VANTAGE POINT: Retailer Data Sharing
A How-To Guide to Operational Benefits
By Jon Golovin, CEO and co-founder, Retail Solutions
The act of retailers sharing detailed operational point-of-sale (POS) and inventory data hardly qualifies as a new concept. Demand Signal Repositories (DSR) have been available for a number of years. However, it was not until the first half of 2008 that a tipping point in this slowly building industry trend was reached. Spurred by industry initiatives -- like "New Ways of Working Together," the availability of new science and applications, and the search for increased profitability in difficult economic times -- most manufacturers and retailers, and not only the larger, leading ones, are designing and implementing data sharing strategies, hoping to find new sources of competitive advantage.
The (Multi) Million Dollar Retailer Question
Most of these companies are still, however, designing these data sharing initiatives in relative uncertainty. Over the past few months, I have met with many executives at leading U.S. and global retailers and the same questions arose over and over again: "What data do suppliers want? What would they do if we shared this data? What else besides data would suppliers want? How does this benefit us, the retailers?"
The truth is most retailers are unaware of what manufacturers currently do or could potentially do with their data. Many expect manufacturers to use POS and inventory data for forecasting, though very few suppliers have forecasting systems designed to even accept this type of data, let alone leverage it. Several retailers mention trade promotion and category management, or supply chain and inventory management; only a handful think of operational improvement.
How POS Data is Being Used Today
Currently, POS data users at manufacturers can be divided into two broad categories with very different needs:
> Corporate-level users in the supply chain, marketing and brand management divisions are focusing on large-scale macro or aggregated sales and operation planning, inventory management or trade promotion planning. They utilize aggregated data (by market or at the distribution center level) by week and don't need it to be very timely. A lag time of couple of weeks for marketing and brand management is generally acceptable, and much of this data comes from syndicated data providers today.
> Customer teams, on the other hand, are focusing on improving sell-through and operations for all SKUs at each store for their retailer. Therefore, they tend to need data at the most granular level (by store, by day and by SKU if possible) and cannot afford to wait a few days, as corrective actions will then be outdated and irrelevant. Their data needs can only be met by direct feeds from the retailer.
The problem faced by users on customer teams is compounded by the fact that manufacturers have no logical place within their legacy systems to host and manage this data. In addition, the customer team is often small, under-funded and consists of two or three people located in a small satellite office, with the notable exception of the Wal-Mart team in Bentonville, Ark. Consequently, they are only able to focus on major exceptions or anomalies in sales and inventory levels.
These teams are in no position to fund a custom DSR project, requiring software and hardware investment as well as corporate IT oversight. Therefore, they resort to custom-designed spreadsheets, particularly ill-designed to handle the extensive scale of data by day, by store and by SKU.
This situation contradicts the fundamental reason why retailers should and are getting increasingly more interested in sharing data. If retailers share detailed data, they expect immediate and tangible improvements to their sales and margins, lower out of stocks, improved inventory levels and decreased shrink from their suppliers' customer teams -- not corporate level usage of their data. This situation has built up to a standstill that is about to be broken.
How POS Data Could (and Should) Be Used
There is no longer need to prove how detailed operational POS and inventory data can be a tremendous source of competitive advantage for both manufacturers and retailers. By going beyond "data-crunching", by applying algorithms designed to extract value from data of this nature, the industry can now see in near-real time into the store and onto the shelf, and know which promotions are executed correctly, which products are about to run out-of-stock, which item introductions work and which ones don't.
This creates a compelling case for the next wave of solutions for consumer goods companies. The following characteristics either are or will soon become essential requirements of any solution:
> Enterprise-Level: covering all functions at consumer goods companies from S&OP to store operations, from the corporate level to each customer team
> Demand-Driven: leveraging POS and supply chain data as a primary input/source for forecasting and demand response
> Scientific: applying advanced algorithms to information. The scale of data available makes simple reporting ineffective. In the United States, a major manufacturer probably sells in 50,000 stores every day. Multiply this by the number of tracked indicators and the number of different products being sold and basic data analysis with drill-down capabilities will be insufficient -- users will get lost in the myriad of details. Supporting systems must proactively find issues and alert the appropriate users.
