2025-12-09 –, Auditorium
At IKEA, retail planning is a complex chain of processes, from sales forecasting to fulfillment and capacity assessment, that involve multiple teams. Each team builds their own predictive models independently, yet their outputs depend on one another to ensure a concise planning chain.
In this talk, we will show how IKEA uses Metaflow, an open-source framework for building and managing real-life ML, to orchestrate and connect the forecasting pipelines for more than thirty countries. We’ll discuss how Metaflow helps align independent teams, improve readability, and enable reproducible workflows and scale.
You will leave with practical approaches for an aligned team workflow and concrete patterns for orchestrating ML/AI pipelines.
Retail planning at IKEA is more than just predicting sales, it is about connecting various forecasts that inform and depend on one another:
Sales forecasting: How much will customers buy?
Fulfillment planning: Can we ensure availability?
Capacity assessment: Do our stores and distribution centers have enough capacity to handle the volume?
Each of these domains has its own models and ways of working. Previously, these teams built and deployed everything independently. Making it difficult to align predictions and maintain visibility across the retail chain. To improve this process, IKEA adopted Metaflow as a data science platform. Metaflow provides a framework for developing, running, and monitoring pipelines.
The patterns and solutions shared during the presentation can apply to any organization dealing with (complex) workflows, interconnected predictions, or resource/scaling challenges.
Target audience:
Data scientists, data engineers, ML practitioners, and technical leads interested in:
Workflow orchestration and reproducibility.
Cross-team collaboration in data science.
Scalable forecasting systems in production environments.
Background knowledge: Basic understanding of data pipelines and workflow orchestration. No Metaflow experience required.
Talk outline:
0-10 minutes:
Introduction to IKEA’s Retail Planning
Problems that you encounter coordinating these processes in 30+ countries
Why traditional pipelines did not succeed
10-15 minutes:
What we needed: A system that could handle complex dependencies, scale and let non-engineers contribute safe and easy
Introduce Metaflow as an OS framework solution
15-25 minutes:
Quick introduction how Metaflow works in practice
Show how Metaflow handles the Retail Planning chain
Show how Metaflow works together with Argo Workflows
Show how Metaflow helps us scale and manage our resources at the same time
25-30 minutes: recap and questions
I'm Yannick Mariman, a data engineer working at Pipple, working with IKEA to build an improved Retail Planning platform. Earlier in my career, I saw good data solutions failing because of a lack of proper workflows and orchestration. This led me to become more interested in tangible solutions that focus on robustness and stability.