Campbell Soup Company, the leading soup maker in the U.S., upgrades to the newest version of Terra Technology’s Demand Sensing solution. Demand Sensing provides an automated way for manufacturers to extract value from big data, using pattern recognition algorithms to create the best possible prediction of future sales for every item in every location. The upgrade will enhance Campbell’s forecast accuracy to better meet the needs of retailers and consumers.
"Sensing product demand is particularly important in today’s increasingly complex marketplace,” said Patrick Folan, Vice President of Supply Chain for Campbell Soup Company. “Following our recent implementation of SAP’s Advanced Planner and Optimizer (APO) tools for supply chain management, upgrading to Demand Sensing 5.0 was the next logical step. Demand Sensing allows us to make supply decisions using forecasts reflecting current market realities rather than historical estimates. Better forecast accuracy will further our efforts to improve customer service, without the burden of carrying unnecessary inventory.”
The availability of data across all parts of the supply chain has grown exponentially in recent years. Automating the use of this data to better understand customer behavior is an important step in creating efficient demand-driven operations, especially for heavily-promoted businesses or companies where innovation is central to their strategy. Access to accurate daily forecasts helps manufacturers improve customer service, grow revenue by increasing on-shelf availability, free cash and improve return on capital. Terra’s Demand Sensing is currently used by manufacturers to plan and execute operations in more than 160 countries.
“In 2004, Campbell Soup Company was the first company to implement Terra’s solutions and reap the benefits of Demand Sensing’s improved forecast accuracy,” said Robert Byrne, Terra Technology’s CEO. “We are proud to play a role in the company’s continued leadership and are glad they continue to see value even with their new planning system.”
