Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?

Artificial intelligence and blockchain application in supply chains: Sustainability and way to create dat a-led profits?

• Naum Zolakis Orcid: Orcid. Org/0000-0003-2042-70471, 2,
• Roman Schumacherorcid: Orcid. Org/0000-0003-0764-58673,
• Manji Dora Orcid: Org/0000-0003-4730-81444 & amp; amp;
• …
• MUKESH KUMARORCID: orcid. Org/0000-0002-1961-50783

• 22K access
• 66 references
• Check all counts

Abstract

Digitalization is expected to transform the en d-t o-end supply chain business by utilizing the technical abilities of advanced technical applications. Despite the individual advantages of the application of digital technology, the effects of the combination have been overlooked due to limited evidence based on facts. In this regard, in this regard, in order to increase the threshold of operation performance and promote sustainable growth and dat a-led revenue, joint application of artificial intelligence (AI) and blockchain technology (BCT). Explore. Specifically, we investigated tuna supply chains in Thailand, identified the corresponding en d-t o-end tasks, observed the management process of materials and data, and assumed the application of AI and BCT. For this reason, we first understand the material, data, and information flow that could be promoted by applying AI and BCT in each supply chain, which may be promoted by multiplying the interaction between the business process and the system level. The result of the mapping shows that AI and BCT play a central role in managing digital supply chains, but the impact of sustainability and data revenue generation is the parameters and goals set by system stakeholders. It indicates that it depends on.

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Avoid mistakes in the manuscript < SPAN> Artificial intelligence and blockchain application in the supply chain: Road to sustainability and dat a-led profits?

1 Introduction

Naum Zolakis Orcid: Orcid. Org/0000-0003-2042-70471, 2,

Roman Schumacherorcid: Orcid. Org/0000-0003-0764-58673,

Manji Dora Orcid: Org/0000-0003-4730-81444 & amp; amp;

MUKESH KUMARORCID: orcid. Org/0000-0002-1961-50783

22K access

66 references

Check all counts

Digitalization is expected to transform the en d-t o-end supply chain business by utilizing the technical abilities of advanced technical applications. Despite the individual advantages of the application of digital technology, the effects of the combination have been overlooked due to limited evidence based on facts. In this regard, in this regard, in order to increase the threshold of operation performance and promote sustainable growth and dat a-led profits, joint application of artificial intelligence (AI) and blockchain technology (BCT). Explore. Specifically, we investigated tuna supply chains in Thailand, identified the corresponding en d-t o-end tasks, observed the management process of materials and data, and assumed the application of AI and BCT. For this reason, we first understand the material, data, and information flow that could be promoted by applying AI and BCT in each supply chain, which may be promoted by multiplying the interaction between the business process and the system level. The result of the mapping shows that AI and BCT play a central role in managing digital supply chains, but the impact of sustainability and data revenue generation is the parameters and goals set by system stakeholders. It indicates that it depends on.

Article Open Access February 16, 2024

2023

2022

2 Artificial intelligence and blockchain in supply chains

artificial intelligence

Avoid mistakes in the manuscript Let's avoid artificial intelligence and blockchain in supply chain: Road to sustainability and dat a-led profits?

Naum Zolakis Orcid: Orcid. Org/0000-0003-2042-70471, 2,

2.1 Artificial intelligence

Roman Schumacherorcid: Orcid. Org/0000-0003-0764-58673,

Manji Dora Orcid: Org/0000-0003-4730-81444 & amp; amp;

MUKESH KUMARORCID: orcid. Org/0000-0002-1961-50783

22K access

2.1.1 Sustainability benefits

66 references

Check all counts

Digitalization is expected to transform the en d-t o-end supply chain business by utilizing the technical abilities of advanced technical applications. Despite the individual advantages of the application of digital technology, the effects of the combination have been overlooked due to limited evidence based on facts. In this regard, in this regard, in order to increase the threshold of operation performance and promote sustainable growth and dat a-led revenue, joint application of artificial intelligence (AI) and blockchain technology (BCT). To explore. Specifically, we investigated tuna supply chains in Thailand, identified the corresponding en d-t o-end tasks, observed the management process of materials and data, and assumed the application of AI and BCT. For this reason, we first understand the material, data, and information flow that could be promoted by applying AI and BCT in each supply chain, which may be promoted by multiplying the interaction between the business process and the system level. The result of the mapping shows that AI and BCT play a central role in managing digital supply chains, but the impact of sustainability and data revenue generation is the parameters and goals set by system stakeholders. It indicates that it depends on.

