Introduction

This learning path is designed for technical developers (ie. Configuration Engineers) that are responsible for the creation and configuration of solutions using the Multifactor Elasticity Accelerator. The use of this Accelerator will provide the framework for building logics that use machine learning to forecast demand and assess price elasticity and assist in forecasting quantity that would be sold at a given price.

It will focus on the core and common tasks associated with developing Groovy logic and configuration of this module and highlight key components of the Multifactor Elasticity Accelerator for Configuration Engineers to build their technical comprehension for the configuration and customization of this Accelerator for customer requirements.

Learning Prerequisites

Make sure that you have these aspects covered before moving on to the course.

• Data Management

• Pricing Basics

• Pricing Terminology

• Multifactor Elasticity Accelerator Learning Path for All Audiences

• Multifactor Elasticity Accelerator Learning Path for Administrators

Learning Synopsis

This learning path will discuss the underlying architecture for price elasticity, use of model classes, associated libraries, and the steps required to define a model, configure it, execute and train the model and generate the output results. It contains the foundational knowledge you need to thrive in your configuration of this Accelerator and expand your understanding of best practices associated with it.

You will also learn core technical concepts, use case customization, practice scenarios and all the details you need to implement solutions for multifactor price elasticity.

Learning Objectives

Core topics and takeaways:

• Demonstrate a comprehensive understanding of the underlying infrastructure that support this Accelerator.

• Illustrate the use of optimization model classes to create your multifactor elasticity model.

• Demonstrate the steps needed create custom logics and associate them with multifactor elasticity process.

Learning Journey

This learning path is separated into sections that provide a basic roadmap for building your developer knowledge and understanding of the technical aspects of Multifactor Elasticity Accelerator.

The components in this path are designed to be experienced in a sequential order, as each subsequent level builds upon the knowledge acquired in the preceding learning path(s). This structured approach ensures that learners have the necessary foundation before progressing to more advanced concepts, optimizing your learning experience.

Multifactor Accelerator Model

• Model Class and Library

  • Multifactor Elasticity Model Class

  • Library

• Model Definition

  • Generate Parameter Table

  • Configurator

  • Advanced Configuration

• Additional Sources

• Model Training

  • Train the Model

  • Dashboard Creation

  • Product Charts

  • Training Curve

  • Elasticity Settings

• Model Prediction

  • Manage and Process Data

  • Post Process

  • Elasticity Calculation Charts

  • Product Elasticity Details

• Evaluation

Time commitment: For this path you should dedicate approximately 2 hours.