Supply Chain Demand forecasting with Python

Supply Chain Demand forecasting with Python, available at $39.99, has an average rating of 3.7, with 15 lectures, based on 5 reviews, and has 30 subscribers.

You will learn about 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 This course is ideal for individuals who are Supply chain analysts or Demand planners or Supply chain students or Supply chain planners or Store managers or Supply chain consultants It is particularly useful for Supply chain analysts or Demand planners or Supply chain students or Supply chain planners or Store managers or Supply chain consultants.

Enroll now: Supply Chain Demand forecasting with Python

Summary

Title: Supply Chain Demand forecasting with Python

Price: $39.99

Average Rating: 3.7

Number of Lectures: 15

Number of Published Lectures: 15

Number of Curriculum Items: 15

Number of Published Curriculum Objects: 15

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 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

Chapter 2: Measure forecast accuracy

Lecture 1: Forecast Bias

Lecture 2: Mean Absolute Percentage Error (MAPE)

Lecture 3: Mean Absolute Error (MAE)

Lecture 4: Root Mean Square Error (RMSE)

Chapter 3: Advanced time series forecasting models

Lecture 1: Weighted Moving Average

Lecture 2: Optimize parameters in Weighted Moving Average Forecast

Lecture 3: Single Exponential Smoothing

Lecture 4: Optimize Alpha Parameter for Single Exponential Smoothing

Lecture 5: Double Exponential Smoothing

Lecture 6: Double Exponential Smoothing with Damped Trend

Lecture 7: Triple Exponential Smoothing

Instructors

Jayakhanthan Sakthivel
Supply Chain Analytics and Optimization Consultant

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 1 votes
4 stars: 2 votes
5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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