Integrated System Dynamics Research Articles

Integrated system dynamics modeling and optimization for artisanal and small-scale gold supply chains

We address artisanal and small-scale gold mining in Peru. Using the data collected through field visits, we develop a System Dynamics (SD) model to examine the current operations of the gold supply chain, and its interplay with mercury and gas suppliers. The current mining operations cause potential health and environmental issues, due to high mercury usage, and other issues (e.g., safety issues). As a potential solution to overcome such problems, we consider incorporating a cyanide processing facility into the SD model, where we examine the amount of ore that a miner would be willing to sell to the facility under several payback scenarios. We formulate a mixed-integer programming model that prescribes the optimal transition plan of a miner from local production to selling their ore to the facility within a time horizon that maximizes a miner’s profit. The optimal solution provides a 22% improvement in the miners’ profit over that of their current operations. We also conduct a sensitivity analysis to test the sensitivity of the optimal solution to the changes in several input parameters. We discuss that the solution is most sensitive to the gold price changes and the least sensitive to the local production cost changes.

Model the complexities in inventories: the case of Tradeasia

Learning outcomes Upon completion of the case study, the students will be able to find the challenges and underlying structures that cause the problem; the students will be able to identify the dynamic variables and develop the interconnection and interlinkages among the time-delayed variables to build the story of the business case; the students could develop the block diagram and could build the system dynamics model using the simulation software STELLA, and if they do not have the simulation software, even then they could have a mental model to understand the problem well; the system dynamics students can design the policies to make the system better behaved and recommend solutions; and the students could make mind maps and develop the mental model and could recommend solutions and way forward to overcome the challenges and solve the issues. Case overview/synopsis Tradeasia is a small-scale manufacturing firm that had started its business activities near Sundar Industrial Estate, Raiwind, in September 2007. The company’s prime focus was to buy the potato starch from chips manufacturing companies and, then, extract the potato starch from the waste potato using its own machinery and sell it as a sizing agent to textile mills. Quality characteristics in terms of better millage and enhanced gullibility made it compatible with Rafhan corn-based starch. The major challenge linked to potato starch was its degree of wetness; the potato starch either extracted from rotten potato or procured from the potato chips manufacturing companies had a high degree of wetness and moisture content. Wet potato starch sometimes had more than 60% moisture content, which was really a challenge. Owing to the high degree of wetness, the wet starch was prone to fungus growth, and within hours, the fungus created toxins if it was not dried immediately, and then after 24 h, toxins acquired a black colour, and they became hardened like pebbles. The starch then was unusable even for sizing purposes for textile products. Reduction in the degree of wetness was really a big challenge and demanded prompt action and high productivity of the operational staff to make that product dry for sale purposes. This was the biggest challenge that ended up in huge inventories of wet starch. Capacity constraints and operational inefficiency killed the company’s productivity and affected the company’s profit. Complexity academic level This case study is written and developed for MBA and MS-level supply chain students of the system dynamics course or those studying management of supply chain complexities. This case study discusses the operational challenges while running the business; huge inventories, capacity constraints and inefficiency in production operations were the challenges associated with almost all manufacturing industries. This case study discussed not only why such challenges are appearing in the business but also the solution that resided in the wisdom shared by the employees in the board meeting. An integrated system dynamics model could be used to design the policies to overcome such challenges. Even the block diagram of the model and causal loop diagram could help to conceptualize the problem and explore the way forward. Supplementary materials Teaching notes are available for educators only. Subject code CSS 7: Management science.

Integrating system dynamics and scenarios: A framework based on personal experience

AbstractSystem dynamics (SD) is a methodology to generate qualitative and quantitative models. SD has two main concepts that are highly suitable to use with scenarios: feedback processes that define the structure of sociotechnical systems and accumulation processes that are responsible for the dynamic behavior of systems over time. This article discusses a framework that integrates methodologically scenarios and SD. The integration can take multiple forms depending on the use of SD for creating or supporting scenarios. The framework is illustrated with multiple examples. Since SDs' practice uses processes similar to scenario practice, mutual enrichment between the communities can be highly successful.

The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach

Open innovation in data science generally takes the form of public competitions where teams exchange messages and solutions by competing and collaborating simultaneously. Team behaviours are widely heterogeneous in terms of the performance of their solutions and the participation in knowledge creation. We present a novel research framework for open innovation by integrating system dynamics and structural topic modelling to extract open factors and adopting a machine learning-based difference-in-differences estimator to understand the impact of team behaviour on their performance using data from Kaggle's competition. Our results identify four team behaviour categories—active, learner, lurker, and passive— in data science open innovation competitions which depend on the performance of their solutions and actions related to posting and reading messages in the forum. Furthermore, the activities of model evaluation, community support, and business understanding are the top three most positive and significant factors affecting team performance. Our research contributes to the literature by highlighting the value of forum feedback and exploring the data science activities in the forum discussion, in relation to innovation performance, to enrich the empirical understanding of open innovation. Research implications for researchers and practitioners participating in, organising, and supporting data science open innovation activities are provided.

