Barriers to Visibility in Supply Chains: Challenges and Opportunities of Artificial Intelligence Driven by Industry 4.0 Technologies

Abstract

Advancements in e-commerce and Industry 4.0 technologies have significantly improved communication and connectivity in supply chains. These technologies, particularly artificial intelligence driven by Industry 4.0 technologies (AI-IT4.0), have reshaped how products, services, and financial transactions are managed, emphasizing the importance of information sharing among supply chain participants to enhance inventory management, sales, and demand forecasting. However, sharing comprehensive information across supply chain stakeholders presents persistent challenges due to various barriers. This study seeks to review the current understanding of visibility barriers in supply chains, identify key obstacles, and suggest directions for future research. Using the PRISMA methodology, the study analyzed 20 articles, identifying 12 critical barriers to visibility. Bibliometric analysis has revealed growing interest in the topic since 2021, although evidence of collaborative research remains limited. A keyword co-occurrence analysis highlighted strong connections between visibility, supply chain management, sustainability, and artificial intelligence driven by industry 4.0 technologies, such as machine learning, predictive analytics, and digital twins. Future research should empirically investigate visibility barriers and explore the interplay between AI-TI4.0, visibility, and sustainable performance through case studies and quantitative approaches.

1. Introduction

The increasing globalization and complexity of modern supply chains have led companies to re-evaluate their management strategies, particularly in relation to visibility [1]. In supply chain management, visibility refers to the ability of organizations to access and monitor accurate, timely information about the processes, actors, and resources within their supply networks [2]. This capability is crucial for ensuring operational efficiency, regulatory compliance, sustainability, and responsiveness to disruptions [3].

Despite its importance, visibility—which focuses on the initial stages of the supply chain, including suppliers and second and third suppliers—can face numerous barriers that hinder its effectiveness, often impacting organizational performance and sustainability [3].

These challenges are particularly pronounced due to the fragmented and expansive nature of global supply chains. Focal companies frequently rely on intricate networks of suppliers, many of which operate in remote regions [4] or have poorly structured management systems [5].

This fragmentation makes accessing critical information, such as the origin of materials, supplier working conditions, and environmental practices, particularly difficult [6]. The lack of visibility exposes companies to reputational risks, as demonstrated by widely publicized cases involving unethical labor practices or environmental compliance failures, which can damage brand reputation and lead to legal consequences [7].

A prominent barrier to visibility is the insufficient adoption of technology to track and collect supply chain data [5]. Although AI-IT4.0 has the potential to revolutionize supply chain management, many organizations, especially small and medium-sized enterprises, lack the financial resources for investment [8] and the technical expertise required to implement these technologies [9].

Additionally, the absence of global standards for data collection, integration, and sharing complicates the harmonization of practices across supply chain participants [10].

Another significant challenge is the lack of trust among supply chain actors [11]. Second and third suppliers may hesitate to share detailed operational information due to concerns about competitiveness or increased compliance demands [12]. This reluctance is often compounded by differing corporate cultures and the absence of clear incentives for information sharing [13]. Furthermore, many focal companies have a limited view visibility beyond their direct suppliers, often remaining unaware of the second and third suppliers involved in earlier stages of production [14].

Financial constraints also present a barrier, as implementing visibility systems requires significant investment in technology platforms, employee training, and regular audits [8]. These costs can deter companies, particularly in industries with narrow profit margins [15]. Compounding this issue is a skills gap within many organizations, where teams may lack the expertise to manage real-time data collection and analysis [16].

Regulatory factors and the absence of external pressures also contribute to the low prioritization of visibility initiatives. In regions with less stringent transparency and sustainability regulations, focal companies and suppliers may feel little motivation to enhance visibility [6] However, as global awareness of sustainability grows, organizations are increasingly subject to scrutiny from consumers, non-governmental organizations, and investors. This trend is encouraging companies to reevaluate their practices and seek solutions to overcome visibility barriers [17].

Given the challenges in achieving visibility and the scarcity of studies on the topic, this research aimed to answer the following research questions:

• What is the state of the art on barriers to visibility in supply chains?
• What are the main barriers to visibility identified in literature?
• What are the key avenues for future research on barriers to visibility in supply chains?

2. Materials and Methods

This section presents the methodology used in this study, focusing on the systematic literature review process. It outlines the steps taken to identify, select, and analyze relevant sources, ensuring a thorough and methodologically sound synthesis of existing research. The purpose of this review is to provide a comprehensive understanding of the current state of knowledge on the subject, while identifying gaps and opportunities for further exploration.

Systematic Literature Review

The systematic literature review for this research adhered to rigorous and detailed criteria, utilizing the PRISMA (Preferred Reporting Items for Reviews and Meta-Analyses) methodology. PRISMA offers a comprehensive checklist and a step-by-step guide for conducting literature reviews [18].

The process in this study consisted of 8 Steps: (1) define research questions; (2) set scope for the review; (3) search strings used; (4) select papers in the Scopus database with the designated strings; (5) select articles in English published in journals in the areas of engineering, management, social sciences and environmental sciences; (6) reading titles and abstracts to select articles that dealt with barriers to visibility in supply chains with a focus on second- and third-tier suppliers; (7) select papers by reading (n = 20); and (8) final sample, as illustrated in Figure 1.

Based on the process outlined in Figure 1, a summary of the papers included in the sample was compiled and organized in Supplementary Material Table S1. The sample articles indicate barriers to visibility in supply chains with a focus on second- and third-tier suppliers. The following chapter presents the Background.

