4625: Demand v2018.1

Lesson Objectives

• Explain what demand forecasting is and its benefits
• Identify the components of a Demand Forecasting Unit (DFU)
• Define the steps that JDA Demand uses to produce a forecast
• Explain the roles and responsibilities of a demand

Lesson Exercises

Not Applicable

Lesson Objectives

• Identify the key input tables
• Explain the key Demand processes
• Identify the key output tables
• Describe how a DFU Hierarchy supports forecasting

Lesson Exercises

Not Applicable

Lesson Objectives

• Navigate from the JDA Demand Home page
• Viewing and modifying data in FE pages
• Using Searches
• Working with Favorites
• Accessing Online Help

Lesson Exercises

• Log on to Demand and Open a Flexible Editor Page
• Use Different Types of Searches to Display Data in a Flexible Editor Page
• Filtering the Data Displayed on a Flexible Editor Page
• Conditional Formatting
• Edit Multiple Records in DMDUNIT FE Page
• Working with Favorites

Lesson Objectives

• Describe the features and functionality of the Demand Workbench
• Navigate in the Demand Workbench to view and manipulate data and model parameters
• Compare the Multi-Model and Single Model Views to determine the best algorithm for your product
• View Measures in the Demand Workbench

Lesson Exercises

• Create a New Hierarchy for Demand Workbench
• Utilize the Demand Workbench
• Display Measures in the Demand Workbench

Lesson Objectives

• Describe the methods to cleanse history
• Perform the steps to run the Cleanse History process

Lesson Exercises

Cleanse history data prior to forecasting

Lesson Objectives

• Describe each algorithm used by JDA Demand
• Define the type of products that are most suitable for each algorithm
• Understand which products in your business would work best with each algorithm

Lesson Exercises

Not Applicable

Lesson Objectives

• Use time series concepts to determine a valid Fourier model
• Explain how the system fits the model
• Describe the key model statistics for Fourier
• Describe how to fine tune the Fourier algorithm

Lesson Exercises

• Fitting One and Two Term Fourier Models
• Fitting a Seasonal Model
• Creating a More Seasonal Model
• Adjusting the Outlier Factor
• Increasing the Significant Amplitude Factor

Lesson Objectives

• Use time series concepts to determine a valid model
• Describe the key model statistics for MLR
• Describe the parameters used to fine tune MLR

Lesson Exercises

Reviewing and Changing Parameters for an MLR Model

Lesson Objectives

• Explain how the system fits the model
• Describe the key model parameters for the algorithm
• Complete the steps to fine tune the algorithm

Lesson Exercises

Working with Moving Average Algorithm

Lesson Objectives

• Explain how the system fits the model
• Describe the key model parameters for the algorithm
• Understand how and when to fine tune the algorithm parameters
• Understand the error measurement calculations

Lesson Exercises

Working with AVS Graves

Lesson Objectives

• Use exponential smoothing concepts to determine a valid Croston model
• Differentiate between the forecast adjustment tools

Lesson Exercises

• Using Demand Size Smoothing Parameter for Croston Algorithm
• Demand Interval Smoothing Parameter

Lesson Objectives

• Describe the key algorithm parameters
• Understand how and when to fine tune the algorithm parameters
• Recognize statistical error measurements

Lesson Exercises

• Changing the Dynamic Mean Impact
• Adjusting the Trend Method
• Utilizing the Seasonality Features of the Lewandowski Model
• Adjusting the Sensitivity of the Dynamic Mean and Seasonality

Lesson Objectives

• Use exponential smoothing concepts to determine a valid Holt-Winters model
• Explain how the system fits the Holt-Winters model
• Describe the key model statistics for Holt-Winters
• Recognize and recall how to fine tune the Holt-Winters algorithm

Lesson Exercises

• Reviewing and Changing Parameters for a Holt-Winters Model (Holt's SES)
• Exploring Holt's Linear Trend model
• Holt Winters Smoothig- Multiplicative
• Exploring Holt-Winters' Linear Model with Damp Factor Impact
• Exploring the Holt-Winters' Parameters Optimization and Smoothed MSE

Lesson Objectives

• Define Profile Based Forecasting
• Explain how the Extract Profile creates profiles
• Explain how the Calculate Model generates forecasts

Lesson Exercises

• Setting up DFUs for Modeling PBF
• Defining the Hierarchy Levels for DmdUnit, DmdGroup, and Loc
• Running the Extract Profile process
• Calculating the Forecast

Lesson Objectives

• Define Short Lifecycle Algorithm
• Describe the Bass Diffusion Model of Short Lifecycle Algorithm
• Explain how the Short Lifecycle process works

Lesson Exercises

• Reviewing the Short Lifecycle DFUs
• Build Short Lifecycle Curves
• Assigning Short Lifecycle Curves
• Calculating and Reviewing Forecasts

Lesson Objectives

• Describe how the Attach Rate Forecasting process works
• Define the various terminologies used in Attach Rate Forecasting
• List the methods and steps to define Attach Rate Forecasting

Lesson Exercises

• Use Static Attach Rate Method
• Use Static Relations Attach Rate Method

Lesson Objectives

• Apply seasonality changes that affect the forecast
• Create a seasonality profile
• Attach a seasonal profile with a DFU

Lesson Exercises

• Modeling Intermittent Seasonality with AVS Graves
• Attaching the Profiles
• Create a Seasonality Profile from another DFLU

Lesson Objectives

• Describe a Moving Event and state the importance of configuring it
• Explain how to work with Moving Events
• Apply a Moving Event to a DFU

Lesson Exercises

Applying a Moving Event to a DFU

Lesson Objectives

• Apply modeling or management approaches to address exceptions
• Explain the system generated exceptions to identify potential model problems
• Interpret exception graphs

Lesson Exercises

Review and Analyze Exception Graphs

Lesson Objectives

• Understand the importance of market intelligence in forecasting
• Cleanse History using Overrides, Data-driven Events (DDEs), Masks, and Mean Value Adjustments
• Explain what is included in good history
• Applying market intelligence to your forecast
• Copying and linking Data-driven Events
• Working with Target, Mean Value Modifications, and Locks to support your forecast
• Describe Related Applications Providing Market Intelligence Data

Lesson Exercises

• Working with History Overrides
• Adding an Override with the Override
• Adding a Data-driven Event to History
• Adding and Linking a Data-Driven Event to the Future
• Apply a Mask
• Optimize the Mean Value in History with Lewandowski Algorithm
• Modify the Mean Value in History with the Lewandowski Algorithm
• Adjusting the Mean Value for the Forecast for AVS-Graves
• Adding in a Locked Forecast Range

Lesson Objectives

• Forecast new products using features such as Copy History, New Product Introduction, Lifecycles, and Launch Profiles
• Discontinue and Phase Out Items within the JDA Demand application

Lesson Exercises

• New DFU Introduction (Replacing an Existing DFU)
• Creating an Allocation Profile in Launch Manager
• Creating a new Lifecycle

Lesson Objectives

• Discuss the purpose, benefits, and the impact of measuring forecast accuracy. Examine factors that impact forecast performance
• Explain the process of closing the forecasting period
• Discuss Storing Forecast Performance Data
• Understand Flexible Editor based Forecast Performance Analysis

Lesson Exercises

Review Forecast Performance

Lesson Objectives

• Recognize Demand Planner Roles and Responsibilities
• Schedule activities
• Identify how to use JDA Demand

Lesson Exercises

• Creating Workflows
• Setting your Home Page
• Compound Workspaces

Lesson Objectives