Blockchain & A.I. In Digital Supply Chain Management In The UK Retail Sector

Effect Of Blockchain & A.I. In Digital Supply Chain Management In The UK Retail Sector

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Chapter 1: Introduction

Research Background

Two cutting-edge technologies that have received a lot of attention recently are “Blockchain” and “Artificial Intelligence”, especially in the context of supply chain management. These technologies have the potential to increase supply chain efficiency, transparency, and consumer satisfaction in the UK retail industry.

A PwC analysis predicts that the global retail sector will expand significantly over the next few years, with e-commerce sales estimated to reach $4.9 trillion by 2021. With this expansion comes a greater demand for supply chain efficiency, which can be done by combining “Blockchain” technology with “Artificial Intelligence” (AI). All parties engaged in the process will have access to the same information thanks to the safe and transparent way that “Blockchain” technology makes it possible to trace products across the supply chain. This can aid in lowering fraud, enhancing inventory control, and providing real-time shipment tracking (Tsolakis et al., 2022). Moreover, smart contracts can be developed using “Blockchain” to automate several supply chain procedures, boosting efficiency even more.

In contrast, supply chain processes can be improved by using “AI” technology, which analyses vast volumes of data and offers useful insights. In areas like demand forecasting, inventory control, and pricing optimization, this can be especially helpful. Retailers may better understand the wants and needs of their customers by employing “AI” algorithms to study consumer behaviour (Gohil and Thakker, 2021). This will allow them to design their products to satisfy these needs. In their supply chain operations, a number of UK merchants have already started to investigate the usage of “Blockchain” and “AI”. For instance, the clothes retailer ASOS has been utilising “AI” algorithms to enhance its pricing and inventory management, while the UK-based store Tesco has teamed with a “Blockchain” start-up to trace the supply chain of its products.

Research Problem

To fully realise the potential of these technologies, a number of issues and constraints must be resolved, according to market research on the efficiency of “Blockchain” and “AI” in digital supply chain management in the UK retail industry (Martin?evi? et al., 2021). The absence of standardisation throughout the supply chain is one of the main problems. It might be challenging to develop a unified platform that can be utilised by all stakeholders because each retailer, supplier, and shipping company may have various systems for monitoring and managing inventory (Sinha et al., 2021). As a result, the supply chain may experience delays, mistakes, and inefficiencies, which may eventually lower consumer satisfaction. The difficulty of incorporating “Blockchain” and “AI” into current supply chain systems is a further problem. Significant time, money, and resource investments are needed to implement these technologies (Paksoy et al., 2020). It can be challenging for shops to entirely rework their current systems in some situations in order to fully benefit from “Blockchain” and “AI”.

“Blockchain” and “AI” have the potential to revolutionize the way digital supply chain management works in the UK retail sector. However, there are also some potential negative impacts that need to be considered. One of the biggest concerns is the potential loss of jobs as automation becomes more widespread. Many of the tasks that are currently performed by human workers, such as order processing and inventory management, could be done more efficiently and accurately by “AI” systems. This could lead to job losses and potentially exacerbate existing inequality and unemployment issues. Another issue is the cost of implementing these technologies. “Blockchain” and “AI” require significant investment in hardware, software, and training. This cost may be prohibitive for smaller retailers, putting them at a competitive disadvantage and potentially leading to market consolidation.

Additionally, there is the risk of cybersecurity threats. “Blockchain” and “AI” systems are only as secure as their underlying infrastructure, and any breach could lead to significant disruption and data loss. Retailers must invest in robust security measures to protect their supply chains from attacks. Finally, there is the issue of standardization. The lack of standardization in the industry makes it difficult to integrate “Blockchain” and “AI” systems with existing supply chain management software. This could lead to inefficiencies and errors in the system, potentially causing delays and increased costs. Overall, while “Blockchain” and “AI” have the potential to bring significant benefits to the UK retail sector, it is important to consider these potential negative impacts and take steps to mitigate them.

Concerns about privacy and security are a sizable obstacle for “Blockchain” and “AI” in supply chain management. Despite the fact that “Blockchain” technology is intended to be safe, data leaks and hacking remain a possibility. Shops must make sure that their systems are safe and that client information is always secured. Also, there is a shortage of qualified professionals to oversee and control these technologies. There is a severe lack of professionals with the essential abilities and understanding to successfully adopt and operate “Blockchain” and “AI” technology (Amentae and Gebresenbet, 2021). Concerns concerning the cost-benefit analysis of using these technologies came up throughout the market study. The advantages of “Blockchain” and “AI” are obvious, but retailers must carefully assess those advantages against the implementation costs.

