Course syllabus

 TEK615 - SUPPLY CHAIN ANALYTICS

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

Supply Chain Management

7.5 Higher Education Credit Units

Spring term 2023

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 organisations.

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, visualisation, 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 organisation, 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 programming language for the course. 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 quantitative models to address problems and challenges within supply chains
• Use popular analytics tools (e.g., Python) to analyse and visualise data
• Develop and analyse the outcome of predictive models in supply chain management problems
• Apply prescriptive models based on mathematical optimisation to solve supply chain management problems (inventory management, transport planning, sourcing, among others)

• Understand the basics of machine learning and how this can be applied to supply chains

Schedule

The course will take place during term 4 in the Spring of 2023. The course will follow a Block B schedule, with lectures on Tuesdays and Thursdays 13:15-17:00, 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 optimisation models and the use of mathematical programming to formulate optimisation 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. Plan to attend guest lectures as these sessions are semi-compulsory, if you cannot attend you should ask the examiner for a compensatory assignment. Selected lectures will require mandatory in-class submissions to train analytical and programming skills for the addressed topics. 

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 the selected references, 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. 

Business Case Studies

The course includes some sessions organised around business case studies. In these student-centred sessions, students read 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. 

Examination

In order to fulfil the course learning outcomes, better integrate the lectures with the examination, and increase student-student interaction, this course will be examined through a course project divided into three parts, i.e., Part 1: visualisation methods, Part 2: predictive analytics, and Part 3: prescriptive analytics. For the course project, Part 1 will be handed-in in week 16, Part 2 in week 19, and, Part 3 at the end of the term.  For the last submission on week 22, students will prepare a video with a group presentation and, during the examination day will oppose another group. Groups will be comprised by 3 students.

Each Part of the report will have a grade, the three examination reports must be passed to pass the course (5/10 for each part). Each project part can have a maximum grade of 10.

Eight (8) lectures were designed to have in-class assignments. Teachers will allocate time during the lecture of the selected sessions to this individual task. Students should submit the Colab notebook and/or the link to a dashboard with the code and answers to the questions. Grading criteria for pass/fail will consider that each assignment was an individual/original work, it is complete, responses were justified, and the code runs. Students will be graded based on their commitment to the task completion rather than correct/incorrect answers. 5/8 submissions are required to pass the course, and 8/8 to get one (1) point in the final grade.    

To pass the course students should:

1. Attend all the literature seminars and guest lectures
2. Submit and pass at least 5 out of 8 in-class submissions
3. Pass each part of the final project including opposition. Opposition is graded individually.

The final course score will be determined based on grades of in-class assignments, project reports and opposition as follow:

• Fulfil the previous requirements: Grade 3
• The final project passed with distinction (>25/30): Grade 4
• The final project passed and 8/8 in-class submissions: Grade 4
• The final project passed with distinction and 8/8 in-class submission: Grade 5