A System Dynamics-Based Simulation Experiment for Testing Mental Model and Performance Effects of Using the Balanced Scorecard

This study develops a theoretical model that explains the effectiveness of the balanced scorecard approach by means of a system dynamics and feedback learning perspective. Presumably, the balanced scorecard leads to a better understanding of context, allowing managers to externalize and improve their mental models. We present a set of hypotheses about the influence of the balanced scorecard approach on mental models and performance. A test based on a simulation experiment that uses a system dynamics model is performed. The experiment included three types of parameters: financial indicators; balanced scorecard indicators; and balanced scorecard indicators with the aid of a strategy map review. Two out of the three hypotheses were confirmed. It was concluded that a strategy map review positively influences mental model similarity, and mental model similarity positively influences performance.

A system dynamics-based simulation

This study develops a theoretical model that explains the effectiveness of the balanced scorecard approach by means of a system dynamics and feedback learning perspective. Presumably, the balanced scorecard leads to a better understanding of context, allowing managers to externalize and improve their mental models. We present a set of hypotheses about the influence of the balanced scorecard approach on mental models and performance. A test based on a simulation experiment that uses a system dynamics model is performed. The experiment included three types of parameters: financial indicators; balanced scorecard indicators; and balanced scorecard indicators with the aid of a strategy map review. Two out of the three hypotheses were confirmed. It was concluded that a strategy map review positively influences mental model similarity, and mental model similarity positively influences performance.

Numerical methods in multibody system dynamics

"Series: Solid mechanics and its applications, vol. 226"

On the frictional contacts in multibody system dynamics

"Series title: Computational methods in applied sciences, ISSN1871-3033, vol. 42"

Forearm surface EMG signals recognition and muscoloskeletal system dynamics identification using intelligent computational methods

Biosignals processing, Biological Nonlinear and time-varying systems identification, Electomyograph signals recognition, Pattern classification, Fuzzy logic and neural networks methods

Combining Advanced Forecasting Methods with System Dynamics - the Case of Finnish Seaports

Seaports play an important part in the wellbeing of a nation. Many nations are highly dependent on foreign trade and most trade is done using sea vessels. This study is part of a larger research project, where a simulation model is required in order to create further analyses on Finnish macro logistical networks. The objective of this study is to create a system dynamic simulation model, which gives an accurate forecast for the development of demand of Finnish seaports up to 2030. The emphasis on this study is to show how it is possible to create a detailed harbor demand System Dynamic model with the help of statistical methods. The used forecasting methods were ARIMA (autoregressive integrated moving average) and regression models. The created simulation model gives a forecast with confidence intervals and allows studying different scenarios. The building process was found to be a useful one and the built model can be expanded to be more detailed. Required capacity for other parts of the Finnish logistical system could easily be included in the model.

Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Understanding construction competitiveness: the contribution of system dynamics

Purpose – Construction sector competitiveness has been a subject of interest for many years. Research too often focuses on the means of overcoming the “barriers to change” as if such barriers were static entities. There has been little attempt to understand the dynamic inter-relationship between the differing factors which impinge upon construction sector competitiveness. The purpose of this paper is to outline the benefits of taking a systems approach to construction competitiveness research. Design/methodology/approach – The system dynamics (SD) modelling methodology is described. This can provide practitioners with “microworlds” within which they can explore the dynamic effects of different policy decisions. The data underpinning the use of SD was provided by interviews and case study research which allowed an understanding of the context within which practitioners operate. Findings – The over-riding conclusion is that the SD methodology has been shown to be capable of providing a means to assess the forces which shape the sustained competitiveness of construction firms. As such, it takes the assessment of strategic policy analysis in the construction sector onto a higher plane. The need to collect data and make retrospective assessments of competitiveness and strategic performance at the statistical level is not now the only modus operandi available. Originality/value – The paper describes a novel research methodology which points towards an alternative research agenda for construction competitiveness research.

Exploring behavioural change through an agent-oriented system dynamics model: the use of personal protective equipment among pesticide applicators in Colombia

The misuse of Personal Protective Equipment results in health risk among smallholders in developing countries, and education is often proposed to promote safer practices. However, evidence point to limited effects of education. This paper presents a System Dynamics model which allows the identification of risk-minimizing policies for behavioural change. The model is based on the IAC framework and survey data. It represents farmers' decision-making from an agent-oriented standpoint. The most successful intervention strategy was the one which intervened in the long term, targeted key stocks in the systems and was diversified. However, the results suggest that, under these conditions, no policy is able to trigger a self sustaining behavioural change. Two implementation approaches were suggested by experts. One, based on constant social control, corresponds to a change of the current model's parameters. The other, based on participation, would lead farmers to new thinking, i.e. changes in their decision-making structure.

The emergence and use of diagramming in system dynamics: a critical account

The paper reviews the leading diagramming methods employed in system dynamics to communicate the contents of models. The main ideas and historical development of the field are first outlined. Two diagramming methods—causal loop diagrams (CLDs) and stock/flow diagrams (SFDs)—are then described and their advantages and limitations discussed. A set of broad research directions is then outlined. These concern: the abilities of different diagrams to communicate different ideas, the role that diagrams have in group model building, and the question of whether diagrams can be an adequate substitute for simulation modelling. The paper closes by suggesting that although diagrams alone are insufficient, they have many benefits. However, since these benefits have emerged only as ‘craft wisdom’, a more rigorous programme of research into the diagrams' respective attributes is called for.

System dynamics mapping of acute patient flows

Department of Health staff wished to use systems modelling to discuss acute patient flows with groups of NHS staff. The aim was to assess the usefulness of system dynamics (SD) in a healthcare context and to elicit proposals concerning ways of improving patient experience. Since time restrictions excluded simulation modelling, a hybrid approach using stock/flow symbols from SD was created. Initial interviews and hospital site visits generated a series of stock/flow maps. A ‘Conceptual Framework’ was then created to introduce the mapping symbols and to generate a series of questions about different patient paths and what might speed or slow patient flows. These materials formed the centre of three workshops for NHS staff. The participants were able to propose ideas for improving patient flows and the elicited data was subsequently employed to create a finalized suite of maps of a general acute hospital. The maps and ideas were communicated back to the Department of Health and subsequently assisted the work of the Modernization Agency.

Theory building with system dynamics: topic and research contributions

The article discusses various reports published within the issue, including the articles "Closing the Loop: Promoting Synergies with other Theory Building Approaches to Improve System Dynamics Practice," by Birgit Kopainsky and Luis Luna-Reyes, and "On improving dynamic decision-making: Implications from multiple-process cognitive theory," by Bent Bakken.