2.1.2 Implementation challenges

Article Open Access February 16, 2024

2023

2022

artificial intelligence

2.2 Blockchain technology

Avoid mistakes that are common in manuscripts

Digitization of business activities is indispensable for traditional companies to compete in the digital economy era (Weill & Amp; WoERner, 2018). In this regard, advanced systems and applications such as the Internet (IoT), blockchain technology (BCT), cloud computing, data analysis, and artificial intelligence (AI) use related digital skills and abilities. Along with development and maturity, it is the basis of business digital transformation (AKTER et al. Each of each digital application is presented with a specific technical advantage, and there is a difference in data and information. Data analysis. In the field, AI is "the ability of the system that uses these lessons to achieve specific goals and tasks through agile adaptation" (Haenlein & Amp; KaPlan, 2019, P. 1) is expected to reach a 2994 billion dollar by 2026 (2021). In the field of wear, BCT is a distributed ledger, enabling safe data sharing to improve supply chain visibility and transparency (Kamble et al. CAGR 48, 1% is expected to reach $ 948 million by 2025 (Marketsandmarkets, 2020). Digitalization of activities is essential (Weill & amp; WoERner, 2018), the Internet (IoT), cloud computing, data analysis, artificial intelligence (AI). The adoption of the system and applications is the basis of business digital transformation, along with related digital skills and abilities (AKTER et al. Each digital application is specific technology. In the field of data and information, AI is "interpreted from external data and achieving specific goals and tasks through agile adaptation. "The ability of the system that uses those lessons" (Haenlein & Amp; Kaplan, 2019, P. 1). The market value of AI in the food and beverage industry is expected to reach $ 2994 billion with an average average growth rate of 45, 8%by 2026 (ResearchandMarkets, 2021). In the hardware field, BCT is a distributed ledger, enabling safe data sharing to improve the visibility and transparency of supply chains (Kamble et al. CAGR 48, 1% is expected to reach the $ 948 million by 2025 (Marketsandmarkets, 2020). Digitalization is indispensable (WeiLL & amp; WoERner, 2018), such as the Internet (IoT), cloud computing, and artificial intelligence (AI). The adoption of systems and applications is based on the development and maturity of related digital skills and abilities, and is the basis of business digital change (AKTER et al. Each digital application is specific technical advantage. In the field of data and information, AI is "interpreted from external data, and to achieve specific goals and tasks through agile adaptation. The ability of the system that uses the lesson "(Haenlein & Amp; Kaplan, 2019, P. 1) is an average annual growth rate of 45, 8%. The US dollar is expected to reach (ResearchandMarkets, 2021), which is a distributed ledger and enables safe data sharing to improve the visibility and transparency of the supply chain. The world BCT market in the farm and food field is expected to reach $ 948 million by 2025 (Marketsandmarkets, 2020).

2.2.1 Sustainability benefits

Despite the advantages of digital systems and applications, their interconnections can overcome their unique restrictions, bring out further technical abilities, utilize the benefits of productivity, and promote corporate growth. (AKTER ET Al, if you compare it, artificial intelligence, which is the most influential application in the early 21st century, usually uses central computing and data storage infrastructure (rea l-time) decisio n-making. To explore (continuous) data streams (Nasar et al.), But the AI ​​system faces complex issues such as data security, interaction, counterattack, ethics and morals (Awad et AI is considered a "black box" in a larger range, and the BCT is also skeptical. As an application, it guarantees the diversified data and decisio n-making, but cannot be used to generate information to analyze data and provide information to the decisio n-making process (). Salah et al., 2019, but in the dynamic global business situation, interactive decisions based on many different sources and rea l-time analysis and interpretation are prominent. In this regard (in this regard, in this regard, the combination of AI, BCT, and other stat e-o f-th e-art technologies (for example, automation of senso r-based sensor bases for collecting store floor data) complements technical abilities and true. Despite the advantages of digital systems and applications that create business value and enable competitiveness (Hughes et al.) < SPAN> Despite the advantages of digital systems and applications, these interconnections have overcome their unique restrictions and further technology. It may be useful for bringing out integrated abilities, using the advantages of productivity, and promoting corporate growth (AKTER et al, for example, the most influential application in the early 21st century, artificial applications. Intelligence usually uses central computing and data storage infrastructure to explore (continuous) data stream for (rea l-time) decisio n-making (Nasar et al.), But the AI ​​system is data. Facing complex issues such as security, interoperability, counterattack, ethics and morals (Awad et al., 2018). In a larger range, AI is regarded as a "black box" and is skeptical of the use of analysis results derived in important decisio n-making. Similarly, the BCT guarantees the diversified data and decisio n-making as a standalone application, but generates information to analyze the data and provide information to the decisio n-making process. It cannot be used for (Salah et al., 2019). However, in the dynamic global business situation, interactive decisions based on rea l-time data and information in real time from many different sources are becoming remarkable (TOO in this respect. In combination with AI, BCT, and other cuttin g-edge technologies (eg, automation of senso r-based senso r-based data), it complements technical abilities, creating true business value, and competitiveness. Despite the advantages of digital systems and applications that enable the catalyst (Hughes et al.), These interconnections overcome unique restrictions, bring out further technical abilities, and utilize the benefits of productivity. However, it may help to promote corporate growth (AKTER et al, for example, artificial intelligence, the most influential application in the early 21st century, is usually the central computing and datastrage. Utilizing infrastructure to explore (continuous) data stream for (rea l-time) decisio n-making (Nasar et al.). However, the AI ​​system has data security, interaction, counterattack, and ethics. Faced a complex problem (AWAD ET AL. Similarly, BCT guarantees the diversified data and decisio n-making as a stan d-alone application, but to analyze the data and provide information to the decisio n-making process. It cannot be used to generate information (Salah et al., 2019), but in real time and information in many different sources and information. Interactive decisions based on analysis and interpretation are becoming more noticeable (too, AI, BCT, and other cuttin g-edge technologies (for example, automation of senso r-based senso r-based senso r-based data)) To complement technical abilities, create true business value, and enable competitiveness (Hughes et al.)

