Supply Chain Risk Assessment Research Articles

A combined approach to risk assessment in the sustainable supply chain

A combined approach to risk assessment in the sustainable supply chain

Perspectives on Supply Chain Management in a Pandemic and the Post-COVID-19 Era

This article draws out some perspectives on the management of product supply chains in the event of a pandemic through cases specific to certain industries: automotive equipment, personal computers (PCs), and home furnishings. In particular, the discussion is based on “distributed management and centralized management of a single location” and the dynamic capability of organizational theory derived from supply chain risk assessment studies. Results show that the automotive industry is shifting to a centralized management model that takes advantage of its inherent closed-integral strengths by increasing proximity to the country of production, while the PC industry is shifting to a model that takes advantage of its global supply chain while maintaining transactions with local suppliers. For the home furnishings industry, results show that “tighter vertical integration” is required.

Risk assessment of agricultural supermarket supply chain in big data environment

With the application of big data in all walks of life, big data thinking is effectively improving the circulation efficiency of agricultural products supply chain by driving management changes in business decision-making and "farmer-supermarket docking" as an innovative mode of agricultural products circulation. Based on the current situation of China's agricultural supermarket supply chain development, this paper makes an in-depth study on the supply chain risk of agricultural products of large retail enterprises under the mode of "agricultural supermarket docking", and then introduces the agricultural supermarket docking supply chain under the big data environment. This paper uses big data to analyze the risks that may arise in the supply chain of "agricultural supermarket docking" in large retail enterprises. This paper from the aspects of production, processing, distribution, retail and consumption, introduces the new risks of agricultural supermarket supply chain after introducing big data. Secondly, Qualitative analysis and quantitative calculation are combined to conduct risk assessment. Through empirical analysis, the ranking of all risk factors is obtained, and the relevant fuzzy evaluation grade and risk evaluation criteria are given. Through expert evaluation, a new risk ranking is also obtained, which is not much different from the results of empirical analysis, and the empirical results are also verified. Therefore, develop this study is helpful to prevent the risk of agricultural supermarket supply chain connection. At the same time, the information integration, sharing and feedback of the big database provide a new idea for the optimization of the supply chain connecting agricultural production.it also has reference significance for other supply chain risk management.

Supply chain risk assessment model based on cloud model with subjective preference weight allocation algorithm

Supply chain risk assessment model based on cloud model with subjective preference weight allocation algorithm

Identifying Tools and Methods for Risk Identification and Assessment in Construction Supply Chain

The construction project is a business full of risk in every process due to its complexity, changes, and involvement from various stakeholders. One of the critical risks in the construction project is in the supply chain. Identifying and assessing the risk with the right tools and methods in that area will inevitably affect the success of the project. Unfortunately, the research for the tools and methods in a construction supply chain is still limited and scattered. This research objective is to analyze the gap between literature and to create improvement in tools and methods for risk identification and assessment in the construction supply chain. This research will use the systematic literature review method in finding and investigating the tools and methods. The four methods that were found are: Analytical Hierical Process (AHP), Failure Mode Effect Analysis (FMEA), Supply Chain Operation Reference (SCOR), and Hazard and Operational (HAZOP). Strength and weakness with their potential use as tools and methods for identifying and assessing the construction supply chain risk then summarized. The use of SCOR combined with FMEA methods has shown to be practical tools and methods for identifying and assessing the construction supply chain risk.

The Role of Fuzzy Logic to Dealing with Epistemic Uncertainty in Supply Chain Risk Assessment: Review Standpoints

The nature of supply chains presents a variety of issues related to uncertainties. Under various uncertainties, risk management plays a crucial role in an effective supply chain decision-making. The uncertainty involved in the risk assessment process can be divided into two types: random uncertainty and epistemic uncertainty. Fuzzy theory has been applied to deal with uncertainties. The purpose of this paper is to analyze the role and contribution of the fuzzy logic in the treatment of epistemic uncertainty into supply chain risk management approaches. A literature review process was performed, followed by analysis and discussions on the examined topic. The results revealed that the integration with multicriteria decision-making and disruptive analysis methods are the most common types adopted, with trend to petri nets and multicriteria decision-making approaches. Supply risks are the most studied type and identification and assessment are the most developed processes in supply chain risk management. Although the publications on the subject has been highlighted, they present some limitations related to the holistic complexity of risks in supply chains, the dynamic nature of the environment and the reliability of the background knowledge in the assessment. In that sense, these remarks reveal interesting future researches lines.

