Resilient Supply Chain Management Research Articles

Editorial: advancements in sustainable and resilient supply chain management: integrative approaches and emerging trends

Editorial: advancements in sustainable and resilient supply chain management: integrative approaches and emerging trends

A system dynamics gamification tool to enhance supply chain resilience in times of disruptions

AbstractIn recent years, significant disruptive events have stressed the global business environment and supply chains worldwide, and these events have become more disturbing and frequent. Consequently, organisations have been facing many troubles in different echelons of their supply chains, such as high inventory stockouts or unplanned supply and recovery costs, significantly affecting their performance and stability. Accordingly, strategists must acknowledge enterprise and supply chain resilience as a vital competence in organisational strategies and ploys to guide decision-makers when dealing with disruptive events. This work aims to help organisations develop a powerful approach for testing and improving a decision-making process to minimise stockouts, through a simulation game that considers essential supply chain components, such as suppliers, products, and disruptive events, coexisting in a dynamic system. Upon implementing the proposed game within an organisation, the main expected outcomes include: (i) enhanced decision-making capabilities; (ii) increased awareness and preparedness to address disruptive events; (iii) improved practical understanding of resilient supply chain management practices; (iv) cost-effective training and awareness-building; and (v) repetitive learning opportunities, thereby enhancing supply chain management and resilience. The game’s simplicity is crucial to ensure that this tool remains accessible and practical for its intended audience, as excessive complexity may potentially discourage its use.

Artificial intelligence for enhancing resilience

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.

Climate and geopolitical risk correlations and response decisions for semiconductor global supply chains

Threats from climate risk (CR) and geopolitical risk (GR) undermine the resilience of the Semiconductor Global Supply Chain (SGSC). Plenty of company profit has plummeted due to the chip supply disruptions and delays. Particularly, the complex risk correlations (RCs) in a multi-project environment, and the inter-entity relationships, render the risk analysis and risk response decisions (RRDs) within CR and GR challenging. To address the problem, this study proposes a novel three-stage approach to explore the impact of CR and GR and obtain RRDs. First, based on the actual operation of SGSC, a system dynamics model of the RCs is conducted to analyze the effects of CR and GR. Second, a Stackelberg game model is formed based on the project interdependencies to find the optimal response resources and profit considering the inter-entity relationships. Third, a binary optimization model constrained in the optimal response resources is developed to determine RRDs. From the simulation results and the numerical example, the proposed approach is a powerful tool for risk managers of SGSCs. The actionable recommendations on managing and responding to CR and GR are summarized. This study provides theoretical and managerial insights for resilient supply chain management.

Resilient and sustainable semiconductor supply chain network design under trade credit and uncertainty of supply and demand

Supply Chain (SC) performance appraisal extends beyond economic evaluation to encompass environmental and social impacts, as well as resilience to supply and demand disruptions. Thus, SC network design decisions should simultaneously optimize all these performance metrics as a primary objective. However, existing studies optimized these objectives independently or in a limited scope, failing to comprehensively address the interconnected nature of these performance metrics. Therefore, this study proposes a mixed integer linear programming (MILP) model with four objectives to address a multi-tier resilient and sustainable semiconductor SC network design problem under trade credit, supply, and demand uncertainties. The model aims to determine the optimal number and location of various facilities, the amount to order from suppliers, economic production quantities, quantity allocation between SC entities, and ideal supplier selections. The proposed supply chain network contributes to the global effort to reduce carbon emissions and enhance the circular economy by considering carbon trading, recycling, and the proper disposal of end-of life products. Uncertainties in input parameters are addressed using a fuzzy programming approach, while lexicographic optimization and the ɛ-constraint method are used to solve the proposed model. This paper presents a numerical example to illustrate the applicability of the proposed model and provide managerial insights. The numerical analysis results show that provided an appropriate confidence level for the uncertain parameters, the sets of Pareto optimal solutions can be generated. And it also shows that the total SC cost increases with decreasing carbon emissions level and increasing job opportunities created. This research advances sustainable and resilient supply chain management practices while offering practical guidance for decision-makers in real-world contexts.

Resilient Supply Chain Management in Manufacturing Sector - A Qualitative Study

This study acknowledges that today’s business environment is dynamic and prone to unanticipated changes. The study aims to understand the implications of such disruptions in the supply chain especially in manufacturing sector. The main goal is to understand the challenges faced and the versatile practices followed to stay resistant to such dynamic environment. The study also focuses on the new practices businesses adopt to form resilient supply chain. Qualitative approach was done to capture the rich essence of the subject matter. The study used focus groups and in-depth interviews as techniques and interviewed members from different nodal points of a supply chain including manufacturers, logistic service providers and mid players in logistics. As a result, it was found out that Multinational companies are well equipped, and they have much better mitigation plans while small scale and mid-scale players need to work more on resiliency instead of just surviving the situations.

Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience

The integration of Artificial Intelligence (AI) into supply chain management has emerged as a pivotal avenue for enhancing efficiency and resilience in contemporary business operations. This paper explores various theoretical approaches to AI within the context of supply chain optimization, delineating pathways to achieve heightened performance and adaptability. Commencing with a historical overview, the paper delves into the evolution of AI techniques in supply chain management, elucidating how these methodologies have transformed the landscape of logistics and operations. Fundamental to this exploration is the discussion on mathematical modeling and algorithmic frameworks that underpin supply chain optimization, providing the theoretical foundation for subsequent AI applications. A key focus of the paper lies in the application of machine learning techniques for demand forecasting and inventory management, which leverage data-driven insights to optimize resource allocation and mitigate risks associated with supply-demand fluctuations. Additionally, network theory and graph algorithms play a crucial role in optimizing the structure and dynamics of supply chain networks, enabling efficient transportation, distribution, and inventory routing. Strategic decision-making in supply chains is addressed through the lens of game theory, which offers theoretical frameworks to model interactions among multiple stakeholders and optimize outcomes in competitive environments. Moreover, swarm intelligence and multi-agent systems provide innovative solutions for coordination and collaboration within complex supply chain ecosystems. Evolutionary algorithms and artificial neural networks are discussed as powerful tools for supply chain design, predictive analytics, and risk management, offering capabilities for optimizing decision-making processes across various operational domains. Furthermore, reinforcement learning techniques empower dynamic decision-making in real-time operational settings, fostering adaptive and resilient supply chain management practices. By integrating multiple AI techniques, hybrid approaches offer synergistic solutions that capitalize on the strengths of diverse methodologies to address multifaceted challenges in supply chain optimization. Through a synthesis of theoretical insights and practical case studies, this paper provides valuable insights into the current state and future directions of AI-driven supply chain optimization.

The Role of Big Data Analytics Capability to Achieve Competitive Advantage with the Mediation of Business Model Innovation: A Dynamic Capability View

The research aims to identify the function of big data analytics to attain competitive advantage through the mediation of business model innovation.260 samples were taken from experts in Pakistan's manufacturing & service sectors. Data analysis was done using (Smart PLS). The findings demonstrate that big data analysis capability (BDAC) has a considerable impact on competitive advantage. By using big data, companies can make rational, data-driven decisions to satisfy customers, sustain and resilient supply chain management, low production costs, and efficient and effective business operations. Similar to how BC affects competitive advantage, business model innovation partially mediates this effect. Additionally, the influence of BDAC on both competitive advantages & new business model innovation is moderated by organizational IT infrastructure. This research presents a unique scenario of big data analytics in which firms use bid data inside to innovate their established business models, resulting in considerable competitive advantage in emerging countries like Pakistan.

COMPLEX INTUITIONISTIC FUZZY DOMBI PRIORITIZED AGGREGATION OPERATORS AND THEIR APPLICATION FOR RESILIENT GREEN SUPPLIER SELECTION

One of the main problems faced by resilient supply chain management is how to solve the problem of supplier selection, which is a typical multi-attribute decision-making (MADM) problem. Given the complexity of the current decision-making environment, the primary influence of this paper is to propose the theory of Dombi operational laws based on complex intuitionistic fuzzy (CIF) information. Moreover, we examined the theory of CIF Dombi prioritized averaging (CIFDPA) and CIF weighted Dombi prioritized averaging (CIFWDPA), where these operators are the modified version of the prioritized aggregation operators and Dombi aggregation operators for fuzzy, intuitionistic fuzzy, complex fuzzy and complex intuitionistic fuzzy information. Some reliable properties for the above operators are also established. Furthermore, to state the art of the proposed operators, an application example in the presence of the invented operators is evaluated for managing resilient green supplier selection problems. Finally, through comparative analysis with mainstream technologies, we provide some mechanism explanations for the proposed method to show the supremacy and worth of the invented theory.

