A system dynamics simulation model of chemical supply chain transportation risk management systems

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.

Assessing Risks in Dairy Supply Chain Systems: A System Dynamics Approach

Due to the dynamic nature of the food supply chain system, food supply management could suffer because of, and be interrupted by, unforeseen events. Considering the perishable nature of fresh food products and their short life cycle, fresh food companies feel immense pressure to adopt an efficient and proactive risk management system. The risk management aspects within the food supply chains have been addressed in several studies. However, only a few studies focus on the complex interactions between the various types of risks impacting food supply chain functionality and dynamic feedback effects, which can generate a reliable risk management system. This paper strives to contribute to this evident research gap by adopting a system dynamics modelling approach to generate a systemic risk management model. The system dynamics model serves as the basis for the simulation of risk index values and can be explored in future work to further analyse the dynamic risk’s effect on the food supply chain system’s behaviour. According to a literature review of published research from 2017 to 2021, nine different risks across the food supply chain were identified as a subsection of the major risk categories: macro-level and operational risks. Following this stage, two of the risk groups identified first were integrated with a developed system dynamics model to conduct this research and to evaluate the interaction between the risks and the functionality of the three main dairy supply chain processes: production, logistics, and retailing. The key findings drawn from this paper can be beneficial for enhancing managerial discernment regarding the critical role of system dynamics models for analysing various types of risks across the food supply chain process and improving its efficiency.

Modelling supply chain risks and their impacts on the performance of the sago starch agro-industry

Purpose

Supply chain risks (SCRs) have uncertainty and interdependency characteristics that must be incorporated into the risk assessment stage of the SCR management framework. This study aims to develop SCR networks and determine the major risk drivers that impact the performance of the sago starch agro-industry (SSA).

Design/methodology/approach

The risk and performance variables were collected from the relevant literature and expert consultations. The Bayesian network (BN) approach was used to model the uncertain and interdependent SCRs. A hybrid method was used to develop the BN structure through the expert’s knowledge acquisitions and the learning algorithm application. Sensitivity analyses were performed to examine the significant risk driver and their related paths.

Findings

The analyses of model indicated several significant risk drivers that could affect the performance of the SSA. These SCR including both operational and disruption risks across sourcing, processing and delivery stage.

Research limitations/implications

The implementation of the methodology was only applied to the Indonesian small-medium size sago starch agro-industry. The generalization of findings is limited to industry characteristics. The modelled system is restricted to inbound, processing and outbound logistics with the risk perspective from the industry point of view.

Practical implications

The results of this study assist the related actors of the sago starch agro-industry in recognizing the major risk drivers and their related paths in impacting the performance measures.

Originality/value

This study proposes the use of a hybrid method in developing SCR networks. This study found the significant risk drivers that impact the performance of the sago starch agro-industry.

Risk Propagation of Concentralized Distribution Logistics Plan Change in Cruise Construction

Compared with the ordinary merchant ship building, the concentralized distribution in cruise building is more complex. Plan change is a common phenomenon in cruise building, and it is easy to lead to mismatch between production and logistics, resulting in risks such as production schedule delay and inventory backlog. In order to reduce the adverse effects of plan change on the shipyard, it is necessary to conduct an in-depth study on the risks of a centralized distribution logistics plan. Based on the analysis of the composition of the centralized distribution logistics planning system, risk factors in different plan links are identified in this paper. A system dynamic model is constructed to simulate the propagation of five basic types of planning risk, including procurement plan, warehousing plan, pallet concentralization plan, distribution plan and production plan. In the case study of HVAC (heating, ventilation and air conditioning) materials, the values of risk factors are estimated though consulting experts with questionnaire. The weight of each risk factor in each subsystem is calculated by a method combined with analytic hierarchy process and coefficient of variation method. Through the simulation experiments carried out in Vensim, it is found that both inventory backlog risk and cruise construction schedule delay risk increase with the increasement of estimated values of risk factors, which is an effective proof of the rationality of the model, and that the most sensitive risk factor for both the two kinds of risk is production planning risk.

Decision-Making of Port Enterprise Safety Investment Based on System Dynamics

Safety is the premise of efficiency and effectiveness in the port operation. Safety investment is becoming a vital part of port operation in current era in order to overcome different types of hazards the port operation exposed to. This paper aims to improve the safety level of port operation through analyzing its influencing factors and exploring the interactions between the safety investment and system risk level. By analyzing the key factors affecting the port operation and their mutual relationship within a man–machine–environment–management system, a decision-making model of safety investment in port enterprise was established by system dynamics (SD). An illustration example and a sensitivity analysis were carried out to justify and validate the proposed model. The results show that increasing the total safety investment of port enterprises, improving the safety management investment on personnel, and strengthening the implementation effect of investment can improve the degree of port security to a certain extent. The strength of the proposed work is its practical application in current scenarios using real time data and the ability to provide a baseline approach for port enterprises to formulate safety investment strategy.