Aims and Objectives

The purpose of this research is to assess how “Blockchain” and “AI” are affecting digital supply chain management in the UK retail industry. It will specifically examine the advantages, difficulties, and best practices for integrating these technologies, as well as stakeholders’ attitudes towards adoption and build a framework for evaluating effectiveness. The objectives of the research are as under:

• To analyze the current state of digital supply chain management in the UK retail sector and identify the key challenges that businesses face in adopting “Blockchain” and “AI” technologies
• To investigate the potential benefits of “Blockchain” and “AI” in digital supply chain management in the UK retail sector
• To evaluate the impact of “Blockchain” and “AI” on the different stages of the supply chain process and identify best practices for integrating the technologies
• To explore the attitudes and perceptions of UK retail sector stakeholders and “AI” in digital supply chain management and identify barriers to adoption
• To develop a framework for assessing the effectiveness of “Blockchain” and “AI” in digital supply chain management in the UK retail sector

Research Questions

• What are the current state of digital supply chain management in the UK retail sector and the key challenges that businesses face in adopting “Blockchain” and “AI” technologies?
• What are the potential benefits of “Blockchain” and “AI” in digital supply chain management in the UK retail sector?
• What are the impact of “Blockchain” and “AI” on the different stages of the supply chain process and the practices for integrating the technologies?
• What is the attitude and perceptions of UK retail sector stakeholders and “AI” in digital supply chain management and barriers to adoption?
• How to develop a framework for assessing the effectiveness of “Blockchain” and “AI” in digital supply chain management in the UK retail sector?

Research Rationale

For a number of reasons, it is crucial to do market research on how well “Blockchain” and “AI” handle digital supply chains in the UK retail industry. First and foremost, the retail industry plays a crucial role in the UK economy (Koh et al., 2020). By enhancing supply chain management, merchants may increase productivity, cut costs, and enhance customer satisfaction. More transparency, real-time tracking of shipments, and enhanced inventory management are some of the advantages that could result from the use of “Blockchain” and “AI” in supply chain management, which has the potential to completely transform the way that merchants conduct business. Additionally, these technologies can be utilised to automate certain supply chain operations, giving retailers more time and resources to concentrate on other aspects of their businesses (Ray et al., 2019). To fully comprehend the possible advantages and difficulties of integrating these technologies in the retail sector, market research is crucial. Retailers can use it to determine the important sections of their supply chain that could benefit from “Blockchain” and “AI”, as well as to get knowledge about the implementation’s costs and difficulties.

Moreover, market research can be used to find possible rivals and collaborators in the industry and reveal important details about consumer preferences and needs. Offerings can be customised using this data, and customer happiness can be raised (Kshetri, 2021). Furthermore, performing market research can assist merchants in making knowledgeable choices regarding their investment in “Blockchain” and “AI” technology. This is especially crucial in light of the high expenses associated with installing these technologies. Retailers may be sure they are making a wise investment that will result in enhanced productivity, lower costs, and higher customer happiness by performing market research.

Retailers can identify the major areas where these technologies can be most useful by doing in-depth research. They can also learn about the implementation’s costs and difficulties. With the help of this knowledge, business owners may decide for themselves whether or not to invest in these technologies and how best to use them to enhance their operational processes.

Research Significance

The construction of a more effective and efficient supply chain for retailers can be aided by market research on the use of “Blockchain” and “AI” in digital supply chain management in the UK retail sector. Retailers can use the knowledge gathered from this research to pinpoint important areas for development and then use these technologies to increase productivity and cut costs while also raising customer happiness. The findings can also aid in debunking presumptions regarding the viability and efficiency of integrating “Blockchain” technology and “Artificial Intelligence” in supply chain management (Kshetri, 2021). For many merchants, it can be a widespread belief that these technologies are either too expensive or too challenging to deploy.

The study might discover that the potential benefits surpass the expenditures and that the implementation costs are appropriate. Market research can assist in identifying potential obstacles to the adoption of these technologies, such as a shortage of experienced workers or worries about the security and privacy of personal information (Zhang, 2021). Retailers may use this research to address these issues and make sure that the adoption of “Blockchain” and “AI” goes smoothly.

