System Dynamics Framework Research Articles

Dynamic model to assess the impacts of government support for electric vehicles on the economy and environment sectors in Indonesia

This research proposes a comprehensive analysis of the environmental and economic impacts of electric vehicle (EVs) adoption using system dynamics modeling. A system dynamics framework is utilized to integrate various aspects of EVs, economy, environment, and impact of policies on those sectors. Stock and flow diagrams were used to model and predict the impact of government support on electric vehicles based on the existing and future conditions through several proposed strategies. This research mainly contributes to providing causal relationships of variables and parameters influencing the number of EVs and their impact on the economy and environment, modeling and simulation of several sub-systems based on the existing condition, and scenario modeling to predict and improve the number of EV, economic value, and environmentally friendly in the future. This research examines how different policies for electric vehicles (EVs) might affect the numbers of people use them, the pollution caused, and the cost spent. They looked at total emissions, yearly budget, and the number of electric cars and motorcycles. The results show that continuing or increasing government help (scenarios SCN2 & SCN3) for EVs leads to the biggest pollution reduction. Focusing on developing new technologies and industries for EVs (SCN4) shows the biggest short-term pollution reduction. The key takeaway is that long-term support for EVs and technological advancements are essential for success. Finding a balance between the initial costs and the long-term benefits is crucial when designing policies for EVs.

Towards a System Dynamics Framework for Human–Machine Learning Decisions: A Case Study of New York Citi Bike

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

Simulation-based genetic algorithm for optimizing a municipal cooperative waste supply chain in a pandemic

The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.

Multi-Scenario Simulation of Land Use and Assessment of Carbon Stocks in Terrestrial Ecosystems Based on SD-PLUS-InVEST Coupled Modeling in Nanjing City

In the context of achieving the goal of carbon neutrality, exploring the changes in land demand and ecological carbon stocks under future scenarios at the urban level is important for optimizing regional ecosystem services and developing a land-use structure consistent with sustainable development strategies. We propose a framework of a coupled system dynamics (SD) model, patch generation land-use simulation (PLUS) model, and integrated valuation of ecosystem services and trade-offs (InVEST) model to dynamically simulate the spatial and temporal changes of land use and land-cover change (LUCC) and ecosystem carbon stocks under the NDS (natural development scenario), EPS (ecological protection scenario), RES (rapid expansion scenario), and HDS (high-quality development scenario) in Nanjing from 2020 to 2040. From 2005 to 2020, the expansion rate of construction land in Nanjing reached 50.76%, a large amount of ecological land shifted to construction land, and the ecological carbon stock declined dramatically. Compared with 2020, the ecosystem carbon stocks of the EPS and HDS increased by 2.4 × 106 t and 1.5 × 106 t, respectively, with a sizable ecological effect. It has been calculated that forest and cultivated land are the two largest carbon pools in Nanjing, and the conservation of both is decisive for the future carbon stock. It is necessary to focus on enhancing the carbon stock of forest ecosystems while designating differentiated carbon sink enhancement plans based on the characteristics of other land types. Fully realizing the carbon sink potential of each ecological functional area will help Nanjing achieve its carbon neutrality goal. The results of the study not only reveal the challenges of ecological conservation in Nanjing but also provide useful guidance for enhancing the carbon stock of urban terrestrial ecosystems and formulating land-use planning in line with sustainable development strategies.

Regional-scale energy-water nexus framework to assess the GHG emissions under climate change and development scenarios via system dynamics approach

Nowadays, the consumption of natural resources is increasing due to population growth, climate change, and development. Considering the crucial impacts of the regional characteristics, this study develops an energy-water-emissions (EWE) nexus model using a system dynamics (SD) framework to assess the pattern of greenhouse gas (GHG) emissions. The model simulates regional GHG emissions (including indirect emissions of the electricity sector) under climate change and development scenarios from 2011 to 2031. The results indicate that total emissions are projected to increase in the future, while water-related emissions are expected to decrease. The study highlights the limitations of global assumptions at the regional scale, emphasizing the need for planning based on specific regional scale conditions. Through evaluating different development scenarios, the energy-saving scenario had the greatest impact on reducing emissions and resource consumption. Additionally, the results showed that development scenarios had varying effects in each region, highlighting the importance of defining region-specific development scenarios separately. This study offers policymakers valuable insights to enhance their understanding of the climate change effects to evaluate a range of development scenarios more effectively.

