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Article

Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective

by
Xiaoyue You
1 and
Shuqi Lou
2,*
1
Sino-German College of Applied Sciences, Tongji University, Shanghai 201804, China
2
School of Economics and Management, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Systems 2025, 13(6), 422; https://doi.org/10.3390/systems13060422
Submission received: 16 April 2025 / Revised: 21 May 2025 / Accepted: 31 May 2025 / Published: 1 June 2025
(This article belongs to the Section Systems Practice in Social Science)
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Abstract

:
Sustainable development has become the universal consensus among nations worldwide, with society increasingly recognizing the importance of fulfilling ESG responsibilities. This recognition has begun to influence companies’ supplier evaluation and selection behaviors. Meanwhile, integrating AI and IoT technologies has brought about new possibilities for enhancing the intelligence of supply chain management. Firstly, the concept of AIoT suppliers was defined, and evaluation and selection criteria were discussed and proposed. Secondly, the impact of ESG factors on the evaluation criteria of AIoT suppliers was explored, while the value of AIoT technology empowerment from an ESG perspective was analyzed. According to a case study of new energy vehicle supply chain management, the application of AIoT technology can assist companies in improving their ESG management capabilities and performance across multiple dimensions, thereby contributing to the long-term sustainable development of companies.

1. Introduction

With the growing international focus on sustainable development, an increasing number of enterprises are integrating environmental, social, and governance (ESG) factors into their business strategies [1]. ESG has not only been a regulatory and capital market requirement for manufacturing enterprises but also a critical criterion for evaluating and selecting suppliers [2]. Particularly in customer-driven markets, suppliers’ ESG performance plays a pivotal role in enhancing the sustainable performance of manufacturers [3]. Meanwhile, the global wave of Artificial Intelligence (AI) has further accelerated the convergence of supply chain management and information technology. Integrating the technical advantages of AI and the Internet of Things (IoT) into supply chain operations is a key initiative for manufacturing enterprises to build intelligent supply chains [4]. Consequently, service providers who offer AIoT (Artificial Intelligence and Internet of Things) technologies or solutions are becoming indispensable partners for manufacturers in the realization of supply chain management optimization.
Although the importance of both ESG and AIoT is widely acknowledged in the field of supply chains [5,6,7,8], there has been little research studying AIoT suppliers’ evaluation from the ESG perspective. The importance of developing intelligent supply chains has been confirmed to improve enterprise efficiency and customer satisfaction [9], thus empowering ESG performance [10]. As one of the key technologies, AIoT connects everything with intelligence while addressing the increasing demand of energy consumption [11]. In order to evaluate the AIoT supplier performance, it is essential to utilize the technique of Industry 5.0 and focus on enhancing sustainability and transitioning towards a digital society simultaneously [12].
Furthermore, how ESG criteria may influence corporate supplier evaluation practices, as well as how the adoption of AIoT technology may impact firms’ ESG performance, warrants further investigation. Based on the technical characteristics of AI and IoT, this study will employ the bibliometric method to study the evaluation and selection criteria of AIoT suppliers. Further, through text analysis, this research delves into supplier management documents and reports from 17 representative enterprises, to investigate how ESG considerations reshape conventional evaluation frameworks. Finally, due to the multi-tiered and highly complex characteristics of the supply chain of the new energy vehicle (NEV) industry, this study conducts a case study to explore the role of AIoT technology in enabling sustainable supply chain management, particularly in ESG-related governance.
The remainder of this paper includes the following sections: Section 2 discusses the connotation of AIoT suppliers and correspondingly proposes evaluation criteria for AIoT suppliers. Section 3 analyzes the influence of ESG factors in the process of supplier evaluation from the environmental, social, and governance dimensions. Section 4 studies the value of AIoT-enabled empowerment based on ESG requirements, combined with the supply chain management practice of the NEV industry. And the last section presents the conclusion of this study.

2. The Evaluation and Selection of AIoT Suppliers

2.1. The Connotation of AIoT Suppliers

The concept and characteristics of AIoT have garnered significant attention [13], and existing research has explored the conveniences and potential applications of AIoT in supply chain management [7]. However, there remains a lack of targeted studies on the conceptualization and capabilities of AIoT suppliers. Based on current discussions on AIoT, this study defines AIoT suppliers as providers who offer technologies or solutions integrating AI and IoT. These suppliers usually specialize in the R&D, manufacturing, and integration of AI/IoT-related hardware, software, or services. They contribute to client demands for automation, intelligence, and connectivity. Specifically, AIoT suppliers may operate in the following domains:
(a)
Hardware Manufacturing: Producing sensors, embedded chips, smart devices (e.g., intelligent cameras, robots), and other equipment. Such hardware often features environmental sensing, data collection, and communication with cloud platforms or other devices.
(b)
Software Development: Designing AI algorithms, machine learning models, data analytics platforms, IoT operating systems, and related applications. This software usually processes data collected from IoT devices and further employs AI for intelligent analysis and decision-making support.
(c)
System Integration: Combining diverse hardware devices and software platforms to deliver turnkey solutions like smart logistics, smart warehousing, and smart manufacturing applications.
(d)
Service Provision: Offering professional services such as technical support, system maintenance, and data analytics, which will be able to help clients maximize the value of AIoT technologies.
The core competency of AIoT suppliers lies in their ability to utilize AI for processing vast datasets, extracting actionable insights, and bridging the physical and digital worlds through IoT devices. This integration facilitates process optimization, efficiency improvements, cost reductions, and enhanced security. With the evolution of technology, the role of AIoT suppliers is expected to diversify beyond mere technology provision. They may emerge as solution architects, data analysts, or even innovators in business models, further expanding their impact across industries.

2.2. Supplier Evaluation and Selection

2.2.1. Bibliometric Analysis

This study employs a bibliometric analysis methodology. To ensure the general coverage of all research related to “supplier evaluation and selection”, we conducted extensive searches using the keywords “supplier evaluation” and “supplier selection”. Given the widely recognized authority and comprehensiveness of the Web of Science Core Collection, this investigation focused on the literature indexed in this database from 2022 to 2024, thereby capturing the most recent high-quality research developments over the past three years. The initial screening yielded 2298 publications. Through meticulous manual review of all the identified literature, this study observed that (1) some papers exhibited similarity; (2) some studies primarily focused on the design of mathematical models or the validation and refinement of algorithms, merely citing evaluation criteria without substantive discussion. Thus, after systematically assessing relevance, this research filtered out articles examining such conditions, ultimately retaining 269 studies that were closely aligned with supplier evaluation criteria and decision models. These selected publications were subsequently analyzed using CiteSpace 6.4.R1 for in-depth literature interpretation and visualization.
First, in the analysis of keywords, 208 unique keywords were identified after consolidating the raw data. Table 1 presents the top 20 high-frequency keywords, revealing that decision models, methodologies, and evaluation criteria for supplier selection constitute the primary research focus in this field. Moreover, adopting a systems-analysis perspective, scholars have increasingly integrated discussions on supplier performance evaluation, supply chain management, and order allocation. As illustrated in the keyword clustering diagram (Figure 1), under the broader imperative of sustainable development, recent studies emphasize suppliers’ sustainable and green attributes, reflecting heightened academic attention to suppliers’ performance in terms of environmental impact and social responsibility.
Regarding research methods, prior studies on supplier evaluation and selection mainly focused on multi-criteria decision-making (MCDM) and group decision-making approaches when constructing evaluation models. Table 2 summarizes the top 10 most frequently employed methods along with their representative studies. It is shown that scholars have integrated fuzzy theory, grey system theory, and rough set theory into data processing in MCDM. At the same time, MCDM approaches have been further enriched through involving aggregation operators, prospect theory, and entropy weight methods.
Regionally, Chinese scholars produced the most significant research output on supplier evaluation, with 135 publications (54% of total) from 2022 to 2024. India, Iran, and Turkey ranked second to fourth with 36, 34, and 31 publications, respectively, as illustrated in Figure 2. Overall, developing Asian nations demonstrate relatively greater attention to supplier management issues like selection decisions, which exhibits some correlations with the scale, development stage, and strategic importance of manufacturing sectors in these countries. What’s more, many studies examine this issue within specific industrial contexts, such as automotive [24], steel [25], construction [26], apparel [27], food [28], and logistics [29] sectors. This pattern further substantiates the close connection between supplier evaluation/selection research and manufacturing industries.

2.2.2. Literature Summary

Early research on supplier selection decisions primarily focused on conventional criteria, such as price, quality, delivery time, historical performance, warranty and claims policies, production facilities and capacity, technical capability, and financial status. The importance of these criteria varied across industries, with price/cost, quality/defects, and delivery time being the three most widely recognized and applied indicators [30,31,32,33,34]. Furthermore, in the context of frequent disruptions such as regional conflicts and public health emergencies, supplier robustness and resilience have become critical factors as well [35,36]. Today, digital transformation prompts enterprises to additionally consider factors, such as smart manufacturing systems, data collection and management tools, big data technologies, and digital system integration capabilities, when evaluating potential suppliers [37,38].
Concurrently, previous studies have found that supplier capabilities, reputation, reliability, and sustainability significantly impact enterprise supply chain performance [39]. Among these, Ghadimi et al. [40] proposed environmental performance, green image, pollution control, and green capability as four environmental indicators, as well as occupational health and safety and employment practices as two social indicators. Incorporating the literature review and Delphi methods, subsequent studies further expanded this framework. While environmental criteria remained relatively consistent, three additional social indicators were proposed: information sharing, stakeholder relationships, and social activities [16]. Building on the Triple Bottom Line (TBL) theoretical framework, common requirements for supplier performance evaluation often include social and environmental dimensions, such as work schedule flexibility, communication channels, waste control, and environmental certifications [41]. Lou et al. [42] further developed this approach in alignment with ESG regulatory requirements, proposing a three-tier indicator system comprising 79 primary indicators.

2.3. Evaluation Criteria for AIoT Supplier

The evaluation and selection of AIoT suppliers require comprehensive consideration across multiple dimensions, as suppliers need to not only meet current capability requirements but also support enterprises’ long-term development objectives. The technical specificity of AIoT necessitates that AIoT suppliers possess stronger technical capabilities and service levels. Among these, technical capabilities encompass both current technological proficiency and the capacity for maintaining leading-edge technological iteration. As for service levels, particular attention must be paid to the adaptability and integration of suppliers’ AIoT hardware/software systems with the enterprise’s existing IT architecture, thereby ensuring high-quality after-sales service.
Nevertheless, research on the evaluation and selection of AIoT suppliers remains extremely limited. As of April 1, 2024, only three articles related to “AI supplier” or “IoT supplier selection” were identified in the Web of Science Core Collection. Among the existing studies, Liang et al. [43] proposed 12 criteria across five dimensions: human resources, infrastructure, enterprise, content and applications, and policy. Nabeeh et al. [44] examined influencing factors for enterprise IoT implementation, identifying security, value, connectivity, intelligence, and telepresence as critical considerations for achieving optimal IoT connectivity. Meanwhile, it is proposed that the sustainability performance in the logistics sector mainly depends on nine aspects: value-added capability, labor productivity, capital productivity, green technology patents, carbon intensity, PM2.5 intensity, employment population, labor compensation, and total investment [45]. Given the current paucity of research in this field, this study synthesizes the aforementioned prior findings with supplier evaluation studies targeting manufacturing or service industries. By integrating the Triple Bottom Line (TBL) theory with the ESG framework and building upon the technical and product characteristics of AIoT, this study proposes an evaluation system for AIoT suppliers along with their interpretations (Table 3).

3. The Impact of ESG Standards on Supplier Evaluation

The mandatory disclosure of ESG information has become a global trend. As of March 2025, among the 134 member stock exchanges of the Sustainable Stock Exchanges Initiative (SSE), 74 have issued official ESG Reporting Guidelines (data source: Sustainable Stock Exchanges Initiative. ESG Disclosure Guidance Database. https://sseinitiative.org/esg-guidance-database/ (accessed on 14 March 2025)). Meanwhile, in July 2024, the European Council formally adopted the Corporate Sustainability Due Diligence Directive (CSDDD), requiring companies to identify, prevent, and mitigate environmental and human rights risks, not only within their own operations but also across their direct and indirect business partners, including every stage of the supply chain—from raw material sourcing to production and transportation (data source: The European Parliament and the Council of the European Union. Corporate Sustainability Due Diligence Directive. (5 July 2024). https://eur-lex.europa.eu/eli/dir/2024/1760/oj (accessed on 14 March 2025)). Nowadays, integrating sustainability requirements into supplier management has become the responsibility of established corporates, and ESG standards are increasingly shaping supplier evaluation practices. Drawing on Lou et al. [42]’s foundational work in reconstructing sustainable supplier evaluation systems based on ESG criteria, this study further examines how ESG standards influence the specific assessment indicators prioritized by enterprises in supplier evaluations through text analysis. By analyzing 42 supplier codes of conduct, supply chain annual reports, and ESG reports from 17 leading global manufacturing companies, this study examines the influence patterns across the three dimensions of environmental, social, and governance.

3.1. Environmental Influence

Against the backdrop of global warming and increasing carbon emission regulations, environmental considerations have undoubtedly become a primary focus for enterprises and a key research area for scholars. Major economies worldwide have committed to timelines for achieving “Carbon Peak” and “Carbon Neutrality”. For supply chains, this necessitates a comprehensive assessment and management of the “Carbon Footprint”. Since the carbon footprint encompasses the entire lifecycle of evaluated entities, it requires end-to-end collaborative management. As a holistic evaluation metric, it not only reflects suppliers’ sustainability awareness and capabilities in green transportation and production but also demonstrates their overall performance in greenhouse gas emission control, when collaborating with upstream and downstream partners. Consequently, environmental requirements for suppliers should extend to the entire supply chain’s green practices, forming a critical basis for supplier evaluation and selection decisions.
Moreover, enterprises pay attention to multi-dimensional environmental impacts during supplier selection as well. Research reveals that the sampled companies consistently prioritize suppliers’ performance in three key areas: material categories, material use efficiency, and energy management. Suppliers are required to utilize renewable or recyclable materials, optimize resource input, and minimize consumption. At the same time, they must reduce waste generation and ensure proper disposal during operations, adopt green transportation methods, and obtain relevant environmental certifications. Through these measures, enterprises aim to mitigate the negative environmental impacts of supplier activities, thereby aligning with corporate sustainability requirements.

3.2. Social Responsibility

Within the social responsibility dimension, regulatory bodies prioritize corporate human rights performance at the legal level, subsequently extending these requirements to their suppliers. In supply chain management, compliance with “conflict minerals” regulations constitutes a mandatory obligation for suppliers. The United States pioneered this regulatory approach through Section 1502 of the Dodd–Frank Wall Street Reform and Consumer Protection Act (2010), which legally mandates U.S. publicly traded companies to disclose whether their products contain conflict minerals sourced from the Democratic Republic of the Congo (DRC) and adjoining regions, while requiring detailed reporting on mineral origins and supply chain due diligence (data source: The 111th United States Congress. Dodd–Frank Wall Street Reform and Consumer Protection Act. (21 July 2021). https://www.congress.gov/111/plaws/publ203/PLAW-111publ203.pdf (accessed on 27 February 2025)). Subsequently, international organizations including the United Nations Security Council (UNSC) (data source: United Nations Security Council. Resolution 1952 (2010). (29 November 2010). https://docs.un.org/en/S/RES/1952(2010) (accessed on 27 February 2025)), Organisation for Economic Co-operation and Development (OECD) (data source: Organisation for Economic Co-operation and Development. OECD Due Diligence Guidance for Responsible Supply Chains of Minerals from Conflict-Affected and High-Risk Areas. (19 May 2011). https://one.oecd.org/document/C/MIN(2011)12/ADD1/en/pdf (accessed on 27 February 2025)), and European Council (data source: The European Parliament and the Council of the European Union. Laying Down Supply Chain Due Diligence Obligations for Union Importers of Tin, Tantalum and Tungsten, their Ores, and Gold Originating from Conflict-Affected and High-Risk Areas. (17 May 2017). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L:2017:130:TOC (accessed on 27 February 2025)), along with various national governments, have established comprehensive regulations to globalize conflict mineral controls. Specifically, for AIoT suppliers, the production and procurement of electronic components (e.g., semiconductors, integrated circuits, communication equipment) heavily rely on tantalum, tin, tungsten, and gold minerals, which are frequently associated with conflict zones. This necessitates a stringent scrutiny of suppliers’ conflict mineral compliance.
Regarding supplier internal operations, corporations emphasize human rights protections in employment practices. Analysis of sampled companies’ policies and reports reveals explicit requirements for the following:
(a)
Workplace Safeguards: Ensuring occupational health and safety standards, comprehensive employee training, and rigorous management of workplace hazards with emergency protocols.
(b)
Labor Ethics: Prohibiting forced or child labor while enforcing non-discrimination and pay equity policies.
(c)
Employee Welfare: Mandating transparent compensation systems and guaranteed benefits.
These measures collectively demonstrate how social responsibility criteria are being institutionalized in supplier evaluation frameworks, particularly for technology-intensive supply chains.

