Dr Wolfgang Garn

Research Interests

  • 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.

Surrey Analytics Summer School

Introduction to Data Analytics and Business Analytics

  • When? Summer 2018, 18-20 June from 9am to 6pm
  • Where? The Rik Medlik Building, University of Surrey
  • Open to: anyone interested in Analytics
  • The Surrey Analytics Research group is hosting a 3 day course this August to provide comprehensive introduction to Data Analytics and Business Analytics.

    Please look at this Website for details.

    Some of my favourite tools

    • Mathworks - Matlab/Simulink
    • R, Excel/Frontline Solver
    • Java, PHP, C++
    • PostgreSQL, SQL Server
    • ILOG - OPL Studio, Power BI
    • Arena, Anylogic, Taylor ED, Simio
    • Blender, QGIS, Unity


  • 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: admissions@surrey.ac.uk, programme enquiries: w.garn@surrey.ac.uk
  • 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

Departmental Duties


  • Head of Business Transformation & Sustainable Enterprise Department


  • Business Analytics (2013-...) - Programme Director
  • Introduction to Management Science (2012-...) - module convenor
  • Supply Chain Analytics (2013-...) - module convenor
  • Information Systems Development (2010-2012)- module convenor
  • Issues in Operations Management (2010-2011)- module convenor
  • Business Research Project (2012-2013) - module convenor and coordinator
  • Business Process Management (2013) - shared convenor
  • Project Management (2012) - computer lab.

Reviewer for the ...

  • European Journal of Operational Research
  • Neurocomputing Journal
  • International Journal of Production Economics
  • International Conference on Information Systems
  • and many more


  • AIS – Association for Information Systems
  • ARGESIM - Working Group Simulation News
  • EURO - The Association of European Operational Research Societies
  • EUROSIM - Federation of European Simulation Societies
  • FITCE  - Federation of Telecommunications Engineers of the European Community
  • INFORMS - Institute for Operations Research and the Management Sciences
  • ÖGOR - Austrian Society of Operations Research.


I am looking for people interested in obtaining a full-time PhD. Candidates should have a strong quantitative background; experience in computer science and interest in Management Science (Operational Research).


  • Drone Delivery Services (e.g. comparison between classic and autonomous drone delivery services)
  • Transportation network optimisations (e.g. bus services, county wide decision support systems)
  • Business analytical/intelligence, Management science
  • Operational research, Simulation
  • Networks and artificial & computational intelligence.

More information for PhD applicants, if you have an interesting idea please send me an email.

Research Projects

KTP ... Knowledge Transfer Partnership.

If you are interested in a KTP please contact me. Potential future projects may be funded by Innovate UK.

Contact Me

Phone: 01483 68 2005

My office hours



Please feel welcome to drop in anytime during these hours (during term times). If you prefer different times, please feel free to send me an email to arrange an appointment. 

My office is  located in 17 MS02.


Journal articles

  • Aitken J, Bozarth C, Garn W . (2016) 'To eliminate or absorb supply chain complexity: A conceptual model and case study'. Emerald Group Publishing Limited Supply Chain Management, 21 (6), pp. 759-774.


    Existing works in the supply chain complexity area have either focused on the overall behavior of multi-firm complex adaptive systems (CAS) or on listing specific tools and techniques that business units (BUs) can use to manage supply chain complexity, but without providing a thorough discussion about when and why they should be deployed. This research seeks to address this gap by developing a conceptually sound model, based on the literature, regarding how an individual BU should reduce versus absorb supply chain complexity. This research synthesizes the supply chain complexity and organizational design literature to present a conceptual model of how a BU should respond to supply chain complexity. We illustrate the model through a longitudinal case study analysis of a packaged foods manufacturer. Regardless of its type or origin, supply chain complexity can arise due to the strategic business requirements of the BU (strategic) or due to suboptimal business practices (dysfunctional complexity). Consistent with the proposed conceptual model, the illustrative case study showed that a firm must first distinguish between strategic and dysfunctional drivers prior to choosing an organizational response. Furthermore, it was found that efforts to address supply chain complexity can reveal other system weaknesses that lie dormant until the system is stressed. The case study provides empirical support for the literature-derived conceptual model. Nevertheless, any findings derived from a single, in-depth case study require further research to produce generalizable results. The conceptual model presented here provides a more granular view of supply chain complexity, and how an individual BU should respond, than what can be found in the existing literature. The model recognizes that an individual BU can simultaneously face both strategic and dysfunctional complexity drivers, each requiring a different organizational response. We are aware of no other research works that have synthesized the supply chain complexity and organizational design literature to present a conceptual model of how an individual business unit (BU) should respond to supply chain complexity. As such, this paper furthers our understanding of supply chain complexity effects and provides a basis for future research, as well as guidance for BUs facing complexity challenges.

