Course syllabus

 TEK615 - SUPPLY CHAIN ANALYTICS

Course is offered by the Department of Technology Management and Economics to the Master Program

Supply Chain Management

7.5 Higher Education Credit Units

Spring term 2021

Course Overview

Course objective and content

Supply chains create a vast amount of data every day. Every transaction, every operation, every claim is often recorded and saved as data. Learning how to analyse these data and convert them into insight for decision making can lead to higher revenues and better services for organizations.

Although supply chain data has been analysed for several years, new developments in technology (e.g., increased use of sensors, developments in intelligent systems, extended use of social networks) and the availability of tools for data analysis (e.g., data storage, visualization, artificial intelligence) have increased the relevance of supply chain analytics in the recent years.

This course aims at providing an introduction to multiple methods and tools related to supply chain analytics that will allow the students to identify how supply chain analytics can benefit an organization, to identify what specific method should be applied to solve a specific problem, to use existing software to analyse data and interpret the results.

Students are expected to understand the mathematics underlying the different methods and are expected to develop basic software programs. However, this course does not focus on mathematical derivations or developing complex codes. Python was selected as the main language for the predictive module and Lindo as the main platform for the prescriptive module. Ready-to-use codes are given to the students, but it is the students’ responsibility to understand input/outputs of the codes, and how these codes use the data.

Learning outcome

After completion of the course, the student should be able to:

• Use several common models of analysis, both quantitative and qualitative, to address problems and challenges within supply chains
• Use common analytics tools (e.g., Tableau, Python) to analyze and visualize data
• Develop and analyze the outcome of predictive models in supply chain management problems
• Apply prescriptive models based on mathematical optimization to solve supply chain management problems (inventory allocation, planning production, select transport providers, among others)

• Understand the basics on data analytics and how this can be applied to supply chains

Schedule

The course will take place during term 4 in the Spring of 2021. The course will follow a Block B schedule, with lectures on Tuesdays and Thursdays 13:00-17:00, and Fridays 13:15 to 15:00.

Content

The course will be structured in different modules in addition to some introductory topics and recap.

The first module (descriptive analytics) will focus on learning different tools to describe and visualise data. The second module (predictive analytics) will start by introducing the role of statistics to handle uncertainty in supply chain management, and then will focus on estimating regression analyses and machine learning algorithms (e.g., cluster analysis, time series) to forecast outcomes in supply chain management problems. The third module (prescriptive analytics) will introduce optimization models and the use of mathematical programming to formulate optimization problems widely used in supply chain management.

The course will include theory lectures where methods will be introduced followed by in-class workshops when possible, guest lectures, cases, and seminars. Be sure you can attend guest lectures as these sessions are mandatory.

Literature

Articles and relevant material will be distributed by instructors

Literature Seminars

The course includes two literature seminars. To prepare for these seminars, students will read two to three papers, prepare relevant questions to be discussed during the seminars, and submit them through Canvas. The seminars will focus on either the use of data analytics tools or their application to supply chain management problems. Each seminar group will be comprised of 4-5 students.

Business Case Studies

The course includes some sessions organized around business case studies. In these student-centred sessions, students reads a real life business case in a handout that the teachers distribute in advance and prepare the questions proposed. During the lecture, the teacher will ask questions to students and key learning points will be derived from students’ responses. It is imperative that students prepare the cases in advance. During these sessions, teachers will use cold calls (ask a student to answer a question without previous notice).

Examination

In order to fulfil the course learning outcomes, to better integrate the lectures with the examination and to increase student-student interaction, this course will be examined through a course project divided in three parts (Part 1: descriptive analytics, Part 2: predictive analytics and Part 3: prescriptive analytics) and an optional paper. For the course project, Part 1 will be handed-in approximately in week 6, while Parts 2 and 3 will be handed-in at the end of the term. Students will prepare a video with a group presentation and during the examination day will oppose another group. Groups will be comprised by 4-5 students.

Each Part of the report will have a pass/fail/distinction grade, and the three examination reports must be passed to pass the course with a grade of 3. Projects with a distinction grade will be assessed with a grade of 4.

The optional paper is an individual assignment in which each student will (i) choose an existing master thesis that used a qualitative method to solve a supply chain management problem and (ii) propose a data analytics approach to solve the same problem. This paper is conceptual and focus on where to get the data, the problem reformulation, collaborations needed within the supply chain to implement data analytics, method description and expected outcomes. It will not focus on the application of a specific method to data. Students who complete this task successfully will get an extra point in the course grade. The two best papers will be presented in the last lecture and a Best Paper Award Diploma will be bestowed.

Attendance to seminars and to guest lectures is compulsory.

In essence, each student has to pass at least the first three examination projects.

The final course score will be determined as follow:

• If one or more of the three parts of the project is not passed: Fail
• The three parts are passed: Grade 3
• The three parts are passed and project is passed with distinction: Grade 4
• The three parts are passed and the student successfully completes the individual paper: Grade 4

The three parts are passed, the project is passed with distinction, and the student successfully completes the individual paper: Grade 5