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Datastreams into erp

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The new data stream sources like Internet of Things, social media and more, contain valuable business information, and it is desirable to get this information transferred into the ERP system. In this talk, I will discuss the design principles to be considered or avoided, when bringing these potentially infinite sequences of data into an ERP system.

Publié dans : Logiciels
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Datastreams into erp

  1. 1. GETTING INFORMATION FROM DATA STREAM SOURCES INTO ERP SAP Inside Track Copenhagen By Søren Amdi Bach Principal Application Architect KMD A/S May the 4th. 2018 Who is Søren: Principal Application Architect at KMD A/S Professional, curious and enthusiastic technology nerd with a sense of business and social skills Some SAP technology words from the recent years: S/4HANA (Public Cloud, On Premise, Conversion), SAP Cloud Platform, SAP Leonardo, SAP Solution Manager, HANA Database, Security, HANA/ABAP: Development, governance, quality, DevOps etc. Various architect roles the last + 15 years, mainly SAP for the last 12- 13 years Origin in development/technology: SAP, Microsoft, IBM MVS, Open Source and Unix
  2. 2. _ Internal - KMD A/S 2 INTELLIGENT ERP, INDUSTRY 4.0 … SOMETHING WITH DATA AS NEW OIL Data Streams ERPMagic The simplified (commercial) perspective A World with wi-fi All the knowledge you ever need - to run your enterprise fully automated
  3. 3. _ Internal - KMD A/S DATA(STREAM) SUPPLY CHAIN Collection • Sensors • Information scanning • SoMe • Internet Cleansing • Remove information of no interest • Normalizing of collected values Enrichment • Aggregation • Time series processing • Correlation with other data sources Management • Monitor source health • Store/archive selected information for further usage Deliver • Analysis • Identification of Events • Notification to business processes Internet Of Things (IOT) Platform Social Media Information Scanning frameworks Other Data Stream frameworks Increasing Entropy in the information
  4. 4. _ Internal - KMD A/S 4 ▪ Scalable (Cloud resources) ▪ Fast innovation mindset ▪ “Microservice” architecture ▪ Exploratory / Experimenting ▪ DevOps Continuous-Integration and -Deployment ▪ Limited and Expensive Scalability (on-prem resources) ▪ Improving / Stability mindset ▪ Monolith (shared Database) ▪ Predictable world ▪ Classical big releases with joint phases FROM DATA STREAM TO ERP Data Supply Chain ERP Events Larger amount of events emitted in short time span might compromise the performance of the receiving ERP
  5. 5. _ Internal - KMD A/S PREFERRED CHARACTERISTICS OF EVENTS PROVIDED FOR THE ERP SYSTEM 5 How-to avoid overloading the ERP system ERP is the critical (less scalable) resource. • The ERP system should only receive events identifying a unique business event • An event should trigger update of a defined set business object in the ERP system. • Only push events that is necessary, of relevance and make sense to the ERP system Don’t disturb unnecessary • Events for the ERP system should be prioritized allowing most important Event types to be processed at first • If more events are provided than the ERP system can consume the Events should be put in a queue Respect Urgency and await turn • Protect against bursts of Events with the same business semantics • Await sending the Events until the existence of the Business Event is certain Aggregate Events before sending to ERP
  6. 6. _ Internal - KMD A/S LIKELY CHARACTERISTICS OF EVENTS IDENTIFIED FROM DATA STREAMS 6 Event is an event by natureRandom •Events is not in general identified/emitted in a nice well-known predictable manner Pattern on time series dataStatistical •A event indicate something in the real-world with a certain probability •A event might be a false prediction Importance of a event might be a function of timeImportance f(t) •Importance of a identified event might varnish over time •Timestamp on event identification might be on importance Possible requiring identification of the same eventRepeated •Identification of events with the same real-world meaning are likely to be repeated •Hysteresis in the detection might be required to ensure certify of the business meaning of the event Pattern of events might have a additional business semanticUnknown Patterns •The identified events might contain new undiscovered knowledge •Requires some domain knowledge on the real-world topic
  7. 7. _ Internal - KMD A/S ▪ Decouple the information for the ERP system ▪ The ERP system load should control the consumption speed of events from the data streams ▪ Consider some sort of prioritized event queue between Data Supply Chain and ERP system HOW TO – PROTECT THE ERP SYSTEM 7 Data Supply Chain ERP 11 1223 ERP System is still the bottleneck, but is protected against information overload
  8. 8. _ Internal - KMD A/S 8 HOW TO – ENSURE RIGHT EVENTS FORWARDED TO ERP No quick fix - to identify Events ▪ Requires domain and data science knowledge ▪ Analysis on collected “live data” streams to identify patterns (supervised learning) Initial go-live set is a proposal • The initial go-live will be based on a priori knowledge and analysis of existing collected data Cotinus validation and retrospective analysis ▪ Non detected real-world events of importance requires analysis of data to search for correlated event patterns ▪ Data patterns defining an event might change over time ▪ Analysis of larger set of identified events to identify new knowledge (new patterns with business semantics)
  9. 9. _ Internal - KMD A/S 9 DATA SCIENCE PROJECTS AS INSPIRATION ▪ The identifications of the right events has similarities to a data science project ▪ The Cross-industry standard process for data mining (CRISP-DM) ▪ Off-line analysis part (model determination) ▪ On-line/real-time usage of model ▪ Consider methods like ▪ Classification ▪ Segmentation or Clustering ▪ Link analysis ▪ Regression ▪ Time Series Analysis