Manage a Distribution Plan in a Supply Chain Model in SAP APO When Demand Exceeds Supply

  • by Alok Jaiswal, Consultant, Infosys Limited
  • May 24, 2017
Learn the process flow and understand the various strategies you can use to deploy supply elements (receipts) in SAP Advanced Planning and Optimization (SAP APO) when the total demand is greater than the available supply.
Learning Objectives

By reading this article, you will learn about:

  • Deployment and the fair share rule
  • A case study used as an example
  • Master data configuration in SAP Advanced Planning and Optimization (SAP APO)
  • Simulation of fair share rule strategies: percentage division by demand, percentage of target stock, division by quota arrangement, and division by priority
Key Concept
Deployment determines which distribution requirements can be met by the supply (receipt elements). Deployment strategies to be used depend on the business requirement. Different strategies available within the fair share rule are used when demand is more than supply. Fair share rule is a set of strategies used by SAP to deploy receipts from a source location that is less than the requested demand.

SAP Advanced Planning and Optimization (SAP APO) supply network planning (SNP) helps to provide medium- or long-term planning for procurement of products to meet customer demand. SNP heuristics help to plan the constant flow of products through the entire network of the supply chain.

Deployment comes into the picture after an SNP run has completed and generated receipt elements. Deployment determines which requirements can be covered by the existing supply. If the produced quantities (supply and demand) match actual plans, then the result of deployment is a confirmation of the supply network plan. If the available quantities are insufficient to cover the demand, the system automatically makes adjustments accordingly.

Fair share rule strategies are deployment strategies that are used if the quantity to be deployed is less than the total demand. If there is less than the required inventory available to fulfill demands of all the distribution centers, then how the system allocates quantity to different centers is based on different options available within the fair share rule.

A Business Scenario

Consider my example of a simple supply chain network of a manufacturer consisting of a single plant and multiple distribution centers. Figure 1 diagrams the supply chain network that is discussed in this article. Products (PROD-1 and PROD-2) are manufactured at PLANT1 and shipped to the distribution centers DC01 and DC02 based on demand and corresponding supply.


Figure 1
An example of a supply chain network

Master Data Configurations in SAP APO

Before running the deployment process, you need to have master data configured in the system for the following objects:

  • Locations
  • Products
  • Transportation lane
  • Resources
  • Production Process Model (PPM)

Note
Refer to the first part of this series, “How to Manage a Distribution Plan in a Supply Chain Model in SAP APO When Supply Exceeds Demand,” to see the setup of master data. I use a similar setup in this article. Also refer to the article to understand the basic navigation needed to carry out a deployment run—for example, Product View and Interactive Planning book heuristics/Location heuristics. Details about these transactions are not explained in greater detail in this article, but I show the simulation scenario and results interpretation.

Alok Jaiswal

Alok Jaiswal is a consultant at Infosys Limited.

He has more than six years of experience in IT and ERP consulting and in supply chain management (SCM). He has worked on various SAP Advanced Planning and Optimization (APO) modules such as Demand Planning (DP), Production Planning/Detailed Scheduling (PP/DS), Supply Network Planning (SNP), and Core Interface (CIF) at various stages of the project life cycle.

He is also an APICS-certified CSCP (Certified Supply Chain Planner) consultant, with exposure in functional areas of demand planning, lean management, value stream mapping, and inventory management across manufacturing, healthcare, and textile sectors.


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