The study may aid in the creation of best practices for the management and application of these technologies in the retail industry. This can involve making recommendations for workforce development and training programmes as well as advice on data security and privacy. Also, the research might aid in locating fresh chances for innovation and expansion in the retail industry. Retailers can improve their supply chain processes and offer customers a smoother and more customised experience by utilising the power of “Blockchain” and “AI”. Retailers may see an increase in sales and market share as a result of this.

Chapter 2: Literature Review

Overview of the Chapter

A literature review is a comprehensive overview and study of current research and literature on a certain topic or research issue (Paul and Criado, 2020). It entails doing a systematic search and analysis of relevant material, such as academic articles, books, and other sources, in order to detect gaps, inconsistencies, and contradictions in the current knowledge base. A literature review’s objective is to offer an overview of the current state of knowledge on a certain issue, identify major themes and trends, and emphasise areas where more study is needed. It may also serve to contextualise and frame a research effort, laying the groundwork for the formulation of research questions, hypotheses, and methods.

The review of different literature mainly focuses on the digital supply chain management in different sectors and provides a contrast on how it differs from the retail sector. The review is taken from scholar papers, articles and journals for the last 10 years and are critically reviewed to find the progress of research on the topic. The research conducted is based on keywords obtained by combining the different points from the topic of research. The keywords are obtained by combining “Artificial Intelligence” (AI), supply chain management, “Blockchain” and “Blockchain management”.

Effective supply chain management and “Blockchain” technology have become essential for firms to remain competitive in today’s quickly evolving business climate. Organisations and activities involved in the production and distribution of goods and services must be coordinated and integrated as part of the supply chain management process. By boosting forecasting, inventory management, logistics, quality control, supplier management, and sustainability, “Artificial Intelligence” (AI) may greatly improve supply chain management.

On the other hand, “Blockchain” technology provides a safe and open method of exchanging data and conducting transactions across a network of computers. A thorough knowledge of the technology and its prospective applications, as well as proficiency in fields like encryption, cybersecurity, and distributed systems, are necessary for effective “Blockchain” administration. Efficiency, accuracy, and productivity may all be significantly increased in supply chain management by integrating “AI” and “Blockchain” technology. It can help companies obtain a competitive advantage, lower costs, increase efficiency, boost customer service, manage risks, encourage sustainability, and promote teamwork. Efficient supply chain management with “Blockchain” technology can open up new doors for innovation and growth while reducing risks and guaranteeing regulatory compliance. It is a crucial component of contemporary corporate operations and can significantly boost overall performance.

Concept of Supply Chain Management

According to Tien et al. (2019), an organisation’s involvement in the manufacturing, delivery and distribution of a good or service from a supplier to the final customer is referred to as a supply chain. It comprises all the processes required to take a good or service from conception to delivery, including obtaining raw materials, producing it, packaging it, storing it, moving it, and giving it to the customer. In order to ensure that goods or services are delivered to the final customer effectively and efficiently, supply chain management is the process of monitoring and coordinating all of the operations involved in the supply chain. Supply chain management aims to optimise the supply chain by lowering costs, cutting lead times, enhancing customer service, and improving product quality.

Mukhamedjanova (2020) explained that demand forecasting, purchasing, managing inventories, logistics, and distribution are just a few of the tasks that go into an efficient supply chain. Moreover, it entails managing risk, working with suppliers and consumers to make sure that their demands are satisfied, and consistently enhancing performance and processes. For organisations to remain competitive and meet customer expectations, supply chain management is essential. Effective supply chain management enables companies to save costs, boost product quality, and improve customer service, all of which can increase client happiness and loyalty.

Many research on supply chain management have been undertaken in the previous 10 years. According to Frederico (2021), the supply chain contains unique and enlightening thoughts concerning Industry 5.0. The author discusses how Industry 4.0 has changed the supply chain and how it is transitioning to Industry 5.0. Conversely, addressing supply chain risks has become a major topic in recent years (Pournader, Kach and Talluri, 2020). The researchers examined supply chain risk papers from nine prestigious management, operations, and supply chain management journals, focusing on their study subjects, techniques, and contributions.

Purchasing and supply management (PSM) has received a lot of attention during the last decade (Wynstra, Suurmond and Nullmeier, 2019). The study’s authors looked at the development of PSM research, namely the themes and ideas that have been studied over time.