Exploring policy options to transform traditional dairy system in Ethiopia: A system dynamics approach

Despite having a larger herd size and a favourable climate, Ethiopia generally lags behind developing nations in terms of both production and consumption of dairy products. For the purpose of finding and assessing ways to increase milk production by the traditional dairy system in Ethiopia's West Shewa zone, we integrated system dynamics (SD) modelling with a participatory model building approach. The main objective of the research was to develop SD model for West Shewa dairy value chain and apply it to assess ex-ante milk production and dairy household profit impacts of various interventions. The interventions evaluated in the study include enhanced urea treatment of crops residue, increased production of improved feed, and investment in more dairy cows. Analysis reveals that policies targeting feed development can boost milk production and household profits above the baseline. They also lead to a higher seasonal variability in milk production. Through the feed development strategy producers can achieve a 70 % and 735 % increase in milk production and household profit, respectively. However, the implementation of improved feed policy still leaves a gap in the average feed protein requirement of a dairy cattle in the study area. On the other hand, policy of increasing cow herd is not profitable. In addition, improved feed policy in drought results in reduced herd size, yet it leads to a higher milk output and household profit. Hence, with the feed development options explored in this study, increasing herd size is not a recommended course of action for improvement of the West Shewa dairy value chain. Therefore, future research should explore for further enhancement of the supply as well as quality of feed resources and the potential of investment in improved breeds.

Widening the Lens: Reflecting on the Mixing of System Dynamics With Action Research Alongside Work Within the Problem Structuring Methods Field Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research".

This commentary reflects upon the insights for improvement cases reported by Holmström et al where they consider the integration of action research (AR)-a research methodology-and system dynamics (SD)-a modelling technique-to manage the complexity of patient care pathways. Whilst this combination can be effective, recognising that SD is a simulation model whereas AR is a research approach is important for both practical and conceptual reasons. In addition, some of the benefits noted can also be achieved through taking a wider examination of modelling techniques, particularly problem structuring methods (PSMs) as SD has been considered a PSM and PSMs are designed to effectively engage multi-disciplinary group members in the search for solutions as this will provide further avenues for both engagement and learning.

A methodology for parameter estimation in system dynamics models using artificial intelligence

AbstractMultiple tools exist for separately simulating and estimating the parameters of system dynamics models. Artificial intelligence (AI) has been increasingly used to estimate the parameters of system dynamics models. The development of modeling tools and advanced environments has resulted in great benefits to the community at large. The incorporation of AI tools into system dynamics presents opportunities for expanding on current decision‐making methods. As systems become complex, the need to incorporate evidence‐based data‐driven methods increases. By integrating system dynamics tools and facilitating AI and system dynamics simulation in an integrated environment, model parameters can be estimated with the latest data, and the integrity of the model can be retained effectively. This provides an advantage to the efficiency and capabilities of the system dynamics model and its analysis. This paper presents a general methodology to incorporate regression AI into system dynamics models for simulation and analysis. To demonstrate the validity of the methodology, a case study involving a susceptible‐infected‐recovered model and empirical data from the COVID‐19 pandemic is performed using support vector machines (SVMs), artificial neural networks (ANNs), and random forests.

Mapping the effect of antimicrobial resistance in poultry production in Senegal: an integrated system dynamics and network analysis approach.

The impact of antimicrobial resistance (AMR) extends beyond the farm-level to other stakeholders warranting the need for a collaborative approach to combat AMR while optimising production objectives and safeguarding human health. This study maps out the effect of AMR originating from poultry production in Senegal and highlights the entry points for interventions from stakeholders' perspectives. A causal loop diagram (CLD) was developed following a group model building procedure with 20 stakeholders and integrated with network analysis by translating the CLD into an unweighted directed network. Results indicate that with an eigenvector centrality of 1, 0.85, and 0.74, the production cost, on-farm profit, and on-farm productivity, respectively are the most ranked influential variables driving the complexity of AMR in the poultry production system. Two reinforcing feedback loops highlight the dual benefits of improving on-farm productivity and increasing on-farm profit. However, one balancing feedback loop that revolves around the causal link between producers' investment in qualified human resource personnel to ensure good farm management practices underline the financial implication of producers' investment decisions. The findings provide precursory groundings for the development of a quantitative SD model, the formulation of intervention scenarios and ex-ante impact assessment of the cost-effectiveness of the interventions.

