Teaching & Academics

Demand Forecasting : Principles , Models & KPIs

What you’ll learn

  • Forecasting Principles
  • Forecasting Models
  • Forecasting KPIs
  • Optimization


  • Should know Basic excel and statistics


Demand Forecasting : Principles , Models & KPIs. This is my second course on Udemy after getting a very good response to my first course ( How to prepare for CPIM).

In this course, I will be sharing the crux of forecasting that I have learned in my CPIM ( Certified in Planning and Inventory Management ) course, MITx – Supply Chain Analytics course, and my 5 years of industrial experience working as a Demand Planner in various MNCs.

Also, you will learn to build models like the Naive Forecasting Method, Moving Average Forecasting Method, Weighted Average Forecasting Method, and Exponential Smoothing Forecasting Method from scratch in excel. Learning forecasting in excel is the foundation of learning forecasting in programming languages like Python and R.

Most importantly you will learn the pros and cons of all these models mentioned above. These models form the fundamentals of all the advanced forecasting models.

Also, you learn about following forecasting KPIs

1. Forecast Accuracy

2. Average Bias

3. MAPE ( Mean Absolute Percentage Error)

4. MAE( Mean Absolute Error)

5. RMSE( Root Mean Square Error)

The lessons cover details of all the KPIs . Which is the right KPI for you ? What is the best KPI ? What are the pros and cons of all and how do calculate it? All this will be covered in the course.

You will learn about how to optimize your forecasting model in Excel using the solver tool.

Those who are planning to appear for the CPIM exam can expect 10-15 questions from this course in the exam.

I wish all the students the very best for this course. If you have any doubts feel free to reach out in the Q&A section.

After this course, I will be soon launching the advanced demand forecasting course.

Who this course is for:

  • Want to learn Demand Forecasting – You are the right place.

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