In particular, BCT is considered to be a sufficient digital application to ensure interpretable and trustworthy AI in real-world settings by verifying data safety, privacy, reliability, usability, dependability, performance, and governance (Nasar et al., 2020). Similarly, AI-based solutions can support BCT applications and redefine industrial operations from multiple perspectives, such as: (ii) enabling autonomous processes; (iii) enabling personalized services; and (iv) moving to forecast-based production planning (Toorajipour et al., 2021). Furthermore, leveraging the synergistic technological capabilities of multiple digital systems and applications, such as AI and BCT, can help pursue the SDGs (Del Río Castro et al., 2020). Collaborative digital ecosystems can promote sustainable supply chain performance by enabling improved resource management, supporting waste monitoring and management, reducing energy consumption, and informing the development and dissemination of sustainable financial products (Belhadi et al., 2020). In particular, the combination of AI and BCT can help to improve the performance of the supply chain by enabling improved resource management, supporting waste monitoring and management, reducing energy consumption, and informing the development and dissemination of sustainable financial products (Belhadi et al., 2020). The integration of blockchain technology will extend data collection, interoperability, and analytical capabilities to the scale of end-to-end operations, enabling supply chain safety. Major retailers have already adopted BCTs, namely (i) Walmart to track U. S. agricultural products and Chinese pork, (ii) Carrefour to monitor milk supply chains, and (iii) Alibaba to address food fraud (Kshetri, 2018). At the institutional level, the U. S. Food and Drug Administration is piloting blockchain applications based on artificial intelligence to dynamically assess foodborne illness risks in imported foods and improve end-to-end tracking and tracing of goods (Mearian, 2019). In particular, BCTs are considered to be sufficient digital applications to ensure interpretable and trustworthy AI in real-world settings by verifying data safety, privacy, reliability, usability, trustworthiness, performance, and governance (Nasar et al. 2019). Similarly, AI-based solutions can support BCT applications and redefine industrial operations in multiple ways, including: (ii) enabling autonomous processes; (iii) enabling personalized services, and (iv) moving towards forecast-based production planning (Toorajipour et al., 2021). Moreover, leveraging the synergistic technological capabilities of multiple digital systems and applications, such as AI and BCT, can help pursue the SDGs (Del Río Castro et al.). Collaborative digital ecosystems promote sustainable supply chain performance by enabling improved resource management, supporting waste monitoring and management, reducing energy consumption, and informing the development and dissemination of sustainable financial products (Belhadi et al.). In particular, the integration of AI and BCT can extend data collection, interoperability, and analytical capabilities to the scale of end-to-end operations, enabling supply chain safety. Major retailers have already adopted BCT; namely, (i) Walmart to track US agricultural products and Chinese pork, (ii) Carrefour to monitor its milk supply chain, and (iii) Alibaba to address food fraud (Kshetri, 2018). At the institutional level, the US Food and Drug Administration is piloting artificial intelligence-based blockchain applications to dynamically assess the foodborne illness risk of imported foods and improve end-to-end tracking and tracing of goods (Mearian, 2019). In particular, BCT is considered to be a sufficient digital application to ensure interpretable and trustworthy AI in real-world settings by verifying data safety, privacy, reliability, usability, dependability, performance, and governance (Nasar et al., 2020). Similarly, AI-based solutions can support BCT applications and redefine industrial operations from multiple perspectives, such as: (ii) enabling autonomous processes; (iii) enabling personalized services; and (iv) moving to forecast-based production planning (Toorajipour et al., 2021). Furthermore, leveraging the synergistic technological capabilities of multiple digital systems and applications, such as AI and BCT, can help pursue the SDGs (Del Río Castro et al., 2020). Collaborative digital ecosystems can promote sustainable supply chain performance by enabling improved resource management, supporting waste monitoring and management, reducing energy consumption, and informing the development and dissemination of sustainable financial products (Belhadi et al., 2020). In particular, the combination of AI and BCT can help to improve the performance of the supply chain by enabling improved resource management, supporting waste monitoring and management, reducing energy consumption, and informing the development and dissemination of sustainable financial products (Belhadi et al., 2020). The integration of blockchain technology will scale data collection, interoperability, and analytical capabilities to end-to-end operations, enabling supply chain security. Major retailers are already adopting blockchain technology: (i) Walmart to track U. S. agricultural products and Chinese pork, (ii) Carrefour to monitor its milk supply chain, and (iii) Alibaba to address food fraud (Kshetri, 2018). At the institutional level, the U. S. Food and Drug Administration is piloting artificial intelligence-based blockchain applications to dynamically assess foodborne illness risks in imported foods and improve end-to-end tracking and tracing of goods (Mearian, 2019).

In terms of international trade, AI and BCT will help to bring out the interests of system officials. For example, the European Union (EU) is the third marine export destination after the United States and Japan in Thailand, accounting for 10 % of the total export. In fact, Thailand's total marine product exports in Thailand were about $ 5. 93 billion (KISHIMOTO, 2019). For this reason, the possibility that Thailand to be excluded from the export of fishery to the EU has a significant economic and social impact. In April 2015, Thailand received a "yellow card" for violating the EU standards for fishery management. "Yellow card" is an official notification that indicates that the export country has not taken appropriate measures against illegal fisheries. After that, if the appropriate measures are not implemented, the country will be excluded from trade activities with the EU (European Commission, 2015). Thailand was excluded in 2019 (IUU Watch, 2020). Illegal overfishing and endangered fisheries are two major concerns on sustainability that Thailand's fisheries industry must work to maintain listing (European Commission, 2015). Illegal fisheries are not only in Thailand, but are the following global issues. < SPAN> In terms of international trade, AI and BCT will help to bring out the interests of system officials. For example, the European Union (EU) is the third marine export destination after the United States and Japan in Thailand, accounting for 10 % of the total export. In fact, Thailand's total marine product exports in Thailand were about $ 5. 93 billion (KISHIMOTO, 2019). For this reason, the possibility that Thailand to be excluded from the export of fishery to the EU has a significant economic and social impact. In April 2015, Thailand received a "yellow card" for violating the EU standards for fishery management. "Yellow card" is an official notification that indicates that the export country has not taken appropriate measures against illegal fisheries. After that, if the appropriate measures are not implemented, the country will be excluded from trade activities with the EU (European Commission, 2015). Thailand was excluded in 2019 (IUU Watch, 2020). Illegal overfishing and endangered fisheries are two major concerns on sustainability that Thailand's fisheries industry must work to maintain listing (European Commission, 2015). Illegal fisheries are not only in Thailand, but are the following global issues. In terms of international trade, AI and BCT will help to bring out the interests of system officials. For example, the European Union (EU) is the third marine export destination after the United States and Japan in Thailand, accounting for 10 % of the total export. In fact, Thailand's total marine product exports in Thailand were about $ 5. 93 billion (KISHIMOTO, 2019). For this reason, the possibility that Thailand to be excluded from the export of fishery to the EU has a significant economic and social impact. In April 2015, Thailand received a "yellow card" for violating the EU standards for fishery management. "Yellow card" is an official notification that indicates that the export country has not taken appropriate measures against illegal fisheries. After that, if the appropriate measures are not implemented, the country will be excluded from trade activities with the EU (European Commission, 2015). Thailand was excluded in 2019 (IUU Watch, 2020). Illegal overfishing and endangered fisheries are two major concerns on sustainability that Thailand's fisheries industry must work to maintain listing (European Commission, 2015). Illegal fisheries are not only in Thailand, but are the following global issues.

2.2.2 Implementation challenges

A central issue in legal action against illegal fishing is the lack of transparency throughout the supply chain. Globally, many fishers do not comply with fishing laws and are able to sell their catch in the market without proof of compliance (MacFadyen et al., 2019). In response to the transparency challenge, AI and BCT have proven to be effective tools to reduce information asymmetries and increase transparency throughout the supply chain (Bumblauskas et al.). However, end-to-end supply chains often contain multiple disparate data archetypes. The main challenges in implementing these technologies are often related to the limited capacity to process unstructured, incomplete, and sometimes inaccurate data (Choi et al.). Furthermore, the lack of systems thinking among supply chain participants can lead to significant challenges in implementing such technologies in complex industrial contexts (Camaréna, 2020). To a larger extent, the implementation of AI and BCT can leverage data to inform decisions in business processes and provide data-driven products and services. Therefore, the concept of “profiting from data”, i. e. “using an organization’s data to generate profits” is emerging (Faroukh et al., 2019). A key challenge for digital traceability and sustainability in food is the lack of appropriate information technology-based tools that can inform supply chain design for agility and dynamic change (Klein et al., 2019). Digital adoption is therefore a key challenge at the conceptual modeling level (Guizzardi et al., 2019). In terms of modeling, methodologies are needed to rapidly redesign digital supply chains and respond to socio-technical and environmental developments, while ensuring data analysis, information flows and operational understanding (Fayoumi & Loucopoulos, 2016). A central issue in legal action against illegal fishing is the lack of transparency throughout the supply chain. Globally, many fishers do not comply with fishing laws and can sell their catch on the market without proof of compliance (MacFadyen et al., 2019). As a response to the transparency challenge, AI and BCT have proven to be effective tools to reduce information asymmetries and increase transparency throughout the supply chain (Bumblauskas et al., 2019). al.) However, end-to-end supply chains often have multiple different data archetypes. The main challenges in implementing these technologies are often related to limited capacity to handle unstructured, incomplete and sometimes inaccurate data (Choi et al.). Furthermore, lack of systems thinking among supply chain participants can lead to significant challenges in implementing such technologies in complex industrial contexts (Camaréna, 2020). To a larger extent, the implementation of AI and BCT can leverage data to inform decisions in business processes and provide data-driven products and services. Therefore, the concept of “profiting from data”, i. e. “using organizational data to generate profits” is emerging (Faroukh

A key challenge for digital traceability and sustainability in food is the lack of appropriate information technology-based tools that can inform supply chain design for agility and dynamic change (Klein et al.) Digital implementation is therefore limited to a conceptual modeling level (Guizzardi et al., In terms of modeling, methodologies are needed to rapidly redesign digital supply chains and respond to socio-technical and environmental developments, while ensuring data analytics, information flows, and operational understanding (Fayoumi & Loucopoulos, 2016). A central issue in legal action against illegal fishing is the lack of transparency throughout the supply chain. Globally, many fishers do not comply with fishing laws and can sell their catch on the market without proof of compliance (MacFadyen et al., 2019). In response to the transparency challenge, AI and BCT have proven to be effective tools to reduce information asymmetries and increase transparency throughout the supply chain (Bumblauskas et al.). However, end-to-end supply chains often contain multiple different data archetypes. The main challenges in implementing these technologies are often related to limited capacity to process unstructured, incomplete, and sometimes inaccurate data (Choi et al.). Furthermore, a lack of systems thinking among supply chain participants can lead to significant challenges in implementing such technologies in complex industrial contexts (Camaréna, 2020). To a larger extent, the implementation of AI and BCT can leverage data to inform decisions in business processes and deliver data-driven products and services. Therefore, the concept of “profiting from data”, i. e. “using organizational data to generate profits” is emerging (Faroukh

A key challenge for digital traceability and sustainability in food is the lack of appropriate information technology-based tools that can inform supply chain design for agility and dynamic change (Klein et al.)

The introduction of digital is therefore a key challenge at the conceptual modeling level (Guizzardi et al., 2016). In terms of modeling, methodologies are needed to rapidly redesign digital supply chains and respond to socio-technical and environmental developments, while ensuring data analysis, information flows and operational understanding (Fayoumi & amp; Loucopoulos, 2016).