A sustainable and resilient supply chain (RS-SCM) by using synchronisation and load-sharing approach: application in the oil and gas refinery

Nowadays sustainable and resilient supply chain management (R&S-SCM) is an interesting and in the meantime vital problem that has grabbed the attention of many researchers. This study is an effort to investigate the simultaneous design of sustainable and resilient flow in the supply chain. To achieve a sustainable and resilient supply chain, there is a wide variety of strategies depending on their impacts on different industries and the features of their supply chain. The fuzzy QFD method is designed to select the most appropriate strategy in the study. Two approaches are used for this purpose. Early planning in sustainable mode is formulated assuming that all members of the network are healthy and final planning in the reactive approach to destruction which formulates the return to the original state in the shortest possible time, cost and scenario-based status. A hybrid priority-based Genetic Algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the optimal solutions. Response Surface Methodology (RSM) method is used to adjust significant parameters for the algorithm. Several test problems are generated showing that the proposed metaheuristic algorithm can find good solutions in reasonable time spans.

Determining the Stationary Enablers of Resilient and Sustainable Supply Chains

One of an organization’s significant challenges in a globalized world is reducing risk by building resilient supply chains (SCs). It is required to realize a competitive advantage in a volatile and fast changing environment. Conversely, the key enablers of such sustainable and resilient supply chain management are not fully analyzed in building projects. This study aims at determining the stationary enablers of resilient and sustainable supply chains. For this to happen, a questionnaire survey comprising 32 enablers of resilient and sustainable supply chains has been conducted with Egyptian engineers to appraise their degree of importance. The results show that the five most important enablers of resilient and sustainable supply chains are: top management support, adaptability, visibility, quality awareness, and responsiveness. This research’s results will allow building administrators to create diverse SCs, while being mindful of how the characteristics of a supply chain decrease or increase its resilience and eventually affect the exposure to risk in the building’s SCs.

Recessions and recoveries in Central African countries: Lessons from the past

AbstractThe Great Financial Crisis and the COVID‐19 pandemic have raised concerns about business cycles. In this paper, we examine the dynamics of past business cycles in CAEMC economies to infer the shapes of recessions and subsequent recoveries. We use annual GDP data covering the period 1960–2020 and non‐parametric approach. The main results are fourfold. First, recessions are, on average, long with a positive duration dependence relationship and the strength of recoveries is independent of the duration and the depth of the associated recessions. Second, recessions and recoveries are not all alike, meaning that history cannot be used as a guide for forecasting issue. Third, nearly 45% of the recessions and subsequent recoveries are V‐shaped, and the L, U and W patterns appear when governments are heavily indebted. Fourth, a regional cycle sometimes emerges. The results suggest a strengthening of the capacities of CAEMC states and a resilient supply chain management.

Supply Chain Modelling of the Automobile Multi-Stage Production Considering Circular Economy by Waste Management Using Recycling and Reworking Operations

The supply chain management plays a crucial role in delivering products from a supplier, through the manufacturer, distributors, and retailers to the targeted customers. The lifecycle of the products can be ended at any stage due to imperfect quality or waste, which are typically not managed well for a good price. This product’s life can be extended and increased with the use of the circular economy for the value addition processes which turn the waste into byproducts, which can be sold with maximum profit. The automobile industry is associated with various other small industries and is very significant for the economy at the local, national, and international levels. However, the industry also requires sustainable development in its supply chain management, gained by introducing the circular economy concept to manage and reduce the generated waste. The consumption of carbon fiber-reinforced composites (CFRCs) in the manufacturing of numerous automotive parts has acquired immense attention this decade, but the process also generates imperfect products (waste). The proposed model is based on a mathematical formulation to manage imperfect production by reworking and recycling, where the former is required to re-add value to the proportion of the rejected parts, and the latter is to recycle the remaining scrap into useful products by using a circular economy. The outsourcing operation is also added to provide an optimal level of inventory and lot sizing for minimizing the total cost of the supply chain management. Data from the automobile part industry are tested to provide the practical implications of the proposed SCM mathematical model. Sensitivity analysis is performed to understand the significance level of the individual parameters affecting the objective function, i.e., the total cost of the SCM. The results show a meaningful insight for the managers to obtain the benefits of the circular economy in multi-stage automobile part production for sustainable and resilient supply chain management.

