Decision Analytics
Databaserat analytiskt beslutsfattande

MION46, 7.5 credits, A (Second Cycle)

Valid for: 2024/25
Faculty: Faculty of Engineering LTH
Decided by: PLED I
Date of Decision: 2024-04-16
Effective: 2024-05-08

General Information

Main field: Logistics and Supply Chain Management Depth of study relative to the degree requirements: Second cycle, in-depth level of the course cannot be classified
Elective mandatory for: MLOG1
Elective for: I4-lf, M4-lp
Language of instruction: The course will be given in English

Aim

The course aims to provide the student with thorough knowledge and understanding of concepts, quantitative methods, tools, and practices for decision-making under risk, both from a theoretical and applied perspective. After the course, the students should be able to apply this knowledge to structure, analyze, and solve complex real-life decision problems. They should also be able to communicate the results, explain how the solution was obtained, motivate why this method is appropriate to use, and reflect on the strengths and weaknesses of the chosen solution method and the quality of the obtained solutions.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

• be able to describe the concepts, methods, and practices for analyzing decision problems under risk covered in the course and the strengths and weaknesses they possess.
• be able to explain how these different concepts, methods, and practices relate to one another and exemplify when they are appropriate to use.
• be able to demonstrate how to use these concepts, methods, and practices to structure, analyze, and solve real-life decision problems. This means that the student is required to possess sufficient knowledge and understanding to:
• be able to articulate and discuss the challenges and impact of uncertainty and variability on the decision process and how to deal with these issues using relevant methods.
• be able to explain the relevance of decisions, uncertainties, and objectives in a decision-making process, and be able to visualize resulting decision problems.
• be able to explain how to solve and analyze decision problems using decision trees.
• be able to explain and discuss the advantages and disadvantages of expected value analysis in a given context and extensions to risk analysis.
• be able to explain the theoretical and practical limitations of decision trees for complex decision problems.
• be able to explain the biases that can occur when generating subjective probabilities and the effect this can have on decision-making.
• be able to conduct Monte Carlo simulations and determine the number of required simulation runs.
• be able to explain the fundamental principles of descriptive decision theory and prospect theory covered in the course, including the relation towards reference points, different risk attitudes towards gains and losses, and non-linear probability weighting functions.
• be able to manage and solve project assignments with high demands on reporting/documentation of results, both in terms of oral presentations and written reports.

Competences and skills
For a passing grade the student must

• demonstrate skills and abilities to independently formulate, analyze, and solve complex decision problems subject to risks and uncertainties using relevant qualitative and quantitative methods.
• be able to use decision trees for analyzing and solving decision problems under risk, and for performing sensitivity analysis.
• be able to use Monte Carlo simulation for solving complex decision problems
• be able to use established terms and concepts to clearly communicate decision problems to different target audiences and interpret quantitative results.
• be able to analyze and solve unstructured decision problems. Important aspects include problem formulation, identifying project objectives, choosing appropriate methods, and performing in-depth analysis.
• be able to clearly report project results, which requires skills in oral and written presentation techniques

Contents

The course covers contemporary concepts, qualitative and quantitative methods, tools, and practices for decision-making under risk, both from theoretical and applied perspectives. Case studies and project assignments are used for introducing and explaining relevant topics, and for training the students to apply the theoretical models to structure, analyze, and solve complex decision problems.

Examination details

Grading scale: TH - (U, 3, 4, 5) - (Fail, Three, Four, Five)
Assessment:

Take home exams/assignments. The examinations are designed to assess the student´s ability to independently solve loosely structured decision problems, typically found in practice. Important assessment criteria are to clearly communicate the results and explain how the problems are solved orally, in well-structured presentations, and in writing by producing well-structured technical reports.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Modules
Code: 0124. Name: Project.
Credits: 7.5. Grading scale: TH - (U, 3, 4, 5). Assessment: Project assignment
Code: 0224. Name: Assignment.
Credits: 0.0. Grading scale: UG - (U, G). Assessment: Compulsory assignments

Admission

Admission requirements:

• Mathematical Statistics, Basic Course, (FMSF20, FMSF50, FMSF70 or FMSF80), or equivalent

Reading list

• Chelst, K. & Canbolat, Y.B: Value-added decision making for managers. Taylor & Francis Group, 2012.
• Albright, S.C. & Winston, W.L: Business Analytics: Data Analysis and Decision Making, 5th edition. Cengage Learning, 2015.
• Related scientific research papers will be announced throughout the course.

Contact

Course coordinator: Danja R. Sonntag, danja.sonntag@iml.lth.se
Course homepage: https://www.pm.lth.se