Smart Manufacturing Demand Forecasting

Who Should Attend?


Duration: 2 Days

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 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.

  • Address production challenges:
    • Improve resource planning
  • Address analytical challenges:
    • Demand forecasting
    • Data quality
  • 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
  1. 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?
  2. Business Use Care
    • The concepts of data science
    • Improve demand forecasting capabilities with predictive analytics
  3. 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
    • Transforming data to insights
  4. EDA: Exploratory Data Analysis
    • Understanding and exploring the information in your datasets
    • Descriptive statistics
    • Data visualization as part of data mining
  5. Predictive Analytics
    • Engineer the right features to model demand characteristics
    • Use machine learning to predict future demands
    • Time-series analysis
  6. Evaluation of Models and Results
    • Validation and prediction performance indicators
    • Mapping the results back to business objectives
  7. 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

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