Deep learning (DL) is a new technique that has been used in a variety of sectors, including supply chain management (Hosseinnia Shavaki and Ebrahimi Ghahnavieh, 2022). Hosseinnia Shavaki and Ebrahimi Ghahnavieh’s (2022) research focuses on the use of deep learning in supply chain management and highlights seven major categories of DL featured in the reviewed literature: “Deep neural network” (DNN), “Convolutional neural network” (CNN), “Recurrent neural network” (RNN), “Deep auto-encoder” (DAE), “Restricted Boltzmann Machine” (RBM), “Generative adversarial network” (GAN), and “Reinforce Learning” (RL).

The COVID-19 epidemic has had a significant impact on worldwide supply networks, causing difficulties in the manufacture and distribution of commodities (Raj et al., 2022). The pandemic has underlined the importance of robust and adaptable supply networks that can respond to rapid changes in demand and supply (AXSOM, 2022). One of the most difficult tasks in supply chain management is controlling the expenses of products delivery (AXSOM, 2022).

Supply chain disruptions have a severe impact on global industrial output and commerce, resulting in inflationary pressures (Attinasi et al., 2022). The supply chain disruption has led in a lack of key items such as medical supplies, food, and technology, resulting in price increases for both consumers and businesses. Yet, if there is a decline in overall consumer demand, the supply chain limitations that seem to be predominantly driven by high demand may begin to alleviate (Attinasi et al., 2022).

To overcome these difficulties, businesses are implementing new technologies such as “Blockchain”, “Artificial Intelligence” (AI), and the Internet of Things (IoT) to increase supply chain visibility (AXSOM, 2022). These technologies enable businesses to trace items from raw ingredients to manufacturing and distribution. They also assist businesses in identifying potential bottlenecks in their supply networks before they become serious problems. Sustainability is another trend that will shape the future of supply chain management. The pandemic has underlined the significance of developing long-term, resilient supply networks that can resist shocks like pandemics or natural catastrophes (AXSOM, 2022). Businesses are increasingly searching for ways to lower their carbon footprint, such as via the use of renewable energy or waste reduction.

Digital Supply Chain Management

Because of the emergence of Industry 4.0 and Industry 5.0, digital supply chain management (DSCM) has grown increasingly significant in recent years (Frederico, 2021). DSCM entails the application of digital technologies such as “Blockchain”, deep learning, and “Artificial Intelligence” to improve supply chain efficiency and solve challenges such as information inequality and inefficiencies in food recalls (Duan et al., 2020; Hosseinnia Shavaki and Ebrahimi Ghahnavieh, 2022). According to a thorough literature analysis, deep learning has been applied in SCM since 2016 and has shown an incremental trend in the previous two years (Hosseinnia Shavaki and Ebrahimi Ghahnavieh, 2022). Deep neural network, convolutional neural network, recurrent neural network, deep auto-encoder, constrained Boltzmann machine, generative adversarial network, and hybrid models were employed in the reviewed studies (Hosseinnia Shavaki and Ebrahimi Ghahnavieh, 2022).

Another technology that has been used in DSCM is “Blockchain”. By eliminating food fraud and enhancing traceability efficiency, including time and cost savings while lowering risks, “Blockchain” can increase traceability efficiency and boost confidence during food recalls (Duan et al., 2020). Literature reviews can also be conducted using “Artificial Intelligence”. According to one research, machine learning may aid in service innovation and design by giving large insights from big data (Wagner, Lukyanenko and Paré, 2022). Closed-loop supply chains 4.0 have emerged as a result of the integration of digital technology into supply networks. The “Internet of Things” (IoT), “smart sensors”, “cloud computing”, “big data analytics”, ““Blockchain””, “artificial intelligence” (AI), “robotics”, “additive manufacturing” (AM), “augmented reality” (AR), “virtual reality” (VR), “drones” or “unmanned aerial vehicles” (UAVs), “autonomous vehicles" (AVs), and other enabling technologies are used in these supply chains (Simonetto et al., 2022). Using reverse logistics operations, closed-loop supply chains 4.0 have been proven to minimise waste output while enhancing resource recovery rates (Simonetto et al., 2022).

Digital supply chain management may be used to enhance efficiency, cut costs, and boost transparency in the automotive, retail, and restaurant industries. In the automobile business, digitalisation may have an impact on the connected supply chain, which has the advantage of cost reduction and improved supplier management (Llopis-Albert, Rubio and Valero, 2021). Digital supply chain management may assist merchants in optimising inventory levels, reducing waste, and improving customer happiness. Retailers may utilise data analytics to properly estimate demand and manage inventory levels accordingly. This method assists merchants in avoiding stockouts while limiting overstocking (Sweeney, 2022).

