For ag retailers, getting inventory levels right is a constant balancing act. Carry too much crop protection product, and valuable working capital gets tied up in the warehouse. Carry too little, and retailers risk being unable to meet grower demand when the season gets underway. Could artificial intelligence (AI) help retailers make better decisions? That question was among the topics discussed at the recent Syngenta Media Summit in Orlando, FL.
Jeff Cecil, Syngenta’s President, North America Crop Protection, sees AI-powered forecasting as one opportunity to address a longstanding challenge for the agricultural industry: predicting product demand accurately enough to support more efficient inventory management.
“In our industry, forecasting is not a strength,” Cecil said, referring specifically to product forecasting.
That weakness can have consequences throughout the supply chain, particularly as companies work to reduce inventory and manage increasingly tight capital resources.
The Inventory Forecasting Challenge
Crop protection inventory decisions involve multiple variables, from anticipated grower demand and seasonal conditions to product availability and changing market dynamics. Forecasting errors can leave retailers holding excess inventory or scrambling to secure products when customers need them.
The challenge becomes even more significant when manufacturers, distributors, and retailers are all attempting to operate with leaner inventories.
Cecil has warned that companies across the crop protection supply chain are reducing inventories to increasingly low levels. In that environment, accurately anticipating demand becomes critical to keeping products moving through the channel.
“You’ve got to be able to make a product in a more kind of way, just-in-time type of thing,” Cecil said. “And if you can’t forecast for that, you can’t do that.”
For ag retailers, the implications extend beyond warehouse management. Inventory decisions affect purchasing schedules, cash flow, customer service, and the ability to deliver products when growers need them.
Better forecasting could help retailers identify demand earlier, reduce unnecessary inventory commitments, and make more informed decisions about which products to stock.
Where AI Fits In
Syngenta is exploring how AI can improve forecasting and other business processes. Cecil described a project the company started approximately two years ago to examine how it approaches forecasting.
The goal is to improve the accuracy and timeliness of those projections, helping the business better align production and inventory with market needs.
Cecil emphasized that the benefits could extend beyond manufacturers. More accurate forecasting can help reduce the financial burden associated with carrying excess inventory throughout the supply chain.
“If you can’t forecast for that, you can’t do that,” Cecil said, referring to the challenge of operating with a more just-in-time approach to product availability.
He added that inventory costs ultimately affect customers, making forecasting improvements relevant to the broader agricultural retail channel.
“And so your ability to forecast more accurately or timely is really important,” Cecil said.
For retailers, AI could potentially help bring together information from sales histories, purchasing patterns, grower demand, and other business data to identify trends that might be difficult to spot through traditional forecasting methods alone.
But the practical value will depend on the quality of the information available, how well forecasting tools integrate with existing business systems, and whether retailers can translate predictions into better purchasing decisions.
From Annual Plans to Continuous Updates
Cecil also discussed how Syngenta is using AI to make business planning more responsive to changing market conditions.
Traditional planning processes can require months of work, yet the resulting plans may quickly become outdated as market conditions change. Syngenta is exploring ways to use AI to update plans much more frequently.
“What we’re trying to do is get that in a position where we can update it almost every week, every day, and be able to look at what’s going on in the market, update it, and then take that to our customers,” Cecil said.
A similar approach could be useful for ag retailers managing seasonal demand, shifting customer purchasing patterns, and changing product availability. More frequent updates could help businesses adjust purchasing plans before small forecasting errors turn into larger inventory problems.
Still, AI should be viewed as a tool to support business decisions rather than a substitute for experienced purchasing managers and local market knowledge.
A Tool for a Tighter Inventory Environment
Cecil sees considerable opportunity for AI across the industry, but he also acknowledged that many companies are experimenting with similar technologies at the same time.
The challenge will be identifying where AI delivers meaningful results and determining how best to apply it to real business problems.
For ag retailers, inventory forecasting is a practical place to start. Better predictions could help businesses balance the competing demands of protecting working capital and maintaining reliable product availability.
As the industry prepares for the 2027 growing season, the ability to anticipate demand more accurately could become an increasingly important competitive advantage.
To hear more from Cecil about AI, inventory management, and other industry challenges, tune in to a recent episode of CropLife Retail Week, featuring additional highlights from the Syngenta Media Summit.