3. Background

This section examines the concept of visibility within supply chains.

3.1. Visibility in Supply Chains

Supply chain management has been undergoing a significant transformation in response to increasing demand for compliance and sustainability [19]. The key concepts associated with transparency in supply chains include traceability, disclosure, and visibility. While these terms are related, they influence the interaction between suppliers and second and third suppliers in the supply chain in different ways [20].

Supply chain refers to an organization’s ability to disclose information about the origin, compliance, and sustainability of products and services in a clear and accessible manner. It is a broad concept that encompasses three main components: traceability, disclosure, and visibility [1].

Traceability involves the technical process of collecting data on supply chain operations, enabling the identification of the origin and history of materials, processes, and products [21]. Technologies such as blockchain and QR code systems allow companies to track everything from raw material extraction to delivery to the end consumer [9].

Disclosure refers to the communication of this information to stakeholders, including consumers, investors, and regulators [22]. Sustainability reports, compliance certificates, and information available on packaging information are examples of disclosure practices that help build trust among stakeholders [19].

Visibility is the ability to access and interpret information in a timely manner, connecting the data gathered through traceability with operational decision-making [2]. Visibility specifically refers to the activities and actors in the early stages of the supply chain, such as first-, second-, or third-tier suppliers [22].

While traceability focuses on data collection and reporting, visibility integrates both, enabling companies to make more informed decisions [23].

Visibility offers significant benefits, addressing strategic, operational, and reputational aspects. Strategically, it supports better risk management by identifying vulnerabilities such as reliance on critical suppliers or non-compliance with regulatory standards [24].

Operationally, visibility enhances process optimization and inventory management, helping to reduce costs and lead times [25]. In terms of reputation, it strengthens the trust of consumers and investors, especially in a context where sustainable practices are increasingly valued [20].

However, achieving visibility presents significant challenges. The fragmentation of global supply chains [14], the resistance of suppliers to sharing information [16], and the lack of global integration standards complicate the process [1]. Additionally, the high costs of implementing technologies [8] and the lack of technical expertise in many organizations create further barriers [3]. These challenges are especially pronounced in long and geographically dispersed supply chains, where communication between links is often limited [4].

In light of these barriers, artificial intelligence driven by Industry 4.0 technologies (AI-IT4.0) have played a crucial role in enhancing visibility, providing advanced solutions that facilitate the monitoring, analysis, and optimization of supply chain information [10]. Key technologies include Internet of Things (IoT), automated systems, cyber-physical systems, augmented reality, big data, cloud computing, additive manufacturing [26], and blockchain technology [7]. Artificial intelligence-driven approaches, such as machine learning, predictive analytics, and computer vision, further enhance these technologies by enabling real-time decision-making, anomaly detection, and process automation, ultimately improving supply chain visibility and efficiency [27].

These technologies not only improve operational efficiency but also promote visibility and sustainability across the supply chain [28]. By increasing visibility, companies can identify and mitigate environmental, social, and economic risks, such as greenhouse gas emissions, poor working conditions, and unethical practices, both within their own operations and throughout their supply chains [29].

Visibility also contributes to sustainable innovation, enabling organizations to develop new products and processes based on more accurate and transparent data [23]. Companies that implement visibility practices often enjoy greater market acceptance [30], strengthen their reputation [20], and are better positioned to meet environmental and social regulations and certifications [29].

In contrast, the lack of visibility can lead to serious consequences, including legal sanctions, financial losses, and reputational damage, particularly in sectors that operate under scrutiny from consumers and regulators [30]. Therefore, adopting visibility practices is not just a competitive advantage [28], but a strategic necessity for companies aiming to thrive in an increasingly sustainability-driven market [17].

Given the importance of visibility for sustainable supply chain performance, and the limited research on barriers to visibility in supply chains [15], this study explores the state of the art on the subject.

In the next session, the main barriers to visibility in supply chains are discussed.

3.2. Identifying Barriers to Visibility in Supply Chains

The widespread use of online shopping platforms and logistics services, which rely on RFID and IoT technology, has transformed how goods are tracked and how customers interact in real time at every stage of the purchase process. Additionally, payment methods for goods and services have undergone significant changes [31].

As a result, sharing information with supply chain partners has become essential for improving inventory management, increasing sales, and gaining a better understanding of demand patterns [1]. However, sharing complete and accurate information across the supply chain remains a challenge for many stakeholders [3].

To address these challenges, it is necessary to first identify the barriers that prevent visibility. This research identifies the critical obstacles to visibility in supply chains, as highlighted in the literature.

One of the key barriers is the lack of data standardization. Supply chain participants often use different formats, systems, and protocols, which complicates the integration and analysis of data [1]. This issue is further exacerbated by inadequate infrastructure, particularly in smaller companies or those in less developed regions. Without reliable infrastructure, data collection, storage, and exchange become inconsistent, resulting in gaps in visibility and hindering effective operational planning [8].

Another significant barrier is the high cost of acquiring and maintaining AI-IT4.0, such as sensors, data management systems (ERP, WMS), and blockchain platforms. Many companies, especially small and medium-sized enterprises, find the upfront investment prohibitive, which limits their ability to integrate advanced technologies that could provide visibility. Furthermore, the rapid pace of technological evolution often leads to system obsolescence, increasing the costs of upgrades [32].

