoT Application Enablement by

11、 SAP Data Science Services *SAP Predictive MaintenanceAvoid unplanned machine downtime and reducemaintenance costs+=SAP S/4HANAAsset ManagementRELEASEDCloud 1702 /OP 1709(planned)SAP LeonardoSAP Leonardo Machine Learning Foundation on SAP Cloud Platform+=SAP S/4HANAFinanceSAP Cash ApplicationAutomat

12、e payment and invoice matchingRELEASEDCloud 1705 /OP 1709(planned)Contract Consumption in ProcureProactively renew contracts by predicting their expiration and consumption+=SAP S/4HANASourcing and ProcurementSAP LeonardoSAP Leonardo Predictive AnalyticsStock In Transit in ProduceMitigate production

13、risks by forecasting stock in transit Delays+=SAP S/4HANAMaterial ManagementSAP LeonardoSAP Leonardo Predictive Analytics6© 2017 SAP SE or an SAP affiliate company.s. NTERNALLABSPREVIEWRELEASEDCloud / OP+Leonardo= THE Intelligent EnterprisePredictive Analytics and Machine LearningEmbedded Analy

14、ticsHumans make decision Data is aggregated for visualizationUI integration at bestAggregateVisualize DataTrain MMachines propose/makedecisionData is de-normalized, flattened, fine grain Process integration at lastPrepare DataApply MMonitorPredictive Analytics7© 2017 SAP SE or an SAP affiliate

15、company.s. INTERNALApp Development Process CDS view §For a classical S/4HANA app, the developer gathers information on where to get the data to bedisplayed, ms the CDS view correspondingly and builds the UI§After the actual development, the app is delivered to the customer8© 2017 SAP

16、SE or an SAP affiliate company.s. INTERNALDeliverAppBuild UIMSelect TablesPredictive App Development Process CDS view §To augment a S/4HANA app with predictive information, it is only needed to create a predictivemas a HANA procedure and wrap it into a CDS view§§The further steps rema

17、in the sameBUT1.2.The predictive mThe predictive mis more precise if trained on customer specific datashould be retrained frequently9© 2017 SAP SE or an SAP affiliate company.s. INTERNALCreatePredictive MDeliver AppBuild UIMSelect TablesPredictive M environmentsing and Development Lifecycles ha

18、ppen in differentDeliver App CDS view Create / Modify (Re-)Train Predictive M Predictive M Generate New Data SAP DevelopmentCustomer10© 2017 SAP SE or an SAP affiliate company.s. INTERNALUse AppBuild UIMSelect TablesNaïve approach of delivering predictive S/4HANA appsCustomerSAP Developmen

19、tDevelopment LifecycleDeliver AppSelect TablesMBuild UI CDS view Create / Modify (Re-) TrainPredictive M Predictive M Generate New Data Predictive MLifecycle§The consequence of such app lifecycle would be that1.2.For each customer, a specific app would have to be deliveredFor each refinement of

20、 the predictive m, a new version of the whole app has to be shipped11© 2017 SAP SE or an SAP affiliate company.s. INTERNALUse AppDelivering predictive S/4HANA apps with Predictive Analytics IntegratorSAP DevelopmentCustomerSelect TablesMDeliver AppBuild UI CDS view Generic PredictiveM Container

21、Development Lifecycle§ Creating a generic object, which defines what the input and output of a instance of a PredictiveMis allows to1.2.3.Separate the lifecycles of the app development and the lifecycle of a Predictive MLet the customer train the Predictive MLet the customer retrain the Predict

22、ive Mon his own dataanytimeà Create one generic predictive S/4HANA app for all customers12© 2017 SAP SE or an SAP affiliate company.s. INTERNAL Create / Modify (Re-)Train Predictive MPredictive M Generate New Data Predictive MLifecycleUse AppEmbed ML MsIntelligent decisions and insights th

23、rough ML seamlessly embedded into business processesEnd-to-end Lifecycle ManagementFlexibility and AgilityDevelopment Efficiency13© 2017 SAP SE or an SAP affiliate company.s. NTERNALCustomers train, run, monitor, retrain embedded predictive msBuild your own ms and inject into the business proce

24、ss where you need itFull integration of ML content with Software Lifecycle ManagementPAI manages transport of Predictive Scenarios and ms (trained/untrained)§ Shipment: SAP à Customer§ Customer: Dev à Test à Prod§ Integration with Key user Extensibility tools in S/4 HAN