In the restaurant business, digital supply chain management may assist restaurants enhance their food procurement process. Restaurants may utilise data analytics to properly estimate demand and change orders appropriately. This method assists restaurants in reducing food waste while also guaranteeing that they have adequate ingredients to fulfil client demand (Organisation for Economic Co-operation and Development, 2020). McDonald’s, for example, employs digital supply chain management to streamline their food procurement process. The organisation employs data analytics to properly estimate demand and change orders appropriately. This method allows McDonald’s to prevent food waste while still having enough ingredients to fulfil consumer demand (Aday and Aday, 2020). Similarly, Walmart optimises inventory levels through digital supply chain management. The corporation employs data analytics to estimate demand properly and change its inventory levels accordingly. This strategy assists Walmart in avoiding stockouts while limiting overstocking.

Concept and Importance of Artificial Intelligence (AI)

As per the opinion of Mäntymäki et al. (2022), “Artificial Intelligence” (AI) is the emulation of intelligent behaviour in machines that are programmed to carry out operations. Computer science’s field of “Artificial Intelligence” (AI) seeks to develop intelligent machines that can reason, acquire knowledge, and adapt just like people. Healthcare, finance, manufacturing, transportation, and retail are just a few of the businesses and areas where “AI” technologies are being used more and more. For instance, “AI” is being applied to healthcare to better disease diagnosis and treatment, forecast disease outbreaks, and identify high-risk individuals. “AI” is used in finance to detect fraud, manage risks, and optimise portfolios. “AI” is applied to the industry for supply chain efficiency, predictive maintenance, and quality control. “AI” is being applied in the transportation industry for autonomous driving and route planning.

Moore (2019) stated that machine learning is the process of teaching algorithms to recognise patterns in data that can be applied to a variety of tasks, including fraud detection, recommender systems, and predictive modelling. Using natural language processing, which may be applied to chatbots, voice assistants, and sentiment analysis, allows robots to comprehend and interpret human language. In computer vision, visual input is made understandable to machines so they can do tasks like object detection, facial recognition, and autonomous driving.

Managing AI

Berente et al. (2019), putforward that strategic planning, data management, talent management, ethical considerations, and performance monitoring are just a few of the different elements that go into effective “AI” management. Setting specific goals and objectives for “AI” efforts and coordinating them with the organisation’s overarching strategy are both components of strategic planning. This includes determining the areas in which “AI” may be used to enhance customer experience, increase efficiency, and improve company operations. Kiron and Schrage (2019) explained that data management entails successfully gathering, storing, and managing data to guarantee the security, correctness, and dependability of data utilised for “AI” activities. This includes putting data governance policies into practice, making sure data is of high quality, and attending to data privacy issues. The goal of talent management is to draw in and keep talented workers with knowledge in “AI” and related fields. This entails establishing training programmes, cultivating an innovative culture, and encouraging cooperation between engineers, data scientists, and business stakeholders.

Ransbotham et al. (2019) discussed that making sure “AI” is created and used in an ethical and responsible manner is one of the ethical considerations. This involves addressing issues with biases in “AI” systems, guaranteeing accountability and transparency in decision-making, and addressing worries about how “AI” will affect society and the workforce. “AI” initiatives’ effectiveness is measured as part of performance monitoring, which also verifies that the desired outcomes are being achieved. This entails keeping track of user input, tracking key performance indicators, and continuously enhancing “AI” algorithms and models.

AI in Supply Chain Management

As per the views of Toorajipour et al. (2021), the use of “AI” technology in supply chain management has the potential to significantly increase productivity, accuracy, and efficiency. The following are some characteristics of the dialogue between supply chain management and “AI”:

• Demand planning and forecasting: Supply chain managers may predict demand more precisely and make better choices about inventory management, production planning, and logistics with the aid of “AI”-powered demand forecasting and planning tools.
• Management of inventory: “AI” systems can examine previous data to improve stock levels, cut waste, and lessen stockouts. Significant cost reductions and better customer service may result from this.
• Logistics and transportation: Routing and scheduling algorithms powered by “AI” can optimise delivery routes, lower transportation costs, and increase the percentage of deliveries that arrive on time.
• Quality control: Supply chain managers may quickly resolve problems by using “AI” to spot flaws and quality issues in real time.
• Supply management: Dash et al. (2019) sttaed that an “AI” can be used for supplier management to track performance and spot potential risks or problems. This can aid supply chain managers in choosing and managing suppliers more effectively.
• Sustainability: “AI” can be used to spot opportunities for waste reduction, carbon footprint reduction, and supply chain sustainability in general.