Distrust in sharing data between partners is another critical challenge. Many organizations are concerned that sensitive information may be misused or exposed to security risks on digital platforms [12]. This mistrust is exacerbated by the lack of robust cybersecurity standards [16] and the complexity of global supply chains, which often involve multiple parties from different regions and cultures. The longer and more diverse the supply chain, the more difficult it becomes to establish reliable data-sharing mechanisms [4].

Effective collaboration between supply chain partners is essential for ensuring visibility, but it is not always prioritized. Many stakeholders hesitate to share critical information, either due to the lack of incentives or concerns about compromising their competitive advantage [1]. Moreover, a lack of technical expertise and training on AI-T4.0 makes it difficult for companies to adopt solutions that enable efficient data integration [33]. This challenge is particularly common among companies still relying on traditional practices without resources to invest in employee training [5].

As digitalization increases, supply chains are also facing data overload [34]. Despite the growing volume, many companies have the technologies and skills necessary to organize, analyze, and integrate this information effectively. The inability to convert raw data into actionable insights leads to wasted resources and the effectiveness of visibility strategies [3].

The absence of uniform international regulations to promote supply chain visibility is another significant barrier. The differing regulations across countries complicate the establishment of global standards for data exchange [6].

Moreover, the lack of clear objectives for using AI-IT4.0 to collect sustainability-related data (environmental, social and economic) [16] and the skepticism about the benefits of visibility for supply chain performance—often seen as a secondary concern to financial objectives [5]—pose additional challenges.

Addressing these barriers can help supply chains, international organizations, and academic institutions to collaborate on viable solutions. By identifying and mitigating the most impactful barriers, organizations can achieve significant improvements in visibility tailored to their specific contexts [3].

The 12 key barriers identified in the literature, which are detailed above, are summarized in

Table 1. Main barriers to the supply chain visibility.

Barriers	Characterization	Authors
B1-Lack of data standardization.	The lack of data standardization for the implementation of SCV systems.	Agrawal et al. (2024) [1]; Aoulad et al. (2023) [35]; Brookbanks; Parry, (2022) [11].
B2-Lack of adequate infrastructure.	Lack of an information infrastructure. The information infrastructure is an open and shared set capable of covering different types of data from different technology sources.	Knol & Tan (2018) [8]; Xu et al. (2024) [5].
B3-Costs associated with the implementation of AI-IT4.0 technologies.	Cost of technologies and lack of balance between technology costs and business benefits	Finkenstadt & Handfield, (2021) [32]; Knol & Tan, (2018) [8]
B4-Distrust in sharing data.	Trust issues and concerns over data accuracy.	Cao et al. (2022) [12]; Wyciślak & Akhtar, (2024b) [16]
B5-Complexities in the supply chain.	Long distances between parties, budget disparities across SC partners, different levels of technological advancement, and different company sizes.	Goh & Garg (2008) [4]; Hilletofth & Lättilä (2012) [14].
B6-Lack of collaboration.	Lack of collaboration in sharing data with supply chain partners.	Mehrotra, (2010) [9]; Wyciślak & Akhtar, (2024) [16].
B7-Lack of technical knowledge and training for the use of AI-IT4.0.	The lack of technical knowledge and training to work with AI-IT4.0 technologies makes data sharing difficult.	Njualem (2022) [36]; Oliveira & Handfield (2019) [37]; Vilko et al. (2019) [38] Moshood et al. (2021) [39]; Panigrahi et al. (2023) [33]
B8-Data overload.	The data needs to be clear and concise to facilitate decision-making. Excessive data hinders data analysis and information flow.	Kalaiarasan et al. (2022) [3]; Meyer-Larsen et al. (2012) [34]
B9-Lack of ability to combine data across a supply chain.	The data needs to be combined concisely and clearly to facilitate faster decision-making.	Moshood et al. (2021) [39]; Njualem (2022) [36]; Oliveira & Handfield (2019) [37]; Panigrahi et al. (2023) [33]; Vilko et al. (2019) [38].
B10-Lack of uniform and international legal regulation	A clear and objective international standard on visibility helps prevent cybercrimes and addresses cultural challenges and communication barriers.	Lafargue et al. (2022) [6].
B11-Lack of clear objectives for using AI-IT4.0 to generate data related to sustainability practices (environmental, social and economic).	There is a lack of awareness that using AI-IT4.0 for increasing visibility throughout the SC can contribute to improving its sustainability performance.	Wyciślak & Akhtar, (2024) [16]
B12-Disbelief in the benefits of visibility for supply chain performance (environmental, social, and economic).	Limited empirical results that prove the benefits of visibility for sustainability.	(Kalaiarasan et al., 2022) [3].

.

In addition to identifying the main barriers, it is valuable to analyze them through a theoretical lens that positions visibility as a strategic resource. This perspective highlights visibility’s role in enabling the supply chain to adapt, integrate, and reconfigure its resources and competencies in dynamic environments. Such an approach is particularly relevant when addressing supply challenges for essential products originating from countries affected by war or significant economic vulnerability [40]. The resource-based view (RBV) theory and the dynamic capabilities theory provide robust foundations for understanding how visibility can meet these challenges by enhancing the supply chain’s ability to respond to uncertainty and sustain competitive advantage.

