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Vantage Point: A "How-To" Guide to Downstream Data in the Consumer Goods Industry

8/2/2011
There is more downstream data being shared in the industry today than ever before. Following Walmart’s lead in the early nineties, all major food, drug and mass retailers are now engaged in some form of point-of-sale (POS) and inventory data sharing, giving their suppliers complete visibility by day, by store and by SKU. CPG leaders have now implemented downstream data programs covering tens of thousands of stores in the United States alone, with international markets the next frontier. If you are not part of this group, what should you look out for?
 
Start with the End in Mind

The business case for downstream data can at first be daunting: documented use cases cover topics ranging from out-of-stocks to trade promotion execution, from baseline forecasting to new product introductions, from unsaleables management to forecasting and from category management to inventory optimization. 
 
As you engage into such a project, defining a small set of core strategic priorities (no more than three to five) will make it easier to:
  • Build the right set of tools that will transform downstream data into insights and alerts
  • Address change management issues: changes to existing processes driven by the new data, adjusted roles and responsibilities…
  • Determine a tangible ROI for the project
Understand the Enterprise Implications of Downstream Data

Most published successes around downstream data read the same way: “Supplier ABC achieves [name your objective] with Retailer XYZ.” These stories, no matter how compelling, tend to mask the real potential of downstream data: the end-goal shared by all the visionary organizations – the early adopters who began this effort years ago – is that the entire organization should be run “shelf-back” – and not just a few individual local account teams.
 
With data now available for 60 percent to 70 percent of US retail sales, downstream data integration creates a tremendous opportunity to solve enterprise-wide problems across functions: early examples cover obvious supply chain opportunities such as improved inventory management and forecasting (using actual end shopper sales vs. customer orders to see demand 3 to 14 days sooner). But they also branch into sales and marketing, in particular trade fund allocation: downstream data provides the opportunity to understand which promotions are really accretive to your business in each market (rather than simply judging them by how much lift they generated at a given retailer) and for which brands promotions actually drive additional consumption and share across retailers rather than just drive internal cannibalization.
 
To achieve these benefits, it is critical to not let a “best-of-breed” approach rise into the organization: only a harmonized, enterprise-wide approach will let you reap the rewards.
 
Build the Right Governance Structure – at the C-Level

Who should own downstream data in the organization? The situation varies widely from company to company, with either sales or supply chain generally owning the project.
 
Both options are perfectly viable, but any downstream data project will fail unless you assemble a joint, cross-functional team from the very beginning. To just take an example, out-of-stock is an issue that cannot be solved by one division alone: marketing needs to build the right planograms, demand planning needs to generate accurate forecasts, supply chain needs to ensure service levels and field sales need to support shelf compliance. Armed with downstream data, each of these functions can perform more effectively, but limiting the reach of the data to one organizational silo will dramatically limit the project ROI and synergies.
 
The sponsor of a downstream data initiative needs to be in a position to coalesce the organization around the topic – to ensure that it really fulfills its promise.
 
Determine the Right Technology Footprint to Achieve your Goals

Many companies still exclusively think of downstream data in terms of Demand Signal Repositories (DSRs), or the database used to collect, cleanse, harmonize and enrich store demand signals across the enterprise. While this is a necessary component to any downstream strategy, a DSR will only enable you to achieve a small part of the benefits. A comprehensive downstream data footprint requires three distinct elements on top of the DSR:
  • A distributed data visibility component: to make sure that the information is disseminated across the organization, in templates matching your roles, responsibilities and processes. To drive the culture change from inside-out to outside-in, downstream data needs to be broadly accessible, with simple, documented processes helping associates leverage that data, see its benefits and turn it into a “business-as-usual” intelligence source
  • Alerting solutions: to ensure you spend more time identifying and addressing existing opportunities or exceptions and issues to solve problems rather than diving into (and likely sinking in) the data. Alerting solutions can then be rolled out across field sales teams, to make store calls more productive but also to other functions (demand planning, marketing…)
  • Automated applications: you can give your demand planners visibility on two years of sales by day, by store and by SKU but they will likely struggle to convert this into a more accurate forecast. The same data set, run through algorithms and applications designed to handle it, will automate forecasting and enable you to gain the 40% reduction in forecast error claimed by CPG leaders. It will automate store-by-store promotion allocations to minimize out-of-stocks and inventory overages and will automate promotions scorecarding, including lift over baseline and lost sales due to out-of-stocks.
Getting Started

The easiest mistake to make is to think of downstream data as an IT or purely data-driven project. CPG companies who have succeeded have taken the opposite approach, focusing on cultural change and joint process definition – with downstream data in the role of the enabler.
 
What we are witnessing today is an exciting evolution, akin to the supplier partnership spirit pioneered by Toyota and now broadly adopted in the auto industry: CPG companies are transforming into true demand-driven organization and much more responsive and agile suppliers for their customers.
 
Downstream data is one of the keys to that transformation, but most organizations will not succeed unless they adopt a new discipline: Retail Execution Management, defined as the set of tools, processes and governance mechanisms supporting the integration of this data at all layers of the company. 
 
Retail Execution Management is becoming a primary source of new competitive advantage and the key to being an innovative and differentiated partner with your retailer customers – bringing them new insights on how to increase revenues, decrease inventories and out of stocks and improve margins. Let me know of your Retail Management Execution experience: I can be reached at [email protected].
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Dr. Jonathan Golovin is the chairman, CEO and co-founder of Retail Solutions. He was also 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.
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