In the dynamic world of agriculture, timing is very crucial. For farmers, the timing of key activities such as sowing and spraying can decide success of cropping season. For instance, delay in wheat sowing can negatively impacts yield by 1% each day of late sowing[1] by limiting tiller formation and increasing the crop’s exposure to heat stress during the grain-filling stage.

Timing isn’t just vital for farmers; it’s equally crucial for agri-input companies. The availability of products at the retailer’s counter during the peak sales window often determines product success or inventory buildup. The Indian agrochemical market faced significant headwinds in FY 2023-24, largely due to excessive inventory buildup and unpredictable weather patterns. Ensuring product availability during the sales period while avoiding inventory buildup requires precision and foresight. The changing technological landscape can help in more accurate demand prediction and smarter inventory planning by using advance technologies to collect and analyse data quickly and precisely.

Understanding Conventional Demand and Supply planning: 

Demand planning involves forecasting what farmers will need, when they’ll need it, and in what quantities.

As shown in Figure 1, this process conventionally relies on input from field staff, historical sales data across regions & crop seasons, and monitoring of sowing & harvesting schedules. By integrating these data points, businesses aim to predict demand with reasonable accuracy.

Once demand is projected, supply planning kicks in to ensure timely product availability. This involves procuring raw materials with sufficient lead time, aligning manufacturing schedules with forecasted demand, maintaining buffer stocks, and optimizing logistics to serve target geographies efficiently.

However, Field-level inputs remain central to this approach; they come with inherent limitations. These include inconsistent market understanding across individuals, the unpredictable nature of pest cycles, variability in data quality, strategic bias in distributors’ feedback and human errors in estimations. Such challenges often render significant room for misaligned forecasts and inefficiencies in supply chain execution, resulting in either stockouts or excess inventory.

Technology as an Enabler for Enhanced Demand and Supply Planning:

Demand and supply planning in agriculture is transforming due to the rapid adoption of advanced technologies. Artificial Intelligence (AI) has opened new possibilities for how companies forecast and respond to market requirements. Deep technologies such as satellite remote sensing, machine learning, Internet of Things (IoT) sensors and big data analytics are fundamentally changing the way of demand planning. Remote sensing enables real-time monitoring of This granular visibility helps planner in timely placement of products at retail channel.

Thermal mapping from satellites adds another layer of insight by providing land surface and air temperature data, which can be analysed at highly localized levels even down to districts levels. When these datasets are superimposed and analysed in correlation with variables such as pest movement, fluctuations, wind direction, vegetative cover, and soil health, companies can generate predictive pest forecasts at the district level. These forecasts can further be enhanced by edge computing to deliver ultra-real-time insights.

Several Agri-tech companies are already enabling this transformation. Cropin[1] offers an enterprise-grade AI platform that provides real-time insights across over a billion acres of cultivable land, supporting yield forecasts, pest and disease alerts, predictive supply chain planning and operational optimization to empower agri-businesses with end-to-end visibility. Similarly, SatSure[2] leverages Earth Observation data and combines crop distribution, irrigation data, and affluence indicators to forecast demand, streamlining inventory and optimizing procurement planning.

This type of integrated, data-driven approach minimizes guesswork, improves responsiveness, and enables more accurate and efficient demand and supply planning based on actual field conditions rather than relying solely on distributor feedback and historical sales trends. It empowers agribusinesses to synchronize their operations with field realities, preventing inventory buildup by aligning production and distribution with real-time demand signals.

[1] https://www.cropin.com/ai-powered-intelligent-agriculture/

[2] https://www.satsure.co/solutions/agri-business/

[1] https://www.sciencedirect.com/science/article/abs/pii/S1161030120301271

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