Digital technologies and circular economy practices: vital enablers to support sustainable and resilient supply chain management in the post-COVID-19 era

PurposeThe COVID-19 pandemic has caused major disruptions and revealed the fragilities in supply chains. This crisis has re-opened the debate on supply chain resilience and sustainability. This paper aims to investigate distinct impacts of COVID-19 on supply chains. It identifies both short- and medium-to-long-term measures taken to mitigate the different effects of the pandemic and highlights potential transformations and their impacts on supply chain sustainability and resilience.Design/methodology/approachTo address the purpose of the study, a qualitative research approach based on case studies and semi-structured interviews with 15 practitioners from various supply chain types and sectors was conducted. Studied organizations included necessary and non-necessary supply chain sectors, which are differently impacted by the COVID-19 pandemic.FindingsThis study reveals five main challenges facing supply chains during COVID-19, including uncertain demand and supply, suppliers' concentration in specific regions, globalized supply chains, reduced visibility in the supply network, and limited supplier capacity. To help mitigate these challenges and develop both sustainability and resilience, this paper identifies some mitigating actions focusing on the promotion of the health and wellbeing of employees and supply chain stabilization. Further, in the post-COVID era, sustainable and resilient supply chains should consider regionalization of the supply chain, diversification of the supply network, agility, collaboration, visibility, and transparency; and should accelerate the use of smart technologies and circular economy practices as dynamic capabilities to improve supply chain resilience and sustainability.Originality/valueThis study contributes to exploring the sustainability- and resilience-related challenges posed by the COVID-19 pandemic. Its findings can be used by researchers and supply chains decision-makers to limit disruptions and improve responsiveness, resilience, sustainability, and restoration of supply chains. The results support benchmarking through sharing of the best practices and organizations can also integrate the different capabilities discussed in this study into the processes of selection and auditing of their suppliers.

Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic

COVID-19 has caused negative impacts on blood supply chain management, due to uncertain supply/demand and logistical disruptions. In the early weeks following the COVID-19 pandemic, 20–30% reduction in blood donation had observed, which adversely affected the whole blood supply chain. Although this shortage was partially compensated through rescheduling of elective surgeries and shifting some inpatient surgeries to outpatient surgeries, resumption of the normal surgeries by hospitals had increased the demands for the blood products. At the same time, the total blood supply was increased by some measures taken to overcome the blood shortage. In this paper, a multivariate time-series deep learning model based on long short-term memory is proposed to forecast the blood donation/demand. It takes daily time-series of blood donation/demand (internal features) as well as daily time-series of new confirmed COVID-19 cases/deaths (external features) as its inputs, and predicts blood donation/demand for the next week. The proposed model is used to achieve a resilient blood inventory management, capable of handling the uncertainties occurring during the COVID-19 pandemic. The proposed blood donation/demand forecasting model has been successfully simulated on the collected data of Tehran Blood Center in Tehran, Iran, for a time period from February 24, 2020, to October 14, 2021. Obtained results show the efficiency of the proposed model by obtaining 6.1% and 6.5% error between the actual and forecasted values of the number of donations and demands, respectively. The results of applying the proposed model for inventory management of blood platelets demonstrate the resiliency of our model to reduce shortage and wastage rates against the existing uncertainty handling models by 32.1% and 26.6%, respectively. The proposed method can be used to assist the decision makers in managing the blood supply chains through prioritizing the blood transfusions during COVID-19 and similar pandemics in the future.

Global Supply Chain Disruption Management Post Covid 19

This study demonstrates this pandemic as absolute catastrophic, which has distracted the overall supply chain activities, with significant shortcomings for businesses, consumers, and the overall global economy. Though in a regular time, it has been extremely challenging to ensure seamless supply chain operations, and senior management had to struggle to respond to critical uncertainties to protect their employees, safeguard supply security, alleviate the financial collision, tackle reputational risks, and steer the market uncertainty. The life-threatening virus has not only caused disaster in our healthcare system but also has destroyed the global economy. During this global economic turbulence, the world’s economy, including Bangladesh’s economy uncovers itself in an unbearable state, particularly due to slumping apparel exports, declining remittances, increasing job losses, and unpredictable consumer demand. Even before the pandemic, managing seamless supply chain operations were highly challenging and stressful. For many years, multinational corporations pursued to aggressively optimize cost through economies of scale, frequently by relocating production facilities to lower-cost labor regions—opted at the expense of other critical supply chain attributes like flexibility and agility. In the meantime, enterprises and governments have occupied in business continuity and resilient supply chain management planning exercises in recent years, outdated risk management undertakings have largely involved responding to national events relating to a specific geography or sector. The current global disruptions emphasize the importance of a new paradigm to improve supply chain resilience at a significant level. Finally, the study classified ten supply chain performance measurement attributes to diagnose the performance and efficiency of an organization. This study illustrates critical challenges and the urgency of the revalidation to be needed in our strategic framework, thus the study emphasizes the importance of comprehensive performance measurement attributes. This study also demonstrates an Integrated Supply Chain Performance Measurement (ISCPM) model connected to these attributes that can supersede the currently practiced models which are not sufficient to address all the issues in the current context.