2.3 Artificial intelligence and blockchain technology integration

This research aims to focus on food networks and explore the interactions of AI, BCT, and supply chain functions that can promote sustainability and value provision. We share the view that the actor of the entire supply chain can be monetized from this data (that is, the use of value) by the material flow, dat a-driven transactions that enable AI and BCT, and information generation. do. For this reason, the purpose of this study is to devise a systematic analysis approach to understand the interaction between digital implementation and supply chain eco system so that it can explore relevant sustainability and opportunities for data revenue generation. To do. Therefore, we will work on the following research issues: How is the interaction of digital technology in food supply chains perceived for sustainability and data revenue?

In order to solve structural research quests, we first understand the flow of materials, data, and information in dynamic supply chain operations, using the business process mapping and system thinking perspective. The mapping process focuses on the role of digital technology, as the application of AI and BCT can promote the basic flow in the en d-t o-end supply chain. By applying such a mapping approach, this study expresses the dynamics of the work in an expensive tuna production supply chain and identifies the performance. In particular, business process mapping allows you to acquire important main data elements, and all stakeholders can be visualized (via BCT infrastructure) and can be interpreted (via AI algorithm). In addition, the perspective of system thinking was a chance to explore the foundation dynamics, capturing the interaction between the components of each "AI-BCT-supply chain". < SPAN> The purpose of this study is to focus on food networks and explore the interaction of AI, BCT, and supply chain functions that can promote sustainability and value provision. We share the view that the actor of the entire supply chain can be monetized from this data (that is, the use of value) by the material flow, dat a-driven transactions that enable AI and BCT, and information generation. do. For this reason, the purpose of this study is to devise a systematic analysis approach to understand the interaction between digital implementation and supply chain eco system so that it can explore relevant sustainability and opportunities for data revenue generation. To do. Therefore, we will work on the following research issues: How is the interaction of digital technology in food supply chains perceived for sustainability and data revenue?

In order to solve structural research quests, we first understand the flow of materials, data, and information in dynamic supply chain operations, using the business process mapping and system thinking perspective. The mapping process focuses on the role of digital technology, as the application of AI and BCT can promote the basic flow in the en d-t o-end supply chain. By applying such a mapping approach, this study expresses the dynamics of the work in an expensive tuna production supply chain and identifies the performance. In particular, business process mapping allows you to acquire important main data elements, and all stakeholders can be visualized (via BCT infrastructure) and can be interpreted (via AI algorithm). In addition, the perspective of system thinking was a chance to explore the foundation dynamics, capturing the interaction between the components of each "AI-BCT-supply chain". This research aims to focus on food networks and explore the interactions of AI, BCT, and supply chain functions that can promote sustainability and value provision. We share the view that the actor of the entire supply chain can be monetized from this data (that is, the use of value) by the material flow, dat a-driven transactions that enable AI and BCT, and information generation. do. For this reason, the purpose of this study is to devise a systematic analysis approach to understand the interaction between digital implementation and supply chain eco system so that it can explore relevant sustainability and opportunities for data revenue generation. To do. Therefore, we will work on the following research issues: How is the interaction of digital technology in food supply chains perceived for sustainability and data revenue?

In order to solve structural research quests, we first understand the flow of materials, data, and information in dynamic supply chain operations, using the business process mapping and system thinking perspective. The mapping process focuses on the role of digital technology, as the application of AI and BCT can promote the basic flow in the en d-t o-end supply chain. By applying such a mapping approach, this study expresses the dynamics of the work in an expensive tuna production supply chain and identifies the performance. In particular, business process mapping allows you to acquire important main data elements, and all stakeholders can be visualized (via BCT infrastructure) and can be interpreted (via AI algorithm). In addition, the perspective of system thinking was a chance to explore the foundation dynamics, capturing the interaction between the components of each "AI-BCT-supply chain".

AKTER et al. (2022) emphasized the need to explore the complex use of new technologies in business digital transformations for operational Excellence and sustainable growth. This study proposes an experienced framework that provides the understanding of the structure of the "Digital Technology-Supply Chain" system and the interaction of these two areas to actively evaluate the benefits obtained from the operation. It contributes to the field of operation management and responds to the documented gap in the community (specifically, the knowledge of this study is to utilize the unique synergistic value. As far as we know, it emphasizes the unique advantages of AI and BCT, as far as we know, and consequently the sustainability and data revenue generated in fish supply chains. It is one of the first research discussing profits.

The remaining part of this study is as follows. Section 2 reviews research backgrounds on AI and BCT in the supply chain, and clarifies its advantages, implementation issues, and impact on sustainability. Section 3 describes the basic research method for designing a supply chain based on AI and BCT. In section 4, the basics of fish supply chains and important data are explained, and section 5 explains in detail the case study of the supply chain using AI and BCT. 5 describes the case study of the fish supply chain ec o-systems in Thailand. In section 6, we will introduce the proposed research framework. 6. The last section describes the conclusions, meaning, limit, and the potential of future research. 7. < SPAN> Akter et al. (2022) emphasized the need to explore the complex use of new technology in digital transformations in business digital transformations for operational Excellence and sustainable growth. This study proposes an experienced framework that provides the understanding of the structure of the "Digital Technology-Supply Chain" system and the interaction of these two areas to actively evaluate the benefits obtained from the operation. It contributes to the field of operation management and responds to the documented gap in the community (specifically, the knowledge of this study is to utilize the unique synergistic value. As far as we know, it emphasizes the unique advantages of AI and BCT, as far as we know, and consequently the sustainability and data revenue generated in fish supply chains. It is one of the first research discussing profits.

