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The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

Transport logistics systems in companies with additional public service roles are complex and could benefit from new approaches to performance management. Existing approaches tend to be fragmented; thus, the purpose of this paper is to integrate balanced performance measures, a dynamics model, and the problem-solving method into a new model.

An integrated framework is developed by reviewing literature and synthesising attributes of performance measurement systems, system dynamics and problem-solving methods. The framework is then applied to a multiple-role company’s sea transportation system. The study uses statistical methods to identify performance indicators, management interviews with document study to develop a dynamics model, and simulation methods to formulate an improvement plan.

The performance measurement design stage allowed for the identification of balanced, aligned performance indicators, while the system dynamics model illuminated the impact of the system components’ interrelationships on performance output. The problem-solving method allowed for analysis of system performance, identification of constraints and formulation of a performance improvement plan.

This framework can help transport logistics system stakeholders in multiple-role companies avoid silo thinking, misaligned performance objectives, local optima and short-term solutions.

This study contributes to the existing body of research by introducing a novel framework integrating performance measurement, system dynamics and the problem-solving method. It also addresses a theoretical gap by showing how interconnecting components of sea transportation systems affect transport logistics performance.

The growth of firms is fundamentally based on selfreinforcing feedback loops, one of the most important of which involves cash flow.When profit margin is positive, sales generate cash, which may then be reinvested to finance the operating cash cycle.We analyze simulations of a sustainable growth model of a generic new venture to assess the importance of taxes, and regulatory costs in determining growth.The results suggest that new ventures are particularly vulnerable to public policy effects, since their working capital resource levels are minimal, and they have few options to raise external funds necessary to fuel their initial operating cash cycles.Clearly, this has potential consequences in terms of gaining competitive advantage from experience effects, word of mouth, scale economies, etc. The results of this work suggest that system dynamics models may provide public policy-makers a cost-effective means to meet the spirit of the U.S. Regulatory Flexibility Act

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

The main streams of the supply chain are defined as material, information and financial flow. There have been many studies and practical cases regarding the flow of material and information including information sharing. However, financial flow related studies have not been widely examined relatively, compared with their importance.

The information sharing is recognized as the method that can reduce the Bullwhip effect in supply chain management. The author intends to analyze the impact of financial information sharing on the results of the supply chain.

In the point of supply chain risk management view, the author examined the impact of financial flow among the various factors that can impede the stability of the supply chain.

In this study, the author embodied the simulation regarding the impact of financial information flow on supply chain performance and stability based on the system dynamics methodology and analyzed the performance.

Assuming the supply chain, composed of supplying company, manufacturing company and sales company , the author embodied the simulation model and assumed that working capital and cash information sharing were achieved. The author embodied the model to affect the settlement conditions according to the results of financial information sharing.

This paper applies systems thinking modelling to enhance the dynamic understanding of how to nurture an innovative culture in healthcare organisations to develop the innovation system in practice and speed up the innovative work. The model aims to provide a holistic view of a studied healthcare organisation's innovation processes, ranging from managerial values to its manifestation in improved results.

The study is based on empirical material from a healthcare unit that, within a few years, changed from having no innovations to repeatedly generating innovations. The study uses the modelling language of causal loop diagrams (CLDs) in the system dynamics methodology to identify the key important aspects found in the empirical material.

The proposed model, based on the stories of the interviewees, explores the dynamics of inertia when nurturing an innovative culture, identifying delays attributed to the internal change processes and system relationships. These findings underscored the need for perseverance when developing an innovative culture in the entrepreneurial phases.

The approach of using systems thinking to make empirical healthcare research results more tangible through the visual notations of CLDs and mental simulations is believed to support exploring complex phenomena to induce and nurture both individual and organisational learning.

The results from this approach provide deepened analysis and provoke the systems view to explain how the nurturing of the culture can accelerate the innovation processes, which helps practitioners and researchers to further expand their understanding of their healthcare contexts.

This paper aims to analyse the main variables and causal relationships in the system structure of the diversification of non-agricultural activities on agricultural holdings using system dynamics (SD) modelling. The SD model aims to simulate depictions of the behaviour of the real system while testing the effects of alternative decisions over time.

An SD methodology was chosen to model diversification in farm tourism.

A system approach increases the authors’ understanding of the transition of agricultural holdings to farm tourism. The results indicate that the transition to farm tourism depends on the level of tourism development in a certain area. The system is influenced by subsidies allocated by authorities to expand primary agricultural activities. The model describes a situation in which the tourism and agricultural industries have been affected by the COVID-19 pandemic.

The research is limited by the small set of available data due to the limited number of farms in Slovenia. One major problem is the difference in statistical data on the same activity collected from different institutions in Slovenia.

The paper includes implications for understanding the transition process to farm tourism, allowing policymakers to experiment with subsidies and promotion to explore the efficacy and efficiency of proposed policies.

This study provides a structured, systemic view of the diversification of non-agricultural activities on agricultural holdings, where the simulation results are a reliable reflection of the behaviour of the actual system being modelled.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines.

Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model.

In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft.

This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics.

A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC.

This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers’ understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses.