A system dynamic simulation model for managing the human error in power tools industries

168 Impact of Connecticut’s Good Samaritan Laws in Preventing Opioid Overdose Deaths – An Applied System Dynamics Approach

Transport is a vast and complex socio-technical system, and despite a clear need to reduce dependence on fossil fuels due to undesirable environmental impacts, it is largely locked into business-as-usual. Systems approaches are a useful way to help make sense of multiple competing influences which may be simultaneously driving change and supporting the status quo. This paper applies qualitative system dynamics modelling to help interpret the results of a Delphi study into global transport transitions, involving 22 international experts in various aspects of transport. The main contribution of the paper is its exploration of the use of system dynamics (SD) modelling to interpret the Delphi findings. SD modelling was used to reveal and elucidate the causal arguments put forward by the expert panel about the factors driving business-as-usual, the factors creating barriers to more sustainable transport systems, and the drivers of change. The SD model is used to explore and expose the key causal patterns at play, and how these interact to both support and hinder change. The resulting model shows the complex, interdependent dynamics involved in supporting the status quo. Even at the relatively high level of analysis reported here, the model is useful in revealing interdependencies between parts of the system, where change in one part may well have knock-on effects elsewhere in the system. In particular the model reveals the strong reinforcing loops that act to minimise the impact of change drivers and thus retain the dominance of automobility. The result is a system that is highly dependent on the continued existence of key reinforcements such as policies that subsidise fossil fuels. From a methodological perspective, the outcomes of the Delphi study provided a rich source of qualitative material which was highly suitable for developing a system dynamics model.

Higher Education (HE) in South Africa has been recently affected by high staff turnover in the science faculties. The higher education system is under severe pressure given that the academic cohort of scientists comprises a majority of ageing white males. Thus, the higher education sector will have to ensure it attracts and retains scientific staff to address this potential crisis. High turnover sometimes occurs as a result of poaching from ‘sister institutions’ but, in the main, is due to staff losses to the private sector because universities cannot match the salaries offered in the private sector. A shortage of scarce skills has left universities with no option but to appoint foreigners in some faculties. This has a negative impact on staff retention. Furthermore, a career in academia is sometimes embraced through default rather than conscious choice. This results in lower productivity as there is little motivation for new staff to perform to their potential since they see academia as a stepping-stone to their ‘real career.’ Therefore, this study, sought to evaluate leadership approaches to talent development and retention using a qualitative system dynamics model. System dynamics modelling is a computer simulation approach to addressing social issues that is rooted in the agency-structure debate. The fundamental thesis of system dynamics is that the behaviour or observed phenomena in any system over time is largely determined by its structure and that effective changes or modifications can only be achieved through an understanding of the structure. The research was primarily qualitative, as system dynamics requires elicitation and understanding of the prevailing mental models in a system as the basis for developing a simulation model. System dynamics modelling primarily follows the systems thinking paradigm, which is a philosophical world view that is rooted in holism. The system thinking paradigm holds that all things and events inadvertently form part of a larger whole and they can be best understood within the context of this larger whole. A purposive sample of 30 members of staff from human resources management, executive management and members of the University Council was used to collect data through interviews and focus groups. Data was analysed using NVivo and a qualitative system dynamic modelling approach. The findings of the study led to identification of four consolidated themes, viz. talent development and retention; a system for talent management and retention; a process for talent management and retention, and recommendations for talent management and retention. The afore-mentioned themes were further categorised into nine sub-themes. The results show that several factors impact talent management, including bureaucracy and a lack of proper promotion processes. The findings from the system dynamics approach led to the formulation of four key variables: work pressurised induced turnover, resource dynamics for staff development, training dynamics and consultative strategic planning. The identified variables led to the development of an integrated SD-CLD model on leadership approaches to talent development and retention, which is a key contribution of this study. Therefore, institutions of higher learning must come up with talent development strategies that adhere to the prevailing laws such as the Employment Equity Act to achieve their strategic goals. A targeted training initiative is fundamental in developing the skills of academics in terms of teaching and learning and research. Hence, departmental courses, seminars and workshops for teaching and learning research excellence and general academic development need to be seriously debated within a given collegial, intellectual, and academic environment. This study has contributed to the literature on talent management, leadership, and retention from a university of technology where resource, constraints, culture, and politics play a crucial role on the final recipe. The study has further provided a springboard for altering or reviewing institutional policies so that the needs of employees are properly addressed, and the talent management framework is enhanced. Future research should consider expanding the scope of the study by other variables that are key to organisational effectiveness using a system dynamics approach.

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather. SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model. DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints. Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term. The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements. An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. A variety of data sources may be used in SD model development, and parametric sensitivity analysis with SD models can determine priority information needs in feedback-rich systems when data are lacking. Although numerous applications of SD modeling to agriculture exist, the approach is underutilized as a useful tool for research, instruction and programmatic development. This presentation highlights key elements of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model, and illustrates the benefits of the SD approach in animal nutrition research and for farm-level nutritional management decisions. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The presentation concludes with recommendations to increase awareness and training is SD methods to enhance its appropriate use in research and instruction.

