One of the significant issues to overcome was seed loss. A single Monsanto seed truck contains tens of thousands of dollars' worth of product. During the journey from the farm to the manufacturing plant, these trucks can queue for miles, becoming extremely hot in the process. Extreme heat can negatively impact germination potential, and Swanson sought to compensate by implementing sensors on the back of the trucks.
"We put sensors in back of these trucks that [measured] temperature, pressure, geolocation, and we streamed that data through IoT to the plant managers through a dashboard," he explains. "And they got to see every truck, where it was in their geography, where it was on track to be able to be offloaded, and if it was in the queue three miles back before it's offloaded - what's the temperature of that truck?"
The sensors allowed Monsanto to prioritize which trucks made it into the manufacturing plant first, providing a streamlined and data-driven set of efficiencies to the supply chain.
The connected supply chain continues within the manufacturing plant, Swanson says.
"The plant workers work on iPads now, looking at sensor data around their equipment, looking at plant logistics, looking at how to optimize the flow of product in the plant via tools and data they've never had before," he says. "And they can optimize that flow, reduce cost of goods, and get a higher [quality] product out to the marketplace to our distributors and dealers."
Bridging the data divide with farm distributors and customers
Data science at Monsanto extends to the most crucial interactions that Monsanto has with its customers. An issue that continues to impact Monsanto's profit and loss (P&L) statement is that of product returns, specifically seed returns. Growers, seeking to optimize their yield, would select different seed varieties at the beginning of the growing season, such as corn and soybeans, and ultimately return seeds they did not plant to Monsanto.
To make an estimate of projected losses due to returns, Monsanto account representatives would call growers weekly.
"There are two problems with that," Swanson says. "One, it's not very value-add for our customer - a rep calling just asking, 'Are you returning seeds? We're trying to calculate at the end of the year how it is going to impact us.' Secondly, time with the grower is precious for a sales rep. They'd much rather be detailing a product or an opportunity than call them about how many seeds they're going to return."
Monsanto IT applied a data model to the issue. Swanson reports that the resulting model provided a high degree of efficacy in predicting returns, providing months of projections and, by extension, lead time. His strategy has been to reinforce such modeling in arenas as diverse as the effectiveness of marketing campaigns and customer segmentations, while following a consistent procedure: Optimize data inputs, test, iterate, scale and then educate colleagues and customers.
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