Modeling of Wind Power Plants and Their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. By changing the structure of the model, projections on the future of wind power plant generation can be estimated. Several scenarios being developed include adding turbines in the Sidrap and Jeneponto regencies to increase the fulfillment ratio of wind energy and scenarios to reduce CO2 emissions using solid direct air capture (S-DAC). The data and information used in this research come from the Central Statistics Agency, articles on wind energy power plants, and data from related previous studies. These models and scenarios can be applied in other regions by adjusting the parameter values of the case study model. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. With the addition of 25 turbines in Sidrap and 20 turbines in Jeneponto, the average fulfillment ratio is estimated to increase by around 7% due to increased production. Meanwhile, total CO2 emissions are estimated to decrease by approximately 45% due to solid direct air capture.

IJCM_214A: Digital interventions for childhood malnutrition in a tribal area of Andhra Pradesh: Understanding the problem through a health system perspective

Background: Childhood malnutrition is a major public health concern in the tribal areas of Andhra Pradesh, India, particularly in the region of ITDA Rampachodavaram. The integration and effectiveness of digital health interventions in these underserved regions are vital for addressing this endemic issue. However, the unique challenges posed by the remote and resource- limited settings of tribal areas make the implementation of such interventions particularly complex. Moreover, the varying complexity of the digital health ecosystem in these areas adds another layer of concern to deploying digital health solutions. Objectives: 1. To explore digital health interventions related to childhood malnutrition in the Integrated Tribal Development Agency (ITDA) of Rampachodavaram. 2. To understand the dynamics of the local health system and the digital health ecosystem in the implementation of these interventions. 3. To examine the role of street-level bureaucrats in implementing digital interventions and identify the barriers and facilitators affecting this process Methodology: The research was conducted in ITDA Rampachodavaram, involving interviews with health system officials and Focus Group Discussions (FGDs) with frontline health workers. The Health System Dynamics (HSD) framework and Lipsky’s theory on street-level bureaucrats were utilized, and enriched with insights into the digital health ecosystem. Results: The study reveals significant systemic, contextual, and human factors influencing the implementation of digital health interventions. Challenges include the complex role of street-level bureaucrats and systemic barriers like inadequate training, technological limitations, and disparities in digital access and literacy. Conclusion: This study highlights the potential of digital health interventions in improving childhood malnutrition outcomes in tribal areas. It stresses the importance of adaptive, inclusive strategies and advocates for digital health equity. Empowering frontline health workers and tailoring interventions to the specific health system dynamics and digital ecosystem of tribal regions are key for effective implementation.

Examining the Behavior of Maize Productivity and Harvest Land to Enhance Maize Production: a System Dynamics Framework

Food security remains a critical concern in Indonesia, prompting the need for adequate food availability nationwide. Maize, as a prominent staple crop, faces demand imbalances across the country. To address this issue, a system dynamics model is developed to analyze the dynamics of maize supply and demand in East Java. The model captures the non-linear and dynamic relationships inherent in maize production and consumption. Through a rigorous research methodology, this study presents a comprehensive system dynamics model that depicts the current state and dynamics of maize supply. The key findings shed light on the factors influencing maize availability in East Java and provide insights for policymakers to enhance food security. By utilizing a systems approach, this research contributes to the understanding of maize supply dynamics and supports the development of strategies to ensure sufficient food supply in the region.

Exploring the Carbon Pricing Strategy to Decarbonizing Coal-Fired Power Plants: A System Thinking Approach

Carbon dioxide is an important component and one of the contributors to greenhouse gas emissions. Taiwan has set new goals to reduce emissions by 23%-25% in 2030. However, electricity production in 2021 is still contributed by coal-fired power plants. This research explores potential carbon pricing strategies to decarbonize coal-fired power plants, which are carbon fees and carbon emission trading. This research uses a system dynamics framework for causal loop diagram development. This research creates a causal loop diagram to describe the carbon pricing strategy's impact on reducing carbon emissions.

Gasoline Policy Simulation to Increase Responsiveness Using System Dynamics: A Case Study of Indonesia’s Gasoline Downstream Supply Chain

In the supply chain, inventory planning plays a crucial role in achieving a balance between supply and demand. This study aims to investigate a supply chain policy that can achieve an impressive level of responsiveness, reaching close to 100%. Additionally, by focusing on the supply chain of RON 92 gasoline in Indonesia, the research aims to identify the key factors influencing responsiveness instability. To achieve these objectives, a simulation model was used to analyze the inventory policy, considering various factors such as demand, production, safety stock coverage, transportation delays, production capacity, and importation due to insufficient production capacity. The results showed that forecasting accuracy is the main determinant of the responsiveness rate. Moreover, maintaining a minimum inventory level of 28 days yielded an impressive 99% responsiveness rate, provided that the deviation in demand does not exceed 5% of the forecast. The analytical tool used in the system dynamics framework was a simulation, which significantly contributed to the research findings. However, it is important to note that this research has limitations, specifically in its inability to analyze crude oil supply. Therefore, further research is necessary to thoroughly examine this aspect and gain a more comprehensive understanding of the overall supply chain dynamics.

