Department of Industrial Engineering
FIELDS OF RESEARCH
The following research groups exist at the department of Industrial Engineering. However, research projects are not limited to these topics. Research can be executed within the context of either the Industrial Engineering or the Engineering Management programme.
Engineering management includes fields such as project-, risk-, innovation-, quality- and performance management, and feasibility studies in the wider sense:
The analysis of enterprises (design, implement, operate) including knowledge and information-, innovation-, financial- and technology management.
The transition to a more sustainable economy and society, which will place emphasis on management of infrastructure/technology, including planning and design.
Health Systems Engineering
Conceptualising novel, engineering-based solutions to the challenges facing the healthcare sector. The research hub is specifically focused on facilitating improved healthcare delivery within the public sector in sub-Saharan Africa.
Innovation for Inclusive Development (I4ID)
Analysis, development and evaluation of inclusive innovations, inclusive innovation systems and innovation platforms. The goal is to explore how I4ID may provide solutions to societal problems (access to clean water, healthcare, financial services, etc.).
Beneficiation of Minerals
Investigates how mineral rich countries may optimally leverage their mineral endowments for sustainable development.
This area focuses on development of resource efficient process chains to ensure sustainable manufacturing as value creation system of products, but also for wider application in the services sector:
Additive manufacturing uses layer technology to create products in metals, polymers and other materials.
This involves micromachining (milling and turning) and microassembly of microproducts in which micromaterial handling systems are utilised.
Physical Asset Management
The systematic and coordinated activities and practices through which an organisation optimally and sustainably manages its assets and related systems.
Supply Chain Management
Supply network design, performance management and feasibility studies in the wider sense, to contribute to efficient supply chains.
The Stellenbosch Learning Factory (SLF) is a small but realistic production facility used for teaching undergraduate students various concepts related the design, management and improvement of production systems (using a “learning by doing” approach), as well as providing a research facility for research topics related to the “smart factory” of the future (in line with the 4th industrial revolution movement).
PRASA Engineering Research Chair
The PRASA Engineering Research Chair which initiates and executes research into aspects of maintenance-management and -processes best suited for the rail sector.
Systems Modelling, Operations Research and Decision Support
This area focuses on the development of mathematical models and their incorporation into computerised systems aimed at supporting scientifically justifiable and effective decisions in industry. These models draw from the scientific fields of applied mathematics, statistics, industrial engineering and computer science and are applicable in the context of complex problems which admit a large variety of trade-off solutions. Strong decision support ties exist with a number of industry partners in the agricultural, retail, banking, insurance and military sectors, as well as various para-statals, NGOs and non-profit organisations. Examples are:
- Routing and scheduling decisions for fleets of delivery vehicles.
- Employee duty roster or timetabling decisions for the manufacturing and health sectors.
- Shelf-space allocation and inventory decisions for retailers.
- Crop irrigation and agricultural pest-control strategy decisions.
- Power generator maintenance scheduling decisions in the energy sector.
- Facility location decisions for effective supply chain logistics.
- Optimal facility or production plant layout.
Data Science and Machine Learning
This programme focuses on enabling students to develop innovative optimisation and machine learning techniques to produce novel, efficient and robust data science technologies, for use in Industrial Engineering, Engineering Management and related applications. Examples include forecasting, customer segmentation and targeted marketing, production quality control, supply chain performance prediction, and process monitoring.