Discover how AI-powered demand forecasting enhances accuracy and responsiveness in India's growing FMCG market. Learn more here!
The New Demand Forecasting Standard India’s FMCG Giants Are Quietly Adopting In Their Supply Chains India’s retail market is racing toward USD 1.4 trillion by 2026 , but over 80% of FMCG volume still flows through general trade channels with almost zero digital visibility.
The result? Tens of thousands of crores annually in stock-outs and excess inventory alone. (Nielsen-IAMAI 2024) .
At the same time, AI adoption in supply chain digitisation in India is accelerating. The use of AI in supply chain management is growing by more than 30% each year, yet only a small share of consumer brands apply it effectively to their forecasting processes. Many continue to rely on fragmented datasets and static spreadsheets.
As the market matures, demand forecasting for FMCG will no longer operate as a quiet back-office function. 2026 will separate the category leaders from the laggards. The winners will treat demand forecasting as strategic intelligence, not a back-office ritual.
Limitations of Current Forecasting Approaches Traditional forecasting methods struggle to keep pace with the complexity of the modern CPG supply chain in India. Legacy systems were not designed for today’s speed, variety, and volatility. They often overlook important demand drivers such as inflation, heat waves, local events, search trends, and competitor pricing.
1. The static forecasting model fails to adapt Many forecasting systems run in monthly or quarterly batches. These static models do not react quickly to rapid changes such as unexpected heat, festive pre-loading, flash e-commerce events, or supply disruptions. When demand moves faster than the planning cycle, the forecast becomes outdated before it is even applied.
2. Manual planning creates errors and version mismatch A large share of planning teams still operate in spreadsheets. They run parallel versions of forecasts, apply judgment-based overrides, and circulate files through email. This causes version mismatches, delays, and manual errors. More importantly, it prevents organisations from building a truly data-first supply chain strategy.
3. Demand and supply planning remain disconnected In many FMCG organisations, demand planning and supply planning continue to function as separate processes. Forecasts are created without visibility into real-time capacity constraints, production bottlenecks, or stock availability at different nodes. As a result, the forecast signal rarely aligns with operational realities. Supply teams then produce based on lagged shipment data rather than real demand. This increases stock-outs in some regions while building excess inventory in others.
These structural limitations create an environment where traditional practices cannot support the level of precision required today. They set the stage for the business challenges that follow.
The New Standard for Forecasting A new approach to forecasting has emerged, powered by AI in supply chain management and deeper data integration. Instead of fragmented, channel-specific datasets, modern forecasting relies on unified visibility from regional distribution centres through distributors, retailers, and finally consumers.
1. Multi-tier forecasting for end-to-end clarity A multi-tier forecasting model creates a connected view across general trade, modern trade, and e-commerce. It aligns demand signals from the warehouse, distributor, retailer, and consumer levels. This provides a clearer and more accurate representation of market movement and reduces reliance on shipment-based approximations.
2. Gen AI enhances accuracy and responsiveness Gen AI systems incorporate external datasets such as weather patterns, inflation indicators, macroeconomic reports, and competitor pricing trends. They learn from past patterns, including returns, out-of-stock periods, and promotional behaviour. The forecast adjusts dynamically, enabling more responsive planning even in volatile environments.
3. Data harmonisation as the foundation A strong data foundation supports every modern forecasting approach. This includes harmonising data across ERP, DMS, CRM, and marketplace systems. A unified taxonomy allows consistent SKU-level and region-level prediction. It also allows brands to fully unlock AI use cases in the FMCG supply chain.
4. Agentic AI for autonomous monitoring Agentic AI systems track anomalies in real time and prompt planners when deviations occur. They can trigger automated replenishment or initiate a review process when sudden spikes or drops appear. This reduces the burden on planning teams and supports a more agile planning cycle.
Together, these advancements can deliver improvements of up to 20 to 30 percent in forecasting accuracy, while reducing stock-outs and lowering inventory holding effort.
The ADA Difference: We Start Where Everyone Else Skips Most AI-driven forecasting solutions assume that data is already clean, connected, and reliable. In the reality of Indian FMCG operations, this is rarely the case. Disparate systems, inconsistent identifiers, and incomplete data streams are the norm, not the exception.
That is why every ADA engagement starts with building a strong data foundation. While often overlooked, this step is critical. It is the difference between a marginal improvement in forecast accuracy and a step-change impact.
One harmonised data spine We stitch together every source you actually have like SAP, Marg, BeatRoute, Bizom, OkCredit, CRM, Amazon/Flipkart Seller Central, 3PL portals, even WhatsApp order screenshots into a single, consistent SKU–region–channel taxonomy. No more “Parle-G 82 g” appearing as 47 different names.
Agentic layer that only works on clean data Once the foundation is rock-solid, the Gen AI and autonomous agents switch on detecting anomalies, adjusting promo lift, and triggering replenishment without human touch.
The brands we partner with don’t buy a tool. They get a co-built, future-proof demand-sensing backbone that becomes their single biggest competitive advantage.
Conclusion Demand forecasting is undergoing a permanent shift as FMCG and CPG brands move from reacting to market trends to anticipating them. As we look toward 2026, forecasting will evolve into a live data ecosystem where every distributor, warehouse, retailer, and channel feeds continuous insight back to the brand.
When forecasting becomes integrated, dynamic, and data-led, it delivers meaningful business impact. It reduces wastage, improves on-shelf availability, and accelerates decision-making. Brands that invest in stronger forecasting today will be better positioned to adapt, compete, and grow in a rapidly changing market.
To begin strengthening your forecasting capabilities, contact ADA. Our team works alongside brands to build connected, future-ready demand systems grounded in strong data foundations. If you’re ready to move beyond traditional forecasting and accelerate your shift to intelligent planning, ADA is here to help.
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