Therefore, applying “AI” to supply chain management has the potential to significantly boost productivity, reduce costs, and increase customer satisfaction, allowing organisations to compete in a constantly shifting market.

Importance of Supply chain Begging

Blanco et al. (2016) explained that sourcing of raw materials marks the start of the supply chain management process, which concludes with the delivery of the finished product to the final customer. Given that it entails the coordination and integration of several activities and organisations involved in the production and distribution of products and services, it is a crucial component of contemporary corporate operations. The significance of supply chain management can be better explained in the following ways:

• Cost savings: By streamlining production procedures, reducing waste, and enhancing inventory control, effective supply chain management can assist firms in cutting costs.
• Enhanced productivity: Supply chain management can aid firms in enhancing production by streamlining procedures, cutting down on lead times, and increasing overall output.
• Improved customer service: By ensuring on-time delivery, lowering stockouts, and enhancing product quality, effective supply chain management can assist organisations in providing better customer service.
• Improved competitiveness: By helping organisations to react rapidly to market changes, cut costs, and enhance product quality, supply chain management can provide them with a competitive edge.
• Risk Management: Gosling et al. (2016) put forward that supply chain management can assist firms in reducing risks by recognising probable pitfalls and creating backup plans to lessen their effects.
• Sustainability: By encouraging sustainable practices like waste reduction, carbon footprint reduction, and social responsibility, effective supply chain management can assist firms in lessening their negative environmental effects.
• Collaboration: Collaboration is necessary for successful supply chain management, including between suppliers, manufacturers, distributors, and retailers. Better communication, better decision-making, and better results for all parties involved can result from effective collaboration.

Alzoubi et al. (2022) opined that supply chain management is significant because it may give organisations the competitive edge they need by lowering costs, increasing efficiency, enhancing customer service, reducing risks, promoting sustainability, and encouraging collaboration. It is a crucial component of contemporary corporate operations and can significantly boost overall performance.

Concept of “Blockchain” and “Blockchain Management”

Wu and Tran (2018) explained that “Blockchain” is a type of digital ledger technology that makes it possible to share data and conduct transactions in a secure and open manner. It employs encryption to build a distributed, impenetrable record of transactions that is accessible to a network of computers. The process of administering and supervising “Blockchain”-based systems and applications is referred to as “Blockchain” management. It entails creating and implementing protocols and standards for the usage of “Blockchain” technology as well as assuring the integrity and security of the “Blockchain” network.

Yli-Huumo et al. (2016) opined that a thorough knowledge of the technology and its prospective applications, as well as proficiency in fields like encryption, cybersecurity, and distributed systems, are necessary for effective “Blockchain” administration. It also entails creating governance frameworks and rules to guarantee the “Blockchain” network is used fairly and equitably. Finance, healthcare, logistics, and supply chain management are just a few of the areas that can use “Blockchain” management. It can save costs, increase accountability and transparency, and enable more secure and effective transactions. Utilising the potential advantages of “Blockchain” technology requires effective “Blockchain” management. Businesses may open up new doors for innovation and growth while reducing risks and guaranteeing regulatory compliance by ensuring the integrity and security of “Blockchain”-based systems and apps.

One relevant model that can be used to explain the impact of “Blockchain” and “AI” in digital supply chain management in the UK retail sector is the Technology Acceptance Model (TAM). The TAM is a widely used model in the field of information systems that seeks to explain how individuals perceive and accept new technology. It consists of two main components: perceived usefulness (PU) and perceived ease of use (PEOU). In the context of “Blockchain” and “AI” in digital supply chain management, PU refers to the perceived benefits of using these technologies, such as increased efficiency, accuracy, and transparency. PEOU refers to the perceived level of difficulty in using these technologies, such as the need for specialized training and technical expertise.

Applying the TAM to the UK retail sector, we can see that the perceived usefulness of “Blockchain” and “AI” in supply chain management is high. These technologies have the potential to streamline processes, reduce costs, and improve supply chain visibility. However, the perceived ease of use may be a barrier to adoption, as retailers may be hesitant to invest in new technology that requires significant training and expertise.

