Data-Driven Operations Planning and Control 5 Credits

The course is primarily aimed at working professionals in industry who wish to increase their knowledge of data-driven operations planning and control of materials and capacity.


The area of use and the role of data-driven Artificial intelligence (AI) is gaining interest also in industrial contexts. Data-driven AI enables the industry to improve on decision support in for instance generating forecasts of capacity and demand for provisioning of products and services. However, to utilize data-driven AI for operations planning and control, it is important to also understand the theoretical foundation of forecasting as well as planning and control of capacity and materials. This is the starting point of this course, where participants are provided with the theoretical foundation of planning and control covering forecasting, capacity management and materials management within an industrial context. This theoretical knowledge is also converted into practice using different practical planning and control assignments to further aid the learning process. Connected to the assignments, participants will also be given the opportunity to work on company specific problems/challenges related to for example data-driven forecasting.

Course 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

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.

Level of Education: Master
Course code/Ladok code: TFSR24
The course is conducted at: School of Engineering Last modified 2024-03-08 09:22:40