Executive Briefings

Secrets to Advanced Store Replenishment

Two capabilities are helping manufacturers and retailers of brand-name consumer goods improve replenishment at the floor and shelf levels, says Kelly Thomas, senior vice president of product strategy and planning at i2 Technologies. These capabilities are the ability to operationally apply insights from demand signals and to collaboratively align measurements.

"Particularly in the last 12 months, with the economic downturn, we have seen manufacturers looking much more closely at how they can pull deeper insights out of their demand data - not for reporting purposes but to use these insights operationally to improve merchandising at the store level," says Thomas.

This is a collaborative effort between manufacturers and retailers that is being supported by the widespread availability of point-of-sale (POS) data, Thomas says. "The movement among big retailers to have dedicated account teams also has fostered a lot more joint decision making."

Gaining useable insights from POS data starts with cleansing and structuring the data in demand-signal repositories, Thomas says. This data can then be used to drive improvements in merchandising, assortment planning and forecasting. "Then you close the loop by linking this into floor-based and store-based replenishment," he says. Thomas says that i2 implemented such a closed-loop system last summer with a large manufacturer of consumer brands. "In an eight week period we were able to achieve a 12-percent improvement in availability at the shelf level, while reducing inventory by 15 percent. We did this - in a very challenging economic environment - by running this capability in the cloud."

Alignment of metrics between manufacturers and retailers also is important, Thomas says. Companies are putting less emphasis on 100 percent forecast accuracy as the "be all and end all," he says. "While accuracy continues to be important, what is more important in today's collaborative relationships is understanding demand changes and being able to react to those."

To view this video interview in its entirety, Click Here.

"Particularly in the last 12 months, with the economic downturn, we have seen manufacturers looking much more closely at how they can pull deeper insights out of their demand data - not for reporting purposes but to use these insights operationally to improve merchandising at the store level," says Thomas.

This is a collaborative effort between manufacturers and retailers that is being supported by the widespread availability of point-of-sale (POS) data, Thomas says. "The movement among big retailers to have dedicated account teams also has fostered a lot more joint decision making."

Gaining useable insights from POS data starts with cleansing and structuring the data in demand-signal repositories, Thomas says. This data can then be used to drive improvements in merchandising, assortment planning and forecasting. "Then you close the loop by linking this into floor-based and store-based replenishment," he says. Thomas says that i2 implemented such a closed-loop system last summer with a large manufacturer of consumer brands. "In an eight week period we were able to achieve a 12-percent improvement in availability at the shelf level, while reducing inventory by 15 percent. We did this - in a very challenging economic environment - by running this capability in the cloud."

Alignment of metrics between manufacturers and retailers also is important, Thomas says. Companies are putting less emphasis on 100 percent forecast accuracy as the "be all and end all," he says. "While accuracy continues to be important, what is more important in today's collaborative relationships is understanding demand changes and being able to react to those."

To view this video interview in its entirety, Click Here.