Risk Assessment of New Energy Vehicle Supply Chain Based on Variable Weight Theory and Cloud Model: A Case Study in China

In order to protect the environment and reduce energy consumption, new energy vehicles have begun to be vigorously promoted in various countries. In recent years, the rise of intelligent technology has had a great impact on the supply chain of new energy vehicles, which, coupled with the complexity of the supply chain itself, puts it at great risk. Therefore, it is quite indispensable to evaluate the risk of the new energy vehicle supply chain. This paper assesses the risks faced by China’s new energy vehicle supply chain in this period of technological transformation. First of all, this paper establishes an evaluation criteria system of 16 sub-criterion related to three dimensions: the market risk, operational risk, and the environmental risk. Then, variable weight theory is proposed to modify the constant weight obtained by the fuzzy analytic hierarchy process (FAHP). Finally, a risk assessment of China’s new energy vehicle supply chain is carried out by combining the variable weight and the cloud model. This method can effectively explain the randomness of matters, and avoid the influence of value abnormality on the criteria system. The results show that China’s new energy vehicle supply chain is at a high level. Through the identification of risk factors, mainly referring to the low clustering risk, technical level risk and information transparency risk, this paper can provide a risk prevention reference for corresponding enterprises.

Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.

Port Supply-Chain Benefit Distribution Model and Algorithm in a Cloud Environment

Huo, H. and Wang, Z., 2019. Port supply-chain benefit distribution model and algorithm in a cloud environment. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 58–61. Coconut Creek (Florida), ISSN 0749-0208.On the basis of the cloud environment, through mathematical analysis, the port supply-chain interest coordination model is established. According to the construction goals and principles of the port supply-chain benefit distribution based on the cloud environment, the technical model and member structure constructed under the cloud environment are described. The overall process of operation describes the port supply; at the same time, we focus on the pre-evaluation and evaluation of the process, and the mathematical model is established and solved by cloud genetic algorithm. Finally, the increased profit of the port supply chain under the cloud environment is rationally distributed to promote the stability of the port supply chain. At the same time, the risk assessment model of port supply chain based on support vector machine is established, and compared with back propagation neural network and radial basis neural network evaluation models. At the same time, considering the error of data acquisition, the fault-tolerant value is introduced into the collected data to form a fault-tolerant interval. The data of the port supply-chain risk assessment model are analyzed by the rough neural network method. The error-tolerant values are evaluated. The model provides a reference for the establishment and development of the port supply-chain risk assessment model.

A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan

Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.

An extended generalized TODIM method for risk assessment of supply chain in social commerce under interval type-2 fuzzy environment

An extended generalized TODIM method for risk assessment of supply chain in social commerce under interval type-2 fuzzy environment

A Fuzzy-Based Holistic Approach for Supply Chain Risk Assessment and Aggregation Considering Risk Interdependencies

Supply chain risk management requires dealing with uncertainty, interrelations, and subjectivity inherent in the risk assessment process. This paper proposes a holistic approach for risk management that considers the impact on multiple performance objectives, the relation between risk agents, and the risk event interdependencies. An aggregated risk score is proposed to capture the cascading effects of common risk triggers and quantify the aggregated score by risk agent and objective. The approach also uses fuzzy logic to allow for the treatment of vague and ambiguity data as input parameters to the model from different domains and scales, according to knowledge and criteria nature. The integration of the balanced scorecard tool improves the analysis and prioritization of mitigation strategies in decision-making, both by risk agent and by strategic objective. A case study of a telecommunication company is presented to illustrate the applicability of the proposed approach.

Supply chain multi-state risk assessment using universal generating function

This paper proposes a new approach to assess the supply risk beyond the classical binary assumption of either delivering the whole quantity or not. Given today’s stochastic nature of supply and the dynamic nature of demand, the different supply activities along with the chain exhibit a multi-state behaviour increasing the complexity of their risk assessment. The new approach utilizes the universal generating function (UGF) to model the different suppliers’ echelons with their various supply risk levels along all the stages of the supply chain as a multi-state risk system. The developed model was successfully implemented to assess the supply risk in a multi-state strawberry supply chain and outperformed classical approaches. Results from the case study and the validation analysis illustrated the ability of the new approach to capture the various supply levels with their associated risks leading to more informative risk assessment process. Furthermore, the developed model improved the visibility for the purchasing managers downstream in terms of the different trade-offs between supply levels and their risks as well as some financial thresholds. The new multi-state approach contributes to the emerging supply chain risk assessment trend by offering a more realistic modelling method to capture the risk of all available supply levels along the delivery chain.

Supply chain risk assessment of cotton shirt production uses the house of risk method

The purpose of this study is to mitigate risks in supply chain activities in the production of cotton shirts. The method used in this research is the House of Risk (HOR) approach based on the Supply Chain Operation Reference (SCOR) model. The House of Risk (HOR) offers a framework for effectively controlling risks in managing risks from risk agents. Aggregate Potential Risk (ARP) is used to determine the priority of risk sources that need to do in the design of mitigation strategies. The research results identify as many as 26 Risk Events and 21 Risk Agents. Three Risk Agents based on ARP values are Procedures for Delivery of Less Organized Raw Materials, limited and Sudden Product Demand from consumers Mitigation actions used to prevent the causes of risk from occurring are creating standard operating procedures for raw material delivery activities. Also, a contract system establishes between suppliers of raw materials and freight forwarding services. Migration in human resources is evaluating employee performance. Empowerment of the community around the company, subcontracting to other similar companies, reviewing raw material stock is an act of meditation in sudden product demand.