To overcome this barrier, retailers may need to provide training and support for employees to ensure that they can effectively use these technologies. They may also need to invest in user-friendly interfaces and tools that simplify the integration of “Blockchain” and “AI” systems into existing supply chain management software. Another model that can be used is the Technology Readiness Index (TRI), which measures individuals’ willingness to adopt new technology based on their attitudes and beliefs. In the context of “Blockchain” and “AI” in digital supply chain management, the TRI can help retailers understand their customers’ readiness to adopt these technologies, which can inform marketing and adoption strategies.

Managing AI

“Blockchain” technology and “AI” are two of the most significant technical advancements of the twenty-first century (Jun, 2018). The combination of these two technologies has resulted in the development of a new paradigm that is altering the way we handle data, transactions, and networks. “Blockchain” technology provides a decentralised and secure platform for data exchange, whilst “AI” allows for the rapid and precise processing of enormous volumes of data (Salah et al., 2019).

In recent years, the integration of “Blockchain” with “AI” has been a hot study area. The potential to construct decentralised “AI” applications is one of the key advantages of combining these two technologies (Salah et al., 2019). Decentralised “AI” applications may be created on “Blockchain” systems and deliver more secure, transparent, and trustless data processing. Ethereum, for example, provides a decentralised platform for generating and executing smart contracts, which may be used for a variety of applications, including “AI” (Wang et al., 2019).

“AI” management in “Blockchain” is an important part of “Blockchain” technology. “Blockchain” provides a safe platform for data storage and exchange, but it also need effective administration in order to realise its full potential. “AI” may be used to automate “Blockchain” network administration, increase data processing, and optimise network performance (Khan et al., 2022). “AI” in “Blockchain” management may assist decrease the risk of human mistake, enhance network administration efficiency, and give real-time insights to better decision-making.

Despite the potential benefits of incorporating “AI” into “Blockchain” technology, there are significant difficulties to overcome (Atlam et al., 2020). Scalability is one of the most critical issues. As “Blockchain” networks grow in size and complexity, the processing power required to execute transactions grows, which can cause performance concerns. To enhance network performance, “AI” may be employed, but it takes a vast quantity of data and computer power to be successful (Thrall et al., 2018). Another thing to consider is privacy and security. “AI” in “Blockchain” administration necessitates access to sensitive data, which must be safeguarded to prevent data breaches.

Notwithstanding these obstacles, incorporating “AI” into “Blockchain” technology offers enormous benefits. “AI” in “Blockchain” management may assist increase network security, minimise fraud risk, and give real-time data to better decision-making (Han et al., 2023). Furthermore, the integration of these two technologies has the potential to provide new business models and revenue streams, such as the development of decentralised “AI” apps.

The incorporation of “Artificial Intelligence” (AI) into “Blockchain” technology is an exciting and intriguing breakthrough that has the potential to alter the way we handle data, transactions, and networks (Allam and Dhunny, 2019). Notwithstanding the limitations, the prospects given by this technology are enormous, and we may anticipate further study and development in this subject in the next years. As the use of “AI” in “Blockchain” technology becomes more common, it is critical to solve the issues and develop new solutions in order to realise the full potential of these two technologies.

AI in Supply Chain Management

SCM is a vital business process that includes controlling the movement of products, services, and information from suppliers to consumers (Azmi et al., 2017). With the fast growth of “Artificial Intelligence” (AI) and “Blockchain” technology, there is an increasing interest in investigating the integration of these two technologies in supply chain management.

“AI” has the potential to significantly improve supply chain management. “Artificial Intelligence” (AI) may be used to optimise supply chain processes by offering real-time data analysis and decision assistance (Modgil, Singh and Hannibal, 2022). This can assist firms in lowering inventory costs, increasing forecasting accuracy, and optimising logistical operations. “Artificial Intelligence” may also be used to automate operations like order processing, inventory management, and logistics planning, decreasing human error and increasing efficiency (Devarajan, 2018).

By offering a safe, transparent, and decentralised platform for data sharing, “Blockchain” technology has the potential to transform supply chain management (Chang, Chen and Lu, 2019). By providing an immutable record of all transactions, “Blockchain” technology can help minimise the risk of fraud and enhance supply chain traceability. Furthermore, by removing intermediaries, “Blockchain” technology may be utilised to automate supply chain procedures and cut transaction costs.

