Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review

Mirzaee, Hossein; Ashtab, Sahand

doi:10.3390/su16198325

Open AccessReview

Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review

by

Hossein Mirzaee

^* and

Sahand Ashtab

^*

Shannon School of Business, Cape Breton University, Sydney, NS B1M 1A2, Canada

^*

Authors to whom correspondence should be addressed.

Sustainability 2024, 16(19), 8325; https://doi.org/10.3390/su16198325

Submission received: 14 August 2024 / Revised: 18 September 2024 / Accepted: 23 September 2024 / Published: 25 September 2024

(This article belongs to the Section Environmental Sustainability and Applications)

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Abstract

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The process of selecting suppliers is a critical and multifaceted aspect of supply chain management, involving numerous criteria and decision-making variables. This complexity escalates when integrating sustainable and resilient factors into supplier evaluation. This literature review paper explores various evaluation criteria that encompass economic, environmental, social, and resilience dimensions for supplier selection. Different methodologies to model and address these complexities are investigated in this research. This review synthesizes the findings of 143 publications spanning the last decade (2013–2023), highlighting the prevalent evaluation criteria and methodologies and identifying existing research gaps. In addition, the feasibility of combining multiple approaches to more accurately reflect real-world scenarios and manage uncertainties in supplier selection is examined. This paper also proposes a decision-making framework to assist practitioners in navigating the intricacies of this process. The paper concludes by suggesting seven potential directions for future research in this evolving field.

Keywords:

supplier selection; sustainability; resiliency; artificial intelligence; mathematical modeling; multi-criteria decision-making

1. Introduction

In the contemporary landscape of supply chain management, the supplier selection process is increasingly recognized as a critical strategic decision [1]. It is pivotal in ensuring competitive advantage and sustainability, particularly in a world where environmental concerns and sustainable practices are becoming central to business operations. This multifaceted problem involves a delicate balance of cost, quality, reliability, and adaptability, all of which are integral to the overall resilience of the supply chain [2]. In light of increasing environmental awareness and regulatory pressures, sustainable supplier selection has emerged as a crucial aspect. This not only poses a challenge in terms of choosing the appropriate criteria for supplier evaluation but also highlights the need to consider environmental and social factors alongside economic ones to select and measure suppliers’ performance [3].

As markets become more dynamic and consumer demands more complex, the ability of a business to align with the right suppliers can lead to enhanced innovation, market responsiveness, and operational excellence [4]. Conversely, inadequate supplier choices can undermine supply chain agility, risk mitigation efforts, and ultimately, customer satisfaction [5]. The volatile economic climate, exacerbated by global disruptions such as pandemics and trade conflicts, further underscores the need for resilient supplier selection strategies that can mitigate the effects of such disruptions. Therefore, navigating the supplier selection conundrum is a strategic endeavor, necessitating advanced methodologies and decision-support systems and considering resilient factors in evaluating suppliers to optimize outcomes [6].

The process of selecting suppliers involves evaluating multiple criteria, making it a complex decision-making scenario [7,8], and it becomes even more intricate when factors of sustainability and resilience are integrated. This integration, significantly complicates the decision-making process [9]. Therefore, the quest to identify and develop effective models and methods for tackling the challenges of sustainable and resilient supplier selection has become increasingly pertinent. Ref. [10] analyzed 60 research papers on supplier selection published from 1991 to 2011, with a focus on environmental factors. However, it did not extensively consider social aspects. In the context of modern supplier selection, where sustainability is increasingly vital, it is important to incorporate all three pillars of sustainability—environmental, economic, and social. This comprehensive approach is essential to reflect the evolving priorities in supplier selection practices. Ref. [7] conducted an extensive review of 33 journal articles published between 1997 and 2011. The focus of this review was on green supplier selection, specifically examining multi-criteria decision-making approaches. Similar to previous studies in this area, this paper primarily concentrated on the environmental aspect of sustainability. Ref. [9] offers a comprehensive literature review of sustainable supplier selection methods. Covering publications from 1990 to 2019, the paper examines 82 studies that use multi-criteria decision-making/aid (MCDM/A) methods. It categorizes these methods based on their application, decision-making perspectives, and their capacity to handle uncertainties. The review highlights a trend towards compensatory methods in sustainable supplier selection and discusses their limitations, especially the insufficient consideration of environmental and social criteria against economic ones. The study calls for more non-compensatory approaches to ensure a balanced evaluation of suppliers across all sustainability dimensions. Ref. [11] conducted a review of how Data Envelopment Analysis (DEA) has been integrated into purchasing and supply management, particularly focusing on supplier selection and evaluation. It covers research published between 2009 and 2018, analyzing 54 papers that propose the use of DEA for supplier-management-related decisions. The paper utilizes descriptive and multivariate statistics to categorize and understand these studies, highlighting the focus on supplier selection, the limited number of practice-oriented papers, and the tendency of sustainability considerations to focus mainly on environmental factors.

The literature reviewed earlier in this paper, spanning 29 years, predominantly focuses on the environmental aspects of sustainability in supplier selection, employing various methodologies for evaluation, ranking, and selection. However, these studies tend to overlook the social aspects of sustainability, with many social criteria remaining unexamined. Additionally, the concept of resiliency and its impact on supplier selection is notably absent in this body of work. Regarding the methodologies utilized, there is a strong preference for multi-criteria decision-making (MCDM) methods. Yet, the emerging trends in artificial intelligence (AI) and the potential of mathematical programming, as well as their integration, are not sufficiently explored.

In contrast, our analysis aims to provide a comprehensive examination of all criteria—traditional, sustainable, and resilience-related—used in supplier evaluation. We focus on identifying and highlighting the most prominent ones and categorize the methodologies and models for solving supplier selection problems into three main groups: mathematical programming, MCDM methods, and AI algorithms. Our goal is to uncover the most effective methods, highlight existing gaps, and explore potential combinations to develop a robust tool for addressing supplier selection issues. Concluding our paper, we propose a decision-making framework complemented by mini-proposals for future research, offering a structured approach and a roadmap for researchers in the field of supplier relations.

The remainder of this paper is organized as follows: Section 2 outlines the research methodology employed in this study. Section 3 presents a descriptive analysis, including the distribution of papers by publication year and journal, applied methods for modeling and solving supplier selection problems, and an evaluation of sustainable criteria and resilience. Section 4 engages in a detailed discussion of the findings, and Section 5 concludes the paper by summarizing the key conclusions.

2. Research Methodology

This literature review focuses on multi-criteria decision-making (MCDM), AI, and mathematical programming methods in the context of sustainable and resilient supplier selection. The primary research questions guiding this study are:

How to formulate the supplier selection problem in the sustainability and resiliency framework?
What are the best practices for ensuring resilience in supplier selection to mitigate disruption effects?
Which issues constitute avenues for future research for sustainable resilience supplier selection regarding the gaps and strengths found in the literature of supplier selection?
What methodologies are best for solving supplier selection problems?

Our methodology for data collection was rooted in the principles of replicability, transparency, and inclusivity of a wide spectrum of high-quality scholarly articles. We utilized the Scopus database for its extensive coverage of the relevant literature, particularly favoring its expansive collection of peer-reviewed articles published between 2013 and 2023. This approach mirrors the methodologies employed in earlier studies such as those by [12].

