Stewart Robinson

Conceptual modelling for simulation Part I: definition and requirements

Conceptual modelling is probably the most important aspect of a simulation study. It is also the most difficult and least understood. Over 40 years of simulation research and practice have provided only limited information on how to go about designing a simulation conceptual model. This paper, the first of two, discusses the meaning of conceptual modelling and the requirements of a conceptual model. Founded on existing literature, a definition of a conceptual model is provided. Four requirements of a conceptual model are described: validity, credibility, utility and feasibility. The need to develop the simplest model possible is also discussed. Owing to a paucity of advice on how to design a conceptual model, the need for a conceptual modelling framework is proposed. Built on the foundations laid in this paper, a conceptual modelling framework is described in the paper that follows.

Measuring service quality: current thinking and future requirements

The publication of the first results of the SERVQUAL instrument provoked a debate on how best to measure service quality. With more than a decade since the publication of those results many researchers have attempted to demonstrate the efficacy, or not, of the SERVQUAL instrument, or to develop their own measurement methods. This paper reviews this debate in relation to six key aspects: the purpose of the measurement instrument; the definition of service quality; models for service quality measurement; the dimensionality of service quality; issues relating to expectations; and the format of the measurement instrument. The main areas of agreement and disagreement are identified. As a result the continued use of the SERVQUAL instrument is called into question, and areas for further research are identified.

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM.

Discrete-event simulation: from the pioneers to the present, what next?

Discrete-event simulation is one of the most popular modelling techniques. It has developed significantly since the inception of computer simulation in the 1950s, most of this in line with developments in computing. The progress of simulation from its early days is charted with a particular focus on recent history. Specific developments in the past 15 years include visual interactive modelling, simulation optimization, virtual reality, integration with other software, simulation in the service sector, distributed simulation and the use of the worldwide web. The future is then speculated upon. Potential changes in model development, model use, the domain of application for simulation and integration with other simulation approaches are all discussed. The desirability of continuing to follow developments in computing, without significant developments in the wider methodology of simulation, is questioned.

Simulation Maidel Verification And Validation: Increasing The Users' Confidence

This paper sets simulation model verification and validation (V&V) in the context of the process of performing a simulation study. Various different forms of V&V need to take place depending on the stage that has been reached. Since the phases of a study are performed in an iterative manner, so too are the various forms of V&V. A number of difficulties with verifying and validating models are discussed, after which a series of V&V methods are described. V&V is seen as a process of increasing confidence in a model, and not one of demonstrating absolute accuracy.

Business process modelling and analysis using discrete-event simulation

Globalisation and competitive pressure urge many organisations to radically change business processes. Although this approach can provide significant benefits such as reducing costs or improving efficiency, there are substantial risks associated with it. Using simulation for modelling and analysis of business processes can reduce that risk and increase the chance for success of business process re-engineering projects. This paper investigates the potential of simulation modelling to be used for modelling business processes and supports the case for a wider use of simulation techniques by the business community. Following a discussion on business process modelling methods and tools, the usability of simulation modelling for evaluating alternative business process strategies is investigated. Examples of simulation models representing business processes are presented and discussed.

Soft with a hard centre: discrete-event simulation in facilitation

Discrete event simulation is normally described as a ‘hard’ OR technique. This may not, however, always be the case. An example of a simulation of a user support helpline is described which, it is argued, has many of the traits of a ‘soft’ OR intervention. In particular, the study involved a facilitated discussion around a simulation model about possible improvements to a problem situation. The nature of the intervention is considered from both a methodological and paradigmatic perspective, and conclusions are drawn about where the intervention lies on a ‘hard’ to ‘soft’ continuum. It is argued that ‘soft’ issues need to be subsumed into the prescribed methodology for discrete-event simulation.

Conceptual modelling for simulation Part II: a framework for conceptual modelling

Following on from the definition of a conceptual model and its requirements laid out in a previous paper, a framework for conceptual modelling is described. The framework consists of five iterative activities: understanding the problem situation, determining the modelling and general project objectives, identifying the model outputs, identify the model inputs, and determining the model content. The framework is demonstrated with a modelling application at a Ford Motor Company engine assembly plant. The paper concludes with a discussion on identifying data requirements from the conceptual model and the assessment of the conceptual model.

Conceptual modeling for simulation: issues and research requirements

It is generally recognized that conceptual modeling is one of the most vital parts of a simulation study. At the same time, it also seems to be one of the least understood. A review of the extant literature on conceptual modeling reveals a range of issues that need to be addressed: the definition of conceptual model(ling), conceptual model requirements, how to develop a conceptual model, conceptual model representation and communication, conceptual model validation, and teaching conceptual modeling. It is clear that this is an area ripe for further research, for the clarification of ideas and the development of new approaches. Some areas in which further research could be carried out are identified

SimLean: utilising simulation in the implementation of lean in healthcare

Discrete-event simulation (DES) and lean are approaches that have a similar motivation: improvement of processes and service delivery. Both are being used to help improve the delivery of healthcare, but rarely are they used together. This paper explores from a theoretical and an empirical perspective the potential complementary roles of DES and lean in healthcare. The aim is to increase the impact of both approaches in the improvement of healthcare systems. Out of this exploration, the ‘SimLean’ approach is developed in which three roles for DES with lean are identified: education, facilitation and evaluation. These roles are demonstrated through three examples of DES in action with lean. The work demonstrates how the fusion of DES with lean can improve both stakeholder engagement with DES and the impact of lean.