3.3. Main Barriers and the View of the Resource-Based View Theory and the Dynamic Capabilities Theory

The Resource-Based View (RBV) theory argues that organizations gain competitive advantages through valuable, rare, inimitable, and irreplaceable resources [41]. In the context of artificial intelligence driven by Industry 4.0 technologies, supply chain visibility is one such strategic resource, enabling organizations to anticipate risks, adapt to market dynamics, and support sustainable practices. However, the adoption and implementation of visibility strategies face numerous barriers, which can be more effectively addressed through the Dynamic Capabilities Theory (DCT) [42].

Dynamic Capabilities Theory complements RBV by emphasizing the organizational capacity to adapt, integrate, and reconfigure resources in response to external environmental changes [20]. This perspective is particularly relevant for overcoming the challenges associated with implementing supply chain visibility in the dynamic and complex environments characteristic of artificial intelligence driven by Industry 4.0 technologies.

For instance, barriers such as B1—Lack of data standardization, and B2—Lack of adequate infrastructure, can be addressed by investing in flexible technologies and interoperable systems that allow seamless integration and continuous upgrades [39].

Barriers like B3—High costs of implementing AI-IT4.0, and B4—Distrust in data sharing, require innovative financing mechanisms and governance frameworks that promote transparency and foster trust among supply chain stakeholders [11,12]. Additionally, B5—Complexity within supply chains, and B6—Lack of collaboration, can be mitigated through strategic partnerships and collaborative initiatives that leverage the complementary capabilities of supply chain partners [5].

Barriers such as B7—Lack of technical knowledge and training, and B8—Data overload, highlight the need for dynamic capabilities that enable organizations to process and prioritize critical information effectively. Empowering teams through targeted training and the adoption of advanced analytical tools is essential to maximize the utility of visibility-enhancing technologies [14]. Similarly, B9—Inability to integrate data across supply chain tiers, and B10—Lack of uniform international legal regulations, underscore the importance of developing collaborative platforms and harmonized global standards to facilitate data sharing and integration [35].

Finally, B11—Lack of clear objectives for using AI-IT4.0 to generate data related to sustainability practices (environmental, social and economic), and B12—Disbelief in the benefits of visibility for supply chain performance (environmental, social and economic), require a strategic realignment of organizational goals. Demonstrating the tangible economic, social, and environmental benefits of visibility initiatives is crucial to overcoming resistance and prioritizing their adoption [36].

By integrating the principles of RBV and DCT, organizations can transform these barriers into opportunities. This dual approach enables the development of unique resources and adaptive capabilities, ensuring competitiveness, resilience, and sustainability in the rapidly evolving landscape of AI-IT4.0 [41].

3.4. Classification of Barriers

According to the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT), barriers can be categorized into three main types: resource-related barriers, which stem from limitations in acquiring, accessing, or managing valuable resources; dynamic capabilities-related barriers, which involve challenges in adapting, integrating, and reconfiguring resources in response to environmental changes; and strategic or cultural barriers, which arise from misaligned priorities, organizational resistance, or cultural differences that hinder collaboration and innovation (

Table 2. Kinds of barriers.

Kinds of Barriers	Barriers
Resource-related barrier (Saqib & Zhang, 2021) [41]	B1-Lack of data standardization
	B2-Lack of adequate infrastructure
	B3-Costs associated with the implementation of AI-IT4.0
	B7-Lack of technical knowledge and training for the use of AI-IT4.0
	B9-Lack of ability to combine data across a supply chain
Dynamic capabilities-related barrier (Sunmola & Apeji, 2024) [20]	B4-Distrust in sharing data
	B5-Complexities in the supply chain
	B6-Lack of collaboration
	B8-Data overload
	B10-Lack of uniform and international legal regulation
Strategic or cultural barrier (Suh & Lee, 2018) [42]	B11-Lack of clear objectives for using AI-IT4.0 to generate data related to sustainability practices
	B12—Disbelief in the benefits of visibility for supply chain performance

).

Data is generated in diverse ways and originates from various technological sources. Ensuring data homogeneity across the supply chain can be a significant competitive advantage, as it accelerates logistics processes and enhances operational efficiency [35]. Developing a technological framework focused on international trade and data standardization offers long-term benefits but requires ongoing effort and multidisciplinary expertise [8].

To fully realize these benefits, the supply chain must adopt AI-IT4.0 that are interoperable and cost-effective for most participants in the network. Without these considerations, adoption and operational costs may exceed the anticipated benefit [12].

The lack of technical knowledge and training in the use of artificial intelligence driven by Industry 4.0 technologies has been identified as a significant barrier by second-tier suppliers [3]. This barrier is closely linked to the time required for technology implementation and the level of senior management commitment to maintaining interoperability and innovation across the supply chain [9].

Another critical barrier, the lack of ability to combine data, underscores the importance of senior management promoting a culture of innovation. Continuous interactions and experience-sharing within the supply chain are essential to monitor and ensure the interoperability of technologies [5].

Barriers such as B1—Lack of data standardization, B2—Lack of adequate infrastructure, B3—Costs associated with the implementation of AI-IT4.0 technologies, B7—Lack of technical knowledge and training for the use of AI-IT4.0, and B9-Lack of ability to combine data across a supply chain are classified as resource-related barriers. Overcoming these obstacles equips supply chains with critical information resources, enabling partners to achieve organizational goals, including sustainability and operational excellence [41].

Other barriers, such as B4—Distrust sharing data, B5—Complexities in the supply chain, B6—Lack of collaboration, B8—Data overload, and B10—Lack of uniform and international legal regulation, are linked to dynamic capabilities.

