Coursera

Supply Chain Planning (Coursera)

Offered by Rutgers University,
Supply Chain Planning (Coursera)

Have you ever wondered how companies know how much to produce in advance so that they do not make too much or too little? Matching supply and demand requires planning. This course introduces you to the exciting area of supply chain planning. Part of a broader specialization on Supply Chain Management, you will master different forecasting techniques, essential for building a Sales and Operations Plan. At the completion of this course you will have the tools and techniques to analyze demand data, construct different forecasting techniques, and choose the most suitable one for projecting future demand.

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Course 3 of 5 in the Supply Chain Management Specialization.

Syllabus

WEEK 1
Simple Forecasting Methods, Naive Forecast and Cumulative Mean
Welcome to the exciting world of planning! This module introduces you to the professor who is teaching the courses in the Supply Chain Management Specialization. You will also construct forecasting models that enable you to predict future demand. By the end of this short module, you'll know what to expect in the course and hopefully be as excited to learn about Supply Chain Planning as I will be to teach you.In this module learners will review two simple forecasting methods, the naive method and the cumulative mean. Finally, you will create forecasts of your own based on a data set and provide the correct answers in the quiz.

WEEK 2
Forecast Accuracy and Moving Average
In this module, learners will cover two more sophisticated forecasting methods, the moving average and exponential smoothing.

WEEK 3
Exponential Smoothing and Forecast Selection
In this module, you will master one more forecasting method - exponential smoothing. In addition, you will learn how to pick the best forecasting approach and what to do with the forecast once it is implemented.

WEEK 4
Supply Chain Planning
In this module you will put your new forecasting skills to the test. You will analyze a real-life dataset and make recommendations on the different forecasting methods.

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