3.3. Governance

Corporate governance encompasses a supplier’s financial management practices, information transparency, and operational compliance. While environmental and social considerations typically receive greater emphasis in supplier assessments, governance requirements remain a critical component, albeit with varying priorities across different enterprises. Generally, companies mandate that suppliers demonstrate sound financial health, robust internal control mechanisms, and strict adherence to relevant laws and regulations to ensure stable and reliable business partnerships.
A detailed review of corporate documents reveals three key governance expectations. Approximately one-third of the materials specifically address “fair trade and competition” practices, requiring suppliers to comply with antitrust regulations by abstaining from price manipulation, market allocation, collusive bidding, or any unlawful exploitation of market dominance. Equally important are anti-corruption and anti-bribery provisions, which compel suppliers to restrict all financial transactions and funds to legitimate purposes, with each payment properly documented and justified. Furthermore, suppliers are expected to proactively disclose and mitigate potential conflicts of interest and uphold business ethics and professional conduct standards. Moreover, they must safeguard client confidentiality and respect trade secrets and intellectual property (IP) rights. These comprehensive governance requirements reflect the growing institutionalization of ethical business practices in modern supply chain relationships.

4. The Enabling Value of AIoT Technology from an ESG Perspective: A Case Study of the NEV Industry

ESG regulations have imposed broader responsibility requirements for the application of AIoT technologies, while these technologies simultaneously facilitate the fulfillment and management of ESG obligations. The NEV sector, particularly intelligent connected vehicles (ICVs), has garnered significant global attention in recent years, with China elevating its development to a national strategic priority. The NEV industry presents unique ESG challenges due to its extended product lifecycles, diverse material inputs with substantial consumption volumes, complex production processes, and multi-tiered supply chains. Additionally, the NEV sector’s labor-intensive operations, capital concentration, and extended ROI cycles fundamentally shape its ESG profile. These structural attributes magnify ESG impacts across three dimensions, (a) operational scale effects, (b) stakeholder implications, and (c) governance complexity, which establish the sector as a critical focus for sustainable development practice.
This study obtained statistical data from two NEV industry enterprises (Company A and Company B). Company A is a complete vehicle manufacturer with 40 years of operation history, whose annual deliveries exceed one million units. Originally a conventional fuel vehicle producer, the company initiated its NEV strategic layout in 2018, achieved mass production of NEV models by 2020, and has been vigorously promoting full-range electrification in recent years. Company B specializes in energy products, focusing on manufacturing AC/DC charging piles and providing green energy solutions, with its residential charging equipment capturing nearly 10% of global market share. These two enterprises, respectively, provided documents and data regarding supplier codes of conduct, evaluation metrics and processes, as well as audit material checklists and statistics, representing both demand-side (OEM) and supply-side perspectives.
Building upon these datasets, this study conducts an in-depth case study of enterprises’ AIoT adoption demands and the key challenges in ESG governance during their production and operational activities. This research finds that, with inherent capabilities of ubiquitous connectivity, real-time communication, data-driven intelligence, and modular scalability, AIoT technology enhances corporate ESG performance across seven critical dimensions (Figure 3): intelligent data analytics and risk early warning, process automation and optimization, waste management and recycling, environmental and social responsibility risk assessment, product design and lifecycle evaluation, supply chain transparency improvement, and supply chain compliance enhancement. This technological empowerment enables enterprises to achieve superior management efficiency while optimizing their ESG outcomes.

4.1. Intelligent Data Analytics and Risk Early Warning

The implementation of AIoT-based management systems enables comprehensive data capture and documentation across all operational processes, covering all suppliers with business interactions. This system-wide visibility facilitates holistic big data analytics to assess supplier performance in key areas such as energy use efficiency, waste treatment methods, and chemical management. By employing AIoT technology, enterprises realize autonomous information acquisition for supplier evaluation, eliminating reliance on supplier-provided data and risks of information falsification. This technological approach significantly enhances the authenticity and accuracy of evaluation outcomes.
Furthermore, with the capability of real-time monitoring and multi-dimensional data analytics, AIoT empowers enterprises to proactively identify potential risks in supplier operations, encompassing both conventional business risks and ESG compliance vulnerabilities. For instance, the data-driven nature of AIoT allows for the agile detection of potential environmental violations or social responsibility breaches by suppliers. The system generates immediate alerts by transmitting real-time behavioral data, enabling advanced intervention before issues escalate. This predictive risk management capability represents a paradigm shift from reactive to preventive supply chain oversight.