Our search strategy was designed to capture articles that explicitly mentioned key terms related to our research in their titles, abstracts, or keywords. The specific search string was meticulously crafted to ensure the comprehensive inclusion of relevant studies while excluding conference papers, reports, notes, and other non-peer-reviewed materials. This string was formulated as follows: TITLE-ABS-KEY (“supplier selection”) AND TITLE-ABS-KEY (“sustainability” OR “Resiliency”) AND TITLE-ABS-KEY (“artificial intelligence” OR “multi-criteria decision-making” OR “machine learning” OR “Mathematical modeling”) AND PUBYEAR > 2013 AND PUBYEAR < 2024 AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)).

The initial search yielded a total of 122 papers, which were then scrutinized for relevancy based on their titles and abstracts. Papers that did not align with the core themes of sustainability or resilience in supplier selection, or did not apply MCDM, AI, or mathematical programming approaches, were excluded. This screening process led to the elimination of 33 papers.

In line with the recommendations of [13], we expanded our search to include Google Scholar to capture the most recent publications. This additional step contributed 54 new papers to our review base.

The resulting collection of articles from this meticulous search provides a robust foundation for our study, offering insights into the current state and evolution of research in sustainable and resilient supplier selection, influenced by advanced decision-making methodologies.

3. Descriptive Analysis

In addressing our research questions on evaluating suppliers based on sustainability and resilience criteria, coupled with modeling and solution strategies, we will initially present descriptive statistics of the identified papers. This will be followed by an analysis of observed gaps and key findings. Subsequently, we will introduce a decision-making framework tailored to these considerations.

3.1. Distribution of Papers Based on Publication Year Journal

The database under review consists of 89 articles published in peer-reviewed journals over the past decade (2013–2023). Figure 1 illustrates the yearly distribution of these reviewed articles.

The provided line graph presents a temporal progression of document counts from 2013 to 2023. An overarching upward trend is observable, suggesting a mounting interest or advancements within the field these documents encompass. The time frame under consideration is substantial, covering nine years, which affords a comprehensive perspective on long-term tendencies.

Upon closer inspection, the graph reveals certain fluctuations: a marginal decline from 2013 to 2014 is followed by a rebound in 2015. A period of stability is noted between 2016 and 2017, after which there is an ascent through 2018. Notably, there is a pronounced surge in document production beginning in 2022. This spike may very well be a consequence of the global supply chain disruptions precipitated by the COVID-19 pandemic, which led to a heightened scholarly focus on resolving the emergent issues in supplier selection. The challenges imposed by the pandemic likely spurred an increased volume of publications as researchers and practitioners sought to address and mitigate the complexities presented by these disruptions.

The steep upward trajectory continues through 2020 to 2023, nearly doubling the figures observed in preceding years. This indicates a robust and growing engagement with the subject matter during these years.

In Table 1, we present a distribution of journal publications related to sustainable and resilient supplier selection. This table encapsulates the frequency of publications across various academic journals, indicating prevailing trends and focal points within the research community.

The journal Sustainability emerges as a leading outlet, with 14 publications, underscoring its prominence as a venue for disseminating research on advanced systems that have practical applications in sustainability and resilience within supply chains. The Journal of Cleaner Production follows with 12 publications, reinforcing its status as a significant journal for research at the intersection of cleaner production processes and sustainable practices.

Computers & Industrial Engineering with 10 publications and International Journal of Production Economics with 9 publications are also noteworthy contributors, which reflect the interdisciplinary nature of sustainable and resilient supplier selection, spanning topics from industrial engineering to economic aspects of production.

3.2. Applied Methods to Model and Solve the Supplier Selection Problem

Table A1 presents a highlight of the studies considered in our literature review, emphasizing the methodologies and techniques applied in the realm of decision-making with an eye toward sustainability and resilience. The curated entries represent a cross-section of recent advancements and applications in the field, detailing the methodological frameworks, the employed techniques, as well as the specific dimensions of sustainability and resilience they seek to enhance. Furthermore, the uncertainty factors addressed by each study are cataloged, underscoring the inherent challenges of predictive modeling and strategic planning in environmentally and socially conscious contexts. This summarization aims to encapsulate the breadth of research approaches within our literature scope, offering a synthesized reference point for the analysis and discourse that follows.

The table consolidates a diverse array of studies that have been examined in our literature review, each contributing unique methodologies and techniques to the field of decision-making with an emphasis on sustainability and resilience. As shown in Figure 2, the methodologies encompass a range of multi-criteria decision-making (MCDM), mathematical programming, artificial intelligence (AI), and combinations of these approaches.

The MCDM framework is often augmented with mathematical programming, as seen in the work of [14], which employs Stochastic Fuzzy Best–Worst Method (SFBWM) alongside a Seasonal Autoregressive Integrated Moving Average (SARIMA) to address environmental and social sustainability aspects. Mathematical programming is employed to develop robust optimization models, with [15,16] using robust-fuzzy multi-objective goal programming to integrate economic, environmental, and social factors under uncertainty. AI techniques are leveraged by [17,18], utilizing Random Forest and Long Short-Term Memory networks, respectively, to forecast and analyze environmental impacts within sustainability-focused decision-making. Interpretive Structural Modeling and Mixed-Integer Linear Programming are examples of mathematical programming techniques used by [19,20] to explore the complexities of decisions within the intertwined realms of the economy, environment, and society. Techniques such as Fuzzy AHP and TOPSIS, highlighted by studies like [21,22], are indicative of a trend towards employing fuzzy logic and grey systems theory. These methods are particularly suited to handle the vagueness and imprecision inherent in sustainability and resilience metrics.

Collectively, the studies cited, such as [1,23,24], contribute to a nuanced understanding of methodological advancements in environmental and social decision-making processes. They illustrate a landscape where uncertainty is a pivotal consideration, demonstrating the application of diverse strategies to navigate complex and often stochastic systems.

More statistical analyses of all the reviewed papers are provided in the subsequent sections.

Multiple Criteria Decision-Making (MCDM): MCDM techniques have been at the forefront of supplier selection research, with 83 studies incorporating this approach. These techniques are particularly suited for complex decision-making scenarios where diverse and sometimes conflicting criteria must be weighed. In supplier selection, factors such as cost, quality, sustainability, and resilience are key. Within the umbrella of MCDM, as depicted in Figure 3, the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Best–Worst Method (BWM), and VIKOR stand out as the most utilized methods, featured in 35, 31, 22, and 12 respective studies. Their widespread use is testament to their robustness in evaluating, comparing, and ranking suppliers based on a set of predefined criteria.

Mathematical Programming: This approach, employed in 18 studies, underscores the importance of a structured quantitative framework in the supplier selection process. It allows for the formulation of complex selection problems in mathematical terms, offering precise and objective decision-making tools. Various mathematical programming techniques such as single and multi-objective optimization, mixed-integer linear programming, as well as robust, fuzzy, and stochastic models, have been applied. These models help capture the intricacies of the supplier selection problem by accommodating multiple objectives and handling uncertainty, thus providing a more resilient decision-making process.

