SC0x - Supply Chain Analytics is delivered on a learner-paced schedule. This course
contains the mathematical concepts used throughout all the other courses and is packaged in
modules based on content. It is the reference course for all the others and remains available
as needed almost continually. Each run of SC0x will have a final exam on a date set by the
course team. The course will close briefly after each scheduled final exam, and learners can
enroll in the next course run if needed. All course content, practice problems, exams,
reading materials, and external links are delivered through the course platform on edX. In SC0x
five module tests will account for 10% of your grade, and one final exam
will account for 90% of your grade.
COURSE CONTENTS
Module 1: Introduction to SCM and analytics basics
In this module, we will provide an overview of supply chains. We will introduce some of the basic
concepts and approaches of the discipline. We will also offer a review of the basics of
analytics: models, algebra, and mathematical functions. And we will explain the basics of data
management using spreadsheets.
UNIT 1: Supply chain management overview
UNIT 2: Models, algebra, and functions
UNIT 3: Data management
Module 2: Probability
This module will teach you how to measure, model, and manage uncertainty and randomness
within supply chains. You will become comfortable with a variety of continuous and discrete
probability distributions widely used in supply chains, such as Normal, Uniform,
Poisson, and others.
UNIT 1: Probability basics
UNIT 2: Discrete distributions
UNIT 3: Continuous distributions
Module 3: Statistics
This module is all about statistics. First, you will learn statistics basics such as the central
limit theorem, sampling, and confidence intervals. Second, you will learn how to conduct
hypothesis testing. You will learn how to formulate, test, and analyze the results of various forms
of tests widely used in practice. Last, you will learn to develop econometric models,
mainly Ordinary Least Squares (OLS) linear regression, that uses history to better estimate
the future. OLS is widely used to estimate future demand for a product and better understand
how different independent factors influence a dependent variable.
UNIT 1: The central limit theorem
UNIT 2: Sampling and confidence intervals
UNIT 3: Hypothesis testing
UNIT 4: Multiple random variables
UNIT 5: Regression models
Module 4: Optimization
This module will teach you when and how to use classic optimization techniques to find
the minimum or maximum values of an unconstrained cost or profit function.
We introduce linear programs (LPs) to solve constrained problems. LPs are the most commonly
used models for decision-making in supply chains. Then we will extend our discussion of
constrained optimization to include integer programming (IP), mixed-integer linear programming
(MILP) and network models. At the end of this module, you will be able to formulate LP, IP and
MILP models that represent real-life supply chain decisions. And you will be adept at solving
and interpreting the results of those models.
UNIT 1: Unconstrained optimization
UNIT 2: Constrained optimization
UNIT 3: Integer and mixed-integer linear programming
UNIT 4: Networks and non-linear programming
Module 5: Algorithms, approximations, and simulation
In this module, you will learn three approaches to problem-solving that are very common in
supply chain management: algorithms, approximations, and simulation. These techniques are
usually applied when exact and optimal solutions are infeasible or unobtainable within the
desired time. You will first learn the basics of developing and deploying algorithms and
how to use them in some fundamental supply chain applications, such as vehicle routing and
inventory planning. Then you will learn about approximation methods, and we will apply one
approximation method for estimating the costs of vehicle routing to illustrate the approach.
Finally, you will learn about simulation, which captures the outcomes of different policies with an
uncertain or stochastic environment.
UNIT 1: Algorithms
UNIT 2: Approximations
UNIT 3: Simulation
Final Exam
The final exam is a timed exam that covers material from the five modules of the course. The exam will only be available for 72 hours, and once you start it, there is a limited time (4 hours) to complete it. See course for exam instructions.