Consumption-Driven Planning Overview

Tip: The Demantra Local Application replaces Collaborator Workbench. You may see both names in this text.

This chapter covers the following topics:

• Introduction
• The Consumption-Driven Planning Process
• CDP Business Process

Introduction

The traditional Demand Planning process focuses on projecting demand at manufacturing plants or manufacturer's depots (this data is typically derived at the store level). This demand is represented as shipments sent or orders received, but it lacks a direct connection to true consumer demand at points of consumption. As this data becomes available, it can be used to support a consumption-driven planning process.

Oracle In-Memory Consumption-Driven Planning (CDP) leverages detailed daily consumption data to produce a consumption-driven forecast which is then leveraged to provide a basis for a shipment forecast as well as a basis for generating store-level safety stock levels and replenishment orders.

Note: Oracle In-Memory Consumption-Driven Planning is a distinct and separately licensed and installed Demantra module.

Tip: Due to the large volume of consumption data required to generate a forecast and plan at a very granular level, the CDP module must be installed with and run on the Oracle Exadata database machine. For optimal performance, Oracle also strongly recommends implementing the Oracle Exalogic server.

Consumption data analysis provides the following benefits:

• Better visibility to true consumer demand.

• Visibility into granular store-level data.

• Better identifies consumer promotions as part of the demand being analyzed.

The following table provides definitions for key terms used in the consumption-driven planning process.

Term	Definition
Store	Final end point of the supply chain. This is where consumers acquire the product.
Site	Customer warehouse being serviced from a manufacturer's plant or warehouse. Stores are typically supplied with products from the site.
Organization	Manufacturer's warehouse or plant, typically providing product to sites or stores.
Sell through	Demand being consumed at a point in the supply chain, typically consumer demand at a store.
Sell in	Demand received at a point in the supply chain, typically shipments to a store or site.

Note: Demantra provides the following predefined CDP users.

Username/Password

• cdp (component owner) / cdp

• cdpanalyst1 / c

• cdpanalyst2 / c

The Consumption-Driven Planning Process

The diagram below provides a high-level view of the Consumption-Driven Planning process.

The process includes the following:

1. Collect the appropriate data from an ERP or other system of record.

   For more information about CDP integration and import interfaces, refer to Integration in this guide.

2. Load the appropriate data to the Demantra database.

3. Generate a forecast.

4. Calculate the sell in, safety stock, and replenishment orders.

   Generating a forecasting and performing calculations involve running the CDP workflows and Business Logic Engine (BLE) to calculate sell-in forecast, safety stock, standard error, replenishment orders, and exceptions.

   For more information, refer to the following:

   • CDP Business Logic Engine Worksheets

   • CDP Workflows

   • Business Logic Engine

5. Use the CDP worksheets to manage and review your sell in, safety stock and replenishment orders.

   For more information, refer to CDP Worksheet.

6. Upload your forecast and then publish orders.

   When you are satisfied with the forecasts and replenishment order values, run the provided CDP workflows to export the forecast(s) and replenishment order quantities to your supply planning system.

   For more information about CDP integration and export interfaces, refer to Integration in this guide.

CDP also allows you to perform new product and new store launches. The new product launch process links a new product (target) with a store, store group, or account based on an existing source product. The new store launch process links a new store (target) based on an existing similar store (source).

For more information, refer to CDP Product Launch Management Worksheets.

CDP Business Process

Use CDP to generate forecasts at different levels, calculate safety stock and replenishment order calculations, and generate various kinds of exceptions.

Business Scenarios

The following diagram illustrates a supply chain with four business channels.

In this example, the following processes appear:

1. For channels where store-level information is available, it is collected and modeled. Store-level consumer demand is referred to as Sell through.

2. Sell-through history is used to generate a sell forecast which is then converted into a Shipment forecast to the stores. This forecast is known as the Sell through forecast.

3. For sales channels where store-level data is not available, shipment history is used to generate a shipment forecast. This forecast is known as the Sell in forecast.

4. The sell-in forecast from the various channels can be combined and aggregated, serving as the basis for the Demand Planning Process.

Supporting Your Business Process

CDP supports your business process by allowing you to:

• Generate consumption forecast for stores.

  • View daily store-level data for stores.

  • View store and site-level in different time buckets. For example, store-level data display could be in daily time buckets and site-level data could be in weekly time buckets.

  • View statistical forecast generated at the store level for each product.

  • View up to a year of history and 90 days of future forecast. There is no restriction on the duration of future forecast and the 90 day value, the default setting, can be adjusted if needed using the "lead" parameter.

    For more information on the "lead" parameter, refer to Forecast in the "Engine Details" chapter of the Oracle Demantra Integration Guide.

  • Modify historical data and execute a simulation.

  • Override forecast values and view your override results.

• Generate sell-in forecast for stores.

  • Generate store sell-in forecast using store sell-through forecast.

  • View calculated target inventory for stores based on target days of supply.

  • View available beginning inventory for current period for each store/item.

  • Based on beginning inventory, target inventory, and forecast, view the calculated inventory amount that needs to be received at the store.

  • Using store lead times and required receipt, view generated sell-in projection.

  • Review the sell-in forecast and override it as needed.

  • Combine sell-in forecast from several channels to drive the Demand Planning process.

• Generate sell-in forecast for sites.

  • Generate sell-through forecast using site-level sell-through data.

  • View calculated targeted inventory for site based on target weeks of supply for site.

  • View available beginning inventory for current period for each site/item.

  • Based on beginning inventory, target inventory, and forecast, view the calculated inventory amount that needs to be received at the site.

  • Using site lead times and required receipts, view generated sell-in projection.

  • Review sell-in forecast and override values as needed.

• Generate store-level safety stock.

  • Select safety stock policy for each Item/Store. A single safety stock policy is used per item/store, regardless of date. The safety stock policy can also be set or maintained for a group of items or stores.

  • View calculated safety stock based on selected safety stock policy and input parameters.

  • View the resulting safety stock associated with all policies and toggle between policies to see how it affects final safety stock.

  • Override safety stock values and view your override results.

• Generate and export replenishment orders.

  • Use sell-through forecast and safety stock to calculate an inventory objective.

  • Respect your maximum and minimum inventory constraints to constrain the inventory objective.

  • Reference On Order and In Transit quantities when determining additional required orders.

  • Generate future orders based on gaps between available quantities and target inventories.

  • Override order values as needed.

  • Use order execution systems, such as Oracle Order Management, to read data from exported orders table.