4625: Demand

Course Type

  • Instructor Led

Duration

  • 3 Days

Target Audience

  • SuperUser
  • EndUser

Course Description

This curriculum will walk you through the key features of Demand, how to work in the Demand Workbench, various forecasting techniques, the importance of market intelligence in forecasting, and how to manage a product throughout its lifecycle. The curriculum is also designed with hands-on exercises to enable you to practice key concepts taught in the lessons.

Course Notes

  • 16 Core Lessons
  • 10 Elective Lessons

Lesson Details

(C) 4625-01: Overview

Lesson Objectives

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

Lesson Exercises

Not Applicable

1 Hour ILT
(C) 4625-02: Introduction to Demand System Architecture and Database Tables

Lesson Objectives

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

Lesson Exercises

Not Applicable

1 Hour 30 Minutes ILT
(C) 4625-03: Demand Navigation

Lesson Objectives

  • Navigate through the Blue Yonder Platform Ul
  • Retrieve and display data using searches
  • Review data in a Flexible Editor (FE) page
  • Populate data in a Compound Workspace

Lesson Exercises

  • Accessing Blue Yonder 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
3 Hours ILT
(C) 4625-04: The Demand Workbench​

Lesson Objectives

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

Lesson Exercises

  • Viewing the Components of Demand Workbench
  • Customizing the Demand Workbench Page
2 Hours ILT
(C) 4625-05: Basic History Cleansing​

Lesson Objectives

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

Lesson Exercises

Use the Cleanse History Process and Review the Results

30 Mins ILT
(C) 4625-06: Forecasting Techniques

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 algorithms

Lesson Exercises

Not Applicable

1 Hour ILT
(C) 4625-07: Demand Classification

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 and Tune Parameters Process Page and Output Results
  • Run Classify DFUs Process and Review Results‭
1 Hour ILT
(C) 4625-08: Moving Average Algorithm​

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 Moving Average Algorithm

45 Mins ILT
(C) 4625-09: Fourier 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
1 Hour 30 Mins ILT
(C) 4625-10: Lewandowski Algorithm

Lesson Objectives

  • Describe the key Lewandowski algorithm parameters
  • Explore how and when to fine-tune the Lewandoski 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
1 Hour 15 Mins ILT
(C) 4625-11: AVS-Graves Algorithm

Lesson Objectives

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

Lesson Exercises

Tuning the Forecast Using the Smoothing Constant and Frequency Factor

45 Mins ILT
(C) 4625-12: Seasonality Manager

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
45 Mins ILT
(C) 4625-13: Managing Exceptions

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

  • Viewing Exceptions for All DFUs
  • Comparing Exception Points for the Selected DFUs
45 Mins ILT
(C) 4625-14: Market Intelligence

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), and Impact of Target
  • Cleanse history using overrides, Data-Driven Events (DDEs), masks, and mean value adjustments
  • Explain what is included in good history
  • Copy and link Data-Driven Events
  • Work with target, mean value modifications, and 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 Event 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
4 Hours ILT
(C) 4625-15: Product Lifecycle Management

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 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
2 Hours 30 Mins ILT
(C) 4625-16: Evaluating Forecast Performance​

Lesson Objectives

  • Describe the purpose, benefits, and the 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

1 Hour 15 Mins ILT
(E) 4625-17: Day In A Life—Forecasting Document​

Lesson Objectives

  • Recognize demand planner Roles and Responsibilities
  • Schedule activities
  • Execute a sequence of activities to forecast demand

Lesson Exercises

Not Applicable

45 Mins ILT
(E) 4625-18: Other History Cleansing Methods

Lesson Objectives

  • Explain the methods to Calculate Lost Sales
  • Explain the methods to Calculate History Adjustments
  • 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

  • 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 ‬
1 Hour 15 Mins ILT
(E) 4625-19: Multiple Linear Regression Algorithm

Lesson Objectives

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

Lesson Exercises

Reviewing the Causal Factors for a DFU

1 Hour ILT
(E) 4625-20: Croston Algorithm

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

30 Mins ILT
(E) 4625-21: Holt-Winters 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 and 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 and 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 and Holdout Periods
1 Hour 30 Mins ILT
(E) 4625-22: Profile-Based Forecasting​

Lesson Objectives

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

Lesson Exercises

  • Reviewing the Profile-Based Forecasting DFU Parameters and Hierarchy Levels
  • Reviewing the Demand Parameter Manager and PBF Model Forecast
45 Mins ILT
(E) 4625-23: Short Lifecycle Algorithm

Lesson Objectives

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

Lesson Exercises

  • Reviewing the Short Lifecycle Model and Its Parameters
  • Reviewing the Impact of Short Lifecycle Algorithm Parameters
1 Hour ILT
(E) 4625-24: Attach Rate Forecasting

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

  • Creating Attach Rate
  • Calculating Dependent Demand
1 Hour 15 Mins ILT
(E) 4625-25: Introduction to Demand 360​

Lesson Objectives

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

Lesson Exercises

Not Applicable

30 Mins ILT
(E) 4625-26: Right Level to Forecast

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

30 Mins ILT