BDE - Ikea

Transcript

1. © Ingka Holding B.V. 2022 Leveraging data and AI to optimize fulfilment operations planning at IKEA Picture by Pixabay 14 September 2022 © Ingka Holding B.V. 2022
2. © Ingka Holding B.V. 2022 Loek Gerrits Machine Learning Engineer BigData Republic Alexander Backus Data & Analytics Leader IKEA Retail (Ingka Group) BILLY
3. “Creating a better everyday life for the many people” Group © Ingka Holding B.V. 2022 465 IKEA stores, shops and planning studios in 32 countries 174,225 co-workers EUR 39.8 billion total revenue Figures from Ingka Group Annual Summary FY21
4. © Ingka Holding B.V. 2022 Towards omnichannel retail Physical store Multichannel Omnichannel
5. © Ingka Holding B.V. 2022 The need for a centralized planning capability Planning system Business objectives World state information Demand plan Supply plan
6. © Ingka Holding B.V. 2022 Three opportunities to improve planning Data-driven Optimisation Automation
7. © Ingka Holding B.V. 2022 Inter IKEA Systems (franchiser) Group Digital Retail Cross-functional product teams A digital product organisation Engineering Data & Analytics Product Xperience & Design
8. © Ingka Holding B.V. 2022 We empower co-workers to make informed planning decisions and positively impact IKEA's future Support the co-worker journey with automation and best-in-class analytics Provide user-friendly and high-performant planning tools for all co-workers Enable co-workers to collaborate with the end-end perspective of an omnichannel environment
9. © Ingka Holding B.V. 2022 Towards human-centred AI-assisted planning [1] The starting point Business objectives World state information Plans Human-centred planning system
10. © Ingka Holding B.V. 2022 Towards human-centred AI-assisted planning [2] A step forward Business objectives World state information Plans Forecasts AI Data
11. © Ingka Holding B.V. 2022
12. © Ingka Holding B.V. 2022 Data Machine learning model Forecast 𝑓 𝑥 = 𝑦 Generating forecasts using three components
13. © Ingka Holding B.V. 2022 Historical demand Events T T - 30 T - 2 T T - 2 T - 1 T + 365 𝑓 𝑥 = 𝑦 Data Our model uses different types of inputs
14. © Ingka Holding B.V. 2022 Expressive Non-linear relationships Scalable & flexible Future iterations Generalizable Minimize subroutines Tailormade IKEA Knowledge 𝑓 𝑥 = 𝑦 Model Design considerations for our machine learning model
15. © Ingka Holding B.V. 2022 Uncertainty Orders Order lines Weight Volume Quantity 𝑓 𝑥 = 𝑦 Forecast Multiple forecasts to support specific plans
16. © Ingka Holding B.V. 2022 Dashboard Model registry Model code Train job Scheduler Predict job Evaluate job App Cloud-based MLOps platform for forecasting and optimisation
17. © Ingka Holding B.V. 2022 Towards human-centred AI-assisted planning [2] A step forward Business objectives World state information Plans Forecasts AI Data
18. © Ingka Holding B.V. 2022 Towards human-centred AI-assisted planning [3] The future Plans AI Reviewing Adding Steering Data
19. Key take-aways Empower cross- functional product teams to deliver planning solutions end-to-end 1 Have a long-term strategy to guide your machine learning algorithm and system design choices 2 3 Together with users, evolve stepwise to human-centred AI- assisted planning © Ingka Holding B.V. 2022
20. © Ingka Holding B.V. 2022 Tack! Picture by Pixabay Find us at booth 53 © Ingka Holding B.V. 2022