Buprenorphine deregulation as an opioid crisis policy response - A comparative analysis between France and the United States.

In passing the Maintstreaming Addiction Treatment Act, the United States has abolished its federal X waiver, considered a major barrier to the wider buprenorphine prescribing needed to respond to opioid-related harms. Advocates for this policy have drawn on the French response of deregulating buprenorphine prescribing to address increasing overdose mortality around the turn of the millennium. So far, such policy advocacy has incompletely accounted for contextual and health system differences between the two countries. Using the health system dynamics framework, this analysis compares France from 1995 to 2003 (the relevant period of buprenorphine reform) to the US from 2018 until today (the comparison period to explore potential impacts of reform). We used it to guide examination of a) contextual issues relating to opioid use epidemiology and b) health system factors including prescriber supply, sector organization, and insurance coverage for primary care to draw relevant policy learning for the contemporary US. We identified that the US had a 22.5-fold higher mortality rate and a 2.3-fold higher opioid use disorder (OUD) rate compared to France, despite having rates of prescribed buprenorphine per-capita higher than, and per-person with OUD comparable to, than that of France. These wide gulfs between the scales and nature of the problems between France and the US suggest that relaxing restrictions on buprenorphine prescribing through abolishing the X waiver will be insufficient for achieving hoped-for reductions in overdose mortality. Health system strengthening with a focus on improvements in primary care prescriber supply, coverage, and coordination are likely higher yield policy complements to relaxing buprenorphine regulation. Such an approach would better prepare the US to adapt to ongoing dynamics and uncertainties in the opioid crisis and to optimize the already relatively high levels of buprenorphine prescribing.

Effect of novel phononic crystals vibration isolator on vibration reduction for floating slab track considering the random dynamical load of the vehicle

ABSTRACT The vibration caused by urban rail transit has led to numerous severe environmental issues. The floating slab track (FST) plays an important role in vibration control and noise reduction for rail transit. In this study, a novel phononic crystal vibration isolator (NPCVI) is developed based on the principle of local resonance to address the deficiencies in the low frequency vibration reduction of existing FST with steel spring vibration isolators (SSVI). The vibration equation of the NPCVI is derived and an analytical method for the dynamic interaction between vehicle, track, and tunnel is proposed. The pseudo excitation method (PEM) is introduced to solve the random vibration response of the vehicle-track-tunnel coupled system under the excitation of full-band track random irregularity. The numerical simulation results are compared with the field measurement results to verify the correctness of the solution model. The vibration reduction analysis of FST using NPCVI is conducted within a system dynamics framework, and a comparison is made between the performance of the conventional SSVI and the proposed NPCVI while a train is passing. The vibration reduction optimization effect of the NPCVI is evaluated using the vibration level (VL) of the tunnel structure. The results demonstrate that the NPCVI exhibits a superior band gap width as compared to that of the SSVI.

Jet Fuel Price Risk and Proxy Hedging in Spot Markets: A Two-Tier Model Analysis

This paper applies a two-tier model based on fuel hedging (model 1) and the testing of the impact of commodity risk on airline capacity forecasting, which is based on a system dynamics framework (model 2). Model 1 provides a comprehensive examination of the worldwide airline industry, including an analysis of the statistical impact of oil price fluctuations on the sector and the corresponding hedging strategies employed by airlines. This study examines a sample of North American and European airlines over a 10-year timeframe to assess the degree to which these airlines have engaged in kerosene hedging for future periods and the potential impact of such hedging on their corporate value and performance. In model 2, the author integrates a capacity-forecasting model within the system dynamics framework, drawing upon the theory of capacity forecasting. The study examines the impact of commodity risk by analysing the influence of fluctuations in the jet fuel spot price on the average airfare and its subsequent effects on other interdependent capacity variables. The hypotheses presented in this study were formulated based on a comprehensive review of the relevant literature and a causal feedback loop diagram. The diagram effectively depicts the dynamic interrelationships between capacity forecasting and risk variables. Furthermore, the diagram capturing causal feedback loops was transformed into a stock-flow diagram. This diagram was then utilised to evaluate the hypotheses that were derived using a dataset that pertains to the domestic airline market in the United States. The verification of the qualitative and quantitative models demonstrates the proven impact of commodity risk on capacity forecasting.

A framework for synthetic power system dynamics.

