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AI Implementation Operational Efficiency
- 1. It’s Not About What You Know It’s About What You Can Do Global Forum Power & Utilities 4.0: Operational Efficiency Through AI Implementation Michal Hodinka, 15.6.2020
- 2. History is the best guide to future Operational company of innogy for the Czech Republic in 2016 rebranded from former RWE 1,6 million customers and 4,000 employees 65,000 kilometers of gas grid three business areas of Renewables, Grid & Infrastructure and Retail supplies reliable energy at a fair price to around 16 million power customers and 7 million gas 2innogy · Název prezentace · DD měsíc RRRR
- 3. Unique downstream position across Europe 3 https://www.eon.com/content/dam/eon/eon-com/investors/presentations/20200201_EON_Creating_the_future_of_energy.pdf
- 5. innogy Czech – our cloud story year by year 5 Stage 0 (2015) Fully on-premise Stage 1 (2016) Transition Stage 2 (2017) IaaS Stage 3 (2018) Optimalization Stage 4 (2019) PaaS Stage 5 (2020+) SaaS ? https://aws.amazon.com/partners/success/innogy-sap/
- 7. innogy · Název prezentace · DD měsíc RRRR 7
- 8. According to Experian’s 2019 Global Data Management Research report, 89% of businesses report that they struggle with managing data…
- 9. The most common use cases are churn and customer value, not all use cases are adopted across all opcos
- 10. How to start an ML-based workload in AWS Two areas that you should evaluate when you build a machine learning workload Machine Learning Stack Phases of Machine Learning Workloads 10innogy · Název prezentace · DD měsíc RRRR
- 11. High-level innogy data processing architecture iDATA:LAB – Simpler, more automated ML on AWS Data catalog iDataHUB Data Science & Analytics Leonardo Lancelot S3 NetApp Redshift ETL Extract, Transform and Load Cisco IPCC SAP Analytics SAP BW4HANA SAP BW on HANA SAP Analytics Cloud SAP ESM IS-U, CRM SAP HR Reporting DataOps Retail Apps
- 12. Continuous machine learning delivery lifecycle iDATA: LAB – Simpler, more automated ML on AWS https://aws.amazon.com/blogs/apn/how-slalom-and-wordstream-used-mlops-to-unify-machine-learning-and-devops-on-aws/
- 13. Preventive retention project I Machine Learning & Multi-Armed Testing Cover a obrázek 1 Step 1: Identification of Customers@Risk • Which customer is more likely to churn? • What affects this risk? • Testing different algorithms (Neural Networks, Decision Trees) • Out-of-sample validation Pilot confirmed that Customers@Risk model is accurate in identifying future churn customers 13innogy · Název prezentace · DD měsíc RRRR AI Customer data Customers@Risk
- 14. Preventive retention project II Machine Learning & Multi-Armed Testing Cover a obrázek 1 Step 2: Multi-armed testing of campaigns and Next Best Offer recommendation • Which retention offering & channel works for which customer? • Optimization across possible offerings & campaigns for each customer First A/B tests confirmed that a reduction in churn coupled with reduction in in-bound calls was observed for some campaigns Next Best Offer to be deployed 14innogy · Název prezentace · DD měsíc RRRR Offer or treatment? Channel? What Message? Customers@Risk + Multi-armed testing
- 15. LESSONS LEARNED
- 16. 1 Enable agility through the availability of high data quality datasets 2 Start simple and evolve through experiments 3 Decouple model training and evaluation from model hosting 4 Detect data drift 5 Automate training and evaluation pipeline 6 Prefer higher abstractions to accelerate outcomes Source: AWS Well-Architectured Framework
- 17. Thank you innogy · Název prezentace · DD měsíc RRRR
- 18. PEOPLE FIRST
- 19. Contact Details 19 Michal Hodinka Enterprise IT Architect Michal.Hodinka@innogy.cz innogy · Název prezentace · DD měsíc RRRR