COURSE SYLLABUS
Data-Driven Operations Planning and Control, 5 credits
Datadriven flödesplanering och -styrning, 5 högskolepoäng
Course Syllabus for students Spring 2025
| Course Code: | TFSR24 |
| Confirmed by: | Dean Sep 7, 2023 |
| Valid From: | Jan 1, 2024 |
| Version: | 2 |
| Education Cycle: | Second-cycle level |
| Disciplinary domain: | Technology |
| Subject group: | IE1 |
| Specialised in: | A1N |
| Main field of study: | Production Systems |
Intended Learning Outcomes (ILO)
After a successful course, the student shall:
Knowledge and understanding
- display knowledge about the fundamentals of operations planning and control
- display knowledge of the role of data-driven operations planning and control for capacity and materials management within an industrial context
- display knowledge of the role of data-driven operations planning and control for capacity and materials management within an industrial context
Skills and abilities
- demonstrate skills of problem identification, analysis and decision making within data-driven operations planning and control
- demonstrate the ability to apply different methods and tools relevant for data-driven operations planning and control
- demonstrate the ability to apply different methods and tools relevant for data-driven operations planning and control
Judgement and approach
- demonstrate the ability to suggest different methods and tools relevant for data-driven operations planning and control
- demonstrate the ability to evaluate results from using data-driven operations planning and control
- demonstrate the ability to evaluate results from using data-driven operations planning and control
Contents
The course is primarily aimed at working professionals in the industry who wish to increase their knowledge of data-driven analysis methods in the context of operations planning and control of materials and capacity.
Data-driven decision making is about taking decisions based on actual data rather than on intuition or observations only. The ability to improve the correctness of one´s own decision making is something that is important for industrial companies and their supply chain partners. The idea of using actual data for decision making is that businesses will be better at anticipating and act proactively on various events, and thus improve their competitiveness.
This course will address data-driven decision making by looking into different data-driven analysis methods in the context of operations planning and control. The participants of the course will be provided with the theoretical foundations of planning and control, capacity management and materials management within the context of the industry. This theoretical knowledge is also converted into practice using practical data-driven planning and control projects to further aid the learning process. Connected to the projects, participants will also be given the possibility to work on company specific data-driven problems/challenges, such as data-driven forecasting.
Being a course on data-driven analysis and the development of decision support, the course is also laying the foundation for participants to use AI techniques in the future.
The course includes the following elements:
- Introduction and overview of:
o planning and control (e.g., allocation of work to resources, scheduling methods, monitoring and control of operations)
o Capacity management (e.g., qualitative and quantitative approaches to forecasting, capacity measurements, capacity dimensioning)
o Materials management (e.g., inventory types, order quantity decisions, re-ordering methods)
- Practical work in Microsoft Excel to generate forecasts based on historical data
Data-driven decision making is about taking decisions based on actual data rather than on intuition or observations only. The ability to improve the correctness of one´s own decision making is something that is important for industrial companies and their supply chain partners. The idea of using actual data for decision making is that businesses will be better at anticipating and act proactively on various events, and thus improve their competitiveness.
This course will address data-driven decision making by looking into different data-driven analysis methods in the context of operations planning and control. The participants of the course will be provided with the theoretical foundations of planning and control, capacity management and materials management within the context of the industry. This theoretical knowledge is also converted into practice using practical data-driven planning and control projects to further aid the learning process. Connected to the projects, participants will also be given the possibility to work on company specific data-driven problems/challenges, such as data-driven forecasting.
Being a course on data-driven analysis and the development of decision support, the course is also laying the foundation for participants to use AI techniques in the future.
The course includes the following elements:
- Introduction and overview of:
o planning and control (e.g., allocation of work to resources, scheduling methods, monitoring and control of operations)
o Capacity management (e.g., qualitative and quantitative approaches to forecasting, capacity measurements, capacity dimensioning)
o Materials management (e.g., inventory types, order quantity decisions, re-ordering methods)
- Practical work in Microsoft Excel to generate forecasts based on historical data
Type of instruction
The course is given as an online course with a mix of digital resources, such as recorded lectures, quizzes, and online meetings. Course work in the form of projects is conducted throughout the course with online supervision, with the possibility of on campus supervision.
The teaching is conducted in English.
Prerequisites
Passed courses of at least 40 credits in the main field of study within Engineering and Technology, Natural Science or Social Sciences, and at least 1 years of work experience (or equivalent). English proficiency is required (level 6 or equivalent).
Applicants with an academic degree of at least 180 credits within Engineering and Technology, Natural Science or Social Sciences field are exempt from the work experience requirement.
Applicants that have at least 4 years of work experience in the industry are exempt from the requirement of academic degree or courses of at least 40 credits within Engineering and Technology, Natural Science or Social Sciences field.
Applicants with an academic degree of at least 180 credits within Engineering and Technology, Natural Science or Social Sciences field are exempt from the work experience requirement.
Applicants that have at least 4 years of work experience in the industry are exempt from the requirement of academic degree or courses of at least 40 credits within Engineering and Technology, Natural Science or Social Sciences field.
Examination and grades
The course is graded Fail (U) or Pass (G).
The final grade will only be issued after satisfactory completion of all mandatory examination elements.
The final grade will only be issued after satisfactory completion of all mandatory examination elements.
Registration of examination:
| Name of the Test | Value | Grading |
|---|---|---|
| Project | 5 credits | U/G |
Course literature
The literature list for the course will be provided eight weeks before the course starts.
Chapman, S., Arnold, T.K., Gatewood, A.K., and Clive, L.M. (2016). Introduction to materials management. 8th ed. Global edition, Pearson Harlow. ISBN: 9781292162355
(as an alternative, the book can also be bought as an eBook [ISBN: 9781292162379])
Chapman, S., Arnold, T.K., Gatewood, A.K., and Clive, L.M. (2016). Introduction to materials management. 8th ed. Global edition, Pearson Harlow. ISBN: 9781292162355
(as an alternative, the book can also be bought as an eBook [ISBN: 9781292162379])
In addition, there are several articles included in the course.
Content updated 2015-06-24
