With the increasing availability of Point of Sale (POS) and other downstream data over the last decade, companies selling through retail channels have strived to use such data to drive their upstream supply chains. The availability of granular data, down to the item, store, and day level, offers the promise of transforming business processes in areas such as demand, replenishment, promotion, and new product planning. However, limitations in computing power have precluded taking full advantage of the huge amounts of data involved. Oracle’s new product, Oracle In-Memory Consumption-Driven Planning (CDP), provides a solution. Based on an optimized version of Oracle’s Demantra product line combined with and designed for the Oracle Engineered Systems platform, CDP offers an extreme step-change in performance and new functionality. Oracle Engineered Systems are the preferred platform for deploying Oracle Applications when performance and scalability are critical, because of the extensive performance optimizations in engineering of hardware and software to work together, and the innovations that are only available with the complete Oracle technology stack. Using the Oracle Exadata Database Machine, and optionally, the Oracle Exalogic Elastic Cloud or SPARC SuperCluster, CDP has been engineered from the ground up to provide unparalleled performance and enable business processes that would otherwise not be feasible.

This paper outlines the capabilities and directions of CDP, applicable to multiple industries such as Consumer Packaged Goods and Durables, Consumer Electronics, Media and Entertainment, Automotive and Communications.

Introduction: The Demand-Driven Value Chain Vision
Over the last decade, many companies that sell through retail channels have been pursuing the vision of the demand-driven value chain, in order to drive their upstream supply chains based on actual end-customer demand – consumption, or sell-through to consumers – rather than shipments, or sell-in to Distribution Centers (DCs) and stores. This approach promises to eliminate the well-documented bullwhip effect, in which lack of visibility to downstream demand and inventory positions propagates upstream and leads to volatile order quantities as well as inventory build-ups and shortages. By monitoring sell-through rather than just sell-in, and minimizing the latency between actual consumer demand and its measurement, companies can gain a more accurate understanding of market conditions and adjust their production and distribution plans accordingly.

By deriving a sell-in forecast based on the sell-through forecast (taking into account channel inventory and lead times) companies can obtain a more accurate forecast of sell-in versus the traditional practice of forecasting sell-in directly. The lack of visibility resulting from observing only shipments can be eliminated and a direct relationship between consumer behavior
and sell-in shipments can be established. With a more accurate forecast, companies can improve on-shelf availability, increase sales, increase turns, and decrease supply chain costs.