On 31st July 2026, the PGDM students attended an insightful guest session on Demand Forecasting by Mr. Naveen Kumar Singh, currently serving as Fashion Lead – North India at Reliance Retail. In his role, he oversees fashion assortment planning, demand forecasting, and merchandise strategies aligned with evolving consumer trends in value retail formats.
The session provided a practical understanding of how demand forecasting influences business decisions and inventory management. Mr Singh began the session by explaining the importance of forecasting and how businesses prepare for uncertain future demand. He emphasized that 100% accurate demand forecasting is impossible in real-life business scenarios, and organizations should instead focus on achieving a high level of accuracy. Drawing from his experience at Big Bazaar, he explained how effective forecasting significantly improved inventory movement, resulting in nearly 80% sales of products that were previously difficult to sell. He also compared retail performance during the 2015 Diwali season, highlighting how better forecasting and inventory planning contributed to stronger business outcomes.
The session introduced practical forecasting techniques such as moving averages, regression analysis, and tracking seven-day moving averages to monitor weekly demand trends. He explained that when demand consistently increases, businesses should proactively increase safety stock to avoid stock-outs. One of the most impactful insights was his statement that "Money is made in retail while buying, not while selling," stressing the importance of smart procurement decisions. He also cautioned students against relying blindly on artificial intelligence in operations, noting that AI primarily depends on historical data and should always be supported by human judgment.
Overall, the session effectively connected theoretical concepts with real-world retail practices, providing students with valuable insights into demand forecasting, inventory planning, and data-driven decision-making.
Key Learnings from the Session
- Perfect demand forecasting is not possible; businesses should aim for high forecasting accuracy rather than perfection.
- Demand forecasting improves inventory management, reduces unsold stock, and enhances overall business performance.
- Techniques such as moving averages and regression analysis are widely used to predict future demand and monitor sales trends.
- Effective inventory planning is crucial—monitoring demand regularly and maintaining appropriate safety stock helps prevent stock shortages.
- AI should support, not replace, managerial decision-making, as it relies heavily on historical data and may not fully capture changing market conditions.