AI / Machine Learning Driven Improvement of Demand Forecasts
AI / Machine Learning Driven Improvement of Demand Forecasts


Stanley Black and Decker has more than 60,000 products in its portfolio across over 90 countries. A large portfolio of products drives robust design needs to handle numerous heterogenous SKUs with different lifecycles, demand profiles (continuous vs discrete), zero demand events, and short and long sales history. With over 60,000 SKUs and more than 100,000 demand forecasting units, planning is computationally intensive task that also requires a lot of people. Robust methods to understand and predict demand could improve the overall planning process and reduce overhead of maintaining service level that is often perceived as a hidden cost of doing business.

This talk will cover technical challenges related to scaling computational architecture, data engineering complexity, demand forecasting approaches and limitations, challenges of integration of AutoML engines, challenges of architecting for future algorithm inclusion to handle the complexities of product demand heterogeneity while building a maintainable system future proofed for business continuity. Additionally, the speaker will cover the full scope of transformational challenges this situation provides and the operating model to implement these challenges using more AI/ML driven approaches. The speaker will also cover how to use such a demand forecasting system to help drive strategic actions in Product Portfolio Management, Promotions, Pricing, Sales and Marketing to showcase an ecosystem of business decision making.


Prabhakar is responsible for delivery of data engineering and analytics solutions in Stanley Black & Decker organization. He has significant experience delivering analytical solutions in the healthcare, automotive, industrial distribution, industrial manufacturing, digital marketing and services industries. Many of his projects involved Machine learning, artificial intelligence, advanced analytics, architecture, strategy development and implementation, process improvement, and data lake creation.

Open Data Science




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