Demand

Lesson Objectives

• Explain what demand forecasting is & its benefits
• Identify the components of a Demand Forecasting Unit
• Explain the roles & responsibilities of a demand planner

Lesson Exercises

• Not Applicable

Lesson Objectives

• Identify the key input tables
• Explain the key Demand processes
• Identify the key output tables
• Review the forecast data on DFU & DFUMAP pages
• Review & validate aggregated history
• Calculate Forecast at all levels
• Compare & reconcile forecast data

Lesson Exercises

• Not Applicable

Lesson Objectives

• Navigate through the JDA Platform Ul
• Retrieve & display data using searches
• Review data on a Flexible Editor (FE) page
• Populate data in a Compound Workspace

Lesson Exercises

• Accessing JDA Demand
• Displaying Data on a Flexible Editor Page Using Search
• Functionality
• Opening an FE Page from Solution Navigation in a New Tab with a Prompted Search
• Opening an FE Page from the Search Bar in a New Tab with a Prompted Entry List Search
• Navigating from One FE to Another Using Related Pages
• Using Filters
• Accessing Online Help
• Editing Data
• Using Compound Workspaces
• Working with Favorites

Lesson Objectives

• Describe the features & functionality of the Demand Workbench
• Navigate in the Demand Workbench to view & manipulate data & model parameters
• Understand how the Demand Workbench is configured

Lesson Exercises

• Viewing the Components of Demand Workbench
• Customizing the Demand Workbench Page

Lesson Objectives

• Describe the history cleansing process & its methods
• Explain the Calculate Lost Sales process & the three methods for calculating the lost sales adjustments
• Explain the calculation history adjustments process & the two methods for calculating history adjustments

Lesson Exercises

• Use the Cleanse History Process & Review the Results

Lesson Objectives

• Describe each algorithm used by 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

• Describe the purpose of Demand Classification
• List the three ways of running the Demand Classification process
• Explain the three stages in the Demand Classification process

Lesson Exercises

• Review Classify DFUs & Tune Parameters Process Page & Output Results
• Run Classify DFUs Process & Review Results

Lesson Objectives

• Define the components of a time series model
• Describe the key model parameters for the Moving Average algorithm
• Explain the steps to fine-tune the Moving Average algorithm

Lesson Exercises

• Working with the Moving Average Algorithm

Lesson Objectives

• Define the components of a time series model
• Explain how the system fits the Model
• Describe the key model parameters for the Fourier algorithm
• Describe how to fine-tune the Fourier algorithm

Lesson Exercises

• Including Seasonal Terms to the Model
• Increasing the Significant Amplitude Factor
• Adjusting the Outlier Factor
• Adjusting the Time Weighting Factor
• Changing the Trend for a DFU

Lesson Objectives

• Describe the key Lewandowski algorithm parameters
• Explore how & when to fine-tune the Lewandoski algorithm parameters
• Recognize statistical error measurements

Lesson Exercises

• Changing the Dynamic means the Impact
• Adjusting the Trend Method
• Utilizing the Seasonality Features of the Lewandowski Model

Lesson Objectives

• Explain how the system fits the Model using the AVS-Graves algorithm
• Describe the key AVS-Graves algorithm parameters
• Explain how & when to fine-tune the AVS-Graves algorithm parameters
• Explain the error measurement calculations

Lesson Exercises

• Tuning the Forecast Using the Smoothing Constant & Frequency Factor

Lesson Objectives

• Apply seasonality changes that affect the Forecast
• Manage Seasonality libraries
• Create a seasonality profile
• Attach a seasonal profile with a DFU
• Explore other options on the Seasonality Manager menu

Lesson Exercises

• Creating a Seasonality Profile
• Attaching a Seasonal Profile in Seasonality Manager
• Attaching a Seasonal Profile in Demand Workbench
• Adjusting the Seasonality Impact Parameter

Lesson Objectives

• Apply modelling or management approaches to address exceptions
• Explain the system-generated exceptions to identify potential model problems
• Interpret Exception Graphs

Lesson Exercises

• Viewing Exceptions for All DFUs
• Comparing Exception Points for the Selected DFUs

Lesson Objectives

• Describe the importance of market intelligence in forecasting
• Describe the nine forecast types—Statistical Forecast, Aggregate
• Market Activities (created by MapDFU-Forecast process), Impact of Lock,
• Reconciled Forecast, Promotion, Forecast Override, Market Activity, - -
• Data-Driven Event (Lewandowski only), & Impact of Target
• Cleanse history using overrides, Data-Driven Events (DDEs), masks, & mean value adjustments
• Explain what is included in good history
• Copy & link Data-Driven Events
• Work with the target, mean value modifications, & locks to support your Forecast
• Describe related applications providing market intelligence data