> Transparent, Collaborative and Integrated with Customers: creating an "end-to-end" supply chain from the manufacturer's factory to each of the retailer's store. Most game-changing opportunities will occur in the last 100 yards of the supply chain -- store execution -- which is historically the area most underserved and least visible.
> Granular: leveraging opportunities at each point in this end-to-end supply chain, at each warehouse, at each store for each SKU and at each step of the supply chain
> Actionable and Proactive: providing each function with recommended actions rather than with reports
> Near-Real Time: leveraging data almost immediately, ensuring no opportunity is lost to improve sales and supply chain effectiveness
> User-Friendly and Quickly Usable: ensuring each party in this supply chain will be empowered to act quickly and effectively. Consumer goods companies should not be in the business of data cleansing, harmonization or translation, but in the business of leveraging information to improve efficiency and effectiveness in their supply chain. This means the data and actions they receive need to match their own product hierarchies, calendars and nomenclature, no matter which retailer it comes from.
> Easy and Quick to Implement: ensuring every customer team, not only the larger ones, quickly benefit. Software-as-a-service (SaaS) creates a compelling solution by bypassing budgetary constraints of large capital and IT investments in a tough economic environment and also allows customer teams to be operational in a few weeks instead of months.
Conclusion
The most important factor in the success of data sharing programs is actually not a system requirement -- it is to establish clarity between business partners on what happens after such systems are operational. Best-in-class alerts and reports without collaborative business processes lead nowhere. Fortunately, industry initiatives, like "New Ways of Working Together" and new generation of systems from Retail Solutions, among other companies, are currently building and operating, are laying the foundation for a new source of joint value creation -- based on real-time data sharing and collaborative business processes -- that add value to the shopper, the retailer and the supplier.
Let me know your thoughts. You can reach me at [email protected].
________________________________________________________________________________
Dr. Jonathan Golovin is the chairman, CEO and co-founder of Retail Solutions. He also was the founder and chairman of Consilium Inc., the largest independent Manufacturing Execution System (MES) Company (now Applied Materials) and of Vigilance, the leading event management company. In 2001, he was awarded the Ernst & Young Entrepreneur of the Year Award for emerging companies and is the author of Achieving Stretch Goals, published by Prentice Hall.
The (Multi) Million Dollar Retailer Question
Most of these companies are still, however, designing these data sharing initiatives in relative uncertainty. Over the past few months, I have met with many executives at leading U.S. and global retailers and the same questions arose over and over again: "What data do suppliers want? What would they do if we shared this data? What else besides data would suppliers want? How does this benefit us, the retailers?"
The truth is most retailers are unaware of what manufacturers currently do or could potentially do with their data. Many expect manufacturers to use POS and inventory data for forecasting, though very few suppliers have forecasting systems designed to even accept this type of data, let alone leverage it. Several retailers mention trade promotion and category management, or supply chain and inventory management; only a handful think of operational improvement.
How POS Data is Being Used Today
Currently, POS data users at manufacturers can be divided into two broad categories with very different needs:
> Corporate-level users in the supply chain, marketing and brand management divisions are focusing on large-scale macro or aggregated sales and operation planning, inventory management or trade promotion planning. They utilize aggregated data (by market or at the distribution center level) by week and don't need it to be very timely. A lag time of couple of weeks for marketing and brand management is generally acceptable, and much of this data comes from syndicated data providers today.
> Customer teams, on the other hand, are focusing on improving sell-through and operations for all SKUs at each store for their retailer. Therefore, they tend to need data at the most granular level (by store, by day and by SKU if possible) and cannot afford to wait a few days, as corrective actions will then be outdated and irrelevant. Their data needs can only be met by direct feeds from the retailer.
The problem faced by users on customer teams is compounded by the fact that manufacturers have no logical place within their legacy systems to host and manage this data. In addition, the customer team is often small, under-funded and consists of two or three people located in a small satellite office, with the notable exception of the Wal-Mart team in Bentonville, Ark. Consequently, they are only able to focus on major exceptions or anomalies in sales and inventory levels.