In terms of having a comprehensive vision toward supplying the water requirements, a multi-criteria decision-making approach was employed on the Zarrine River Basin (ZRB) in the northwest of Iran. First, the climate change impacts were analyzed with the Long Ashton Research Station Weather Generator (LARS-WG) downscaling approach by using General Circulation Models (GCMs) including the European Consortium Earth System Model (EC-EARTH), Hadley Centre Global Environment Model version 2 (HADGEM2), Model for Interdisciplinary Research on Climate, version 5 (MIROC5), and Max Planck Institute Earth System Model (MPI-ESM), from Coupled Model Intercomparison Project 5 (CMIP5) under Representative Concentration Pathway (RCP4.5, RCP8.5) scenarios for 2021–2080. Afterward, the downscaled variables were utilized as inputs to the Artificial Neural Network (ANN) model to predict future runoff under the climate change impact. Finally, the system dynamics (SD) model was employed to simulate various scenarios for assessing water balance utilizing the Vensim software. The results of downscaling models suggested that the temperature of the basin will increase by 0.47 and 0.91 °C under RCPs4.5 and 8.5 by 2040, respectively. Additionally, the precipitation will decrease by 3.5 percent under RCP4.5 and 14 percent under RCP8.5, respectively. Moreover, simulation results revealed that the water demand in various sectors will be enormously increased. The contribution of the climate change impact on the future run-off was a seven percent decrease, on average, over the basin. The SD model, according to presented plausible scenarios including decreasing agriculture product and shifting irrigation efficiency, cloud-seeding, population control, and household consumption reduction, reducing meat and animal-husbandry production, and groundwater consumption control, resulted in a water balance equilibrium over five years. However, the performance of individual scenarios was not effective; instead, a combination of several scenarios led to effective performance in managing reduced runoff under climate change.

Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model

Forecasting unemployment and employment: A system dynamics approach

PurposeThe purpose of this research is to investigate sustainable strategies for skills development that is specific to the youth of South Africa. International and South African data are statistically analysed and quantified to provide inputs for the systems dynamics (SD)-based predictive skills model. The skills model simulates the impact of barriers and drivers on youth skills development towards identification of focus areas for improvement.Design/methodology/approachThe research adopts a mixed-methods approach. The study begins with an explorative literature study on skills development, with the findings applied in developing (1) South African specific research instruments for small, medium and micro enterprises (SMMEs) and skills programme grant recipients and (2) a conceptual framework of the SD predictive skills model. The responses to the South African specific instruments are analysed via confirmatory factor analysis (CFA), which quantifies the input coefficients to the system dynamics model. To quantify the global inputs for the SD model, an in-depth literature review of the global skills development initiatives is conducted. The SD model output on skills, for the South African inputs, is comparatively evaluated against global inputs.FindingsThe paper details the results of the literature analysis, instrument analyses, CFA and SD model. The instrument results rank experience, skills and interactions with experts and work-based learning as most important. South African and global learners identify networking as the primary medium for identifying training and employment opportunities. South African and global learners also identify qualifications and work-based experience as key to finding employment. The quantified results of the SA and global analysis are used as inputs in the SD model to deliver a forecasting tool. The SD model finds that the global data provide for better development of the skills base than the South African inputs. The key focus areas identified for improvement in South Africa include networking, work-based experience and a reduction in administrative requirements.Originality/valueThe research's originality resides in the ability to predict the impact of drivers and barriers on skills development. This research sought to transform qualitative global and South African inputs into a consolidated, predictive systems-based model. The SD model can be adopted as an indicator of drivers and barriers focused towards the optimisation of skills development.

System Dynamics (SD) modelling is a highly complex process. Although the SD methodology has been discussed extensively in most breakthroughs and present literature, discussions on data collection methods for SD modelling are not explained in details in most studies. To date, comprehensive descriptions of knowledge extraction for SD modelling is still scarce in the literature either. In an attempt to fill in the gap, three primary groups of data sources proposed by Forrester: (1) mental database, (2) written database and (3) numerical database, were reviewed, including the potential data collections methods for each database by taking into account the advancement of current computer and information technology. The contributions of this paper come in threefolds. First, this paper highlights the potential data sources that deserved to be acknowledged and reflected in the SD domain. Second, this paper provides insights into the appropriate mix and match of data collection methods for SD development. Third, this paper provides a practical synthesis of potential data sources and their suitability according to the SD modelling stage, which can serve as modelling practice guidelines.

A system dynamics approach to the efficiency thoroughness tradeoff

A study of regional sustainable development based on GIS/RS and SD model — Case of Hadaqi industrial corridor

An Early Warning System of Water Shortage in Basins Based on SD Model

The purpose of this paper is to model dynamic feedback effects and complex interactions among risks affecting foodgrains transportation using a system dynamics approach. The risk scenario is simulated using a system dynamics model considering risk index values. It has been observed that there is a significant increase in inventory level from 8.39% to 28.4% and vehicle capacity from 8.99% to 28.4% with a change in risk value by 30%. This can help managers to develop inventory and transportation policies. It will, therefore, help to generate risk reduction scenarios for better availability of foodgrains through integrated risk control and mitigation in a supply chain. The key findings drawn for transportation and inventory management of foodgrains will help policy-makers to improve the efficiency of foodgrains supply chain. This research uniquely analyses the dynamic implication of inventory-transportation policies on foodgrains transportation system using system dynamics modelling approach.

The big data in Accident and Emergency services in hospitals worth being analyzed to provide clinical decision support to clinicians and medical information to patients. System Dynamics modelling is a technique used for modelling complex behaviors of organizational and social systems. In this study, a system dynamics approach is used to model the patient flow in Accident and Emergency in Hong Kong. The study aims to examine the trade-offs of various safety and quality outcomes in an Emergency Department: waiting time and occupancy (acute beds and waiting room) in primary, on the adjustments of various factors (e.g. Admission volumes and staff numbers) in order to evaluate how an accident and emergency system in Hong Kong could be more efficiently operated.