Artificial Intelligence (AI): AI’s contribution to supplier selection is evident in its application across various studies. This surge reflects the evolving landscape of supply chain management, which increasingly demands the processing of large datasets and the identification of complex patterns that traditional methods may not easily discern. AI techniques such as support vector machines (SVMs) and random forest have been applied to derive insights from data, aiding in the identification of optimal suppliers. These methods are particularly useful for predicting supplier performance and risk, thereby enabling more informed and data-driven decision-making.

Hybrid Methodologies

Mathematical Programming + MCDM: The hybridization of mathematical programming and MCDM, observed in 26 studies, indicates a growing appreciation for integrated approaches. This combination capitalizes on the strengths of both methodologies: the systematic rigor of mathematical programming and the multi-criteria analysis capability of MCDM. By doing so, it paves the way for developing sophisticated models that can tackle the complexity of supplier selection while providing clear and actionable insights.

AI + MCDM: The intersection of AI and MCDM, explored in four studies, is a testament to the innovative progression in the field. It harnesses the computational power of AI for handling large-scale data and complex patterns while utilizing the decision-making structure of MCDM to systematically evaluate the outcomes. This blend aims to enhance the decision-making process by providing a nuanced analysis that is both data-rich and criteria-specific.

Mathematical Programming + AI: The collaboration between mathematical programming and AI, evidenced in five studies, suggests a strategic move towards leveraging the predictive capabilities of AI along with the systematic procedures of mathematical programming. Such an approach is indicative of a shift towards integrating traditional and contemporary techniques to foster a more dynamic, responsive, and intelligent supplier selection process.

While the adoption of hybrid methodologies is a developing trend, established methods like MCDM and mathematical programming continue to be integral in supplier selection research. The inclination towards AI and machine learning points to a paradigm shift to data-centric and automated systems in addressing the complexities of modern supply chains.

3.3. Sustainability Criteria

Table 2 shows the frequency of various criteria used in the literature for sustainable and resilient supplier selection. The table categorizes the criteria into four main groups: economic, environmental, social, and resilience.

Economic Criteria: The most frequently mentioned economic criteria are cost and quality, which suggests that these are considered primary considerations when selecting suppliers in the context of sustainability and resilience. This is understandable as cost-effectiveness is crucial for business sustainability, while quality assurance is key to long-term resilience and customer satisfaction. Delivery performance also ranks high, indicating the importance of reliable logistics in supplier selection. Other criteria such as flexibility, technology level, and financial capability/stability are also significant but mentioned less frequently. These factors are essential for adapting to market changes and maintaining operations during disruptions.

Environmental Criteria: GHG emissions (Air Pollution Control) and environmental management systems are the most frequently cited environmental criteria. This reflects a strong awareness of the need for suppliers to manage their carbon footprint and have systems in place for environmental management. Green design (Eco-design) and waste management are also important, highlighting the trend towards products that are environmentally friendly throughout their lifecycle. Criteria such as product recyclability and energy (resource) consumption show a recognition of the role of technology and energy efficiency in environmental sustainability.

Social Criteria: Work safety and labor health is the leading social criterion, emphasizing the importance of human factors in the sustainability of supply chains. Worker education and training and information disclosure show that there is a focus on enhancing skills and safeguarding stakeholder interests. Other social factors such as human rights (rights of employees) and stakeholder rights protection are considered important but are less frequently mentioned, suggesting they are secondary considerations.

Resilience Criteria: Risk management (Awareness) is the most repeated criterion under resilience, indicating that awareness and management of risks are paramount for resilience in supplier selection. Other aspects such as responsiveness, agility, flexibility, and having a surplus inventory are recognized as important for the ability to respond to and recover from adverse events. The category labeled ’others’ in each section suggests there are many more criteria considered in the literature that are not as frequently mentioned as those listed but are nonetheless important for a comprehensive evaluation of suppliers.

In summary, the analysis shows that while economic factors are still dominant in supplier selection, there is a considerable and growing emphasis on environmental and social considerations. Resilience criteria, while not as prevalent, are recognized as crucial for ensuring supply chain robustness. The data imply a holistic approach to supplier selection that balances cost and quality with sustainability and the ability to withstand and recover from disruptions.

4. Discussion

The literature reveals a significant focus on the inclusion of sustainability into supplier selection processes. Economic factors like cost, quality, and delivery performance continue to dominate the supplier selection criteria; however, there is a noticeable shift towards incorporating environmental, social, and resilience factors. This shift is driven by a growing recognition of the risks posed by environmental degradation, social instability, and global supply chain disruptions.

In the economic realm, the emphasis is on cost and quality as critical factors for supplier evaluation, which aligns with traditional business priorities aiming for profitability and customer satisfaction. The high frequency of mentions for ’Delivery Performance’ suggests that timeliness and reliability remain significant considerations, reflecting the need for responsive and dependable supply chains.

Environmental concerns are prominently featured, with ’GHG emissions’ and ’environmental management systems’ being the most cited criteria. This indicates that businesses are increasingly aware of their carbon footprint and the need for environmental stewardship. The focus on ’green design’ and ’product recyclability’ underscores a trend towards lifecycle thinking in product design, which is crucial for the circular economy.

The social aspect of supplier selection, led by ’work safety and labor health’, highlights the importance of human-centric factors in sustainability. This suggests that companies are paying more attention to the welfare and rights of workers in their supply chains, which can contribute to a more equitable and socially responsible business model.

Resilience criteria, while less prevalent, are nonetheless recognized for their importance in ensuring supply chain continuity and recovery capabilities. The prioritization of ’risk management’ indicates a strategic approach to anticipating and mitigating potential disruptions.

The methodologies for supplier selection have evolved to incorporate complex decision-making tools that address sustainability and resilience criteria. The literature indicates a preference for multi-criteria decision-making (MCDM) methods, which are capable of handling multiple, often conflicting criteria. Techniques such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are frequently used due to their ability to provide a structured framework for evaluating diverse factors.

AHP and ANP, in particular, are noted for their ability to decompose the decision-making process into a hierarchy of more manageable sub-problems, allowing decision-makers to assess various criteria systematically. TOPSIS is recognized for its ability to rank alternatives based on their distance from an ideal solution, which aligns well with the goal of identifying suppliers who best meet the desired criteria.

Furthermore, the use of artificial intelligence (AI) and machine learning techniques in supplier selection is a significant development, as these technologies offer powerful capabilities for data processing and pattern recognition. AI methods, such as neural networks and support vector machines, have the potential to analyze large datasets and identify complex relationships between supplier attributes and selection criteria.

However, despite these advancements, the literature reveals a gap in the integration of AI with traditional MCDM methods. While hybrid approaches are mentioned, there is a lack of detailed exploration into how these integrations can be effectively implemented. Combining AI’s predictive power with MCDM’s structured decision-making could lead to more robust and adaptive supplier selection processes, particularly in dynamic and uncertain environments.

The investigation into supplier selection methodologies reveals that mathematical programming has been employed in multiple studies, highlighting its significance in structuring the supplier selection process quantitatively. This approach is crucial for formulating complex problems in mathematical terms, which then allows for precise and objective decision-making. The utilization of various mathematical programming-based techniques, such as single- and multi-objective optimization, mixed-integer linear programming, as well as robust fuzzy and stochastic models, signifies the depth of the quantitative analysis required in contemporary supplier selection.

