Dr Wolfgang Garn
Wolfgang Garn worked as Programme director of Business Analytics and Acting Head of the Business Transformation Department at the University of Surrey. His research interests are in the area of Operations Research and Business Analytics. Currently he is working on the modernisation, optimisation and simulation of processes within a delivery service company. Formerly a Management Scientist at Telekom Austria in the Department of Operations Research. His duties entailed Network Optimisations, Transportation, Market Analysis amongst others. One of his major achievements was a nationwide transition strategy from a copper to a fibre network. At the Defence Technology Centre (DTC) he worked on Agent and Decision Support Systems for MoD funded research. One of the outcomes enabled autonomous agents to evaluate and act on military effects using Artificial Intelligence. His role as Senior Scientist and Project Manager for Eurobios involved him with key clients such as Serco, Biffa, Unilever, DHL and BP. In this context solutions for environmental and delivery services were implemented. He is the CEO and founder of Smartana, which offers SMART Analytics solutions and consulting services to businesses. He is a member of the Institute for Operations Research and Management Sciences, and the Society for Modeling & Simulation. He earned his PhD in Simulation and Optimisation of Telecommunication processes at the Vienna University of Technology, in the area of Technical Mathematics and Computer Science.
Affiliations and memberships
- Business analytics; business optimisation and economics
- Logistics, transportation, traffic flows, routing, scheduling
- Management science, combinatorial optimisations, network flows, meta heuristics, e.g. genetic algorithms, simulated annealing
- Applied mathematics, Artificial Intelligence, discrete event simulation, queueing systems
- Kernel Density Estimators, decision support systems, Bayesian networks, statistical learning.
- machine learning techniques to gain insights into the sustainability of homes
- The rise of the machines - learning for environmental good and cost savings (Blog, May 24, 2017)
- Are we “boiled” for choice? (Blog, August 3, 2017)
- AI detects roof status using Drones (BA MSc dissertation summary, October 10, 2017)
Indicators of esteem
Reviewer for the ...
- European Journal of Operational Research
- Neurocomputing Journal
- International Journal of Production Economics
- International Conference on Information Systems
- and many more
- Business Analytics
- Big Data drives Big Decisions! Business Analytics arms you with the expertise in analysing data and creating knowledge - leading to competitive advantages for business-decisions. It equips you with state-of-the art and new emerging skills to solve business transforming challenges. General enquiries: firstname.lastname@example.org, programme enquiries: email@example.com
- Supply Chain Analytics (level M) - Semester 2
- Management Science is used to solve supply chain (SC) aspects analytically. Techniques examine the Supply Chain's underlying transportation network which connects suppliers via transhipment nodes to its demand locations. Best locations for warehouses (or transhipment nodes) are determined using quantitative methods. Decision Science is used for in rational decision making under uncertainty. For instance optimal inventory levels are determined for warehouses and manufacturing. All kinds of business activities are optimised to give businesses a competitive advantage by maximising profit and minimising costs.
- Module catalogue (MANM304)
- Introduction to Management Science - Semester 1
- Methods and tools are used to tackle challenges occurring in the business and industrial environment. The obtained results are used for qualified decision making.
- Module catalogue (MAN2093)
- Information Systems Development (PG) - Semester 2 (2012)
- A hands-on approach to the development of Information Systems - using practical State-of-the-Art methods, tools and techniques.
- Module catalogue (MAN114)
- Issues in Operations Management (UL2) - Semester 1 (2010/2011)
- This lecture explores a set of critical areas in Operations Management in depth using a Management Science approach.
- Module catalogue (MAN2086)
- Project Management & Computer Lab. (PG, UG) - Semester 2 (2012)
- Business Research Project (FHEQ6 - year 3) - Semester 2 (2014)
- To analyse and critically evaluate existing work in order to deliver value to businesses.
- Module catalogue (MAN3116)
- Business Process Management (PG) - Semester 2 (2013)
- Shows the relationship between operations management and information systems, with hands-on experience in SAP.
- Module catalogue
Postgraduate research supervision
I am looking for PhD - students
I am looking for people interested in obtaining a PhD. Candidates should have a strong quantitative background (e.g. mathematics, computer science, physics, etc.) and be interested in Management Science (Operational Research).
Potential topic areas:
- Business analytical/intelligence, Management science
- Operational research (e.g. Simulation, Networks) - ranging from operations to finance applications
- Artificial Intelligence
The contents covers topics such as:
- Waiting Line Models (e.g. Multiple Server Waiting Line)
- Transportation and Network Models (e.g. Shortest Route)
- Inventory Management (e.g. Economic Order Quantity model)
This will enable the reader:
- To increase the efficiency and productivity of business firms
- To observe and define ?challenges? in a concise, precise and logical manner
- To be familiar with a selected number of classical and state-of-the art Management Science methods and tools to solve management problems
- To create solution models, to develop and create procedures that offer competitive advantage to the business/organisation
- To communicate and provide results to the management for decision making and implementation
Through modelling and experimentation organisations and individuals can reshape their cognitive maps to develop and recognise new opportunities to improve their processes through improving the presentation and use of information.
services has been decreasing in a county in South of England. Hence,
methods that increase patronage, focus subsidies and stimulate the bus
industry are required. Our surveys and market research identified and
quantified essential factors. The top three factors are price, frequency,
and dependability. The model was further enhanced by taking into
account real time passenger information (RTPI), socio-demographics
and ticket machine data along targeted bus routes. These allowed the
design of predictive models. Here, feature engineering was essential
to boost the solution quality. We compared several models such as
regression, decision tress and random forest. Additionally, traditional
price elasticity formulas have been confirmed. Our results indicate
that more accuracy can be gained using prediction methods based on
the engineered features. This allows to identify routes that have the
potential to increase in profitability - allowing a more focused subsidy