Lesson Exercises

• Applying History Overrides to the DFUs History
• Applying History Overrides Using the Override Manager Tool
• Flexible Allocation
• Adding Data-Driven Events Using the Demand Workbench
• Linking a Data-Driven Event to the Forecast
• 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
• Adding in a Locked Forecast Range

Lesson Objectives

• Forecast new products using features such as Copy History, New
• Product Introduction, Lifecycles, & Launch Profiles
• Discontinue & phase out items within the Demand application
• New DFU Introduction (Replacing an Existing DFU) Creating an
• Allocation Profile in Launch Manager

Lesson Exercises

• Copy History
• New DFU Introduction (Replacing an Existing DFU) Creating an
• Allocation Profile in Launch Manager

Lesson Objectives

• Describe the purpose, benefits, & Impact of measuring forecast accuracy
• Examine the factors that impact forecast performance
• Explain the process of closing the forecasting period
• Explain how to store forecast performance data
• Explain how to conduct a flexible editor-based forecast performance analysis

Lesson Exercises

• Review Forecast Performance

Lesson Objectives

• Recognize demand planner Roles & Responsibilities
• Schedule activities
• Execute a sequence of activities to forecast the Demand

Lesson Exercises

• Not Applicable

Lesson Objectives

• Explain the methods to Calculate Lost Sales
• Explain the methods to Calculate History Adjustments
• Describe a Moving Event & state the importance of configuring it
• Explain how to work with Moving Events
• Apply a Moving Event to a DFU

Lesson Exercises

• Calculating Lost Sales Using the History Average Method
• Calculating Lost Sales Using the Forecast Performance Metrics Method
• Calculating Lost Sales Using the Allocated Forecast Method
• Calculating History Adjustments
• Calculating History Adjustments
• Applying a Moving Event to a DFU 

Lesson Objectives

• Use time series concepts to determine a valid Multiple Linear
• Regression (MLR) model
• Describe the key model statistics for the MLR algorithm
• Describe the parameters used to fine-tune MLR models

Lesson Exercises

• Reviewing the Causal Factors for a DFU

Lesson Objectives

• Explain how the Croston model uses exponential smoothing concepts to determine the Forecast
• Describe the Croston Model parameters

Lesson Exercises

• Using Demand Size Smoothing Parameter for Croston Algorithm

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 & recall how to fine-tune the Holt-Winters algorithm

Lesson Exercises

• Exploring Holt's Single or Simple Exponential Smoothing Method
• Exploring Holt's Linear Trend Model
• Using the Multiplicative Seasonality Option & Reviewing the
• Impact on Holt's Seasonal Smoothing Algorithm
• Exploring Holt-Winters' Linear Model with Damp Factor Impact
• Exploring the Holt-Winters' Parameters Optimization Option & Holdout Periods

Lesson Objectives

• Define Profile-Based Forecasting
• Explain how to create profiles using the Extract Profile process
• Explain how to Calculate Model generates forecasts using Profile-
• Based Forecasting algorithm

Lesson Exercises

• Reviewing the Profile-Based Forecasting DFU Parameters & Hierarchy Levels
• Reviewing the Demand Parameter Manager & PBF Model Forecast

Lesson Objectives

• Define the Short Lifecycle algorithm
• Define & prioritize DFU attributes
• Explain how the Short Lifecycle process works
• Describe the Bass Diffusion model of the Short Lifecycle algorithm

Lesson Exercises

• Reviewing the Short Lifecycle Model & Its Parameters
• Reviewing the Impact of Short Lifecycle Algorithm Parameters

Lesson Objectives

• Describe how the attach rate forecasting process works
• Define the various terminologies used in attach rate forecasting
• List the methods & steps to define attach rate forecasting

Lesson Exercises

• Creating Attach Rate
• Calculating Dependent Demand

Lesson Objectives

• Define Demand 360
• Recognize the capabilities of Demand 360
• Identify the different Worksheet components

Lesson Exercises

• Not Applicable

Lesson Objectives

• Explain what Right Level to Forecast is
• Explain what Right Level to Forecast is
• Explain the steps involved in Right Level to Forecast process

Lesson Exercises

• Not Applicable

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