Coursera

Supply Chain Planning (Coursera)

Offered by University of California, Irvine,
Supply Chain Planning (Coursera)

Supply chain planning is an important activity in any supply chain. This is where organizations get an idea of the upcoming demand, realize if they have the capacity to meet the demand, and determine how to realize these demands. In this course, we will explore how to use data science to conduct demand and supply planning, how to constrain the forecast, and how to measure the results. As we walk through this process, we will also explore how to use Excel to quantify each step.

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Course 1 of 3 in the Leverage Data Science for a More Agile Supply Chain Specialization.

What You Will Learn

  • Describe how demand planning, supply planning, and constrained forecast are associated with one another.
  • Use Excel to analyze historical data to quantify future needs.
  • Measure and quantify results for improvement.

Syllabus

WEEK 1
Demand Planning
Welcome to Module 1, Demand Planning. In this module, we will explore demand planning, how to identify influencing factors, how to differentiate between the three simple statistical forecast types, and how to compute a few forecast values.

WEEK 2
Supply Planning
Welcome to Module 2, Supply Planning. In this module, we will discuss the basics of supply planning, how to confirm internal & external capacity, how to confirm material availability, and the importance of communication and data sharing to supply planning.

WEEK 3
Constrained Forecast
Welcome to Module 3, Constrained Forecast. In this module, we will discuss constrained forecasts and consensus meetings. Specifically, we will cover the purpose and creation of constrained forecasts and how consensus meetings are utilized to reach organizational goals.

WEEK 4
Measure Results
Welcome to Module 4, Measure Results. In this module, we will discuss measuring results through collecting actual performance results and calculating forecast errors. Specifically, we will discuss the purpose of measuring results, the types of metrics used when measuring results, and common error measurements and their advantages and disadvantages.

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