SCM 340: Introduction to Supply Chain Analytics

Textbook Information

Required:

  • All required text and video-based materials are provided in Canvas.

Optional:

  • Liu, K.Y. (2022). Supply Chain Analytics: Concepts, Techniques, and Applications, 1st edition, Palgrave Macmillian. This textbook is available as an E-Book through the Penn State Libraries.

Published Remarks

None

Hardware Requirements

A computer with stable internet connection.

Software Requirements

  • Microsoft Excel
  • Python Environment

Proctored Exams

This course uses Honorlock for proctoring quizzes exams. You will need to take all quizzes and exams using Chrome. You will need to download and install the Chrome extension for Honorlock. 

Course Description

Method of Course Delivery: Asynchronous Online

SCM 340 - Introduction to Supply Chain Analytics (3 credits): 

Supply Chain Analytics studies key decision areas in supply chain design and operation using data-driven methodologies. The course introduces students to the key components of supply chains, the role of data in supply chain management, data manipulation, visualization techniques, customer management approaches, supply management techniques, warehouse and inventory management, demand forecasting, and logistics management. The course integrates Python programming to implement data-driven supply chain strategies.

Course Pre-requisite(s): SCM 301: Supply Chain Management.

To be successful in this course, each student should have a solid understanding of statistics and spreadsheet (Excel) operations and familiarity with basic programming and business concepts especially in operations and supply chain management. It is your responsibility to brush up on these areas if needed.

Objectives

By the end of the course, students will be able to:

  • Foundational Understanding of Supply Chain Analytics: Describe and use the fundamental components, flows, and objectives of supply chains and discern the role of business analytics in enhancing supply chain operations.
  • Data Mastery in Supply Chain Context: Recognize the importance of data-driven decisions in supply chain management, identify primary data sources, and understand the characteristics of big data while gaining proficiency in Python and its key libraries for data manipulation and visualization.
  • Analytical Approaches to Supply Chain Management: Apply data manipulation, visualization, and advanced analytical techniques to address key challenges in customer management, supply management, warehouse management, demand management, and logistics.
  • Strategic Implementation of Analytical Techniques: Implement Python-based solutions, encompassing regression, classification, time series forecasting, and optimization algorithms, to solve real-world supply chain problems, emphasizing customer-centric strategies, inventory optimization, and logistics management.
  • Holistic Perspective on Supply Chain Operations: Develop a comprehensive perspective on supply chain operations, differentiating between various supply chain strategies, forecasting methods, and logistics practices, and appreciate the importance of a data-driven approach in enhancing supply chain efficiency and effectiveness.

How to Succeed in This Course

The most important ingredients for success in this course are:

  • Thorough preparation and analyses of the weekly assigned problems and case studies, and success on the weekly knowledge and skills checks.
  • Timely submission of ALL assignments.
  • A thorough reading of the text and module material.
  • Weekly work on the Final Project to ensure timely and accurate submission.
  • Professionalism in all we do, including writing and submitting of assignments and communicating with the instructor and other students.

Course Grading

The requirement weightings and the final grade determination is based on your results on assignments, projects, midterm exam, final exam, and your participation and discussions.

The following table summarizes assignments and their associated values.

 
Assignment Percentage Points
Assignments, Discussions, and Quizzes 55% 385
Midterm Exam 10% 70
Final Exam 15% 105
Project 20% 140
Total 100% 700

Letter grades will be based on the following scale:

Grade Percentages
Grade Percentage Range
A > 93.0%
A- 90.0 – 92.99%
B+ 87.0 – 89.99%
B 83.0 – 86.99%
B- 80.0 – 82.99%
C+ 77.0 – 79.99%
C 70.0 – 76.99%
D 60.0 – 69.99%
F <59.99%

Late Policy

NO late assignments will be accepted for ANY Reason. Only under VERY RARE and EXTRAORDINARY CIRCUMSTANCES may the instructor accept a late assignment at his discretion. Should a student have a medically related or military related or emergency circumstance that may cause concern for lack of timely assignment completion, he/she MUST communicate the issue with the instructor immediately. The instructor will ask for any/all supporting documentation to validate the circumstance. Technology issues are NOT valid circumstances. Start assignments early, ask questions, and DO NOT WAIT UNTIL THE LAST MINUTE to submit, as technology sometimes has issues. Thus, submit early! I’m willing to work with students if there’s an open line of communication. Sometimes things (life) happen, so don’t hesitate to get in touch as soon as the emergency arises.