4.2. Process Automation and Optimization

The data collection capabilities of AIoT technology extend beyond generating basic statistical dashboards to enabling comprehensive, data-driven business analysis and management. Through the integration of hardware devices and software systems, enterprises can digitize physical operational processes into programmable workflows within management platforms. Since vehicle manufacturing faces process complexity, stringent precision requirements, and error prevention difficulties, this technological integration will effectively address such challenges, thereby enhancing operational efficiency while reducing error-related costs.
Specific applications demonstrate AIoT’s transformative potential: First, in direct and indirect production material management, AIoT systems employ predictive analytics to optimize inventory levels, minimizing resource waste from overstocking. Second, for environmentally hazardous materials (e.g., chemicals and their byproducts), AIoT enables precise control of dispensing ratios and timing to reduce harmful emissions. Moreover, AI capabilities further facilitate the identification of eco-friendly alternatives to conventional materials, decreasing reliance on natural resources.
Fundamentally, AIoT technology creates value by systematically exposing current management deficiencies and revealing latent optimization opportunities across operations. This diagnostic capability establishes a continuous improvement cycle for both operational and environmental performance.

4.3. Waste Management and Recycling

The finite stock of natural resources necessitates that all parties improve utilization efficiency. On one hand, it is essential to reduce redundancy and waste in the processing of primary resources during production; on the other hand, maximizing the value of existing resources through recycling should be prioritized. A notable example is the closed-loop supply chain model established between NEV manufacturers and power battery suppliers, which enables a sustainable lifecycle from battery production to recycling, component remanufacturing, and cascading utilization.
Through image recognition technology, AIoT-based management systems can classify different types of domestic waste or industrial byproducts, guiding robotic systems in accurate sorting and further coordinating material transportation through IoT networks. The application of AIoT in vehicle production management extends beyond in-house manufacturing processes within individual enterprises; it also facilitates the coordination and integration of production resources across supply chain nodes, achieving resource optimization at an industrial level. With widespread adoption in the future, there will be greater potential to explore cross-industry resource coordination. Furthermore, AI technology can assist enterprises in incorporating recyclability considerations during the product design phase. By simulating the environmental impact of different design solutions, companies can choose more eco-friendly alternatives, both embedding sustainability awareness upstream in the development process and promoting a forward-thinking approach to circular economy practices.

4.4. Environmental and Social Responsibility Risk Assessment

Compared to economic dimensions, quantifying the environmental and social impacts of corporate activities presents greater complexity, as similar events may be recorded and measured differently across reporting frameworks. With IoT-enabled data capture, AI technologies can assist in constructing robust environmental and social impact assessment models, which have already been applied in corporate carbon reduction initiatives and industrial green development strategies [46]. In environmental performance evaluation, AIoT systems employ comprehensive data analysis to assess a company’s production impacts across multiple dimensions, including air, water, and soil quality, while not only diagnosing current conditions but also predicting potential trends through simulation. For social responsibility assessment, AI’s natural language processing capabilities supplement quantitative data with qualitative insights from textual sources.
The automotive industry provides a salient example: aluminum, extensively used in NEV manufacturing, carries significant carbon emissions from smelting to processing. The choice between primary and recycled materials for components substantially influences the vehicle’s overall carbon footprint. In this context, AIoT contributes to both material-type identification and real-time resource availability tracking. Moreover, through the continuous monitoring and comparative analysis of multi-dimensional data, AIoT systems allow companies to: (1) track environmental and social performance across their operations and supply chains; (2) identify emerging risks and latent vulnerabilities; (3) strategically adjust practices to mitigate negative environmental effects; (4) prevent potential social responsibility crises. These data-driven insights facilitate evidence-based decision making for long-term ESG improvements.

4.5. Product Design and Lifecycle Evaluation

The environmental and social sustainability of products extends beyond manufacturing processes, requiring the mitigation of adverse impacts throughout their entire lifecycle, including distribution, usage, and end-of-life recycling. As such, a product’s ESG performance must be comprehensively evaluated across all stages, from initial design and production to transportation, utilization, and final disposal [47]. This holistic approach necessitates thorough consideration during the earliest phases of product development. The application of AI technologies enables enterprises to conduct simulation experiments and data-driven analysis, modeling the evolution and performance of products throughout their lifecycle. Such capabilities provide manufacturers with a systematic understanding of a product’s environmental impacts at each stage, facilitating targeted measures to enhance ESG outcomes. Specifically, AI-enhanced product development contributes to optimized design through improved functional planning precision, while allowing comprehensive lifecycle assessments of environmental and social impacts. These advanced analytical tools not only inform the optimization of usage phases and recycling strategies but also support waste classification and reuse initiatives, thereby promoting circular economy principles in product innovation.