The term “dynamic capability” refers to an organization’s ability to strategically reallocate its resources [43]. It is the ability to adapt to unforeseen situations [40]. Analyzing the concept of dynamic capabilities helps in analyzing how supply chains generate value for products and services and still manage to achieve external goals, such as fiscal, legal, and sustainability goals [6].

For instance, overcoming B4—Distrust sharing data enhances supply chain visibility by enabling better detection and learning [44]. Addressing B6—Lack of Collaboration will bring greater confidence in the supply chain, ensuring that companies feel confident in sharing knowledge and resources, ultimately reducing risks and disruptions [11]. Mitigating B8—Overload data helps organizations identify and prioritize essential data, determine appropriate formats, and define access protocols for shared platforms [16].

One of the most critical barriers in this category is B10—Lack of uniform and international legal regulations. Supply chains operating across multiple countries must navigate diverse legal frameworks, making it vital to establish standardized practices for data management and compliance [1].

Lastly, barriers B1—Lack of clear objectives for using AI-IT4.0 to generate data related to sustainability, and B12—Disbelief in the benefits of visibility for supply chain performance, fall into the category of strategic or cultural barriers. Overcoming these requires a realignment of corporate strategies and cultural attitudes across the supply chain [3].

Top management must commit to supporting the adoption of tailored AI-IT4.0 that suit specific organizational needs [1]. In some cases, incremental innovations may be more advantageous than radical changes [8]. Additionally, cultivating a culture that views visibility as a strategic asset for innovation and sustainability is crucial. This involves embedding visibility into processes, monitoring outcomes, and sharing results to reinforce its value throughout the organization [36].

4. Results and Discussion

In this section, the results of the bibliometric analysis of the sample are systematically presented and organized.

4.1. State-of-the-Art Bibliometric Results

Research on barriers to visibility in supply chains began modestly in 2008. Goh and Garg (2008) [4] conducted a case study on a Chinese company that overcame challenges posed by its unfavorable location using an IT system. This system enhanced visibility across the three basic flows of its supply chain: information, products, and finance.

In 2010, Mehrotra (2010) [9] identified barriers faced by Indian supply chains in implementing AI-IT4.0. Using factor analysis, the study categorized these barriers into four main groups: time, cost, lack of employee skills, and lack of senior management support.

Hilletofth and Lättilä (2012) [14] investigated the benefits and barriers of employing an agent-based decision support system in a Swedish home appliance supply chain. While the system improved visibility, barriers included difficulties in accessing partner information, lengthy validation processes, training time, challenges in establishing common rules among partners, and selecting relevant data. In the same year, Meyer-Larsen et al., (2012) [34] analyzed the Chino project, which integrated logistics operators with innovative artificial intelligence technology to improve fleet visibility along the Hong Kong–Europe route.

Knol and Tan (2018) [8] explored the challenges of implementing artificial intelligence technology in international trade through a European Union project. They identified barriers such as mobilizing stakeholders, high costs, the need for governance agreements, and process reengineering.

In 2019, the focus shifted from the challenges of implementing artificial intelligence technologies to their application. For instance, Oliveira and Handfield (2019) [37] examined issues of data quality, while Vilko et al. (2019) [38] analyzed risks associated with data sharing.

In 2020, more advanced technologies, such as blockchain, began gaining attention. Authors such as Rogerson and Parry (2020) [7] explored how blockchain can enhance visibility and trust in supply chains, identifying barriers including trust in technology, human error, fraud at borders, governance, access to consumer data, and willingness to pay. They concluded that industries with higher consumer value, such as the food sector, were more inclined to adopt artificial intelligence driven by industry 4.0 technologies.

In 2021, research focused on healthcare supply chains and logistics, highlighting how visibility supports decision-making, inventory control, and forecasting. Barriers identified included high implementation costs, lack of technical expertise, and interoperability challenges between technologies [32,39].

In 2022, the focus shifted to overcoming visibility barriers, as evidenced by five notable studies. Brookbanks and Parry (2022) [11] examined how a blockchain platform impacted trust in buyer–supplier relationships in a UK wine supply chain. Similarly, Cao et al. (2022) [12] analyzed the effects of blockchain adoption in a Chinese agricultural supply chain. Kalaiarasan et al. (2022) [3] conducted a systematic review identifying antecedents, challenges, motivators, and effects of visibility, emphasizing the need for empirical case studies. Lafargue et al. (2022) [6] mapped the cocoa supply chain between Ecuador and the Netherlands, demonstrating how biomarkers can address visibility gaps, although specialized skills were required to implement the technology effectively. Lastly, Njualem (2022) [36] highlighted the benefits and challenges of blockchain adoption.

By 2023 and 2024, discussions on visibility barriers increasingly centered on artificial intelligence driven by industry 4.0 technologies. Aoulad et al. (2023) [35] explored improvements in interoperability and knowledge sharing through IoT, sensors, and RFID technologies. Panigrahi et al. (2023) [33] investigated the interplay between visible supply chains, innovation capacity, and sustainable performance. Agrawal et al. (2024) [1] conducted a systematic review emphasizing managerial factors that influence visibility and the need for standardization, trust, and data handling skills.

Wyciślak and Akhtar (2024) [16] identified barriers such as technological immaturity, trust issues, lack of industry guidance, and legislative ambiguity through a case study in Poland and Germany.

