Machine Learning for Inventory Management

1. Machine Learning for Inventory Management

2. BHGE Introduction

3. Product CompaniesDelivering market-leading solutions across the energy value chain OILFIELD SERVICES OILFIELD EQUIPMENT DIGITAL SOLUTIONS TURBOMACHINERY & PROCESS SOLUTIONS Our Oilfield Services (OFS) business lowers the cost per barrel of oil equivalent for the life of a well by improving well efficiency, optimizing production and increasing ultimate recovery. In Oilfield Equipment (OFE), we provide customers with a portfolio of ultra-reliable technologies, including subsea trees, manifolds, blowout preventers (BOPs), flexible risers and advanced control systems. Our Digital Solutions business (DS) combines sophisticated hardware technologies with enterprise-class software products and analytics to connect industrial assets, providing customers with the data, safety and security needed to reliably and efficiently improve operations. In Turbomachinery & Process Solutions (TPS), we provide industry-leading availability and reliability in mechanical-drive, compression, and power-generation applications across a diverse range of industry segments.

4. Turbomachinery & Process Solutions (TPS) Structured to serve our customers • 10,000+ employees • Operating across 120 countries in 7 Regions: Europe, Russia & CIS, Africa, Middle East & India, Asia Pacific & China, North America and Latin America • 30 manufacturing facilities • 10 service facilities • 1,000 Field Service Engineers • Solutions tailored to industry segment needs Portfolio • Aeroderivative and heavy-duty gas turbines • Small- to medium-sized steam turbines • Centrifugal and axial compressors • Reciprocating compressors • Process, control and safety valves • Integrated power, compression and LNG modular systems • Service solutions Onshore & offshore production Pipeline & gas processing Liquefied natural gas Refinery & petrochemical Industrial Confidential. Not to be copied, distributed, or reproduced without prior approval.

5. Segment financial performance($ in millions) Orders – Revenue = 341 M$ Conversion Rate = Revenues/Orders = 84% Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

6. The Supply Chain Model (simplified) ORDER FROM CUSTOMER ORDER TO SUPPLIER PRODUCTION ORDER FULFILLMENT Goods Shipment Customer Order LOGISTICS FORECASTING t NEW LEAD TIME TOTAL LEAD TIME Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif May 2, 2019 6 Confidential. Not to be copied, distributed, or reproduced without prior approval.

7. What is the most important thing for the Forecasting Manager? The Turns indicator Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif May 2, 2019 7 Confidential. Not to be copied, distributed, or reproduced without prior approval.

8. How to increase Turns? 1st Option: Increase Revenues 2nd Option: Reduce Inventory How do we reduce the inventory while keeping the same Revenues? We need to choose the ‘’right’’ parts to stock!! Right parts = MTS Wrong parts = MTO Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif

9. Machine Learning Approach Binary Classification (MTO-MTS) Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

10. Machine Learning Application

11. Machine Learning for Materials Forecast The team scope is to maximize Service Level for strategic and frequently asked items, buying them in advance, while minimizing inventory. Parts classification means deciding which are the items to hold in stock (MTS) and their reorder levels. Machine Learning Application Pre-Machine Learning Process ML is used to improve the two main steps of the process: Parts Classification Supervised neural network algorithm Based on historical sales order shipments (6 years) and items features (cost, price..) 25% Make To Stock items Improvement areas: • Huge amount of data managed manually • Multi-validation process • Level calculation based on static statistical model Stock Levels Optimization Reinforcement Learning algorithm Heuristic process can be efficiently conveyed into a machine learning algorithm Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

12. Machine Learning Steps Data preprocessing Features extraction Classification & Validation ML automates classification and highlights inconsistencies Supervised algorithm (KERAS dense layers with Drop Out) Full deep learning same Accuracy 200 K items, 6 years, 1M sales transactions Huge amount of data require time consuming analysis Domain knowledge translated into features to condense items demand pattern Statistics & Unsupervised learning Integrated in Python 99% Accuracy Machine learning highlights clusters, learns human validation and allows single flow process Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

13. Reinforcement learning approach for levels setting • MIN and MAX levels decide when we have to buy material • Optimal policy (MIN and MAX values) is found through epsilon-greedy, every visit MC algorithm • Reward Function is made up of: • Carrying cost • Purchasing cost • Sales of items ready in stock • Sales of items not in stock • Discount factor due to delayed sale • Demand quantities are sampled from historical pattern • Optimal levels allow 95% + service level Customer demand Inventory Optimal level Reinforcement learning explores possible levels and exploits previous attempts. Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

14. Main Interface (Django & Keras) Django app makes results available also for non-expert users Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

15. Conclusions Faster decision making Workload reduction 50% Accurate results Single flow process Errors risk mitigation Rapid adaptability Model continous improvement Validation will require less time any time Performance evaluation Key indicators (stock plan, sales..) automatic calculation Inventory optimization Regular items identification, levels optimization Inventory reduction 15% • Domain expertise combined with Machine Learning drives Inventory optimization & Workload reduction Prepared by Ciro Campese, Laura De Stefanis, Amr Abdullatif Confidential. Not to be copied, distributed, or reproduced without prior approval.

16. Thank you

17. Model input features

18. DNN Model parameters for MTO/MTS classifier Accuracy98%

19. DNN Model parameters for Capital/Flow classifier Accuracy98.9%

20. Models comparison DNN model was selected as it run more efficiently on the GPU DNN with Word2Vec achieve an accuracy of 0.98% on the test set