These teams are in no position to fund a custom DSR project, requiring software and hardware investment as well as corporate IT oversight. Therefore, they resort to custom-designed spreadsheets, particularly ill-designed to handle the extensive scale of data by day, by store and by SKU.
This situation contradicts the fundamental reason why retailers should and are getting increasingly more interested in sharing data. If retailers share detailed data, they expect immediate and tangible improvements to their sales and margins, lower out of stocks, improved inventory levels and decreased shrink from their suppliers' customer teams -- not corporate level usage of their data. This situation has built up to a standstill that is about to be broken.
How POS Data Could (and Should) Be Used
There is no longer need to prove how detailed operational POS and inventory data can be a tremendous source of competitive advantage for both manufacturers and retailers. By going beyond "data-crunching", by applying algorithms designed to extract value from data of this nature, the industry can now see in near-real time into the store and onto the shelf, and know which promotions are executed correctly, which products are about to run out-of-stock, which item introductions work and which ones don't.
This creates a compelling case for the next wave of solutions for consumer goods companies. The following characteristics either are or will soon become essential requirements of any solution:
> Enterprise-Level: covering all functions at consumer goods companies from S&OP to store operations, from the corporate level to each customer team
> Demand-Driven: leveraging POS and supply chain data as a primary input/source for forecasting and demand response
> Scientific: applying advanced algorithms to information. The scale of data available makes simple reporting ineffective. In the United States, a major manufacturer probably sells in 50,000 stores every day. Multiply this by the number of tracked indicators and the number of different products being sold and basic data analysis with drill-down capabilities will be insufficient -- users will get lost in the myriad of details. Supporting systems must proactively find issues and alert the appropriate users.
> Transparent, Collaborative and Integrated with Customers: creating an "end-to-end" supply chain from the manufacturer's factory to each of the retailer's store. Most game-changing opportunities will occur in the last 100 yards of the supply chain -- store execution -- which is historically the area most underserved and least visible.
> Granular: leveraging opportunities at each point in this end-to-end supply chain, at each warehouse, at each store for each SKU and at each step of the supply chain
> Actionable and Proactive: providing each function with recommended actions rather than with reports
> Near-Real Time: leveraging data almost immediately, ensuring no opportunity is lost to improve sales and supply chain effectiveness
> User-Friendly and Quickly Usable: ensuring each party in this supply chain will be empowered to act quickly and effectively. Consumer goods companies should not be in the business of data cleansing, harmonization or translation, but in the business of leveraging information to improve efficiency and effectiveness in their supply chain. This means the data and actions they receive need to match their own product hierarchies, calendars and nomenclature, no matter which retailer it comes from.
> Easy and Quick to Implement: ensuring every customer team, not only the larger ones, quickly benefit. Software-as-a-service (SaaS) creates a compelling solution by bypassing budgetary constraints of large capital and IT investments in a tough economic environment and also allows customer teams to be operational in a few weeks instead of months.
Conclusion
The most important factor in the success of data sharing programs is actually not a system requirement -- it is to establish clarity between business partners on what happens after such systems are operational. Best-in-class alerts and reports without collaborative business processes lead nowhere. Fortunately, industry initiatives, like "New Ways of Working Together" and new generation of systems from Retail Solutions, among other companies, are currently building and operating, are laying the foundation for a new source of joint value creation -- based on real-time data sharing and collaborative business processes -- that add value to the shopper, the retailer and the supplier.
Let me know your thoughts. You can reach me at [email protected].
________________________________________________________________________________
Dr. Jonathan Golovin is the chairman, CEO and co-founder of Retail Solutions. He also was the founder and chairman of Consilium Inc., the largest independent Manufacturing Execution System (MES) Company (now Applied Materials) and of Vigilance, the leading event management company. In 2001, he was awarded the Ernst & Young Entrepreneur of the Year Award for emerging companies and is the author of Achieving Stretch Goals, published by Prentice Hall.