This progression of research highlights a growing understanding of the barriers to visibility in supply chains, as well as the increasing focus on leveraging advanced technologies to overcome these challenges.

Figure 2 shows the number of papers published per year on the topic of barriers to visibility.

The largest concentration of papers in the sample emerges in 2022, highlighting the novelty of the topic and the existing gap in the literature regarding barriers to visibility in supply chains, particularly in the context of systematic reviews.

The authors in the sample do not demonstrate a significant collaborative network, with their connection largely limited to the shared focus on barriers to visibility. This fragmentation may be attributed to the diverse range of technologies explored in the studies, each addressing specific technologies in isolation rather than as part of a cohesive framework. Furthermore, there is limited evidence of business or research collaboration between the countries and regions represented by the authors, suggesting a lack of integration or coordinated efforts in this research domain.

The most frequently cited authors in the sample are summarized in

Table 3. Five most cited articles in the sample.

Authors	Title	Journal	Citations
Agrawal et al. (2024) [1]	Supply chain visibility: A Delphi study on managerial perspectives and priorities.	International Journal of Production Research	23
Brookbanks; Parry (2022) [11]	The impact of a blockchain platform on trust in established relationships: a case study of wine supply chains.	Supply Chain Management	70
Cao et al. (2022) [12]	An analysis on the role of blockchain-based platforms in agricultural supply chains.	Transportation Research Part E: Logistics and Transportation Review	87
Finkenstadt; Handfield. (2021) [32]	Blurry vision: Supply chain visibility for personal protective equipment during COVID-19.	Journal of Purchasing and Supply Management	62
Hilletofth; Lattila (2012) [14]	Agent based decision support in the supply chain context.	Industrial Management and Data Systems	32

, providing an overview of their contributions to the field and highlighting the foundational works that have shaped the discussion on barriers to visibility in supply chains.

The most cited authors in the sample primarily represent recent publications, highlighting the increasing relevance and timeliness of the topic in academic discourse.

Table 4. Number of papers per journal in the sample.

Journals	Papers
Accident Analysis and Prevention	1
Applied System Innovation	1
Asian Journal of Management Cases	1
Global Business Review	1
Industrial Management and Data Systems	1
International Journal of Production Economics	2
International Journal of Production Research	1
International Journal of RF Technologies: Research and Applications	1
International Journal of Supply and Operations Management	1
Journal of Purchasing and Supply Management	1
Journal of Theoretical and Applied Electronic Commerce Research	1
Logforum	1
Production Planning and Control	3
Supply Chain Management	2
Sustainability	1
Transportation Research Part E: Logistics and Transportation Review	1

provides an overview of the journals with the highest frequency in the sample, reflecting the key platforms where research on barriers to visibility in supply chains is being published.

The sample includes contributions from high-impact journals, primarily focused on the fields of engineering, management, social sciences, and environmental sciences. Figure 3 illustrates the distribution of publications across different countries, providing insights into the geographical trends in research on barriers to visibility in supply chains.

Studies involving Sweden are notably prominent in the sample. Sweden’s dedication to sustainable practices and ambitious climate goals, such as achieving net-zero emissions by 2045 [45], underscores its focus on visibility in supply chains. Visibility is particularly significant for Sweden due to its role as a global export hub, especially in sectors like technology, manufacturing, and pulp and paper. Enhanced visibility helps optimize costs, improve predictability, and increase logistical efficiency [1].

Moreover, Sweden’s emphasis on rigorous labor standards, human rights, and environmental protection highlights the necessity of visibility for compliance with European Union regulations and global trade standards [14]. With a strong foundation in technology and digitalization, Sweden leverages visibility to improve data collection and decision-making through IoT and AI integration, ensure transaction reliability via blockchain, and enhance energy efficiency in logistics chains [3].

China, the United Kingdom, and India each appear with two publications. China is one of the largest global exporters and importers and has very extensive supply chains, so visibility and AI-IT4.0 are very important to ensure product compliance [12].

The UK, following its exit from the European Union in 2020, uses visibility to comply with fares and regulations, keeping trade flowing. In addition, visibility into supply chains helps the UK monitor its suppliers for carbon dioxide emissions and working conditions. This monitoring will help guide companies’ actions towards the government’s commitment to achieving net-zero emissions by 2050 [7,11].

India, a major exporter of textiles, information technology and pharmaceuticals, relies on supply chain visibility to meet export deadlines, reduce logistics costs, and maintain quality standards [33]. The pharmaceutical industry in India was an early adopter of visibility-enhancing AI-IT4.0 due to its highly regulated nature and need for operational control [13]. In contrast, the textile sector’s interest in visibility was driven by external pressures to ensure transparency and compliance [27].

Figure 4 shows the research methods explored by the papers in the sample.

The results presented in Figure 5 indicate that the most common research methods in the sample were case studies [35], followed by interviews [10]. This trend highlights the conceptual nature of the topic and suggests that it remains at an early stage of development within academic literature. Furthermore, only three systematic reviews were identified, none of which specifically focused on barriers to visibility in supply chains with a focus on second- and third-tier suppliers, and no studies were found that related and classified the barriers in a strategic manner based on the theory of vision based on resources and dynamic capabilities.

In terms of keyword co-occurrence, Figure 5 illustrates a strong alignment between the keywords employed in the sample papers and the overarching theme of barriers to visibility. This cohesion reflects a clear thematic focus within the research.