Integrating System Dynamics and Action Research: Towards a Consideration of Normative Complexity Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research".

Holmström and co-authors argue for the value of integrating system dynamics into action research to deal with increasing complexity in healthcare. We argue that despite merits, the authors overlook the key aspect of normative complexity, which refers to the existence of multiple, often conflicting values that actors in healthcare systems have to pragmatically develop responses to in their daily practices. We argue that a better theoretical and empirical understanding of the multiplicity of values and how actors deal with value conflicts in daily practices can enrich discussions about complexity in healthcare. We introduce the alternative methodology of 'value exnovation' for action researchers to broaden the scope of system-based thinking and action research in healthcare.

Nonlinear tribodynamics of an elastic shaft with a flexible bearing outer race

In this paper, a mathematical model of a rotor-bearing system is presented. The model includes modal elastodynamics of a flexible rotor as well as the in-plane radial dynamics of the bearing with a flexible outer race. Elastodynamics of the flexible shaft utilises a solution based on Green's function to provide a computationally efficient approach. The flexible bearing outer race is modelled using Timoshenko beam theory. The system model also includes detailed lubricated contact mechanics of balls-to-races contacts with viscous friction. Therefore, the rotor-bearing analysis represents a detailed multi-physics tribodynamics and modal elastodynamic responses of the system which closely represents broad-band vibration response of such systems in practice, an approach not hitherto reported in the literature. It is also demonstrated that the outer race flexibility changes the location of the stability orbital centres, as well as the spread of limit cycle vibrations. Furthermore, it accentuates the occurrence of multiples of ball pass frequency. The importance of integrated system dynamics and lubricated contact mechanics is highlighted, showing that although the elastodynamic response of the rotor's flexible elements may not be clear in the acquired vibration signal, its effect on energy efficiency of the system can be quite important.

Modelling a System to Help Improve It Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research".

The article that this commentary considers describes the use of systems modelling in an action research (AR) project that helped improvement teams to understand the dynamics of their service as a system. This commentary seeks to make the complex article easier to understand for those unfamiliar with the subjects. It describes the advantages, disadvantages and benefits, and suggests developments of this approach for research and practice using digital technologies. The conclusion of the commentary is that dynamic system modelling combined with AR is useful for certain purposes and can produce benefits in terms of a more sophisticated understanding of systems and feedback loops for practitioners. However, there are challenges for researchers unfamiliar with AR and dynamic system modelling as well workshop facilitation expertise.

A Pragmatic and Systemic Approach to Advance Research in Health Policy and Management Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research".

"Healthcare is complex" - or similar sentences - is a statement that introduces a wide number of scientific articles dealing with health policy and management issues. We all agree that healthcare is complex, but most studies, although using this kind of sentence to introduce their background, do little to effectively deal with such complexity in their analyses. Holmström et al proposed a methodological approach to tackle healthcare complexity by integrating system dynamics (SD) into action research (AR). This commentary highlights three touch points that makes the combination of AR and SD feasible, namely the epistemological ground, the use of experimentation and the collaborative approach. The proposed approach addresses some of the key sources of the complexity characterizing healthcare settings.

Dynamic Analysis and Active Stabilization of a Utility-Scale Grid-Connected Current-Source Inverter-Based PV System Considering Source Dynamics

This paper investigates the effects of the dynamic properties of a photovoltaic generator (PVG) on the stability of a grid-connected current-source inverter (CSI)-based photovoltaic system and presents a simple yet effective active compensator to ensure system stability. A detailed equivalent model of the PVG that considers the dynamic resistance, diffusion capacitance, series inductance, and dc cable resistance and inductance is used, and the effects of these parameters are assessed. It has been found that when the PVG operates in the constant-current region (CCR), the phase margin becomes negative in the control-to-input dynamics, which alters the stability of the dc-link current control. The change in the system dynamics when the operating point of the PVG shifts from the constant-voltage region (CVR) to CCR is verified through a detailed small-signal state-space model of the complete system. Furthermore, an active compensator is proposed to ensure stable dc-link current control operation at all operating points of the PVG. The influence of different control parameters on the integrated system dynamics is examined to determine the range of the control parameters required for stable operation. The performance of the proposed system under utility-grid fault, grid voltage parameter variation, weak grid, and changes in insolation level is also evaluated. Detailed nonlinear time-domain simulation results, using a typical utility-scale CSI-based PV system, validate the analytical results and the effectiveness of the proposed active stabilization under different operating conditions.