We present a modular framework for generating synthetic power grids that consider the heterogeneity of real power grid dynamics but remain simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. For the first time, our synthetic model also includes the major drivers of fluctuations on short-time scales and a set of validators that ensure the resulting system dynamics are plausible. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper.

Green energy mix modeling under supply uncertainty: Hybrid system dynamics and adaptive PSO approach

Reducing global warming is crucial for sustainability. Carbon emissions primarily stem from energy supply, prompting a shift from Non-Renewable Energy Supply (NRES) to Renewable Energy Supply (RES). However, transitioning to RES entails substantial investment and faces supply uncertainty due to weather dependency. Therefore, the transition should be done gradually, requiring a reliable approach to energy mix modeling. This study proposes a System Dynamics framework integrated with Adaptive Particle Swarm Optimization (APSO) and Machine Learning to optimize the energy mix under supply uncertainty. Due to energy system dynamicity, the proposed framework considers not only supply, but also demand, energy storage, electric vehicle, and emission subsystems. The experiment has been conducted by taking the United States as a case under various scenarios namely to minimize the total system cost, total carbon emissions, and both, accounting for the static and dynamic cost of RES. Results of this study reveal four main points: (i) A 38% reduction in total system cost is achievable by decreasing the RES Ratio to 6%, but total emissions will rise by 8%; (ii) A 55% reduction in total emissions is possible by directly transitioning to 100% RES, but total system cost increases by 68%; (iii) Both objective functions can be significantly minimized at a time by increasing the RES ratio; (iv) Dynamic cost offers a better opportunity for reducing costs and emissions than static cost.

Laboratory services in the context of prevention of mother-to-child transmission of HIV testing requirements in Copperbelt Province, Zambia: a qualitative inquiry

IntroductionReliable and timely laboratory results are crucial for monitoring the Prevention of the Mother-to-Child Transmission (PMTCT) cascade, particularly to enable early HIV diagnosis and early intervention. We sought to explore whether and how laboratory services have been prepared to absorb new testing requirements following PMTCT Test-and-Treat policy changes in three districts of Zambia.MethodWe employed in-depth interviews and thematic data analysis, informed by the health system dynamic framework. Twenty-Six health workers were purposively selected and a document review of laboratory services in the context of PMTCT was undertaken. All face-to-face interviews were conducted in three local government areas in the Copperbelt Province (one urban and two rural) between February 2019 and July 2020. We extracted notes and markings from the transcripts for coding. Different codes were sorted into potential themes and the data extracted were put within the identified themes. Trustworthiness was confirmed by keeping records of all data field notes, transcripts, and reflexive journals.ResultsThe findings revealed that the health system inputs (infrastructure and supplies, human resources, knowledge, and information and finance) and service delivery were unequal between the rural and urban sites, and this affected the ability of health facilities to apply the new testing requirements, especially, in the rural-based health facilities. The major barriers identified include gaps in the capacity of the existing laboratory system to perform crucial PMTCT clinical and surveillance functions in a coordinated manner and insufficient skilled human resources to absorb the increased testing demands. The centralized laboratory system for HIV testing of mothers and exposed neonates meant facilities had to send specimens to other facilities and districts which resulted in high turnaround time and hence delayed HIV diagnosis.ConclusionNew guidelines implemented without sufficient capacitation of health system laboratory capacity severely limited the effectiveness of PMTCT program implementation. This study documented the areas relating to health system inputs and laboratory service delivery where greater support to enable the absorption of the new testing requirements is needed.

An economic evaluation of potential offshore wind farm sites in South Korea using a real options approach

In the era of energy transition, climate and topography characteristics make wind energy a promising renewable energy source in South Korea. Following the successful operations of onshore wind power generation, the South Korean government has implemented the 3020 renewable energy implementation plan with a target of establishing 12GW offshore wind farms by 2030. Considering financial and time constraints, it is important to determine the most suitable locations to enable renewable energy farms to achieve the proposed targets for renewable energy production. Although several studies conducted in South Korea have assessed the economic feasibility of potential wind farm sites using the discounted cash-flow method, this method involves some limitations regarding incorporating the value of flexibility and uncertainty factors in the site evaluation process. Thus, this study utilized a real options approach – the Geske compound option model – in a system dynamics framework to evaluate potential sites for offshore wind farms on the mid-west coast of South Korea. Using the developed potential site evaluation model, this study obtained different results from the reference study, which conducted cost-benefit analyses of the same areas. The results of this study demonstrate the importance of flexibility value and uncertainty factors in the site evaluation process. This study also estimates the environmental mitigation of each potential site concerning carbon emission reduction for offshore wind farms.