4.6. Supply Chain Transparency Improvement

The European Union has mandated that qualifying enterprises implement comprehensive operational controls over environmental and social responsibility performance throughout their secondary supply chains. This regulatory requirement extends corporate accountability beyond direct suppliers to encompass indirect suppliers (such as tier-2 and tier-3 providers). In the NEV sector, this specifically obligates original equipment manufacturers (OEMs) to exercise oversight over sub-component suppliers and raw material providers within their extended supply network. The establishment of end-to-end data linkages constitutes a fundamental prerequisite for compliance.
AIoT technologies significantly enhance supply chain transparency through real-time, comprehensive data exchange by:
(a)
Enabling cross-verification of business information across supply chain nodes;
(b)
Supporting precise tracking of material provenance, manufacturing processes, logistics, and inventory management;
(c)
Establishing interconnected audit trails throughout operational workflows [48].
Particularly, regarding the globally emphasized conflict minerals management issue, AIoT technology effectively enables enterprises to mitigate operational risks through its closed-loop traceability capabilities. When integrated with blockchain and other complementary technologies, AIoT further ensures data authenticity, integrity, and accuracy. Such technological integration transforms regulatory compliance from a passive obligation into a strategic capability, enabling the proactive identification and management of ESG risks across multi-tier supply networks.

4.7. Supply Chain Compliance Enhancement

The significant contribution of AIoT technology to the intelligent transformation of supplier management is evident, particularly in its effective supervision and facilitation of supply chain compliance. The assessment of supplier operational compliance varies according to regulatory differences across sales regions, countries, or securities markets, which requires corporations to make great efforts to closely monitor supplier behavior as well as market-specific regulations.
AIoT technology addresses these challenges by enabling the real-time monitoring of supplier operations to identify potential compliance violations, while simultaneously assisting in the review of supplier-submitted compliance documentation. Through precise evaluation against regional regulatory frameworks, AIoT ensures suppliers meet the diverse requirements of different target markets. Consequently, by continuously tracking supply chain activities, AIoT maintains adherence to established standards and regulations across all operational stages, while proactively detecting and alerting potential anomalous activity patterns.

5. Conclusions

The global dissemination of ESG principles has precipitated a fundamental transformation in supplier evaluation and selection criteria. The application of AIoT technologies has significantly accelerated this paradigm shift. This technology allows for the enhancement of corporate ESG management capabilities and improvements in sustainability performance, thereby contributing to the development of a greener and more sustainable global economy.
Through bibliometric analysis and examination of AIoT’s technical characteristics, this study establishes an evaluation criteria system for AIoT suppliers. Meanwhile, this study proposes that enterprises should place particular emphasis on multi-dimensional ESG performance assessment, such as carbon footprint, conflict mineral compliance, and ethical transaction practices. Focusing on the NEV industry as a representative case, the research elucidates seven critical dimensions where AIoT generates value from an ESG perspective: intelligent data analytics and risk early warning, process automation and optimization, waste management and recycling, environmental and social responsibility risk assessment, product design and lifecycle evaluation, supply chain transparency enhancement, and supply chain compliance improvement.
The implementation of AIoT not only elevates suppliers’ sustainable development capacities but also creates strategic synergies for manufacturers. As ESG standards continue to undergo refinement and broader implementation, the supplier evaluation and selection process will increasingly prioritize long-term sustainability considerations, consequently fostering high-quality development throughout industrial value chains. This evolutionary trajectory underscores the growing convergence of technological innovation and sustainable business practices in reshaping global supply networks.