Closing the Implementation Gap Comment on "Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research".

Holmström et al provide an interesting and thought-provoking contribution to a perennial problem: why, despite a vast number of applications of simulation modelling in healthcare over the past 70 years, there is still remarkably little evidence of successful implementation of model results. Their paper is a retrospective analysis of five case studies, all undertaken as consultancy, that used a blend of system dynamics (SD) modelling and action research (AR). This commentary assesses the effectiveness of this approach in achieving implementation, based on the evidence presented, and discusses some of the issues raised. These issues include a comparison of Holmström's approach with group model building (GMB) in SD, the differences between healthcare modelling projects undertaken by (a) business consultants and (b) academics, and the challenges of undertaking 'systematic' reviews of the grey literature.

An Integrated System Dynamics Model and Life Cycle Assessment for Cement Production in South Africa

Cement is one of the most produced materials globally. Population growth and urbanization cause an increased demand for the cement needed for expanding infrastructures. As a result of this circumstance, the cement industry must find the optimum compromise between increasing cement production and reducing the negative environmental impact of that production. Since cement production uses a lot of energy, resources and raw materials, it is essential to assess its environmental impact and determine methods for the sector to move forward in sustainable ways. This paper uses an integrated life cycle assessment (LCA) and a system dynamics (SDs) model to predict the long-term environmental impact and future dynamics of cement production in South Africa. The first step used the LCA midpoint method to investigate the environmental impact of 1 kg of Portland cement produced in South Africa. In the cement production process, carbon dioxide (CO2), nitrogen oxides (NOx), sulphur dioxide (SO2), methane (CH4) and particulate matter (PM) were the major gases emitted. Therefore, the LCA concentrated on the impact of these pollutants on global warming potential (GWP), ozone formation, human health, fine particulate matter formation and terrestrial acidification. The system dynamics model is used to predict the dynamics of cement production in South Africa. The LCA translates its results into input variables into a system dynamics model to predict the long-term environmental impact of cement production in South Africa. From our projections, the pollutant outputs of cement production in South Africa will each approximately double by the year 2040 with the associated long-term impact of an increase in global warming. These results are an important guide for South Africa’s future cement production and environmental impact because it is essential that regulations for cement production are maintained to achieve long-term environmental impact goals. The proposed LCA–SD model methodology used here enables us to predict the future dynamics of cement production and its long-term environmental impact, which is the primary research objective. Using these results, a number of policy changes are suggested for reducing emissions, such as introducing more eco-blended cement productions, carbon budgets and carbon tax.

The Importance of Prevention in Tackling Desertification: An Approach to Anticipate Risks of Degradation in Coastal Aquifers

Groundwater degradation is a major issue on an increasingly hot and thirsty planet. The problem is critical in drylands, where recharge rates are low and groundwater is the only reliable resource in a context of water scarcity and stress. Aquifer depletion and contamination is a process of desertification. Land Degradation Neutrality is regarded as the main initiative to tackle land degradation and desertification. It is embedded in target 15.3 of the Sustainable Development Goals and focused on preventing these dynamics. Within this framework, we present an approach to assess risks of degradation and desertification in coastal basins with aquifers threatened by seawater intrusion. The approach utilizes an integrated system dynamics model representing the main relationships between the aquifer and an intensively irrigated area (greenhouses) driven by short- and medium-term profitability. The study area is located in a semi-arid region in Southern Spain, the Gualchos stream basin, which contains the Castell de Ferro aquifer. We found that the risk of salinization of the aquifer is 73%, while there is a 70% risk that the system would increases its demand for surface water in the future, and the chance of doubling the current demand is almost 50%. If the current system of reservoirs in the area were not able to satisfy such an increase in demand because of climate change, the basin would be at a serious risk of desertification.

An integrated System Dynamics model for Closed Loop Supply Chains under disaster effects: The case of COVID-19.

For a Closed Loop Supply Chain (CLSC), disaster is a risk source of unknown-unknowns, which may result in production disruptions with significant consequences on -but not limited to-profitability. For this reason, we provide a System Dynamics (SD)-based analysis for disaster events on the operation of CLSCs in order to study the system response (production/collection/disassembly/remanufacturing/recycling rates, inventories, cost, profit). This response is examined through the dynamics at a manufacturer, parts producer, collector, and disassembly center level, by providing control mechanisms for resilient CLSCs under disaster effects. In this dynamic analysis, COVID-19 is treated as a disaster event. Five different business scenario settings are presented for the manufacturer, which are considered as alternative mitigation policies in responding to product demand. The extensive simulation results provide insights for policy-makers, which depend on the reduction in manufacturer's production, reduction in product demand and duration of recovery period which are considered as causal effects due to the COVID-19 outbreak. For all combinations, holding base stocks during the pre-disaster period is proposed as the best mitigation policy in terms of manufacturer's inventory. In terms of economic impact, holding base stocks or coordination with third party are revealed as the best choice depending on the combination, while remote inventory policy adoption as the worst choice.