Adaptive Framework Aims To Develop Resilient Supply Chain Using 3D Printing

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203089, “Adaptive Framework for Resilient Supply Chain Using 3D Printing in the Oil and Gas Industry,” by Yousef Al Tartoor, Adel Khalaf, and Mohammed Awadallah, ADNOC, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. Resilient supply-chain management plays an essential role in organizational success. While an effective supply chain is dependent on logistics, inventory control, and materials handling, it is also strongly interlinked with the use of new technologies that will enhance the complete supply-chain cycle. The complete paper develops a framework that introduces 3D printing in the oil and gas industry, taking into consideration the challenges that this technology is facing to penetrate a generally conservative industry and realizing its potential benefits. Sourcing Materials Challenges (As-Is Situation) Additive manufacturing can play a vital role in resolving pains experienced by global aftermarket supply chains. It can help in dealing with obsolescence of materials after the end of service, when the materials are either not available in market, very costly, or missing technical drawings and data sheets. Also, high uncertainty in the planning of demand in oil and gas leads to reordering of unnecessary materials because of high order quantity minimums or having the required parts as a part of kits (usually other items in such kits are nonmoving). Another supply-chain issue is long lead times for purchasing materials, especially internationally. Moreover, additive manufacturing can be a cost-effective solution with less material waste, especially for precious materials. Finally, additive manufacturing can help relieve the problem of working capital being locked up in inventory as a result of accumulation of dead inventory (nonmoving items), leading to scrapping or write-off of unused spare parts. Fig. 1 compares the cost of traditional methods of reverse engineering with 3D printing. Conventional methods of reverse engineering, such as casting or forging, can be cost-effective for noncomplicated configurations. As the complexity of the part increases, however, the cost increases exponentially, revealing 3D printing as the more cost-effective approach. Several commodities in the oil and gas industry can be plotted beyond the breakeven point in the figure. Such materials include 2D and 3D impellers, various valves, control-valve plugs and diffusers, casing scrapers, fuel injectors, nozzles, vanes, and blades. Market Analysis and Self-Assessment Political, economic, sociological, technological, legal, and environmental (PESTLE) analysis of their operating company was performed by the authors to gain a better understanding of the current situation of the business and external environment factors that may affect the company’s activities without much direct control from the company. From the standpoint of PESTLE analysis, the technological factor seems to be promising, considering reverse engineering and 3D printing of materials on demand. If any certified local workshop or 3D printing facility is able to produce like-original-equipment-manufacturers’ (OEM) spares, then certificates of conformity and warranty should be provided that match OEM warranties (generally 12 months from installation or 18 months from receiving the materials, whichever comes first).

Latest Advancement of Technologies in Supply Chain Management: An Overview

Supply chain management (SCM) involves an interconnected structure of facilities that produces elements or compounds for transforming into a semifinished compound. After completion, the final compound is delivered to customers via a distribution system. Owing to ever-changing customer demands, increasingly efficient and resilient SCM is necessary, which requires support from the latest technologies. Therefore, this paper presents an overview of the latest technologies in SCM and logistics. The management of a supply chain and role of the different actors involved differ from industry to industry and company to company. Consequently, SCM has become a crucial issue to manufacturers, professionals and researchers. The latest technologies identified in this paper include radio frequency identification, vehicular ad hoc networks, the Internet of things, robotics and artificial intelligence and blockchain. Moreover, the application of the aforementioned technologies in different industries and systems is discussed to understand how the technologies impact practitioners. Although not discussed thoroughly, other recent technology trends in SCM and logistics, such as machine learning, autonomous vehicles/drones, advanced analytics, cloud logistics, supergrid logistics, anticipatory logistics, omnichannel logistics and additive manufacturing (3D printing), are also highlighted in this paper. By understanding the advancements in SCM technology, the possible direction of future innovations can be effectively planned and articulated.

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem

The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision-makers and experts to adopt a fuzzy-based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision-making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t-conorms and t-norms have the same properties as those of the general t-conorm and t-norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision-making environment with uncertain condition and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multi-attribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy-neutrosophic-based approach for resilient supplier selection. The main contributions of this research work are the design, implementation and analysis of a multi-attribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the propsed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.