H&M Group

H&M operates a highly complex global supply chain, where accurate demand forecasting and timely procurement decisions are critical to inventory availability, cost control, reduced waste, and speed-to-market.

The complexity was amplified by rapidly changing consumer behaviour and the need for fast, reliable decision support.

SK Good Tech AB worked hands-on to build and improve the AI and ML foundations supporting demand forecasting and supply chain optimisation.

The focus was on enabling trustworthy AI systems that could be used directly in business-critical planning processes.

The engagement required close collaboration across both technical and business domains, including procurement and supply chain stakeholders.

SK Good Tech AB worked directly with procurement and supply chain teams to understand planning pain points, translated business planning needs into ML and AI solutions, and operated effectively within a large, distributed enterprise environment.

The work established a scalable foundation for AI-driven decision support across H&M's global operations, improving data reliability and enabling business-critical planning processes.

Built on GCP with BigQuery, Apache Spark, and dbt for data engineering. ML models developed using Python, TensorFlow/PyTorch, and scikit-learn with specialized time-series forecasting. Integrated LLM-based workflows for analytical decision support with robust data quality monitoring.