Modeling the effects of ecosystem changes on seagrass wrack valorization: Merging system dynamics with life cycle assessment

Seagrass meadows, while recognized as essential ecosystem service providers, are degrading worldwide. This has a profound impact on the environment but also on socioeconomic systems which hope to utilize beach-cast seagrass (wrack) as a bioresource. This study integrates system dynamics (SD) thinking with life cycle assessment (LCA) and life cycle costing (LCC) to understand how a degraded ecosystem feedbacks into the circular bioeconomy. An SD model was created to assess the impacts of seagrass meadow changes on wrack production and on ecosystem services accounting, considering an Italian case study of wrack deposited on a beach. Environmental and economic impacts of wrack valorization through anaerobic digestion (AD) were then determined through LCA and LCC. Finally, an extended LCC combined the results of the SD model, LCA, and LCC to demonstrate the cost of seagrass meadow degradation and the value of restoration. The results confirmed complexities in stakeholder perspective within the waste-to-resource framework. For the AD operator, meadow restoration would increase the profits from wrack valorization (23.10 €/ton), while for the municipality, meadow degradation would reduce the high costs associated with management (104.29–140.00 €/ton). When also considering the impacts on the environment and local community, valuation of ecosystem services and cost of restoration were influential. Meadow restoration with wrack valorization was the most favorable option if the natural capital of the seagrass meadows was valued appropriately (>0.065 €/m2) and direct costs of restoration could be kept relatively low (<1179 €/ha). Overall, the model resulted in a total net present cost of −3.161,462.40 € for the baseline scenario, −1,488,277.28 € for the scenario of wrack valorization, and −1,231,325.12 € for the scenario of wrack valorization and meadow restoration.

A framework for drought adaption under deep uncertainties: Application of Portfolio theory (Markowitz 2.0)

Hydrological uncertainty, climate change, and water infrastructure development upstream transboundary basins may affect the safe access to water resources downstream, making socioeconomic and biophysical subsystems vulnerable. Therefore, it is vital to apply decision-making approaches to reduce vulnerability, especially in arid and semi-arid basins. This study evaluated an adaptation framework to reduce vulnerability through optimal water allocation in drought-prone areas. This framework was implemented in an area dependent on upstream transboundary water (Helmand River Basin). An integrated system dynamics model was developed using socioeconomic and biophysical factors identified by the Füssel Vulnerability Framework. The climatic variables of the basin were simulated and predicted by the output of AOGCM models. The trend of upstream annual precipitation and temperature changes during the 2021–2050 period based on the RCP4.5 emission scenario was −2% and +1.9 C compared to those in the historical period, respectively. The results indicated the drought intensification due to a 13% decrease in future runoff (2050) compared to the base period, leading to a reduction in value-added agriculture (8%) and the population (31%). Therefore, the latest version of portfolio optimization called Markowitz 2.0 was used. In order to decide on potential long-term growth versus short-term loss, an efficient frontier was formed between the expected return and the conditional value at risk (CVaR) based on the Markowitz 2.0 model. According to the model results, the development of the industrial sector as an approach with moderate (reasonable) risk could increase financial return by 43% and allocation to the environmental sector by 2.3 times. Finally, prioritizing the environmental sector was a high-risk approach with high returns that increased efficiency by 45%. The results showed that the proposed framework could help decision-makers, based on their risk-taking level, activate and develop decisions on water resource allocation and water-dependent productive sectors on the basis of efficient frontiers.

Redesigning a network of primary healthcare centres using system dynamics simulation and optimisation

This paper presents the redesign of a network of health providing facilities, as far as geographical location and allocation of service levels are concerned. A novel methodology which integrates system dynamics (SD) and mathematical programming is used. Simulations of SD models of different patients-facilities system configurations provide forecasting scenarios for demand, which are then supplied as input to a stochastic facility location model formulated as a two-stage process. The first stage determines the strategic decisions concerning the range and type of services that every health centre should provide, while the second stage, which is scenario dependent, assigns patients to health centres. The proposed methodology allows experimentation with different system structures to gain a robust understanding of patient flows and facility service dynamics over the entire planning horizon. The methodology was applied in the (re)location of health services in a network of 32 primary care health centres and 13 hospitals in Southern Greece.