Executive Briefings

The Challenges of Demand Management

Janice Gullo, lean supply chain master and black belt at the DuPont Center of Competency, shows how Lean concepts can be applied to the demand-forecasting process, to improve accuracy, profitability and customer-service levels.

Gullo is "frustrated" by those who claim that forecast accuracy can't be improved. With so many inputs to consider, the process can be confusing, she acknowledges. But collecting the right day is key.

The DuPont Center has been working to eliminate waste in the forecasting process. The data should be exactly what's needed, and nothing more. Information on discontinued products should be weeded out. Once the data is cleaned up, the company should segment its product base, using an "ABC" approach. Top priority should be assigned to the 20 percent of items that generate 80 percent of revenue. "You take that segment," she says, "and focus all your resources on making it more accurate." Fewer data points to consider means less work, and the greater chance of coming up with accurate numbers. Slower-selling items shouldn't be ignored, but should be managed differently. "You don't necessarily have to forecast them," Gullo says, cautioning against "having a sales person focusing on the minute details of a product that's purchased once a year."

Essentially, says Gullo, the company is applying Lean concepts to the flow of demand data.  In the process, "you're focusing on what matters."

It isn't all about the numbers, however. The assumptions behind the data are equally important. Much depends on whether a particular product can be accurately forecasted, or that the company is aware of the margin of error attached to that item. "The assumption is that history will repeat itself," Gullo says, adding that this isn't always the case. Companies should know which products are subject to high levels of variability, before they commit to new plants or increased orders.

For new products, which have no history of demand, companies should rely on collaborative relationships with customers. In particular, they need to understand buyer demographics. The Collaborative Planning, Forecasting and Replenishment (CPFR) model, created to address demand questions in a variety of retail environments, can help. "It's a beautiful thing," says Gullo. "You can move the push-pull interface to where it matters for that company."

To view video in its entirety, click here

Janice Gullo, lean supply chain master and black belt at the DuPont Center of Competency, shows how Lean concepts can be applied to the demand-forecasting process, to improve accuracy, profitability and customer-service levels.

Gullo is "frustrated" by those who claim that forecast accuracy can't be improved. With so many inputs to consider, the process can be confusing, she acknowledges. But collecting the right day is key.

The DuPont Center has been working to eliminate waste in the forecasting process. The data should be exactly what's needed, and nothing more. Information on discontinued products should be weeded out. Once the data is cleaned up, the company should segment its product base, using an "ABC" approach. Top priority should be assigned to the 20 percent of items that generate 80 percent of revenue. "You take that segment," she says, "and focus all your resources on making it more accurate." Fewer data points to consider means less work, and the greater chance of coming up with accurate numbers. Slower-selling items shouldn't be ignored, but should be managed differently. "You don't necessarily have to forecast them," Gullo says, cautioning against "having a sales person focusing on the minute details of a product that's purchased once a year."

Essentially, says Gullo, the company is applying Lean concepts to the flow of demand data.  In the process, "you're focusing on what matters."

It isn't all about the numbers, however. The assumptions behind the data are equally important. Much depends on whether a particular product can be accurately forecasted, or that the company is aware of the margin of error attached to that item. "The assumption is that history will repeat itself," Gullo says, adding that this isn't always the case. Companies should know which products are subject to high levels of variability, before they commit to new plants or increased orders.

For new products, which have no history of demand, companies should rely on collaborative relationships with customers. In particular, they need to understand buyer demographics. The Collaborative Planning, Forecasting and Replenishment (CPFR) model, created to address demand questions in a variety of retail environments, can help. "It's a beautiful thing," says Gullo. "You can move the push-pull interface to where it matters for that company."

To view video in its entirety, click here