2.4 Literature remarks

The remaining part of this study is as follows. Section 2 reviews research backgrounds on AI and BCT in the supply chain, and clarifies its advantages, implementation issues, and impact on sustainability. Section 3 describes the basic research method for designing a supply chain based on AI and BCT. In section 4, the basics of fish supply chains and important data are explained, and section 5 explains in detail the case study of the supply chain using AI and BCT. 5 describes the case study of the fish supply chain ec o-systems in Thailand. In section 6, we will introduce the proposed research framework. 6. The last section describes the conclusions, meaning, limit, and the potential of future research. 7. Akter et al. (2022) emphasized the need to explore the complex use of new technologies in business digital transformations for operational excellence and sustainable growth. This study proposes an experienced framework that provides the understanding of the structure of the "Digital Technology-Supply Chain" system and the interaction of these two areas to actively evaluate the benefits obtained from the operation. It contributes to the field of operation management and responds to the documented gap in the community (specifically, the knowledge of this study is to utilize the unique synergistic value. As far as we know, it emphasizes the unique advantages of AI and BCT, as far as we know, and consequently the sustainability and data revenue generated in fish supply chains. It is one of the first research discussing profits.

The remaining part of this study is as follows. Section 2 reviews research backgrounds on AI and BCT in the supply chain, and clarifies its advantages, implementation issues, and impact on sustainability. Section 3 describes the basic research method for designing a supply chain based on AI and BCT. In section 4, the basics of fish supply chains and important data are explained, and section 5 explains in detail the case study of the supply chain using AI and BCT. 5 describes the case study of the fish supply chain ec o-systems in Thailand. In section 6, we will introduce the proposed research framework. 6. The last section describes the conclusions, meaning, limit, and the potential of future research. 7.

3 Methodology

The introduction of AI in the supply chain management promotes orchestration and optimization of network operations. (III) Providing information on the planning, simulation, and scheduling of the supply chain, and enables (IV) modeling based on cooperative negotiations (TOORAJIPOUR et al., 2021). In addition, BCT is an application that enables "transparent, secure distributed ledger, smart contract, reliable network for sustainable supply chain management" (Kouhizadeh et al.) So this study shows the food field. We propose to apply AI and BCTs in a unified and complementary way to increase the efficiency and sustainability of supply chains.

3.1 Theoretical lens

To identify new research in the context of the supply chain, especially the existing research on the use of AI and BCT, we conducted critical reviews. For this purpose, SCOPUS database (AIVAZIDOU ET AL, Scopus database and Web of Science database covers most of the research, management, science, and engineering arts magazines related to this research. I admit (MONGEON & AMP; AMP; PAUL-HUS, 2016) is a database that is widely accepted for searching and mapping for existing literature for queries for document search. The focus of Fahimnia et al., 2019; In the following combination, the search results were limited to the publication of the journal. After reading the abstract, the remaining 27 papers were excluded.

Table 1 The introduction of AI in the critical classification of existing research < SPAN> Supply Chain Management promotes orchestration and optimization of network operations. (III) Providing information on the planning, simulation, and scheduling of the supply chain, and enables (IV) modeling based on cooperative negotiations (TOORAJIPOUR et al., 2021). In addition, the BCT is an application that enables "transparent, safe distributed ledger, smart contract, and reliable network" for sustainable supply chain management (Kouhizadeh et al.) So this study shows the food field. We propose to apply AI and BCTs in a unified and complementary way to increase the efficiency and sustainability of supply chains.

To identify new research in the context of the supply chain, especially the existing research on the use of AI and BCT, we conducted critical reviews. For this purpose, SCOPUS database (AIVAZIDOU ET AL, Scopus database and Web of Science database covers most of the research, management, science, and engineering arts magazines related to this research. I admit (MONGEON & AMP; AMP; PAUL-HUS, 2016) is a database that is widely accepted for searching and mapping for existing literature for queries for document search. The focus of Fahimnia et al., 2019; In the following combination, the search results were limited to the publication of the journal. After reading 36, the remaining 27 papers were excluded.

Table 1 The introduction of AI in the critical classification supply chain management of existing research promotes orchestration and optimization of network operations. (III) Providing information on the planning, simulation, and scheduling of the supply chain, and enables (IV) modeling based on cooperative negotiations (TOORAJIPOUR et al., 2021). In addition, BCT is an application that enables "transparent, secure distributed ledger, smart contract, reliable network for sustainable supply chain management" (Kouhizadeh et al.) So this study shows the food field. We propose to apply AI and BCTs in a unified and complementary way to increase the efficiency and sustainability of supply chains.

To identify new research in the context of the supply chain, especially the existing research on the use of AI and BCT, we conducted critical reviews. For this purpose, SCOPUS database (AIVAZIDOU ET AL, Scopus database and Web of Science database covers most of the research, management, science, and engineering arts magazines related to this research. I admit (MONGEON & AMP; AMP; PAUL-HUS, 2016) is a database that is widely accepted for searching and mapping for existing literature for queries for document search. The focus of Fahimnia et al., 2019; In the following combination, the search results were limited to the publication of the journal. After reading 36, the remaining 27 papers were excluded.