Scaling-up integrated type-2 diabetes and hypertension care in Cambodia: what are the barriers to health system performance?

Non-communicable diseases (NCDs) such as type-2 diabetes (T2D) and hypertension (HTN) pose a massive burden on health systems, especially in low- and middle-income countries. In Cambodia, to tackle this issue, the government and partners have introduced several limited interventions to ensure service availability. However, scaling-up these health system interventions is needed to ensure universal supply and access to NCDs care for Cambodians. This study aims to explore the macro-level barriers of the health system that have impeded the scaling-up of integrated T2D and HTN care in Cambodia. Using qualitative research design comprised an articulation between (i) semi-structured interviews (33 key informant interviews and 14 focus group discussions), (ii) a review of the National Strategic Plan and policy documents related to NCD/T2D/HTN care using qualitative document analysis, and (iii) direct field observation to gain an overview into health system factors. We used a health system dynamic framework to map macro-level barriers to the health system elements in thematic content analysis. Scaling-up the T2D and HTN care was impeded by the major macro-level barriers of the health system including weak leadership and governance, resource constraints (dominantly financial resources), and poor arrangement of the current health service delivery. These were the result of the complex interaction of the health system elements including the absence of a roadmap as a strategic plan for the NCD approach in health service delivery, limited government investment in NCDs, lack of collaboration between key actors, limited competency of healthcare workers due to insufficient training and lack of supporting resources, mis-match the demand and supply of medicine, and absence of local data to generate evidence-based for the decision-making. The health system plays a vital role in responding to the disease burden through the implementation and scale-up of health system interventions. To respond to barriers across the entire health system and the inter-relatedness of each element, and to gear toward the outcome and goals of the health system for a (cost-)effective scale-up of integrated T2D and HTN care, key strategic priorities are: (1) Cultivating leadership and governance, (2) Revitalizing the health service delivery, (3) Addressing resource constraints, and (4) Renovating the social protection schemes.

Analyzing the Application of SIR Model to Study the Outbreak of COVID-19: A Case Study in Pakistan

The current study aims to examine the exponential rate of the spread of COVID-19 by employing a system dynamic model. The outbreak of COVID-19 was first evidenced on Feb 26, 2020 in Pakistan. The local bodies and law enforcing agencies took the initial preventive measures to restrict COVID-19 to a particular locality but all in vain. A large number of people were infected by this virus which increased the death rate countrywide. The numbers of infected people were alarming and a need was felt to develop the model to calculate the existing reproduction number and transmission rate and highlight its varied values in the coming days. People-friendly measures and government-based policies must be explored to fight against this deadly disease. This paper aims to develop an epidemic model using the system dynamic framework of simulation software STELLA. Additionally, the current study’s purpose is to experiment with the system dynamic model to replicate the progression of the communicable disease and probe multiple combinations of people-based and government-based measures to reduce the spread of the COVID-19 pandemic. These containment measures are of two types; people-based measures and government-based measures which directly affect the reproduction number and infection growth rate of the mitigating circumstances due to COVID-19. Combined efforts of the public and government can combat this global pandemic. The reduced number of reproduction number/recurring cases and infection growth rate are the key metrics to judge and evaluate the effectiveness of containment/ control measures. Therefore, this research points to a more holistic combination of public and government-oriented measures that play a vital role in reducing the increasing infection rate of COVID-19. Simulation results were traced to replicate the real-life settings against four combinations of containmentmeasures in tabular form and graphical form.

Horizontal collaboration between suppliers to mitigate supply chain disruption: A secure resource sharing strategy

Resource sharing is a flexible recovery strategy that has attracted attention with the outbreak of global disruption. Horizontal cooperation could lead to mutual benefits and reduce the negative consequences during the recovery period. This paper emphasizes the advantages of sharing resources between two competing suppliers under disruption. Suppliers share a secure source of inventory to absorb the shocks of disruption. The stability of relationships between suppliers is sensitive to several factors. Accordingly, an evolutionary game model is developed followed by a replicator dynamic analysis to study the stability of strategies under disruption scenarios. A hybrid agent-based system dynamics framework is built using AnyLogic software to simulate the relationships, interactions, and the effect of parameters in the adopted strategies of suppliers. Compared with mutual incentives and penalty costs, findings indicate that the value of secure resources and the number of shortages have higher impacts on the stability of cooperation. A higher rate of shortages could undermine collaboration. Results show that the initial mindset of suppliers to follow a collaborative approach is a key driver for the stable implementation of collaborative resource sharing. This paper highlights the benefits of implementing new technologies to improve supply flexibility through horizontal integration with competitors.