These models excel in capturing the complexities of supplier selection by accommodating multiple objectives and handling the inherent uncertainties within the supply chain. The flexibility of these models to adapt to different scenarios is especially valuable in crafting a supplier selection process that is not only efficient but also resilient to the fluctuations and disruptions that are typical in global supply chains.

Furthermore, the implementation of mathematical programming-based methods provides a decision-making tool that can quantify and integrate the sustainability and resilience criteria into the supplier selection framework. This integration is indicative of a sophisticated approach to supplier selection, aligning with the strategic goals of organizations to maintain competitive advantage while adhering to sustainability and resilience imperatives.

The application of these mathematical models in conjunction with AI technologies suggests a progressive trend towards more dynamic, responsive, and intelligent supplier selection processes. As the landscape of supply chain management continues to evolve, the synergy between mathematical modeling and AI could potentially pave the way for developing advanced models that can handle the increasing complexity of supplier selection criteria, thereby leading to more informed and robust decision-making in the field.

4.1. Gaps and Findings

Despite the comprehensive nature of the current literature, several gaps remain. Firstly, while economic criteria are well explored, the integration of environmental and social criteria into a unified supplier selection framework is less developed. There is a need for more research on how to balance these often competing priorities effectively. It is particularly crucial to identify and refine the most effective evaluation criteria that pertain to environmental and social considerations.

Secondly, the literature indicates an emerging interest in resilience, but there is a lack of deep dives into specific strategies and frameworks that can be adopted to enhance supply chain resilience in the face of disruptions like global pandemics or climate change. The current literature primarily concentrates on the criteria for assessing supplier resilience, yet there are a variety of strategies—including multiple sourcing, backup suppliers, stockpiling, and the use of temporary facilities—that supply chain managers can implement to fortify their supply chains against unforeseen events.

The findings suggest that while there is a trend towards more holistic approaches to supplier selection, there is still a need for practical frameworks and tools that can help businesses operationalize these criteria. Additionally, the growing interest in the use of AI and machine learning presents an opportunity to develop more sophisticated decision-support systems. However, there is a gap in understanding the best practices for integrating these technologies with traditional decision-making processes to enhance supplier selection.

Another significant finding is the increasing adoption of hybrid methodologies that combine mathematical programming, MCDM, and AI. This trend suggests a move towards more nuanced and complex decision-making frameworks that can handle the multifaceted nature of supplier selection in a sustainable and resilient supply chain context.

Furthermore, while there is an acknowledgment of the importance of social criteria in supplier selection, there is a noticeable gap in the literature regarding the operationalization of these criteria. Social aspects are often less quantifiable and may be subject to bias, indicating a need for more robust methods of evaluation.

In terms of resilience, the focus is primarily on risk management and awareness, with less emphasis on the practical implementation of resilience strategies. This gap points to the need for research that translates resilience concepts into actionable business practices.

Overall, the gaps identified in the literature suggest several avenues for future research:

Developing comprehensive frameworks that integrate economic, environmental, social, and resilience criteria in supplier selection while expanding on the social criteria to include Diversity, Equity, and Inclusion (DEI).
Creating practical tools and models that incorporate AI and machine learning to handle the complexities of modern supply chains.
Exploring best practices for operationalizing social and resilience criteria in the supplier evaluation process.
Investigating the role of supplier diversity and flexibility in enhancing supply chain resilience.
Examining the long-term impacts of sustainable and resilient supplier selection on business performance and competitiveness.

These findings and gaps offer a roadmap for future research endeavors aimed at advancing the field of supplier selection in the context of sustainability and resilience, ensuring that supply chains are not only cost-effective and quality-oriented but also environmentally responsible, socially equitable, and robust against disruptions.

4.2. A Decision-Making Framework for Supplier Selection

The steps outlined below are used to make a decision in selecting the best suppliers, as illustrated in Figure 4.

1. Data Gathering: Collect data on the most relevant and effective evaluation criteria for supplier selection, considering economic, environmental, social, and resilience factors. These data can be gathered based on this literature review paper, which investigates the most frequently used and efficient criteria in the literature.

2. Expert Involvement: Engage with experts to weigh and rank these criteria according to their importance and relevance to the organization’s specific context.

3. Criteria Customization: Introduce new criteria that align with the organization’s unique goals, vision, and strategic direction. DEI, as newly identified social criteria, can be added to the criteria list.

4. Supplier Evaluation: Gather and assess data related to potential suppliers’ performance or scores based on the selected evaluation criteria.

5. Methodological Application: Choose and apply the most suitable method or combination of methods to model the supplier selection problem, incorporating approaches to manage uncertainties.

6. Problem-Solving: Execute the chosen model(s) to solve the supplier selection problem.

7. Result Analysis and Decision Making: Analyze the results obtained from the model(s) and make informed decisions to select the best supplier(s) based on the analysis.

This framework would not only serve as a theoretical construct but also as a practical guideline for practitioners in the field, encapsulating best practices and a systematic approach to making informed supplier selection decisions. It emphasizes the importance of a structured and data-driven selection process, integrating both quantitative and qualitative factors, and leveraging expert knowledge to inform the weighting of criteria.

The involvement of AI and advanced analytics in this framework could enhance decision-making accuracy and efficiency. This could involve using machine learning algorithms to predict supplier performance or employing optimization techniques to find the best fit for the organization’s needs.

4.3. Avenues for Future Research

4.3.1. Developing a Weighting System for Supplier Evaluation Criteria Using Machine Learning

In the realm of supplier evaluation within the context of supply chain management, there exists a promising avenue for future research that revolves around the application of machine learning techniques. As demonstrated in recent research proposals, the integration of machine learning methodologies offers the potential to revolutionize the supplier selection process. This emerging field aims to create dynamic weighting systems for evaluation criteria, which, when informed by machine learning insights, can significantly enhance the precision and effectiveness of supplier selection.

The proposed research, which leverages machine learning techniques such as support vector machines, gradient boosting, and random forest primary tools, explores a holistic approach to supplier evaluation by incorporating both traditional and unconventional criteria, including factors like import and export indexes. This forward-thinking approach recognizes the importance of assessing a supplier’s operational competence and global market influence comprehensively. Additionally, the dynamic nature of the developed weighting system allows it to adapt to evolving market conditions and business needs, addressing a crucial need in the field.

Furthermore, it is essential to acknowledge the growing significance of sustainability within supplier selection processes. As part of future research endeavors, sustainable criteria for supplier selection should be considered. While our review has touched upon social sustainability criteria, the existing literature on the topic has not given this dimension the attention it deserves. Future research should address this gap by developing supplier selection frameworks that integrate social sustainability more comprehensively. This could involve examining the impact of supplier practices on labor rights, community well-being, and ethical standards, alongside economic and environmental criteria. Among the social sustainability criteria, Diversity, Equity, and Inclusion (DEI) can be added to the list of commonly used criteria in the literature on supplier selection. The incorporation of DEI criteria aligns with the evolving corporate values and societal expectations related to ethical and socially responsible supplier practices. This inclusion represents a progressive step towards more holistic and responsible supplier evaluations.