The co-occurrences of keywords in the abstracts are grouped into three distinct clusters. The central theme of visibility serves as the link between all other clusters, reflecting that each of the topics considered in the sample revolves around this core concept.

The blue cluster, associated with the keyword visibility, also includes analytics and sustainability. This alignment reinforces the notion that visibility, coupled with data analysis, can enhance sustainability in supply chains [16,36].

The green cluster features the terms supply chain management, challenges, and logistics. These keywords emphasize that effective management of both material and human resources is crucial for achieving a paradigm shift toward greater visibility and sustainability in supply chains [7,39,46]. Additionally, the term challenges reflect the consistent identification of barriers to visibility in the research.

In the red cluster, the keywords blockchain, supply chain, trust, and risk appear. These terms underscore the importance of adopting secure technologies that ensure accurate and consistent data across the supply chain [1,12].

However, the implementation of AI-IT4.0 requires both financial investment [8,33] and an organizational culture aligned with the adoption of innovation [5]. In summary, achieving sustainability through visibility enabled by AI-IT4.0 involves overcoming the twelve primary barriers identified and categorized earlier in the analysis.

4.2. Discussion

This study provides a comprehensive analysis of the barriers to visibility in supply chains, offering a structured classification and examining these barriers in the context of artificial intelligence driven by industry 4.0 technologies. The findings highlight the complexity of the challenges associated with achieving visibility and the need for further exploration of solutions to overcome these barriers. The discussion is divided into several key areas: the barriers identified, the role of AI-IT4.0, sector-specific insights, and the implications for future research.

The research on barriers to visibility is relatively recent, with significant growth in the topic beginning in 2022 [3]. Notably, the authors in the sample have no clear co-authorship relationships, and the most cited studies are predominantly from recent publications [1,11,12].

Furthermore, the bibliometric results indicate that the journals with the highest incidence are high-impact publications within the fields of engineering [5], management [6], social sciences, and environmental sciences [33].

The countries with the highest number of publications on this topic are Sweden [14], China [4], United Kingdom [11], and India [33]. Notably, the textile [1], pharmaceutical [37], and food sectors [7] are the most prominent in exploring the application of visibility within supply chains.

The results are important to detect which barriers are most common in second and third suppliers so that focal companies can define action plans together with their partners. Solving visibility problems between partners and the focal company avoids labor and environmental fines, in addition to helping maintain or improve the reputation of the entire supply chain.

Identifying the main relationships between barriers provides a generic path for supply chains, which can apply brainstorming, interviews and surveys to establish the main relationships in each specific case.

4.2.1. Barriers to Visibility: Classification and Insights

This study offers a comprehensive analysis of barriers to visibility in supply chains, classifying them into three key categories: resource-related barriers, dynamic capabilities-related barriers, and strategic/cultural barriers. This classification provides a framework for understanding the diverse challenges organizations face when implementing visibility solutions.

• Resource-Related Barriers

Resource-related barriers are primarily driven by challenges in acquiring, managing, and utilizing necessary resources. These include the lack of data standardization (B1), inadequate infrastructure (B2), and high implementation costs of AI-IT4.0 technologies (B3). These challenges are consistent with the existing literature, which stresses the need for technical standards and infrastructure investment for successful digital transformation. The costs associated with adopting AI-IT4.0 technologies can be especially challenging for organizations with limited resources. Moreover, insufficient technical knowledge (B7) and difficulties in integrating data across the supply chain (B9) further limit the potential for effective visibility solutions. These barriers hinder efficient collection, integration, and use of data, which are essential for enhancing supply chain transparency and responsiveness.

• Dynamic Capabilities-Related Barriers

Barriers in this category concern the ability of organizations to adapt, integrate, and reconfigure resources in response to environmental changes. These challenges often involve the relationships and collaboration between supply chain actors. Issues such as distrust in data sharing (B4), supply chain complexity (B5), and lack of collaboration (B6) align with prior research [14], which emphasizes the role of trust and coordination in supply chain management. These barriers highlight the need for improved communication and cooperation among supply chain partners to facilitate data sharing and joint decision-making. The inherent complexity of global supply chains further exacerbates these challenges, requiring stronger frameworks for collaboration and data standardization.

• Strategic and Cultural Barriers

Strategic and cultural barriers include the lack of senior management support (B11) and skepticism about the benefits of visibility (B12). These findings align with the existing literature [1,5], which underscores the importance of organizational leadership in recognizing the strategic importance of visibility for improving supply chain performance. The absence of clear objectives regarding the use of AI-IT4.0 technologies to enhance sustainability data suggests that many organizations have yet to integrate visibility into their strategic goals. Overcoming these barriers requires a shift in organizational culture, investments in leadership development, and alignment with sustainability objectives.

4.2.2. Interrelationships Between Barriers

The study identifies twelve key barriers to visibility, which can be mitigated through targeted actions. The mitigation strategies include developing frameworks for data standardization, leveraging technological infrastructure, fostering trust and collaboration, investing in training, and aligning organizational objectives with sustainability goals. Each of these actions aims to address the specific challenges identified in the research, helping organizations improve their supply chain visibility and sustainability performance.

