Antuela A. Tako

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.

Model development in discrete-event simulation and system dynamics: an empirical study of expert modellers

An empirical study comparing the model development process followed by experts in discrete-event simulation (DES) and system dynamics (SD) modelling is undertaken. verbal protocol analysis (VPA) is used to study the modelling process followed by ten expert modellers (5 SD and 5 DES). Participants are asked to build simulation models based on a case study and to think aloud while modelling. The generated verbal protocols are divided into seven modelling topics: problem structuring, conceptual modelling, data inputs, model coding, verification & validation, results & experimentation and implementation and then analyzed. Our results suggest that all modellers switch between modelling topics, however DES modellers follow a more linear progression. DES modellers focus significantly more on model coding and verification & validation, whereas SD modellers on conceptual modelling. Observations are made revealing some interesting differences in the way the two groups of modellers tackle the case. This paper contributes towards the comparison of DES and SD.

A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.

PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare

Discrete event simulation (DES) studies in healthcare are thought to benefit from stakeholder participation during the study lifecycle. This paper reports on a multi-methodology framework, called PartiSim that is intended to support participative simulation studies. PartiSim combines DES, a traditionally hard OR approach, with soft systems methodology (SSM) in order to incorporate stakeholder involvement in the study lifecycle. The framework consists of a number of prescribed activities and outputs as part of the stages involved in the simulation lifecycle, which include study initiation, finding out about the problem, defining a conceptual model, model coding, experimentation and implementation. In PartiSim four of these stages involve facilitated workshops with a group of stakeholders. We explain the organisation of workshops, the key roles assigned to analysts and stakeholders, and how facilitation is embedded in the framework. We discuss our experience of using the framework, provide guidance on when to use it and conclude with future research directions.

Comparing model development in discrete event simulation and system dynamics

This paper provides an empirical study on the comparison of model building in Discrete-Event Simulation (DES) and System Dynamics (SD). Verbal Protocol Analysis (VPA) is used to study the model building process of ten expert modellers (5 SD and 5 DES). Participants are asked to build a simulation model based on a prison population case study and to think aloud while modelling. The generated verbal protocols are divided into 7 modelling topics: problem structuring, conceptual modelling, data inputs, model coding, validation & verification, results & experimentation and implementation and then analyzed. Our results suggest that all modellers switch between modelling topics, however DES modellers follow a more linear progression compared to SD modellers. DES modellers focus significantly more on model coding and verification & validation, whereas SD modellers on conceptual modelling. This quantitative analysis of the processes followed by expert modellers contributes towards the comparison of DES and SD modelling.

A participative modelling framework for developing conceptual models in healthcare simulation studies

Conceptual modelling, one of the first stages in a simulation study, is about understanding the situation under study and deciding what and how to model. We argue that stakeholder involvement as part of conceptual modelling could lead to a more successful simulation study with better prospects for implementation. Our work is mainly applied in health care studies, which are characterized by many stakeholders with multiple views and objectives and often a politically charged environment. We develop a participative conceptual modelling framework, which uses tools from soft systems methodology, a problem structuring approach. The benefit of this approach lies in that it supports the conceptual modelling process by engaging stakeholders in a structured and participative way. It involves facilitated workshops, using a set of tools developed. A case study of the conceptual modelling process undertaken for an obesity system is provided to illustrate the proposed framework and tools.

Using a model of the performance measures in Soft Systems Methodology (SSM) to take action: a case study in health care

This paper uses a case study of a multidisciplinary colorectal cancer team in health care to explain how a model of performance measures can lead to debate and action in Soft System Methodology (SSM). This study gives a greater emphasis and role to the performance measures than currently given in typical SSM studies. Currently, the concept of performance measurement in SSM is in the form of a set of criteria used to judge the performance of the SSM model (the purposeful activity model). These performance criteria are definitions of efficacy, efficiency, effectiveness, ethicality and elegance. However, the use of performance measures within SSM is not clear and therefore criticized by some as nebulous. This paper uses a case study to explain how to involve the stakeholders in deriving a performance measurement model (PMM), which is a more detailed expansion of the performance criteria. The paper concludes with some reflections about how the PMM can fit in the SSM cycle, with two modes of practice put forward.

Is Simulation in Health Different

It is often stated that health simulation is quite different and even that it is more difficult than in other sectors. But is simulation in health really different to simulation elsewhere? In this paper we explore this question through a survey of simulation modellers and academics. We elicit their opinions across a range of factors concerning the difficulties of health modelling against modelling in other domains. The analysis considers the responses of the whole group of respondents and the sub-group of respondents who have experience both in and outside of health modelling. The results show that overall, there is a perception that health modelling is different and that it is more difficult across a range of factors. The implications for simulation research and practice in health are discussed.

Improving patient waiting times: a simulation study of an obesity care service

Background Obesity care services are often faced with the need to adapt their resources to rising levels of demand. The main focus of this study was to help prioritise planned investments in new capacity allowing the service to improve patient experience and meet future anticipated demand. Methods We developed computer models of patient flows in an obesity service in an Academic Health Science Centre that provides lifestyle, pharmacotherapy and surgery treatment options for the UKs National Health Service. Using these models we experiment with different scenarios to investigate the likely impact of alternative resource configurations on patient waiting times. Results Simulation results show that the timing and combination of adding extra resources (eg, surgeons and physicians) to the service are important. For example, increasing the capacity of the pharmacotherapy clinics equivalent to adding one physician reduced the relevant waiting list size and waiting times, but it then led to increased waiting times for surgical patients. Better service levels were achieved when the service operates with the resource capacity of two physicians and three surgeons. The results obtained from this study had an impact on the planning and organisation of the obesity service. Conclusions Resource configuration combined with demand management (reduction in referral rates) along the care service can help improve patient waiting time targets for obesity services, such as the 18 week target of UKs National Health Service. The use of simulation models can help stakeholders understand the interconnectedness of the multiple microsystems (eg, clinics) comprising a complex clinical service for the same patient population, therefore, making stakeholders aware of the likely impact of resourcing decisions on the different microsystems.

An experimental investigation into the role of simulation models in generating insights

It is often claimed that discrete-event simulation (DES) models are useful for generating insights. There is, however, almost no empirical evidence to support this claim. To address this issue we perform an experimental study which investigates the role of DES, specifically the simulation animation and statistical results, in generating insight (an ‘Aha!’ moment). Undergraduate students were placed in three separate groups and given a task to solve using a model with only animation, a model with only statistical results, or using no model at all. The task was based around the UKs NHS111 telephone service for non-emergency health care. Performance was measured based on whether participants solved the task with insight, the time taken to achieve insight and the participants’ problem-solving patterns. The results show that there is some association between insight generation and the use of a simulation model, particularly the use of the statistical results generated from the model. While there is no evidence that insights were generated more frequently from statistical results than the use of animation, the participants using the statistical results generated insights more rapidly.