As we look to the future, researchers are encouraged to investigate deeper into the application of machine learning techniques for supplier evaluation while also expanding the criteria set to encompass sustainability, including DEI considerations. Exploring alternative machine learning methods, refining the data collection process, and validating the developed models with real-world data remain essential steps. The potential for this research avenue to contribute to the efficiency and accuracy of supplier selection processes, as well as its broader implications for global supply chain networks, make it a promising area of inquiry for scholars and practitioners alike. As such, it represents a valuable future direction for the advancement of supply chain management practices.

4.3.2. Combination of Multi-Objective Mathematical Modeling and Machine Learning in Developing a Sustainable Supplier Selection Framework

This proposal presents a forward-thinking strategy for sustainable supplier selection, integrating machine learning and multi-objective MILP models. The proposal emphasizes the importance of a nuanced approach to evaluating suppliers. This involves not only identifying and weighing criteria through machine learning but also refining these criteria by considering sub-criteria, thereby enhancing the precision of the evaluation process. Additionally, the research suggests a hybrid approach of combining literature reviews with expert questionnaires to identify the most relevant criteria effectively.

Moreover, the proposal underlines the significance of selecting the optimal machine learning technique tailored to the problem, with a focus on accuracy. This approach aims to create a robust decision-support tool that incorporates predictive analytics for forecasting market trends and supply risks. The outcome envisaged is a comprehensive and adaptable model, suitable for various industries, that revolutionizes supply chain management by synergizing advanced ML techniques and mathematical optimization for a more sustainable, cost-effective, and resilient supplier selection process.

While the current literature has begun to explore the theoretical potential of AI in supplier selection, there is a significant gap in practical, sector-specific applications. Future research should focus on developing and testing AI-driven models tailored to the unique needs of various industries, such as manufacturing, healthcare, and retail. This would involve creating detailed case studies, frameworks, and implementation guidelines that highlight how AI can be effectively integrated into supplier selection processes to enhance sustainability and resilience. Additionally, research should address the ethical challenges of AI application by proposing transparent, fair, and accountable AI frameworks that can be adapted across different sectors. Improving data collection methods and quality is also critical to ensuring that AI models can operate effectively in real-world settings.

4.3.3. Fortifying Supply Chains: Strategic Supplier Selection for Enhanced Resilience

This proposal proposes enhancing supply chain resilience by strategically selecting suppliers based on resilience-focused criteria. The research aims to develop a robust framework for evaluating suppliers, emphasizing their adaptability and recovery capabilities in the face of disruptions. This involves defining specific resilience criteria, exploring strategic aspects of resilient supply chain management, and assessing the effectiveness of these criteria through empirical methods. The anticipated outcome is a comprehensive decision-making process for supplier selection, integrating resilience alongside traditional metrics like cost and quality, thereby equipping businesses to navigate the complexities of a volatile global market. Moreover, supply chain managers have a range of tactics at their disposal to enhance chain resilience. These tactics include diversifying sourcing options, establishing backup suppliers, creating stockpiles, and utilizing temporary facilities. These measures, implemented alongside established resilience criteria, serve to strengthen supply chains, making them more robust and better prepared for unexpected disruptions.

The study also plans to analyze how resilient strategies such as back up suppliers, multiple sourcing, temporary facilities, stockpiling, using safety stock, etc., can adapt to long-term global challenges, such as climate change and geopolitical shifts. By focusing on resilience and sustainability, the proposed framework seeks to balance robustness and adaptability in supply chains. This research is expected to make significant contributions to the supply chain management literature and provide practical value to industry practitioners, setting new standards for resilience-focused supply chain management in an uncertain global environment.

4.3.4. Strategic Maneuvers in Supply Chain: A Game Theory Approach to Buyer Competition in Constrained Supplier Markets

This research explores the application of game theory to understand competitive dynamics in supplier selection, particularly in constrained markets where suppliers also choose their customers. This research will create game-theoretical models to analyze the interactions between competing firms and selective suppliers, focusing on how these dynamics affect supplier choice, pricing, and supply chain stability. This study will consider factors like limited procurement capacity, market regulations, and economic shifts. The anticipated outcome is a strategic framework for businesses to improve negotiation and positioning for supplier contracts, contributing to both academic knowledge and practical strategies in supply chain management.

4.3.5. The Green Link: Deciphering the Dynamics of Sustainability in Supplier Selection and Consumer Demand

This research will focus on the dynamics of sustainability in supplier selection and its impact on consumer demand. It examines how supply chains balance economic efficiency, environmental stewardship, and social responsibility, and how these factors influence consumer behavior towards eco-friendly products. This study aims to explore the connection between consumer preferences for sustainability and supplier practices, potentially leading to a feedback loop promoting greener practices. Utilizing consumer surveys, case studies, and data analytics, this research seeks to provide strategic recommendations for businesses to align their supplier strategies with the growing consumer demand for sustainable products. Using machine learning approaches as efficient ways to find the relationship between the sustainability score of a supplier and customer demand can be useful.

4.3.6. Application of Exact Algorithms in Obtaining Robust Solutions for Large-Scale Supplier Evaluation Models: Benders Decomposition Algorithm

This research explores the use of exact algorithms, specifically Benders Decomposition, for large-scale supplier evaluation models. This research focuses on overcoming the complexities of extensive supplier networks by efficiently breaking down large optimization problems into manageable sub-problems. This study aims to assess the adaptability of this technique in different supplier selection scenarios and compare its performance with other algorithms. The findings are expected to offer strategic insights for advanced, efficient supplier selection in supply chain management.

5. Conclusions

This study provides a literature review of 89 peer-reviewed journal papers on sustainable and resilient supplier selection. This comprehensive review has delineated the intricate relationships and intersections between sustainability, resiliency, and the use of AI in supplier selection. The emergent landscape in supply chain management underscores the necessity for an integrated approach, considering these three critical dimensions. Among several gaps identified in this research, we highlight developing a comprehensive framework that integrates economic, environmental, social, and resilience criteria in supplier selection while expanding on the social criteria to include Diversity, Equity, and Inclusion (DEI).

The implementation of AI technologies in supplier selection has demonstrated potential in enhancing sustainability and resilience. By leveraging AI, organizations can make more informed, data-driven decisions that not only emphasize cost and efficiency but also prioritize environmental and social responsibilities. AI’s predictive capabilities further empower supply chains to be more adaptive and resilient to disruptions.

However, challenges remain, particularly in the ethical application of AI and the need for comprehensive data for accurate decision-making. The complexity of measuring and integrating sustainability and resilience factors in AI algorithms also presents a significant challenge.

Future research directions should aim at developing ethical AI frameworks that are transparent and accountable, enhancing data collection and quality for better sustainability and resilience assessment and exploring sector-specific applications of these integrated approaches. This will not only contribute to the theoretical advancements in the field but also provide practical guidance for organizations aiming to achieve sustainable and resilient supply chains through the effective use of AI in supplier selection.

In conclusion, the synergy of sustainability, resiliency, and AI in supplier selection heralds a new era in supply chain management, promising a more holistic, responsible, and forward-looking approach to global challenges.

Author Contributions

Conceptualization, H.M. and S.A.; methodology, H.M. and S.A.; analysis, H.M.; writing—original draft preparation, H.M.; editing/revising, S.A.; visualization, H.M.; supervision, S.A.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by NSERC grant number [ALLRP 576988-22].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Summary of literature review.