25、A CloudApplication developers code business logicML managed by Predictive Scenario container§ Stable API§ Managed mbindings§ CDS views (ABAP) or Rest Service (SCP)Build Your Own ML MsFlexible adaption tocustomer needsSAP Predictive Analytics Application EditionExtensibility by Custome

26、rs and Partners14© 2017 SAP SE or an SAP affiliate company.s. NTERNALEnhance Predictive Scenarios§ Add more data and widen dataset§ Build a bespoke mand replace the shipped oneBuild your own predictive scenarios§ PAi will be fully integrated with extensibility tools§ Blend i

27、n predictions in custom queries, UIs, business logicBuild predictive ms in a rich authoring environment usingAutomated Mling: Regression, Classification, Clustering, Time Series, Link AnalysisPredictive Composer: complex pipelines with data manipulations, algorithms from PAL, APL, RComprehensive deb

28、riefing of mtrainingsPublish ms into PAi and create Predictive ScenariosAutomated MerPredictive ComposerData PreparationPredictive FactoryBuild Your Own ML ApplicationScale-Out of Embedded ML Use CasesInnovation by Customers and Partners15© 2017 SAP SE or an SAP affiliate company.s. NTERNALBuil

29、d a new side-by-side application on SCP§ Implement new application logic§ Access S/4 data and enrich with data from other sources§ Use the full development capabilities on SCPBuild your own predictive scenarios using PAi REST§ Build powerful predictive ms§ Same development m

30、ethodology as for embedded ML§ All managed in one placeStart simple and empower the customer to flexibly extend the application and to grow beyond the boundaries of S/4Delegate mtraining and costly operations to a dedicated ML environment on HCPAccess to data and engines outside of S/4 Smooth t

31、ransition with stable interfacesDemo How to Develop a Predictive SAP S/4HANA AppPAi Machine Learning FrameworkBusiness UserData ScientistDeveloper / Analytics Specialist* Not available in SAP S/4HANA 170918© 2017 SAP SE or an SAP affiliate company.s. NTERNALTranspoPA * MAuthoringAutomatedPipeli

32、nesOperationalizationMonitoringSchedulingData PreparationData ManagerCustomer ApplicationsS/4HANA ApplicationsPlatform ServicesrtASHANAPAiPredictive ScenarioMManagementRepositoryConfigurationBusiness LogicPredictive EnginesAPLR*PAL*TensorFlow*TrainingCDS ViewsPredictive Analytics Integrator Architec

33、ture Management of MHANA Creation of APL m (Re-)training of ms which are stored onswith Fiori UI Applying of ms via CDS views19© 2017 SAP SE or an SAP affiliate company.s. INTERNALClasses of Predictive Analytics ChallengesClassification / Scoring§ Who will (churn | fraud | buy) next (week

34、| month | year)?Regression§ How many products will a customer buy next (week| month| year)?Clustering§ What are the groups of customers with similar (behavior | profile )?Forecasting§ How much will be the (revenue | # churners) over the next year on a monthly basis?Recommendations/ Li

35、nk Analysis§ What is the best ( Next offer | Next action | ) for (customers | Internet )?20© 2017 SAP SE or an SAP affiliate company.s. INTERNALUse Case: SAP S/4HANA Sourcing and ProcurementOperational Contract ManagementKey Message§Contract management is the process of managing contr

36、act creation, execution and monitor toize operational and financialperformance. It is essential that buyers have a effective and efficient system support for monitoring contracts. With Machine Learning we can predict the consumption date of each contract to allow Buyers to proactively engage with Su

37、ppliers.Unique Value of S/4HANA Today: Contract End date 3 months Predicted contract consumption date analyzing all attributes for contracts, POs and vendors (tables EKKO, EKPO, LFA1, LFB1 and derived attributes) Insight to action capabilities: Dynamic and flexible contract worklist Contract and sup

38、plier factsheet Contract renewal workflow Powered by SAP Predictive AnalyticsAvailable in SAP S/4HANA 170921© 2017 SAP SE or an SAP affiliate company.s. INTERNALSAP S/4HANA Produce + PAIStock Management Stock In Transit Key Message§For companies issuing and receiving good from and to their

39、 plants, it is important to track the status of the materials in transit in order to take action in case of problems. The "Materials Overdue Stock in Transit” app gives an overview of the open shipments allowing the business user to take action. With PAI we are enhancing the app with Predicted Shipment Dates for each Goods Movement to allow users to take action to manage delivery delays.Unique Value of S/4HANA Classify the status of a shipment into different classes “Still ok” “Longer as planned” “Unacceptable dela