Objectives and competences
Students acquire and enhance the knowledge of modern quantitative methods in supply chain management. Using the open-source software tool R, they can analyze real-world cases, select the appropriate methodological approach, critically evaluate, and appropriately interpret the results.
Content (Syllabus outline)
1. Overview of optimization models in supply chain management
(linear optimization models, simplex algorithm, integer models and mixed-integer models, with examples from supply chain management)
2. Transportation optimization
(model formulation, comparison of methods for solving)
3. Forecasting with regression models
(regression models, time series models, demand forecasting and forecasting of other quantities with regression models)
4. Deep learning
(deep learning fundamentals, examples of the application of deep neural networks for forecasting in supply chain management)
In all chapters, the open-source software tool R will be used to solve empirical cases.
Learning and teaching methods
lectures
- active individual and group work
- case studies
Intended learning outcomes - knowledge and understanding
In the course Quantitative Methods in Supply Chain Management, students:
1. Systematically acquire and enhance their knowledge of modern quantitative methods in supply chain management.
2. Develop the ability to apply theoretical knowledge from the field of supply chain management in quantitative models. (PILO 2a)
3. Learn to compare and critically evaluate different methodological approaches.
4. Formulate or develop an appropriate model and estimate it using the open-source software tool R. (PILO 3a)
5. Become proficient in using the R software tool on practical examples. (PILO 1a, PILO 3a)
6. Appropriately present and interpret the results obtained through quantitative methods. (PILO 3b)
7. Develop skills for independent and group empirical work and enhance their ability for collaboration and communication. (PILO 3 c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
In the course Quantitative Methods in Supply Chain Management, students:
1. Systematically acquire and enhance their knowledge of modern quantitative methods in supply chain management.
2. Develop the ability to apply theoretical knowledge from the field of supply chain management in quantitative models. (PILO 2a)
3. Learn to compare and critically evaluate different methodological approaches.
4. Formulate or develop an appropriate model and estimate it using the open-source software tool R. (PILO 3a)
5. Become proficient in using the R software tool on practical examples. (PILO 1a, PILO 3a)
6. Appropriately present and interpret the results obtained through quantitative methods. (PILO 3b)
7. Develop skills for independent and group empirical work and enhance their ability for collaboration and communication. (PILO 3 c)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
- Anderson, T.R. (2022) Optimization modelling using R. Boca Raton: Chapman & Hall/CRC. (izbrana poglavja/selected chapters)
- Christou, I.T. (2013) Quantitative methods in Supply Chain Management: Models and algorithms. London: Springer. CRC. (izbrana poglavja/selected chapters)
- Izbrani članki za prikaz uporabe globokega učenja v managementu dobavnih verig. (Selected papers on the application of deep learning in supply chain management.)
Additional information on implementation and assessment Written exam (70%)
In-class participation (30%)
