What if agricultural companies could know the demand for their products in advance?

Juan Besari||Digital transformation|3 min read

Demand forecasting in food companies

Demand forecasting in food companies is planned and organized, as in all other sectors, according to the future estimate the manufacturer intends to meet in order to avoid losing sales and even customers. However, in the food industry, there is another major challenge besides meeting demand and avoiding additional stock levels, for which demand forecasting could be essential: the seasonality of some products and, on the other hand, the short shelf life of others.

Anticipating the future is already possible through Artificial Intelligence (AI) solutions

The production and consumption of agricultural commodities are often not aligned, as the production time and consumption rate can be disjointed. Food commodities are often produced periodically (certain crops are harvested only once a year) but are continuously consumed throughout the year. These temporary mismatches in production and consumption require agri-food companies to forecast future consumption to avoid making erroneous decisions, eventually causing food waste, price volatility and overstocks.

Other food companies are characterized by raw materials and products that normally cannot be stored for the long term, so they must manage business processes extremely well according to accurate forecasts.

How can AI help agri-food companies with demand forecasting?

The demand planning process is currently undergoing a huge transformation. While historically it has been a reactive process that involved responding to changing market conditions, the advent of technology is enabling, and at the same time forcing, demand planning to become much more strategic. Digitizing demand planning is becoming essential in demand forecasting for food companies.

Efficient demand planning must fulfill three core functions in a company: demand forecasting, demand control, and coordination of supply chain opportunities and capacities to the expected demand.

A more accurate estimation of future demand leads to better optimization of the raw material inventory on the one hand, and to production planning which optimizes finished goods stock levels, on the other. This reduces lost sales and excessively long storage time for products with short shelf life.

For this reason, demand forecasting solutions in food companies based on Artificial Intelligence (AI) can be a very useful tool to improve supply chain management.

AI drives the automation of the most traditional and labor-intensive tasks within demand planning, in particular, the analysis and interpretation of batches of data. Not only can AI do this more accurately and quickly, but by automating these critical but complex tasks, the team's time is freed up, allowing them to focus on more strategic business tasks.

Exponentia's Artificial Intelligence solutions for demand forecasting in food companies perform demand and stock estimation and forecasting. In addition, they avoid the massive storage of unstructured data in multiple databases or systems that make analyzing such data in an efficient and effective way impossible.

Through the analysis of historical demand, production or warehouse, our artificial intelligence solution for demand forecasting in food companies can approximate which products or procedures to optimize (for example, harvests), reduce wastage or increase quality. Moreover, this AI-based solution can help you to better manage your warehouses, helping to improve logistics and decision making.

If you think you need help to define your company's demand forecasts, choose an automated and intelligent solution that can collect your data from multiple sources and provide you with forecasts in a matter of minutes.

Exponentia's artificial intelligence solution for food companies will provide you with useful information from in-depth data analysis, and a user-friendly and innovative interface.