Table 1 critical classification of existing research

As information is increasingly available throughout the global supply chain, expectations for the use of this information are increasing (Sanders et al., 2019). In fact, McKinsey survey states that AI analytics can add about 13 trillion dollars (or 16%) to the world's annual GDP by 2030, while conducting a substance chai n-related activity. For example, 2018). As a result, it is expected that the use of AI will significantly improve the efficiency and productivity of the supply chain in the next 10 years. In the supply chain area, the introduction of AI applications promotes (i) the plans and reconnection of the supplementary network through (i) potential stakeholders (alternative suppliers, etc.), facilities and technical screening and classifications (Govindan et al). ., 2017), (II) Analyzing big data, explaining and evaluating risks, providing a supply chain's resilience (Papadopoulos et al, 2017), (III), uncertainty and demand fluctuations. To deal with the analysis of large amounts of data from various sources (web, social media, supply chain information system, etc.), support the optimal decisio n-making that is automated in almost rea l-time (baryannis et al) .

3.2 Research approach

Today, the construction of a sustainable global supply chain has emerged as one of the most urgent but unresolved industrial issues (Dauvergne, 2020). The global supply chain's en d-t o-end operation has a great impact on sustainability (Carter & Amp; Amp; WashiSpack, 2018). Most of the negative effects on the environment are not directly manufactured, but from en d-t o-end supply chain operations, including procurement, distribution, production, and logistics (Sanders et al., 2019). Many researchers have advertised AI as pioneers of "environmentally friendly" supply chain design, but on the other hand, the application to AI's supply chain has negative impact on existing sustainability. Some researchers see that they will accelerate.

This study recognizes that the debate on AI for sustainability can be conducted at many levels. Therefore, Table 2 is an overview of the issues of the benefits and sustainability that are often pointed out by the application of AI to the supply chain.

Table 2 Literary classification of AI applications for sustainability of supply chains

3.2.1 Empirical evidence

The application of AI has various business benefits such as productivity and efficiency improvement (Camaréna, 2020; Cubric, 2020; DI VAIO et al., 2020; Sanders et al., 2019). It is recognized to lead to improvement. As an example, AI is used to optimize harvesting and crop processing. For example, a drone with a camera and a machine learning algorithm determine the decomposition rate of vegetables (Camaréna, 2020). In addition, AI, which is supported by other elemental technologies, is a fish harvesting and industrial processing downstream in the fishery that makes it easier to sort food supply while monitoring the hygiene level of the entire work. From monitoring to transparency of international commercial activities and ensuring traceability, the same benefits can be obtained in many places (TSOLAKIS et al.) AI is an artificial mistake, labor force. It can contribute by reducing operating costs related to equipment (CURIC, 2020) and costs related to fuel consumption for production and transportation (Dauvergne, 2020). I n-house), in addition to the benefits at the operation level, AI is also recognized that it will benefit the en d-t o-end supply chain management. Such

From the viewpoint of social sustainability, transparency is one of the important benefits that AI can provide, especially downstream of the supply chain. For example, AI informs customers that they make purchases based on more information on responsible products with advanced data processing capacity that can sensing the upstream of the supply chain at the stage of raw materials. (CHIDEPATIL et al.) In addition, A I-based applications can contribute to social welfare. For example, social support robots can reduce the burden on caregivers and enhance the happiness of the elderly by enabling movement, social contact, and cognitive support (CUBRIC, 2020). < SPAN> Table 2 Literary classification of AI applications for sustainability of supply chains

The application of AI has various business benefits such as productivity and efficiency improvement (Camaréna, 2020; Cubric, 2020; DI VAIO et al., 2020; Sanders et al., 2019). It is recognized to lead to improvement. As an example, AI is used to optimize harvesting and crop processing. For example, a drone with a camera and a machine learning algorithm determine the decomposition rate of vegetables (Camaréna, 2020). In addition, AI, which is supported by other elemental technologies, is a fish harvesting and industrial processing downstream in the fishery that makes it easier to sort food supply while monitoring the hygiene level of the entire work. From monitoring to transparency of international commercial activities and ensuring traceability, the same benefits can be obtained in many places (TSOLAKIS et al.) AI is an artificial mistake, labor force. It can contribute by reducing operating costs related to equipment (CURIC, 2020) and costs related to fuel consumption for production and transportation (Dauvergne, 2020). I n-house), in addition to the benefits at the operation level, AI is also recognized that it will benefit the en d-t o-end supply chain management. Such

From the viewpoint of social sustainability, transparency is one of the important benefits that AI can be provided, especially downstream of the supply chain. For example, AI informs customers that they make purchases based on more information on responsible products with advanced data processing capacity that can sensing the upstream of the supply chain at the stage of raw materials. (CHIDEPATIL et al.) In addition, A I-based applications can contribute to social welfare. For example, social support robots can reduce the burden on caregivers and enhance the happiness of the elderly by enabling movement, social contact, and cognitive support (CUBRIC, 2020). Table 2 Literary classification of AI applications for sustainability of supply chains

3.2.2 Supply chain mapping

The application of AI has various business benefits such as productivity and efficiency improvement (Camaréna, 2020; Cubric, 2020; DI VAIO et al., 2020; Sanders et al., 2019). It is recognized to lead to improvement. As an example, AI is used to optimize harvesting and crop processing. For example, a drone with a camera and a machine learning algorithm determine the decomposition rate of vegetables (Camaréna, 2020). In addition, AI, which is supported by other elemental technologies, is a fish harvesting and industrial processing downstream in the fishery that makes it easier to sort food supply while monitoring the hygiene level of the entire work. From monitoring to transparency of international commercial activities and ensuring traceability, the same benefits can be obtained in many places (TSOLAKIS et al.) AI is an artificial mistake, labor force. It can contribute by reducing operating costs related to equipment (CURIC, 2020) and costs related to fuel consumption for production and transportation (Dauvergne, 2020). I n-house), in addition to the benefits at the operations level, AI is also recognized that the en d-t o-end supply chain management will also benefit. Such

From the viewpoint of social sustainability, transparency is one of the important benefits that AI can provide, especially downstream of the supply chain. For example, AI informs customers that they make purchases based on more information on responsible products with advanced data processing capacity that can sensing the upstream of the supply chain at the stage of raw materials. (CHIDEPATIL et al.) In addition, A I-based applications can contribute to social welfare. For example, social support robots can reduce the burden on caregivers and enhance the happiness of the elderly by enabling movement, social contact, and cognitive support (CUBRIC, 2020).

