Matt Johnson of Oracle on Demand Signal Management
After guarding their data for years, retailers have recently begun to bury consumer packaged goods manufacturers in a greatly expanded set of retail data sources. Many companies are currently trying to leverage patterns and exceptions in this data to win business and avoid disruptions. I recently spoke with Matt Johnson, senior director of Consumer Goods Industry Strategy for Oracle, to discuss the opportunities and challenges of demand signal management.
How did demand signal management get its start?
JOHNSON: Some people would trace the roots of demand signal management to continuous replenishment, efficient consumer response and business-to-business initiatives in the 1990s. Certainly, retailers have been sharing sales and consumption data selectively with manufacturers for years. But the biggest trigger point was Wal-Mart's decision to stop selling point-of-sale (POS) data to syndicated data providers in 2001. Suddenly, to get a detailed understanding of the largest retailer's activity, manufacturers had to go straight to Wal-Mart. It didn't take long for a cottage industry of solutions to spring up that queried Wal-Mart's Retail Link system to download the data to a system at a field sales office so it could be analyzed there.
So would you say that demand signal management is
primarily a Wal-Mart phenomenon?
JOHNSON: Hardly. While Wal-Mart's move brought the concept into the mainstream, many other retailers take the same approach of sharing data directly with their supplier rather than syndicating it through third parties. In the United Kingdom, for example, POS data is available from all of the largest grocery retailers. Syndicated data is still very important for understanding competitor behavior, share, trends and the sales volumes of more traditional retailers, but it is no longer a complete source. Demand signal management has become an imperative for all consumer packaged goods manufacturers, whether they realize it or not.
How does demand signal management solve the problem
of proliferating customer sales analysis systems?
JOHNSON: Demand signal management takes an enterprise approach to the retail data problem. It applies a common framework and tools that are aligned with the corporate IT strategy to the diversity of retailer data sources and analytical requirements.
Typically, with demand signal management, an account team can review results in its customers' terms (for example, market week, customer category, customer region) while presenting insights in terms of the manufacturer's calendar and brand hierarchy, aligned with syndicated data. This approach allows manufacturers to rationalize the field data marts, centralize processing for efficiency and free up the account teams to spend more time servicing their customers.
But most importantly, demand signal management makes it possible to drive better decision making using enterprise applications. For years, companies have wanted to drive their demand planning off of store sales, see the progress of promotional sales while promotions are still in flight, and preview sales and inventory positions before dispatching merchandising personnel to a store. The real benefits come from pulling the data out of the field offices and putting it to work company-wide.
Even if they can see what's going on, aren't manufacturers limited in their ability to address issues that occur in
a retailer's stores?
JOHNSON: Ultimately, the retailers own their stores, and, for most categories, they stock their own store shelves. But there is still plenty that manufacturers and retailers can do to align their efforts in order to win more business as well as eliminate expensive disruptions.
An industry initiative called "New Ways of Working Together" (sponsored by the Grocery Manufacturers Association, among others) provides a framework for retailer/manufacturer collaboration. One area that the team uncovered early was the lack of common goals and common measures between retailers and manufacturers. We formed the GS1 Trading Partner Performance Management (TPPM) work group and created a global standard of 17 performance metrics that can help to guide retailer/manufacturer relationships.
Demand signal management is as critical to collecting and managing this joint scorecard as it is to identifying the specific issues and opportunities in the store data.
How did demand signal management get its start?
JOHNSON: Some people would trace the roots of demand signal management to continuous replenishment, efficient consumer response and business-to-business initiatives in the 1990s. Certainly, retailers have been sharing sales and consumption data selectively with manufacturers for years. But the biggest trigger point was Wal-Mart's decision to stop selling point-of-sale (POS) data to syndicated data providers in 2001. Suddenly, to get a detailed understanding of the largest retailer's activity, manufacturers had to go straight to Wal-Mart. It didn't take long for a cottage industry of solutions to spring up that queried Wal-Mart's Retail Link system to download the data to a system at a field sales office so it could be analyzed there.
So would you say that demand signal management is
primarily a Wal-Mart phenomenon?
JOHNSON: Hardly. While Wal-Mart's move brought the concept into the mainstream, many other retailers take the same approach of sharing data directly with their supplier rather than syndicating it through third parties. In the United Kingdom, for example, POS data is available from all of the largest grocery retailers. Syndicated data is still very important for understanding competitor behavior, share, trends and the sales volumes of more traditional retailers, but it is no longer a complete source. Demand signal management has become an imperative for all consumer packaged goods manufacturers, whether they realize it or not.
How does demand signal management solve the problem
of proliferating customer sales analysis systems?
JOHNSON: Demand signal management takes an enterprise approach to the retail data problem. It applies a common framework and tools that are aligned with the corporate IT strategy to the diversity of retailer data sources and analytical requirements.
Typically, with demand signal management, an account team can review results in its customers' terms (for example, market week, customer category, customer region) while presenting insights in terms of the manufacturer's calendar and brand hierarchy, aligned with syndicated data. This approach allows manufacturers to rationalize the field data marts, centralize processing for efficiency and free up the account teams to spend more time servicing their customers.
But most importantly, demand signal management makes it possible to drive better decision making using enterprise applications. For years, companies have wanted to drive their demand planning off of store sales, see the progress of promotional sales while promotions are still in flight, and preview sales and inventory positions before dispatching merchandising personnel to a store. The real benefits come from pulling the data out of the field offices and putting it to work company-wide.
Even if they can see what's going on, aren't manufacturers limited in their ability to address issues that occur in
a retailer's stores?
JOHNSON: Ultimately, the retailers own their stores, and, for most categories, they stock their own store shelves. But there is still plenty that manufacturers and retailers can do to align their efforts in order to win more business as well as eliminate expensive disruptions.
An industry initiative called "New Ways of Working Together" (sponsored by the Grocery Manufacturers Association, among others) provides a framework for retailer/manufacturer collaboration. One area that the team uncovered early was the lack of common goals and common measures between retailers and manufacturers. We formed the GS1 Trading Partner Performance Management (TPPM) work group and created a global standard of 17 performance metrics that can help to guide retailer/manufacturer relationships.
Demand signal management is as critical to collecting and managing this joint scorecard as it is to identifying the specific issues and opportunities in the store data.