The barriers identified in this study are interconnected, creating a cycle of challenges that can significantly impede supply chain efficiency and sustainability. Several key relationships between these barriers illustrate how they influence one another:

Implementation costs of AI-IT4.0 technologies (B3) [32] and lack of adequate infrastructure (B2) [8]: The high costs associated with implementing AI-IT4.0 technologies are exacerbated by inadequate infrastructure. Companies with mature infrastructure are better positioned to tackle these challenges at a lower cost, as they already have the necessary foundation for implementing new technologies [5].

Another important relationship is the lack of data standardization (B1), which can hinder efficient data management, leading to data overload (B8) [35], due to the lack of ability to combine data across a supply chain (B9), which hinders systems integration [33]. Without standardized data, integrating and analyzing it becomes more difficult, leading to data overload and limiting the ability to integrate data across the supply chain, hindering decision-making.

Lack of data standardization (B1) also exacerbates distrust in sharing data (B4) [11] and creates an environment of lack of collaboration (B6) [38], increasing complexity in the supply chain (B5) [4]. The absence of data standardization hampers trust and collaboration among supply chain partners. When data is not standardized, partners may hesitate to share it due to concerns about misinterpretation or misuse, which in turn increases supply chain complexity and further inhibits visibility.

In addition, lack of uniform and international legal regulation (B10) makes it difficult to define sustainable strategies (B11) [1] and perceive the value generated by the visibility of the chain (B12) [3]. Furthermore, lack of technical knowledge and training for the use of IT4.0 (B7) leads to data integration problems (B9) [14], making it difficult to define sustainability goals and objectives (B11) [16]. The absence of clear international regulations creates uncertainty about how visibility will be regulated or rewarded. This, in turn, affects organizations’ ability to define sustainability strategies and diminishes the perceived value of visibility, preventing widespread adoption of visibility practices. This creates a cycle where insufficient visibility hinders the improvement of sustainability performance, which further limits the capacity for data integration and technological adoption.

By categorizing barriers into resource-related, dynamic capabilities-related, and strategic/cultural categories, organizations can prioritize initiatives to overcome these challenges. This classification ensures strategic alignment with organizational objectives and enhances adaptability to the external environment, ultimately leading to more efficient, sustainable, and collaborative supply chains. Understanding the interrelationships between these barriers is critical to developing effective solutions and achieving supply chain visibility and sustainability goals.

4.2.3. The Role of Artificial Intelligence Driven by Industry 4.0 Technologies (AI-IT4.0)

Artificial intelligence driven by industry 4.0 technologies, such as IoT, blockchain and big data, has been shown to play a critical role in addressing the barriers to visibility. These technologies offer potential solutions to many of the challenges identified, particularly in terms of real-time data collection, integration, and transparency. Blockchain, for example, can enhance trust and security in data sharing [1,12], addressing concerns about data privacy and intellectual property. Similarly, IoT and big data technologies enable the collection and analysis of large volumes of data, facilitating decision-making and improving supply chain responsiveness.

Despite the potential of AI-IT4.0, the barriers to their adoption remain significant. Issues such as the lack of data standardization (B1) and system interoperability (B9) continue to impede the seamless integration of these technologies across supply chains. These challenges highlight the need for industry-wide standards and the development of interoperable systems that can support the effective use of AI-IT4.0. Furthermore, the high costs of implementation (B3) and the lack of technical expertise (B7) present additional hurdles for organizations, particularly those with limited resources.

4.2.4. Sector-Specific Insights

The food, pharmaceutical, and textile sectors were identified as particularly engaged in efforts to improve supply chain visibility. The food sector has made significant progress in adopting AI-IT4.0 to ensure compliance with regulatory standards and improve sustainability practices [12,35]. Given the sector’s connection to public health, achieving visibility is critical for ensuring the safety and quality of products throughout the supply chain.

The pharmaceutical sector, similarly, has long been a leader in implementing visibility solutions, driven by the need to maintain strict control over product safety and regulatory compliance [13]. The textile sector, while relatively newer in its adoption of visibility technologies, is increasingly recognizing the importance of transparency in response to sustainability pressures and ethical concerns [27].

These sector-specific findings highlight the varying drivers of visibility across industries, as well as the shared barriers to achieving effective visibility. Despite differences in sectoral focus, common challenges—such as the need for improved data sharing, greater collaboration, and alignment with sustainability goals—emerge as central themes.

4.2.5. Implications for Future Research

This study presents several prospects for future research. First, empirical studies that explore how the identified barriers manifest in real-world supply chains could provide valuable insights into the practical challenges of achieving visibility.

Furthermore, research on the effectiveness of emerging technologies, such as 5G and blockchain, in overcoming these barriers is needed. Examining how these technologies perform in operational settings would provide more concrete evidence of their potential to enhance visibility and improve supply chain management.

Future research can use the barriers identified in this study to assess the most relevant ones in specific cases through interviews, surveys, or the AHP method. Additionally, it is possible to analyze which barriers act as drivers or dependents in a study using interpretative structural modeling.

5. Conclusions

In conclusion, this study identifies and classifies the key barriers to visibility in supply chains, offering a framework for understanding the challenges organizations face in implementing effective visibility solutions. The barriers identified—resource-related, dynamic capabilities-related, and strategic/cultural—represent critical obstacles to achieving greater supply chain visibility and efficiency. Overcoming these barriers requires both technological advancements and organizational changes, particularly in terms of leadership support, collaboration, and strategic alignment. Future research should continue to explore these barriers empirically, examine the role of emerging technologies, and investigate the potential for integrated visibility solutions across the entire supply chain.