Big Data Analytics in Smart Manufacturing: Demand Forecast

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Course Objectives

  • Address production challenges:
    • Improve resources planning
  • Address analytical challenges:
    • Demand forecasting
    • Data quality
  • After the training, students will have the ability to:
    • Articulate demand forecasting scenario
    • Identify relevant data sources and perform common data preparations
    • Build sophisticated forecasting models
    • Evaluate model quality to relate back to business requirements
    • Deliver results to enhance planning

Description

One of the most challenging aspects of supply chain is to predict the future demand. Using data from relevant sources, we can profile customer demands based on historical data and apply predictive analytics approaches to harvest patterns and predict the demand of the future. By knowing what the future wants, manufacturers can plan their resources, such as raw materials, operators, engineers, and machines ahead of time to meet the demands. Hence, it allows manufacturers to secure more orders in a leaner and cost-effective environment.

In this 2-days course incorporating a mixture of theories and hands-on, we will guide you through the approach to carry out a demand forecasting project with relevant data, via exercises carried out using state-of-the-art analytics tools.

Target Audience

This course is suitable for those wish to improve their demand forecast using machine learning methodology.

Training Outline

Overview
  • What is Industry 4.0?
  • How Big Data Analytics play a role in Industry 4.0 and Smart Manufacturing?
  • What could be done with Big Data Analytics to solve business problems?
Business Use Case
  • The concepts of data science
  • Improve demand forecasting capabilities with predictive analytics
Identifying and Getting Your Data Ready for Demand Forecasting
  • Where to obtain the relevant datasets?
  • How to cleanse and prepare your datasets?
  • The importance of incorporating multiple data sources
EDA: Exploratory Data Analysis
  • Understanding and exploring the information in your datasets
  • Descriptive statistics
  • Data visualization as part of data mining
Predictive Analytics
  • Engineer the right features to model demand characteristics
  • Use machine learning to predict future demands
  • Time-series analysis
Evaluation of Models and Results
  • Validation and prediction performance indicators
  • Mapping the results back to business objectives
Delivering and Operationalizing Analysis Outcomes
  • Predicting future demands using a trained model
  • Integration of the demand forecasting workflows into the smart factory ecosystem
  • Consuming forecasting results via dashboards for non-technical users

Prerequisite

Basic knowledge of computer programs and mathematics.