Supply Chain Demand Forecasting With Python

Learn to build advanced time series demand forecasting models in python

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

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.

Overview

Section 1: Introduction to time series forecasting in python

Lecture 1 Introduction to moving average forecasting

Lecture 2 Learn to clean and prepare time series data in python

Lecture 3 Build a function for moving average forecast

Lecture 4 Visualize the time series forecast forecast results

Section 2: Measure forecast accuracy

Lecture 5 Forecast Bias

Lecture 6 Mean Absolute Percentage Error (MAPE)

Lecture 7 Mean Absolute Error (MAE)

Lecture 8 Root Mean Square Error (RMSE)

Section 3: Advanced time series forecasting models

Lecture 9 Weighted Moving Average

Lecture 10 Optimize parameters in Weighted Moving Average Forecast

Lecture 11 Single Exponential Smoothing

Lecture 12 Optimize Alpha Parameter for Single Exponential Smoothing

Lecture 13 Double Exponential Smoothing

Lecture 14 Double Exponential Smoothing with Damped Trend

Lecture 15 Triple Exponential Smoothing

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