In addition, the use of AI is also useful for promoting environmental sustainability. In the energy field, AI can contribute to reducing fuel consumption by increasing energy conversion and logistics efficiency. In addition, AI can increase the efficiency and reliability of renewable energy sources by increasing the accuracy of weather forecasts and improving energy storage (Dauvergne, 2020).

3.2.3 System conceptualisation

AI's introductions include technical, ethical, legal, management, and economic considerations. The major technical challenge when introducing AI to work is related to the use and utilization of data. Data that can be used by companies is often not structured, making it difficult to share between supply chain members. Structuring such data is very expensive. For example, overloading AI algorithms may occur if the AI ​​algorithm is applied to (small) datasets that do not accurately reflect reality or training datasets. On the other hand, if the learning data is insufficient, the performance of the processed AI algorithm may be reduced (CURIC, 2020). Furthermore, since the information is not standardized, it may be difficult to select an appropriate AI solution. Through internal data architecture, it tends to provide customized digital solutions for companies (EBINGER & AMP; Omondi, 2020).

Another task caused by the use of data for AI is that privacy rights may be infringed. For example, the fact that governments and competitors to achieve food traceability in supply chains with AI can cause farmers' privacy rights (Leone, 2017). In addition, project datasets often contain confidential information, bringing a major technical barrier to adopting A I-based solutions in industrial use. In addition, the application of AI may cause social issues related to ethnic and racial profiles. As an example, in St. Peterburg's shopping mall, facial recognition algorithm has already sorted customers by age, ethnic groups (Dauvergne, 2020), and there is concern about privacy. < SPAN> In addition, the use of AI is also useful for promoting sustainability of the environment. In the energy field, AI can contribute to reducing fuel consumption by increasing energy conversion and logistics efficiency. In addition, AI can increase the efficiency and reliability of renewable energy sources by increasing the accuracy of weather forecasts and improving energy storage (Dauvergne, 2020).

4 Fish supply chains

AI's introductions include technical, ethical, legal, management, and economic considerations. The major technical challenge when introducing AI to work is related to the use and utilization of data. Data that can be used by companies is often not structured, making it difficult to share between supply chain members. Structuring such data is very expensive. For example, overloading AI algorithms may occur if the AI ​​algorithm is applied to (small) datasets that do not accurately reflect reality or training datasets. On the other hand, if the learning data is insufficient, the performance of the processed AI algorithm may be reduced (CURIC, 2020). Furthermore, since the information is not standardized, it may be difficult to select an appropriate AI solution. Through internal data architecture, it tends to provide customized digital solutions for companies (EBINGER & AMP; Omondi, 2020).

Another task caused by the use of data for AI is that privacy rights may be infringed. For example, the fact that governments and competitors to achieve food traceability in supply chains with AI can cause farmers' privacy rights (Leone, 2017). In addition, project datasets often contain confidential information, bringing a major technical barrier to adopting A I-based solutions in industrial use. In addition, the application of AI may cause social issues related to ethnic and racial profiles. As an example, in St. Peterburg's shopping mall, facial recognition algorithm has already sorted customers by age, ethnic groups (Dauvergne, 2020), and there is concern about privacy. In addition, the use of AI is also useful for promoting environmental sustainability. In the energy field, AI can contribute to reducing fuel consumption by increasing energy conversion and logistics efficiency. In addition, AI can increase the efficiency and reliability of renewable energy sources by increasing the accuracy of weather forecasts and improving energy storage (Dauvergne, 2020).

AI's introductions include technical, ethical, legal, management, and economic considerations. The major technical challenge when introducing AI to work is related to the use and utilization of data. Data that can be used by companies is often not structured, making it difficult to share between supply chain members. Structuring such data is very expensive. For example, overloading AI algorithms may occur if the AI ​​algorithm is applied to (small) datasets that do not accurately reflect reality or training datasets. On the other hand, if the learning data is insufficient, the performance of the processed AI algorithm may be reduced (CURIC, 2020). Furthermore, since the information is not standardized, it may be difficult to select an appropriate AI solution. Through internal data architecture, it tends to provide customized digital solutions for companies (EBINGER & AMP; Omondi, 2020).

Another task caused by the use of data for AI is that privacy rights may be infringed. For example, the fact that governments and competitors to achieve food traceability in supply chains with AI can cause farmers' privacy rights (Leone, 2017). In addition, project datasets often contain confidential information, bringing a major technical barrier to adopting A I-based solutions in industrial use. In addition, the application of AI may cause social issues related to ethnic and racial profiles. As an example, in St. Peterburg's shopping mall, facial recognition algorithm has already sorted customers by age, ethnic groups (Dauvergne, 2020), and there is concern about privacy.

In addition to ethical and legal issues related to data, the application of AI is facing a large barrier because it is relatively young and has a wide range of industries (min, 2010). Because it is an early stage of development and introduction, many solutions utilizing AI are proven only in pilots / test demonstration projects, and practical solutions are limited (EBINGER & AMP; Omondi, 2020). Similarly, the management team is often lacking in the benefits of the introduction of AI in companies (CUBRIC, 2020). In this regard, solutions using AI can be complicated by the decisio n-determined users (min, 2010).