Author Contributions

Conceptualization, X.Y.; methodology, X.Y. and S.L.; software, S.L.; validation, X.Y. and S.L.; formal analysis, X.Y.; investigation, X.Y.; resources, S.L.; data curation, S.L.; writing—original draft preparation, X.Y. and S.L.; writing—review and editing, X.Y.; visualization, S.L.; supervision, X.Y.; project administration, X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new created data.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Keywords cluster in supplier evaluation and selection research (2022–2024).
Figure 1. Keywords cluster in supplier evaluation and selection research (2022–2024).
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Figure 2. Regional distribution in supplier evaluation and selection research (2022–2024).
Figure 2. Regional distribution in supplier evaluation and selection research (2022–2024).
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Figure 3. The enabling value of AIoT technology from an ESG perspective.
Figure 3. The enabling value of AIoT technology from an ESG perspective.
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Table 1. Top 20 keywords in supplier evaluation and selection research (2022–2024).
Table 1. Top 20 keywords in supplier evaluation and selection research (2022–2024).
No.KeywordsFrequencyNo.KeywordsFrequency
1supplier selection/evaluation17611management41
2decision making13812framework41
3model10913order allocation37
4supply chain (management)8414dea24
5topsis7015sustainable/green supplier selection23
6performance (evaluation)6916system22
7fuzzy6817group decision making22
8ahp6518sets20
9criteria6219aggregation operators17
10mcdm5720sustainability15
Table 2. Top 10 research methods in supplier evaluation and selection research (2022–2024).
Table 2. Top 10 research methods in supplier evaluation and selection research (2022–2024).
No.MethodFrequencyRepresentative Study
1TOPSIS70Giri (2022) [14]
2Fuzzy Theory68Dang (2022) [15]
3AHP65Chai (2023) [16]
4DEA24Nguyen (2022) [17]
5Aggregation Operators17Kamacı (2022) [18]
6VIKOR12Shang (2022) [19]
7Entropy11Song (2022) [20]
8ANP11Jessin (2023) [21]
9BWM10Afrasiabi (2022) [22]
10Prospect Theory7Liang (2022) [23]
Table 3. Evaluation criteria for AIoT suppliers.
Table 3. Evaluation criteria for AIoT suppliers.
DimensionConcrete CriteriaExplanation
EconomicPriceAssess price reasonableness and market competitiveness of products/services
QualityEvaluate product/service reliability and consistency to avoid quality-related additional costs
Delivery TimeVerify ability to meet delivery timelines and maintain project schedules
ServiceExamine support services including after-sales support and training provisions
Technical CapabilityAssess industry-leading technical capabilities and cutting-edge support, evaluate compatibility of AIoT solutions with existing enterprise IT architecture for seamless integration
Financial CapabilityMeasure financial stability and cash flow reliability
Market CompetitivenessEvaluate co-development capability, assess innovative solution provision, and verify rapid response to market dynamics and customer needs
EnvironmentalCarbon Footprint ManagementEvaluate sustainability performance in green transportation and production across the supply chain
Material ManagementAudit raw material classifications and productivity metrics
Resources ManagementMeasure water, electricity, and natural gas consumption and utilization efficiency
Waste and Pollution ManagementAssess environmental impact, pollution control, and waste recycling measures
Environmental Management SystemReview environmental protection measures and management systems
Green ProductEvaluate product environmental friendliness and recyclability
Green ImageInvestigate market reputation regarding environmental responsibility
Green InnovationMeasure eco-innovation capabilities and green technology R&D
Green TransportationAudit circular packaging solutions and low-carbon transportation
SocialConflict MineralVerify conflict-free mineral sourcing and human rights compliance
Occupational Health and
Security		Examine employee health/safety protections and workplace conditions
Staff Right and InterestAssess employee treatment including compensation, benefits, and development opportunities
Non-discrimination and EqualityEvaluate understanding and respect for diverse cultural backgrounds in global operations
Child Labor and Forced LaborVerify compliance with forced labor prohibitions
Community ImpactEvaluate social program support and community rights compliance
GovernanceFair Trade & CompetitionVerify bid-rigging/market manipulation absence and conflict-of-interest disclosures
Internal Management & ComplianceAudit internal control effectiveness and anti-corruption compliance
Data Security and ProtectionExamine data protection mechanisms and client data security
Business EthicsReview trade secret/IP protections and ethical code adherence
Supply Chain TransparencyAssess willingness to provide transparent supply chain information regarding environmental/social impacts
Collaborative CapacityTest co-development capabilities, contract fairness, and long-term partnership commitment
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You, X.; Lou, S. Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective. Systems 2025, 13, 422. https://doi.org/10.3390/systems13060422

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You X, Lou S. Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective. Systems. 2025; 13(6):422. https://doi.org/10.3390/systems13060422

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You, Xiaoyue, and Shuqi Lou. 2025. "Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective" Systems 13, no. 6: 422. https://doi.org/10.3390/systems13060422

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You, X., & Lou, S. (2025). Research on the Evaluation and Selection of AIoT Suppliers from an ESG Perspective. Systems, 13(6), 422. https://doi.org/10.3390/systems13060422

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