  • Garn W, Louvieris P. (2015) 'Conditional probability generation methods for high reliability effects-based decision making'. arXiv, (Computer Science > Artificial Intelligence) Article number arXiv:1512.08553 [cs.AI]


    Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.

  • Garn W, Aitken J . (2015) 'Splitting hybrid Make-To-Order and Make-To-Stock demand profiles'. arXiv,


    In this paper a demand time series is analysed to support Make-To-Stock (MTS) and Make-To-Order (MTO) production decisions. Using a purely MTS production strategy based on the given demand can lead to unnecessarily high inventory levels thus it is necessary to identify likely MTO episodes. This research proposes a novel outlier detection algorithm based on special density measures. We divide the time series' histogram into three clusters. One with frequent-low volume covers MTS items whilst a second accounts for high volumes which is dedicated to MTO items. The third cluster resides between the previous two with its elements being assigned to either the MTO or MTS class. The algorithm can be applied to a variety of time series such as stationary and non-stationary ones. We use empirical data from manufacturing to study the extent of inventory savings. The percentage of MTO items is reflected in the inventory savings which were shown to be an average of 18.1%.

  • Van Der Heijden H, Garn W. (2013) 'Profitability in the car industry: New measures for estimating targets and target directions'. Elsevier European Journal of Operational Research, 225 (3), pp. 420-428.


    In this paper we study the profitability of car manufacturers in relation to industry-wide profitability targets such as industry averages. Specifically we are interested in whether firms adjust their profitability in the direction of these targets, whether it is possible to detect any such change, and, if so, what the precise nature is of these changes. This paper introduces several novel methods to assess the trajectory of profitability over time. In doing so we make two contributions to the current body of knowledge regarding the dynamics of profitability. First, we develop a method to identify multiple profitability targets. We define these targets in addition to the commonly used industry average target. Second, we develop new methods to express movements in the profitability space from t to t + j, and define a notion of agreement between one movement and another. We use empirical data from the car industry to study the extent to which actual movements are in alignment with these targets. Here we calculate the three targets that we have previously identified, and contrast them with the actual profitability movements using our new agreement measure. We find that firms tend to move more towards to the new targets we have identified than to the common industry average. © 2012 Elsevier B.V. All rights reserved.

  • Louvieris P, Gregoriades A, Garn W. (2010) 'Assessing critical success factors for military decision support'. PERGAMON-ELSEVIER SCIENCE LTD EXPERT SYSTEMS WITH APPLICATIONS, 37 (12), pp. 8229-8241.

Conference papers

  • Garn WOLFGANG. (2015) 'Drones reveal efficiency savings in delivery services'.
  • aitken J, garn W, iyer K. (2015) 'Impact of Discrete-Event-Simulation on Lean and Swift-Even-Flow processes in sorting facilities'. Conference: 27th European Conference on Operational Research
  • Aitken J, Garn W. (2014) 'Stochastic operations optimisation for delivery services'. Palermo, Italy: 21st EurOMA Conference
  • Garn W, Aitken J. (2013) 'Production scheduling in the food industry'. Rome, Italy: 26th European Conference on Operational Research
  • Aitken J, Garn W. (2012) 'Process variance: Competing Against Customer Demand'. Brussels, Belgium: 5TH EUROPEAN FORUM ON MARKET DRIVEN SUPPLY CHAINS
  • Garn W. (2010) 'Predictive Power Decisions with Generated Conditional Probabilities'. Beijing: INFORMS
  • Garn W. (2003) 'A Real-World S-MD-mVRP-TW'. Heidelberg, Germany: International conference of the German Operations Research Society


  • Garn W. (2010) Issues in Operations Management. Pearson


  • Garn W. (2009) Chess with "Greedy Edi". http://www.mathworks.com/ : Matlab-Central


    You can play against "Edi" a chess program using Matlab. It uses a greedy heuristic to find the "best" move.

Working Papers

  • Garn W, Aitken J, Schmenner R. (2017) Smoothly Pass the Parcel: Implementing the Theory of Swift, Even Flow. Emerald
  • Louvieris P, Garn W. (2013) Novel conditional probability generation methods for high reliability effects-based decision making. Decision Support Systems,
    [ Status: Submitted ]

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