Reference	Methodology	Technique Used	Sustainability and Resiliency Aspects	Uncertainty
[25]	MCDM + AI	VC-DRSA, CRITIC, and CTOPSIS	Economic, environmental, and social	Machine Learning
[14]	MCDM + Mathematical programming	Stochastic Fuzzy Best–Worst Method (SFBWM), SARIMA	Environmental and social	Fuzzy set theory, Stochastic optimization, Robust optimization
[26]	AI	Artificial Neural Network (ANN)	Resilience	Fuzzy DEA
[27]	MCDM	DAHP-DEMATEL hybrid method	Economic, environmental, and resilience	The D number method
[15]	Mathematical programming	Multi-objective robust optimization model	Environmental and economic	Robust optimization
[16]	Mathematical programming	Robust fuzzy multi-objective goal programming	Economic, environmental, and social	Robust optimization, Fuzzy set theory
[28]	MCDM + Mathematical programming	Augmented epsilon-constrained, AHP	Economic, environmental, and social
[17]	AI	Random forest method	Economic, environment, and social
[29]	MCDM + AI	Integration of Best–Worst Method (BWM) and gradient boosting machine learning	Resilience	—
[30]	Mathematical programming + MCDM	MILP, fuzzy DEMATEL, TOPSIS, and AHP	Economic, environmental, and resilience	Fuzzy triangular numbers
[31]	MCDM	BWM	Economic, environmental, and social	—
[32]	Mathematical programming + AI	MILP + Distributed Artificial Intelligence as part of the MAS. AI = MAS (multi-agent system)	Economic, environmental, and social	Managed through MAS for real-time data processing
[33]	MCDM	q-rung orthopair fuzzy hypersoft (q-ROFH)	Environmental	Fuzzy logic
[34]	MCDM	AHP–R method	Resilience	—
[35]	MCDM	AHP and TOPSIS	Economic, social, environmental	—
[36]	MCDM	DEMATEL-based ANP (DANP), VIKOR	Economic, environmental, social	—
[3]	Mathematical programming	DEA	Environmental	—
[37]	MCDM	D numbers-based fuzzy Ordinal Priority Approach (OPA) and Combinative Distance-based Assessment (CODAS)	Economic, environmental, and resilience	Fuzzy
[38]	MCDM	TOPSIS	Economic, social, and resilience	Trapezoidal intuitionistic fuzzy
[39]	MCDM	EDAS (Evaluation Based on Distance from Average Solution)	Economic, social, environmental, and resilience	Fuzzy logic
[40]	MCDM	Fuzzy BWM	Economic, social, environmental, and resilience	Fuzzy
[41]	MCDM	fuzzy SECA (Simultaneous Evaluation of Criteria and Alternatives)	Economic, social, environmental, and resilience	Fuzzy logic
[42]	MCDM + Mathematical programming	BWM, WASPAS, Type-2 Neutrosophic Fuzzy Numbers, and Robust multi-objective optimization model	Resilience	—
[43]	MCDM	VIKOR	Economic, snvironmental, and social	Single-Valued Neutrosophic Sets (SVNS)
[44]	Mathematical programming	Genetic algorithm	Economic, environmental, social	—
[45]	MCDM	Delphi, AHP, and EDAS	Economic, environmental, social	q-rung orthopair fuzzy sets
[46]	MCDM	BWM and TRUST	Environmental and resilience	Fuzzy sets
[47]	MCDM	MACBETH and CODAS	Economic, environmental, social, and resilience	Fuzzy rough numbers
[48]	MCDM	Rough BWM and Interval Rough MABAC	Economic, environmental, social	Rough numbers
[49]	MCDM + Mathematical programming	BWM, MARCOS, Epsilon constraint method and min-max fuzzy approach	Economic, environmental, social	Fuzzy
[50]	MCDM	AHP, Fuzzy TOPSIS, and SECA	Economic, environmental, social	Fuzzy logic
[51]	MCDM + Mathematical programming	AHP, Fuzzy TOPSIS, and Fuzzy MINLP	Economic, environmental, social and resilience	Fuzzy set theory
[52]	MCDM	BWM, TOPSIS	Environmental	—
[53]	MCDM	Delphi Method and BWM	Economic, environmental, social, and resilience	Neutrosophic sets and fuzzy set theory
[54]	MCDM	COPRAS (Complex Proportional Assessment)	Economic, environmental, social	Fuzzy sets
[55]	Mathematical programming	Novel Grey Stratified Decision-Making	Social	Grey numbers
[23]	MCDM + Mathematical modeling	Fuzzy-Delphi method, FBWM, GC-TOPSIS, Multi-objective planning model	Environmental and resilience	Fuzzy set theory
[1]	MCDM	Fuzzy TOPSIS	Economic, environmental, and social	Fuzzy numbers
[56]	Mathematical programming + AI	mixed-integer optimal control model + dynamic Bayesian network	Resilience	—
[57]	MCDM	DEMATEL	Economic, social, and environmental	Pythagorean fuzzy sets
[18]	AI + Mathematical programming	LSTM networks, MLP, Multi-objective programming model	Environmental	Trapezoidal fuzzy numbers
[58]	MCDM	BWM, SEM, DMM, Fuzzy MULTIMOORA method	Economic, environmental, and social	Fuzzy set theory
[59]	MCDM	AHP, TOPSIS	Economic and environmental
[60]	MCDM +AI	PROMETHEE, ANP, and K-means cluster analysis	Economic, environmental, and social	—
[61]	MCDM	AHP, TOPSIS	Economic, environmental, and social	Fuzzy logic
[62]	MCDM	ARAS, BWM	Economic, environmental, social, and resilience	Fuzzy
[63]	MCDM	DEMATEL, ANP	Environmantal, social	—
[64]	MCDM	Weighted Sum-Product, BWM	Economic, environmental, and social	Grey theory
[65]	MCDM	Spherical Fuzzy AHP, CoCoSo	Economic, environmental, and social	Spherical fuzzy sets
[66]	MCDM	BWM, TOPSIS	Economic, environmental, social, and resilience	Fuzzy + grey relational analysis
[67]	MCDM	normalized Euclidean distance + Taguchi loss function	Economic, environmental, and social	Fuzzy sets
[68]	AI + Mathematical programming	Pythagorean Fuzzy Entropy SWARA-COPRAS method	Economic, environmental, and social	Pythagorean fuzzy sets
[69]	MCDM	COPRAS + ANP	Economic, environmental, and social	Spherical fuzzy sets + Grey numbers
[70]	MCDM	DEA, AHP, WASPAS	Economic, environmental, and social	Spherical fuzzy sets
[71]	MCDM + Mathematical programming	MILP + ANP + TOPSIS	Economic, environmental, and social	Fuzzy set theory
[72]	MCDM + Mathematical programming	VIKOR + MARCOS	Economic, environmental, and social	Interval-Valued Intuitionistic Fuzzy Sets
[73]	MCDM	COPRAS + AHP	Economic, environmental, and social	Interval-Valued Intuitionistic Fuzzy Sets
[74]	MCDM	TODIM	Economic, environmental, and social	Fuzzy logic + probabilistic linguistic term sets
[75]	AI + Mathematical programming	Relational regression chain (RRC), ARIMA, Stochastic MILP	Environmental	Stochastic optimization
[76]	Mathmatical programming	MIP	Resilience	Fuzzy set theory
[77]	AI + MCDM	Machine learning, BWM	Economic, environmental, and social	Fuzzy Inference System
[78]	MCDM	ITARA + PROMETHEE	Economic, environmental, and social	—
[79]	MCDM	Failure Mode and Effects Analysis, entropy weight method, and DEMATEL.	Economic, environmental, and social	Fuzzy sets and entropy methods
[80]	Mathematical programming + MCDM	ITARA, multi-objective linear programming	Economic, environmental, and social	Fuzzy logic
[81]	MCDM	BWM, TOMID	Economic, environmental, and social	—
[82]	MCDM	FBWM, Two-stage Fuzzy inference system (FIS)	Economic, environmental, and social	Fuzzy set theory
[83]	Mathematical programming	Multi-objective MIP	Economic, environmental, social, and resilience	—