Shallot supply chain analysis using rapid agricultural supply chain risk assessment method: case in Bantul Regency, Special Region of Yogyakarta, Indonesia

The supply chain of shallot commodities in Bantul Regency involves several business actors including farmers, middlemen, collectors, wholesalers, retailers, and small traders. In the process of each supply chain, risks will always be encountered. The risks in each tier of shallot supply chain certainly need to be identified and analyzed. The categories of each of these risks will determine the appropriate supply chain risk management steps based on the risk categories obtained. This study was conducted to analyze the risk of shallot supply chains in the three largest shallot producing sub-districts. The Rapid Agricultural Supply Chain Risk Assessment (RapAgRisk) method is used to carry out risk analysis, and the sampling method used was purposive sampling and snowball sampling. The results show that the farmer is the tier with most risks faced compared to the others. Excessive stock is the risk that requires adequate priority to manage, and it is faced by all levels of the shallot supply chain. Meanwhile, farmers are the weakest supply chain actors in bargaining power, and the small traders are the most vulnerable actors in the face of risks associated with quality damage and slow sales rate.

Study on risk assessment of pharmaceutical distribution supply chain with bipolar fuzzy information

We investigate the multiple attribute decision making problems with bipolar fuzzy information. Then, we have developed the bipolar fuzzy Hamacher correlated averaging (BFHCA) operator. The prominent characteristic of the operators is that they can not only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have applied the BFHCA operators to multiple attribute decision making for risk assessment of pharmaceutical distribution supply chain with bipolar fuzzy information. Finally an illustrative example for for risk assessment of pharmaceutical distribution supply chain has been given to show the developed method.

Research on hotel supply chain risk assessment with dual generalized triangular fuzzy Bonferroni mean operators

In this paper, we investigate the multiple attribute decision making (MADM) problems with triangular fuzzy information. Motivated by the ideal of dual generalized Bonferroni mean and dual generalized geometric Bonferroni mean, we develop two aggregat

House of Risk Approach for Assessing Supply Chain Risk Management of Material Procurement in Construction Industry

The purpose of this study is to identify the potential risk events in material procurement supply chain and to arrange risk agents priority using House of Risk (HOR) approach where priority can be the basis to create mitigation strategies. The approach started with HOR Phase 1 by mapping supply chain activities using Supply Chain Operation Reference (SCOR) model and risk assessment. There are 30 risk events in supply chain are adapted from several previous studies, but after the preliminary studies by deep interview with the Manager and the Purchasing Supervisor of Accounting Division obtained 23 valid risk events with 17 risk agents. Using the Aggregate Risk Potential (ARP) values and Pareto 80:20 principle, obtained 3 selected risk agents needs immediate handling with mitigation strategies. HOR Phase 2 with the mitigation strategies arrangement will be performed in the further study.

CVaR-based risk assessment and control of the agricultural supply chain

Purpose The purpose of this paper is to establish an effective risk assessment approach based on the conditional value-at-risk (CVaR) in the agricultural supply chain. Design/methodology/approach This study analyzes and assesses the risks of breeding, processing, transportation and warehousing in the agricultural supply chain. The ordered weighted averaging operator is used to sort risk control factors according to their importance and determine the main risk indicators of an enterprise. The CVaR model is utilized to establish the risk loss function, and an improved genetic algorithm is employed to identify the optimal risk control portfolios in the case of the smallest risk loss. Findings Based on the approach, the optimal combination of risk control to minimize risk losses is determined. Results show that the proportion of capital investment in risk control differs at three confidence levels, and a large amount of money needs to be invested in the production process at the source. Thus, any attempt to control the risks inherent in the agricultural supply chain must begin with the production process at the source. Originality/value Supply chain risk management has become increasingly important and significant to the operation and production of enterprises in recent years. The proposed method to assess the risk in the agricultural supply chain can benefit managers in making smart decisions to control total risk.

Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China.

The objective of this study was to develop predictive models for describing the inoculated Salmonella reductions on chicken during the scalding process in China. Salmonella reductions on chicken breasts at a 100 s treatment were 1.12 ± 0.07, 1.38 ± 0.01, and 2.17 ± 0.11 log CFU/g at scalding temperatures of 50, 60 and 70 °C, respectively. For chicken wingettes, 0.87 ± 0.02, 0.99 ± 0.14 and 1.11 ± 0.17 log CFU/g reductions were obtained at 50, 60 and 70 °C after the 100 s treatment, respectively. Greater bacterial reductions were observed on chicken breasts than on chicken wingettes (p < 0.05). A logistic (−1.12, 0.06) distribution could describe the bacterial reductions on chicken breasts at 50–60 °C. Weibull, exponential and log-linear models were compared for describing the bacterial reduction on chicken breasts at 70 °C and the Weibull model showed the best fit as indicated by the pseudo-R2, root mean square error (RMSE) and standard error of prediction (SEP) values. For chicken wingettes, a logistic (−0.95, 0.07) distribution could be used to describe the bacterial reduction at 50–70 °C. The developed predictive models could provide parts of the input data for microbial risk assessment of the poultry supply chain in China.