Threshold behavior of optimal safety stock coverage in the presence of extended production disruptions

Purpose

The purpose of this paper is to investigate the effects of extended production disruptions because of process quality breakdowns on operational and financial performance. The investigation is conducted over the market cycle of a highly profitable product, e.g. a patented pharmaceutical. In particular, the study evaluates performance effects of different inventory level policies. The paper considers different degrees of availability of a substitute product.

Design/methodology/approach

The study uses simulation experiments based on system dynamics methodology to derive inventory policies on handling infrequently happening but severe production process breakdowns. The simulation model is inspired by empirical case study research and is based on standard modeling formulations from the literature.

Findings

The scenario analyses show that the optimal level of safety stock coverage time depends in a highly non-linear manner on various economic parameters and shows threshold behavior.

Research limitations/implications

In subsequent studies, the effect of the degree of backlogging and the effect of the repeated occurrence of quality breakdowns on the results can be investigated.

Practical implications

The critical importance of safety stock and its non-linear relationship to economic product characteristics is emphasized.

Originality/value

Motivated by a real-world case study, the paper uses standard model formulations to derive insights for a specific business situation after considering uncertainty in the environment.

Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models

Liquefied nature gas (LNG) is a green energy. LNG-fueled vessels are extremely complex engineering systems. In view of the inherent hazardous properties of LNG fuel, LNG fueling is not only an important part, but it is also full of high risks in the operation of LNG-fueled vessels (LNGFVs). Therefore, it is necessary to study the risk factors, and the intrinsic relationship among them between the LNG and the vessel, and to simulate the system dynamics in the process of LNGFV operation. During the process of fueling of LNGFV, at every moment the vessel interacts with the energy and information of the surrounding environment. First, the impact of the three interactions of the fueling operation process, ship factors, and environmental factors were analyzed on the risk of fueling operation, and a complete node system was proposed as to the complex system dynamics mode. Second, by analyzing the boundary conditions of the system, the relationship of factors was established via the tools of system dynamics (SD). Based on the catastrophe theory (CA), the dynamics model for the fueling of LNG is set up to study the system’s risk mutation phenomenon. Third, combined with the simulation results of the case analysis, the risk evolution mode of the LNGFV during the fueling process was obtained, and constructive opinions were put forward for improving the safe fueling of the LNGFV. Application examples show that formal description of risk emergence and transition is a prerequisite for the quantitative analysis of the risk evolution mode. In order to prevent accidents, the coupling synchronization of risk emergence should be weakened, and meanwhile risk control should be implemented.

Risk-agility interactive model: a new look at agility drivers

Purpose

According to the definitions presented for agility drivers, it can be mentioned that supply chain risk factors in terms of feature have very close roles to agility drivers and both of these factors impact the uncertainty in the environment. However, the risk factors with a wider domain are more comprehensive. This study aims to examine the effectiveness of risk as a driver on the organizational agility.

Design/methodology/approach

To conduct the research, after identification of influential risk factors of supply chain and indicators of organizational agility, questionnaires needed for the research were designed and after confirming their validity and reliability were distributed among the member of the sample. To assess the relationship between supply chain risk and the amount of organizational agility, data obtained from the questionnaires were analyzed using structural equation modeling technique.

Findings

The results of data analysis showed that supply chain risk factors could be considered as a driver affecting the organizational agility. In addition, in this study, supply chain risk factors were ranked using interpretive structural modeling. The presented comprehensive model indicated that based on causal relationships between risk factors, sovereign risk as the basis for model (three levels) and product and customer satisfaction risks as the output of the model (Level 1) were considered.

Originality/value

This paper fulfills an identified need to study how the risk factors as drivers of supply chain agility can have effect on agility.

Modelling critical risk factors for Indian construction project using interpretive ranking process (IRP) and system dynamics (SD)

Purpose

The purpose of this paper is to employ an integrated approach of interpretive ranking process (IRP) and system dynamics (SD) for modelling the key risk factors for a typical construction project.

Design/methodology/approach

The risk parameters and performance measures applicable in the construction industry have been identified through extensive literature review and discussions with experts from the construction industry. Based on the literature review, a questionnaire was designed and 64 responses were considered. The list of 20 risk parameters and 32 performance measures relevant for a construction industry is reduced to five risk factors and five performance measure factors using factor analysis. IRP modelling is employed to examine the contextual relationships among risk factors and to rank them with respect to performance measure factors. Subsequently, the results of IRP model were utilised as inputs to SD analysis. The SD analysis is conducted for two models, namely, risk factor model and risk variable model to understand the impact of interventions offered by project management team on risk reduction and mitigation.

Findings

The developed IRP model shows that the risk factor dimension “construction management” has a high likeliness to occur during the construction phase.

Research limitations/implications

The research demonstrates an application of proposed approach for a typical construction environment and hence the results cannot be generalised.

Originality/value

This research addresses real life complexities in construction project by modelling critical risk factors using an integrate approach of IRP and SD. The proposed approach would facilitate project managers to devise appropriate risk mitigation strategies for a construction project.