By Santosh Singh, Senior Vice President, IT, DS Group
Walk into a kirana store, scroll through a quick-commerce app, or watch a delivery vehicle navigate the last mile, and you are seeing only the visible end of an increasingly intelligent FMCG supply chain. Behind that simple consumer experience lies a complex ecosystem of demand, manufacturing, procurement, inventory, geography, seasonality, logistics and consumer behaviour.
In India, this complexity is amplified by regional preferences, festive demand, weather volatility, rural accessibility, seasonal commodities and thousands of product-SKU combinations. Technology’s role is therefore no longer limited to automating transactions. It is about enabling the supply chain to sense, predict, decide and respond faster.
From historical forecasting to demand sensing
The fundamental question is gradually shifting from “How much did we sell?” to “Where will the next unit be required?”
Traditional forecasting relied heavily on historical sales and experience. We are progressively moving towards AI-driven demand sensing, combining primary and secondary sales, distributor inventory, promotions, seasonality and market signals to create more granular SKU-level intelligence.
This is particularly relevant in India, where Diwali, Holi, Eid, Onam, Pongal and regional festivals can dramatically alter consumption patterns. Weather conditions, local events and changing consumer preferences can create equally significant demand shifts. Increasingly, these are not merely external variables; they are becoming digital supply-chain signals.
Optimising a complex manufacturing network
With manufacturing spread across more than 20 plants, the challenge is not simply capacity utilisation. Technology must help answer three interconnected questions: What should we manufacture, where should we manufacture it, and which location should optimally serve which market?
Integrated planning allows us to evaluate plant capacity, raw-material availability, inventory positions, regional demand and transportation requirements together. The objective is to continuously improve network optimisation, cost-to-serve and inventory velocity, while reducing unnecessary movement across the network.
Smarter procurement and working capital
Seasonal procurement of agricultural and commodity-based raw materials introduces another layer of complexity. Many commodities have specific harvest and buying windows, requiring businesses to balance supply assurance and procurement opportunities against inventory-carrying costs and working-capital deployment.
Data and analytics are helping improve decisions around when to buy, how much to buy and where to hold inventory. The objective is not maximum inventory; it is optimal inventory—balancing availability, quality, cost, risk and working capital.
One India, multiple supply chains
India cannot be treated as one homogeneous market. Product preferences, flavours, pack sizes and price points vary significantly across geographies. Rural markets add challenges of fragmented retail networks, difficult terrain and longer replenishment cycles.
At the same time, FMCG companies are effectively operating multiple supply chains—general trade, modern trade, e-commerce, quick commerce and institutional channels—each with different service expectations and replenishment cycles.
Distributor management systems, mobility platforms, IoT, GPS-enabled logistics and integrated analytics are helping improve downstream visibility. The real opportunity lies in connecting these channels rather than optimising each in isolation.
The digital supply chain control tower
An important part of our journey is creating greater end-to-end visibility through a digital supply chain control tower approach connecting ERP, manufacturing plants, procurement, suppliers, warehouses, transportation, distributors and demand signals into a common decision-making framework.
For us, a control tower should not become another dashboard. Its real value lies in exception-based management: identifying potential stock-outs, excess inventory, supply constraints, plant bottlenecks, quality issues or logistics disruptions early enough for teams to intervene.
Over time, digital twins and AI-driven scenario modelling can further strengthen this capability by allowing teams to simulate disruptions and evaluate alternatives before making decisions.
From prediction to intelligent action
The next evolution will be towards prescriptive intelligence and agentic AI. Instead of merely reporting what happened or predicting what may happen, intelligent systems will increasingly recommend the next-best action—whether that means reallocating inventory, adjusting production, changing a replenishment plan or highlighting an emerging supply risk.
We see this evolution as:
Visibility → Prediction → Prescription → Intelligent Action
However, technology alone cannot create an intelligent supply chain. Sustainable transformation depends equally on data quality, master-data governance, system integration, process discipline, cybersecurity and people adoption.
The real opportunity for FMCG enterprises is therefore not simply to digitise supply chains but to make them connected, intelligent, predictive and resilient. In a country as diverse and dynamic as India, the organisations that can sense change early, intelligently orchestrate their networks and respond at speed will be better positioned to convert India’s complexity from an operational challenge into a sustainable competitive advantage.