Inventory Intelligence: Forecasting Retail Demand with AI and SAP CAR

In the retail world, the battle for customer loyalty is often won or lost in the supply chain. Demand forecasting sits at the heart of this challenge. Traditional methods, reliant on historical data and static models, have struggled to keep pace with today’s dynamic, multi-channel retail environment. The integration of Artificial Intelligence (AI) with SAP’s Customer Activity Repository (CAR) is rewriting the playbook, giving retailers the ability to anticipate demand shifts in real time, optimize stock levels, and respond with agility to market changes.

SAP CAR consolidates vast volumes of transactional, inventory, and customer activity data from multiple sales channels into a single, harmonized source. Layering AI and machine learning algorithms on this foundation transforms raw data into actionable intelligence. Retailers can forecast demand for thousands of SKUs with precision, accounting for factors such as new product launches, seasonal trends, and promotional events. The result is a smarter, more resilient inventory strategy reducing stockouts, minimizing excess inventory, and elevating the customer experience.

Sivasubramanian Kalaiselvan has been a driving force behind this evolution. With deep expertise in architecting enterprise-scale inventory intelligence platforms, he has built systems that bridge the gap between cutting-edge technology and real-world business needs. His career is marked by leading high-impact supply chain innovations, steering large-scale ERP transformations, and pushing the boundaries of predictive analytics in retail operations.

“The future of supply chain forecasting isn’t just about prediction, it's about building systems that adapt, respond, and even heal themselves in real time,” says Kalaiselvan.

In one of his initiatives, He designed a unified planning platform that seamlessly connects demand forecasting to retail allocation in a single integrated system. Powered by SAP CAR’s predictive libraries, this architecture improved forecast accuracy by 25% and enabled planning for 14,000 SKUs across more than 2,000 locations. By eliminating fragmented data silos and creating a reliable, real-time “single source of truth,” he ensured that AI models could deliver consistently accurate results regardless of market volatility.

The Effect has been both operational and financial. Vendors now benefit from just-in-time, AI-powered inventory replenishment, reducing holding costs and improving supply chain responsiveness. Optimized processes for returned inventory have unlocked billions in annual revenue streams, turning what was once a cost center into a profit generator. Order fulfillment has become faster and more precise, with planning cycles that adapt dynamically to shifts in consumer demand.

Yet for Sivasubramanian, this is only the beginning. He envisions the rise of the “self-healing” supply chain systems capable of detecting disruptions in real time, modeling the best course of action, and executing corrective measures autonomously. His ongoing work focuses on building the high-speed, reliable data pipelines and automation capabilities that will make this vision a reality.

In an industry where responsiveness is key and margins are tight, AI-driven forecasting powered by SAP CAR is emerging as a game-changer. Through his innovative approach, Sivasubramanian Kalaiselvan is showing how advanced technology, applied with precision and strategic insight, can transform inventory management into a proactive, value-generating force reshaping the competitive landscape of retail.