WEEK 0: Overview & Logistics: In Week 0, we will provide an overview of the course and the software you will need.
WEEK 1: Data Management I: In this first week, we ask 'why look to data for answers and introduce data modeling. We dive into data modeling and the basics of relational models, including entities, attributes, keys, tables, and of course, databases. You will become familiar with Entity-Relationship Diagrams that describe the business rules through a combination of relationships and cardinality between entities. We will show examples of taking real-world data and putting it into fully normalized databases. We will finish up with a short review of client-server architecture.
WEEK 2: Data Management II: In this week, you will become thoroughly acquainted with using the Structured Query Language, or SQL, the language that allows humans (as well as other systems) to interact with relational databases. You will learn how to use SELECT, INSERT, JOIN, VIEW, and other statements through extensive examples. We will be using MySQL for these assignments, but you can use any database implementation that you are comfortable with.
WEEK 3: Data Management III: This week, we will introduce additional topics in data management. Building on the data management skills mastered in weeks 1-2 and the first lesson of this week, you will learn about supervised and unsupervised learning techniques and model quality. We will also provide a quick overview of the burgeoning field of Machine Learning.
WEEK 4: Machine Learning: In Week 4, we will dive more deeply into the field of Machine Learning. We will present examples of commonly used machine learning algorithms and some applications. Next, we will discuss methods to train and validate classifiers and the trade-offs between classifier performance and overfitting. We will be using the open-source software package Orange (implemented in Python) for machine learning.
WEEK 5: Prep Week: This is a prep week in which no new graded assignments will be released. It provides you time to complete previous assignments and prepare for the Midterm next week. We will also offer a Virtual Field Trip to New York City, where learners can apply machine learning to logistical problems at a real company.
WEEK 6: Midterm Exam: The Midterm exam is a timed exam covering all material presented to date.
WEEK 7: Supply Chain Systems I: ERPs, SC Modules, and Software Selection and Implementation: So far, we have discussed how to manage large sets of data. This week, we take a specific look at supply chain systems starting with typical data structures and communication methods (such as EDI, XML, etc.). Building on this, we will provide an overview of the traditional supply chain operational and execution systems, such as ERP, MRP, TMS, OMS, and WMS. Finally, we will review how supply chain planning systems are used in practice and some of the challenges embedded in supply chain systems.
WEEK 8: Supply Chain Visibility: You will learn about the importance of visibility in supply chains and the key challenges and benefits associated with it. Dr. Inma Borrella will introduce this topic in Lesson 1 and provide a framework to implement and evaluate supply chain visibility initiatives. In the second lesson, Dr. Borrella will explore a series of cases that illustrate common supply chain visibility challenges as well as the solutions implemented by several companies. This week will bring together many lessons you have learned in this course, including how to work with big data, supply chain systems, platform interoperability, and data analytics to achieve supply chain visibility.
WEEK 9: Automation in Supply Chains:: You will learn about the key considerations and challenges with automation in supply chains. In the first lesson, Dr. Eva Ponce will discuss why we consider automating processes, levels of automation, strategies to achieve it, and other fundamental concepts. In the second lesson, Dr. Ponce will illustrate the key considerations and challenges with examples of automation in different areas of the supply chain, from warehouses to trucks.
WEEK 10: Virtual Field Trip plus New Technologies and Trends: In the last week of content, we will wrap up the course with a virtual field trip to New York City, where we will see how machine learning is applied to logistics problems at a real company. We also have short lessons on new technologies and trends, and how they may impact supply chains. This week is not graded, but we encourage you to participate in the polls and discussions.
WEEK 11: Prep Week: Again, this is a prep week with no new graded assignments so that you can get prepared for the Final Exam!
WEEK 12: Final Exam: The Final exam is a timed exam that covers all the material in the course.
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