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Supply Chain Demand forecasting with Python
Rating: 4.0 out of 5(8 ratings)
44 students

Supply Chain Demand forecasting with Python

Learn to build advanced time series demand forecasting models in python
Created byJayakhanthan Sakthivel
Last updated 1/2023
English

What you'll learn

  • Timeseries data cleaning and preparation
  • Implement simple moving average forecast in python
  • Learn different KPIs (Bias, MAPE, MAE, RMSE) to measure forecast accuracy & implement in Python
  • Implement weighted moving average forecast & optimize parameters in python
  • Implement single exponential smoothing model in python
  • Implement double exponential smoothing model in python
  • Implement double exponential smoothing with damped trend
  • Implement triple exponential smoothing model in python
  • Simulate and optimize Alpha, Beta, Gamma and Phi parameters for automatic model selection
  • Calculate error KPIs for each models
  • Visualize the results with actuals, forecast and forecast errors

Course content

3 sections • 15 lectures • 2h 19m total length

  • Introduction to moving average forecasting1:57
  • Learn to clean and prepare time series data in python7:18
  • Build a function for moving average forecast11:18
  • Visualize the time series forecast forecast results4:11

    Visualize time series forecast results using a moving average in Python, plot actual and forecast lines with Plotly, and assess forecast accuracy with error calculations.

Requirements

  • Basic understanding of how to start coding with python
  • Understanding of basic demand planning and supply chain business process
  • Curiosity to learn and optimize supply chain with advanced analytics

Description

Understanding and predicting the demand is one of the key challenge in Supply chain planning. Having better forecasting meaning better supply planning and optimized business operations with good customer service, therefore learn to build better forecast is a key skill to master in Supply chain management. Demand forecasting sounds simple but it will get complex when we have thousands of SKUs and each with its own demand pattern such as seasonal, intermittent and lumpy.

In this course you will learn demand forecasting models from basic to more advanced. And implement each of the models in Python. You will gain practical knowledge with real life data with over 3000 skus and over 5 years of data and millions of transactions.

By the time you complete the course you would have learned how advanced demand forecasting engine works in expensive commercial software and you would build your own fully automated forecasting engine.

In this course you will not only learn to build forecasting models and predict demand but also learn to build a python tool which can automatically optimize and select the best forecasting model based on your data.

Last but not least, you will learn to visualize all the forecasted data and errors in an intutive way.


Who this course is for:

  • Supply chain analysts
  • Demand planners
  • Supply chain students
  • Supply chain planners
  • Store managers
  • Supply chain consultants

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