[84]	MCDM + Mathematical programming	Choquet integral-based geometric Bonferroni mean and Bonferroni mean operators, DEMATEL, MABAC, Multi-objective optimization model	Economic, environmental, social, and resilience	Interval type-2 Pythagorean fuzzy set + Grey relational analysis
[85]	Mathematical programming + MCDM	Multi-objective MINLP, fuzzy MCDM	Economic, environmental, and social	Fuzzy sets
[86]	MCDM	Interpretive Structural Modeling (ISM), VIKOR	Economic, environmental, and social	Fuzzy logic
[87]	MCDM	PIPRECIA + MABAC	Economic, environmental, and social	Interval fuzzy logic
[88]	MCDM	AHP, TOPSIS	Social	—
[89]	MCDM	BWM, WASPAS, TOPSIS	Economic, environmental, and social	Grey theory
[90]	MCDM	TOPSIS	Environmental	Q-ROF
[91]	MCDM	AHP, DEMATEL, TOPSIS	Economic, environmental, and social	Fuzzy logic
[92]	Mathematical programming + MCDM	AHP + MULTIMOORA	Economic, environmental, social, and resilience	Fuzzy logic
[93]	Mathematical programming	multi-objective MINLP	Economic, environmental, and social	Fuzzy logic
[94]	MCDM	SWARA, WASPAS	Economic, environmental, and social	—
[95]	Mathematical programming	Multi-objective Genetic Algorithm, Multi-objective Particle Swarm Optimization	Economic, environmental, and social	—
[96]	AI + MCDM	AHP, TOPSIS, ELECTRE + Artificial Neural Networks	Environmental	Fuzzy logic
[97]	MCDM	Fuzzy BWM, Interval VIKOR method	Social and environmental	Fuzzy set theory
[98]	MCDM	Analytic network Process (ANP)	Environmental, social, economic	—
[99]	MCDM	Fuzzy AHP, TOPSIS-Grey	Environmental	Fuzzy set theory, Grey theory
[19]	Mathematical programming	Interpretive structural modeling (ISM)	Economic, environmental, and social	—
[100]	MCDM	DEMATEL, VIKOR	Economic, environmental, and resilience	—
[101]	MCDM	Interval Type-2 Fuzzy Sets in MCDM	Economic, environmental, and social	Interval type-2 trapezoidal fuzzy sets
[102]	MCDM	MARCOS	Economic, environmental, and social	—
[103]	Mathematical programming + MCDM	multi-objective optimization + AHP	Economic, environmental, and social	Fuzzy sets
[104]	MCDM	SWARA, DNMA	Economic and environmental	Hesitant fuzzy linguistic term sets
[105]	MCDM	AHP, TOPSIS	Economic, environmental, and social	Fuzzy logic
[106]	MCDM	AHP, TOPSIS, VIKOR, MULTIMOORA	Economic, environmental, and social	Fuzzy logic
[107]	MCDM	TOPSIS	Environmental	Fuzzy set theory
[108]	Mathematical programming	MINLP	Economic and resiliency	—
[109]	MCDM	Copeland method, AHP, ELECTRE-TR	Economic, environmental, and social	—
[110]	Mathematical programming	Stochastic bi-objective MIP	Resiliency	Stochatic optimization
[111]	Mathematical programming	DEA	Environmental	Sensitivity analysis
[21]	MCDM	Fuzzy AHP	Economic, environmental, social	Fuzzy set theory
[112]	AI	Supervised Machine Learning	Resilience	Data analytics
[113]	MCDM	BWM and TOPMID	Economic, environmental, and social	Grey numbers
[114]	MCDM	Voting AHP + game-theoretic approaches	Economic, environmental, and social	—
[115]	Mathematical programming	MILP	Economic, environmental, and social	—
[116]	MCDM + Mathematical programming	Multi-objective optimization, Markowitz portfolio theory, ANP	Economic, environmental, and social	—
[117]	MCDM	AHP	Economic, environmental, and social	—
[118]	MCDM + Mathematical programming	TOPSIS + Fuzzy Goal Programming	Environmental	Fuzzy logic
[119]	MCDM	FUCOM, rough Dombi aggregator, and rough COPRAS method	Economic, environmental, and social	Rough set theory
[120]	MCDM + Mathematical programming	AHP, TOPSIS + Multi-Objective Optimization	Economic, environmental, and social	Fuzzy logic
[121]	Mathematical programming	Epsolin-constraint	Environmental	—
[122]	MCDM	DEMATEL, ANP, and modified VIKOR	Economic, environmental, and social	Intuitionistic fuzzy set theory
[123]	MCDM	FUCOM, the SAW	Economic, environmental, and social	Interval rough numbers
[22]	MCDM	TOPSIS	Economic, environmental, and social	Grey Theory
[124]	MCDM	Shannon Entropy	Economic, environmental, and social	Fuzzy logic
[125]	MCDM	ANP	Resiliency	Fuzzy logic
[126]	Mathematical programming	MAX-MIN method	Economic, environmental, and social	Fuzzy logic
[127]	MCDM	TOMID	Economic, environmental, and social	Rough set theory
[128]	MCDM	AHP, VIKOR	Economic, environmental, and social	Fuzzy set theory
[129]	MCDM + Mathematical programming	AHP, TOPSIS, E-constraint method, LP-metrics method	Economic, environmental, and social	Fuzzy logic
[130]	MCDM	AHP, TOPSIS	Economic and environmental	—
[131]	MCDM + Mathematical programming	Fuzzy possibilistic statistical approach + VIKOR and MULTIMOORA methods.	Economic, environmental, and social	Interval-valued fuzzy sets and asymmetric uncertainty information
[132]	MCDM	ANP, DEMATEL, FPP, TOPSIS	Economic, environmental, and social	Fuzzy set theory
[133]	MCDM	Delphi, ISM, ANP, and COPRAS-G	Economic and social	Fuzzy set theory
[134]	MCDM	portfolio approach	Economic, environmental, social, and resiliency	—
[135]	MCDM	ELECTRE	Economic, environmental, and social	Rough set theory
[136]	MCDM	AHP, VIKOR	Economic, environmental, social	—
[137]	MCDM	Analytic Network Process (ANP)	Environmental, social, and economic	Sensitivity analysis
[138]	MCDM + Mathematical programming	Integrated ANP-QFD, AHP, WASPAS, MOORA, Multi-objective optimization model	Economic, environmental, social	—
[139]	MCDM + Mathematical programming	AHP, Improved grey relational Analysis (IGRA), Mathematical modeling	Environmental, social, and economic	Grey theory
[140]	AI + Mathematical programming	Least Squares–Support Vector Machine (LS-SVM), Cuckoo Optimization Algorithm (COA)	Economic, environmental, social	—
[141]	MCDM	TODIM, PROMETHEE	Environmental	Fuzzy set theory
[142]	Mathematical programming	DEA	Economic, environmental, and social	Type-2 fuzzy sets
[143]	Mathematical programming	Multidimensional decision-making framework	Economic, environmental, and social	Stochastic optimization
[144]	MCDM	TOPSIS	Resiliency	Fuzzy set theory
[145]	MCDM	BWM	Environmental	—
[20]	Mathematical programming	Mixed-integer linear programming	Economic, environmental, and social	—
[146]	MCDM	ISM, ANP, ELECTRE II, VIKOR	Economic, environmental, and social	Fuzzy logic
[147]	MCDM	DEMATEL	Economic, environmental, social	Grey theory
[148]	MCDM + Mathematical programming	ILP + AHP, TOPSIS, IRP	Economic, environmental, and social	—
[149]	Mathematical programming	Monte Carlo Markov Chain	Economic, environmental, and social	Bayesian framework
[150]	MCDM + Simulation	System dynamic simulation	Economic, environmental	Fuzzy logic
[151]	MCDM	AHP	Environmental	—
[152]	MCDM	TOPSIS	Economic, environmental, and social	Fuzzy logic
[153]	Mathematical programming + MCDM	LP + AHP	Environmental	—
[24]	MCDM	Fuzzy TOPSIS	Economic, environmental, and social	Fuzzy set theory

