CTL.SC0x - Supply Chain Analytics

SC0x - Supply Chain Analytics is delivered on a learner-paced schedule. This course teaches the mathematical concepts used throughout the rest of the SCx courses and acts as a reference course for the whole SCM MicroMasters Program. To ensure availability to SCM learners, SC0x remains open for enrollment almost always. All course content, practice problems, exams, reading materials, and external links are delivered through the course platform on edX.

The course content of SC0x is packaged into 5 modules, with each module ending in a Module Test. The course session will then end with a Final Exam. Learners may work through the course content at their own pace, including the Module Tests, which are due near the end of the course, when the Final Exam becomes available. Unlike the rest of the course content, the Final Exam is available to learners only during a specific time period, set by the course team. The course will close briefly after each Final Exam, in order to process scores and make Course Certificates available to passing learners.

The five module tests will account for 10% of the Overall Course Score, and the Final Exam will account for 90% of the Overall Course Score. The Verification (Payment) deadline for SC0x is three weeks before the scheduled Final Exam becomes available.

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 all the material in the course.