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EncartaLabs

Blue Yonder Demand Workbench

( Duration: 3 Days )

This Blue Yonder Demand Workbench training course will walk 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.

The Blue Yonder Demand Workbench workshop is ideal for:

  • End Users
  • Super Users

COURSE AGENDA

1

Overview of Demand

  • Explain what demand forecasting is and its benefits
  • Identify the components of a Demand Forecasting Unit
  • Identify the components of a Demand Forecasting Unit
  • Explain the roles & responsibilities of a demand planner
2

Intro to Demand System Architecture & Database Tables

  • 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
3

Demand Navigation

  • Navigate through the JDA Platform Ul
  • Retrieve and display data using searches
  • Review data in a Flexible Editor (FE) page
  • Populate data in a Compound Workspace
4

The Demand Workbench

  • 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
5

Basic History Cleansing

  • 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
6

Forecasting Techniques

  • 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
7

Demand Classification

  • 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
8

Moving Average Algorithm

  • 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
9

Fourier Algorithm

  • 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
10

Lewandowski Algorithm

  • Describe the key Lewandowski algorithm parameters
  • Explore how and when to fine-tune the Lewandoski algorithm parameters
  • Recognize statistical error measurements
11

AVS-Graves Algorithm

  • 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
12

Seasonality Manager

  • 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
13

Managing Exceptions

  • Apply modeling or management approaches to address exceptions
  • Explain the system-generated exceptions to identify potential model problems
  • Interpret Exception Graphs
14

Market Intelligence

  • 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
15

Product Lifecycle Management

  • 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
16

Evaluating Forecast Performance

  • 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
17

Day In A Life

  • Recognize demand planner Roles and Responsibilities
  • Schedule activities
  • Execute a sequence of activities to forecast demand
18

Other History Cleansing Methods

  • 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
19

Multiple Linear Regression Algorithm

  • 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
20

Croston Algorithm

  • Explain how the Croston model uses exponential smoothing concepts to determine the forecast
  • Describe the Croston Model parameters
21

Holt-Winters Algorithm

  • 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
22

Profile-Based Forecasting

  • 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
23

Short Lifecycle Algorithm

  • 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
24

Attach Rate Forecasting

  • 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
25

Introduction to Demand 360

  • Define Demand 360
  • Recognize the capabilities of Demand 360
  • Identify the different Worksheet components
26

Right Level to Forecast

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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting https://www.encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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