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Figure 1. Quantitative distribution of articles reviewed by publication year from 2013 to 2023.

Figure 2. Distribution of methods applied in the reviewed articles.

Figure 3. AI and MCDM methods used in the reviewed papers.

Figure 4. Decision-making framework for supplier selection.

Table 1. Number of publications in each journal.

Journal	Number of Publications
Sustainability	14
Journal of Cleaner Production	12
Computers & Industrial Engineering	10
International Journal of Production Economics	9
Expert Systems with Applications	7
Annals of Operations Research	5
Environmental Science and Pollution Research	4
Processes	3
Symmetry	3
Applied Soft Computing	3
Others	73

Table 2. Supplier selection criteria.

Criteria	Subcriteria	Repeat
Economic	Cost	101
	Quality	95
	Delivery Performance	72
	Flexibility	35
	Financial Capability/Stability	33
	Technology Capability	31
	Service Efficiency	21
	Production Facilities and Capacity	18
	Reputation	16
	Innovation	15
	R&D	14
	After Sales Service	13
	Relationship/Partnership	13
	Supplier’s Past Performance	12
	Geographical Location	10
	Logistics Performance/Cost	10
	Continuous Improvement	8
	Management Capacity and Organization	8
	Product Reliability	7
	Technical Capability	7
	Payment Terms	6
	Predetermined Order Quantity	4
	Productivity	4
	Foundation of Industry 4.0	4
	Attitude	4
	Efficient Production Methods	4
	Process Capability	3
	Quantity Discount	3
	Enterprise Size	3
	E-commerce Capability	3
	Information Sharing	2
	Political Situation	2
	Organization Commitment	2
	Product Durability	2
	Others	148
Environmental	GHG Emissions (Air Pollution Control)	48
	Environmental Management Systems	41
	Waste Management	33
	Green Design (Eco-design)	32
	Energy (Resource) Consumption	30
	Product Recyclability	24
	Environmental Competencies	16
	Green Image	15
	Use of Environmentally Friendly Material/Green Products	12
	Green Technology	9
	Green R&D/Innovation	8
	GHG Legislation	7
	Green Packing and Labeling	3
	Reverse Logistics	5
	Use of Clean Energy	3
	Environmental Training of Staff	3
	End-of-Pipe Pollution Control	2
	Management Commitment	2
	Green Warehousing	2
	Others	66
Social	Work Safety and Labor Health	53
	Worker Education and Training	28
	Information Disclosure (Sharing)	26
	Human Rights (Rights of Employees)	24
	Stakeholders’ Rights Protection	20
	Social Commitment (Responsibility)	17
	Local Communities Influence	14
	Respect for Policy	11
	Job Safety (Employee Unemployment)	6
	Attention to the Child and Forced-Labor Problem	6
	Reputation	4
	Ethical Issues and Legal Complaints	3
	Job Creation	3
	Employee Welfare and Protection	3
	Employment Compensation (Contracts)	3
	Philanthropy and Ethics	3
	No Discrimination (Gender, Salary)	3
	Mutual Trust	3
	Social Management	2
	Legal Requirements	2
	Others	62
Resilience	Risk Management (Awareness)	15
	Responsiveness	12
	Flexibility	12
	Surplus Inventory	6
	Agility	6
	Robustness	5
	Backup Supplier	3
	Adaptability	2
	Vulnerability	2
	Reliability	2
	Others	28

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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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MDPI and ACS Style

Mirzaee, H.; Ashtab, S. Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review. Sustainability 2024, 16, 8325. https://doi.org/10.3390/su16198325

AMA Style

Mirzaee H, Ashtab S. Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review. Sustainability. 2024; 16(19):8325. https://doi.org/10.3390/su16198325

Chicago/Turabian Style

Mirzaee, Hossein, and Sahand Ashtab. 2024. "Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review" Sustainability 16, no. 19: 8325. https://doi.org/10.3390/su16198325

APA Style

Mirzaee, H., & Ashtab, S. (2024). Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review. Sustainability, 16(19), 8325. https://doi.org/10.3390/su16198325

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Sustainability, Resiliency, and Artificial Intelligence in Supplier Selection: A Triple-Themed Review

Abstract

1. Introduction

2. Research Methodology

3. Descriptive Analysis

3.1. Distribution of Papers Based on Publication Year Journal

3.2. Applied Methods to Model and Solve the Supplier Selection Problem

3.3. Sustainability Criteria

4. Discussion

4.1. Gaps and Findings

4.2. A Decision-Making Framework for Supplier Selection

4.3. Avenues for Future Research

4.3.1. Developing a Weighting System for Supplier Evaluation Criteria Using Machine Learning

4.3.2. Combination of Multi-Objective Mathematical Modeling and Machine Learning in Developing a Sustainable Supplier Selection Framework

4.3.3. Fortifying Supply Chains: Strategic Supplier Selection for Enhanced Resilience

4.3.4. Strategic Maneuvers in Supply Chain: A Game Theory Approach to Buyer Competition in Constrained Supplier Markets

4.3.5. The Green Link: Deciphering the Dynamics of Sustainability in Supplier Selection and Consumer Demand

4.3.6. Application of Exact Algorithms in Obtaining Robust Solutions for Large-Scale Supplier Evaluation Models: Benders Decomposition Algorithm

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Appendix A

References

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