“AI” can potentially play an important role in “Blockchain” network management. “AI” may be used to improve data processing, increase network performance, and automate network administration (Benzaid and Taleb, 2020). This can help firms decrease the risk of human error, enhance network administration efficiency, and give real-time data to assist decision-making. By enhancing network speed, the application of “AI” in “Blockchain” administration can also assist to address the issue of scalability.

Notwithstanding the potential benefits of using “AI” and “Blockchain” technology in supply chain management, various hurdles must be solved. Interoperability is one of the most major problems. As more enterprises use “Blockchain” technology, interoperability between “Blockchain” networks is required to allow smooth data exchange (Dagher et al., 2018). Furthermore, the application of “AI” in “Blockchain” administration necessitates access to sensitive data, which must be safeguarded to avoid data breaches.

Notwithstanding these obstacles, the use of “AI” and “Blockchain” technology in supply chain management offers tremendous prospects. “AI” in supply chain management may assist firms in lowering costs, increasing efficiency, and improving decision-making (Mou, 2019). “Blockchain” technology has the potential to improve supply chain traceability, minimise fraud, and automate supply chain activities.

The use of “Artificial Intelligence” (AI) and “Blockchain” technology in supply chain management is an exciting and promising development that has the potential to alter the way we manage supply chain activities (Rodríguez-Espíndola et al., 2020). Notwithstanding the limitations, the prospects given by this technology are enormous, and we may anticipate further study and development in this subject in the next years. As the integration of “AI” and “Blockchain” technology in supply chain management becomes increasingly common, it is critical to solve the obstacles and develop innovative solutions to realise the full potential of these two technologies.

Key Challenges that Businesses face in Adopting “Blockchain” and “AI” Technologies

“Blockchain” and “Artificial Intelligence” (AI) technologies have the potential to transform the way organisations function. “Blockchain” technology can provide a safe, transparent, and decentralised platform for data exchange, whilst “Artificial Intelligence” (AI) can be utilised to automate processes and enhance decision-making (Angelis and Da Silva, 2019). Despite the potential benefits of new technologies, many firms encounter major barriers to adoption.

Technical Challenges

One of the most major technical hurdles that organisations confront when integrating “Blockchain” and “AI” technology is one of scale. “Blockchain” technology and “Artificial Intelligence” (AI) are both difficult technologies that need extensive technical skill to deploy and maintain (Attkan and Ranga, 2022). Furthermore, due to a lack of standardisation in these technologies, organisations may find it challenging to integrate them with current systems.

Regulatory Challenges

Another important barrier to company adoption of “Blockchain” and “AI” technology is regulatory in nature. Several nations have complicated regulatory structures that control how these technologies are used. These requirements can be difficult to understand, especially for organisations operating in numerous countries. Furthermore, a lack of clarity on regulatory frameworks might make it difficult for enterprises to design and implement “Blockchain” and “AI” initiatives (El Khatib, Al Mulla and Al Ketbi, 2022).

Data Privacy and Security Challenges

While embracing “Blockchain” and “AI” technology, enterprises must prioritise data privacy and security. The usage of these technologies frequently necessitates access to sensitive data, and the risk of data breaches is high (Nair and Tyagi, 2021). Furthermore, because “Blockchain” technology is decentralised, it might be difficult to monitor who has access to data, creating worries about data privacy.

Cost and Investment Challenges

Implementing “Blockchain” and “AI” technology can be expensive for organisations, especially small and medium-sized firms (SMEs). Hardware, software, and labour expenditures can all be incurred while deploying these technologies (Maddikunta et al., 2022). Moreover, the return on investment for these technologies may be delayed, making it difficult for firms to justify the expenditure.

Organisational Challenges

Businesses may face substantial organisational obstacles when adopting “Blockchain” and “AI” technology. To achieve effective adoption, these technologies necessitate a considerable cultural shift, and firms may need to engage in staff training and education (Chang et al., 2020). Furthermore, new technologies have the potential to disrupt traditional company procedures, leading to employee resistance.

Additional Challenges

In addition to the problems described above, organisations may face additional obstacles when implementing “Blockchain” and “AI” technology. They include a lack of compatibility across multiple “Blockchain” platforms and a lack of “Blockchain” technology scalability. Furthermore, organisations may struggle with the ethical implications of utilising “AI”, particularly when it comes to issues like prejudice and discrimination (Yapo and Weiss, 2018). Lastly, because of the quick speed of technological change, it can be difficult for organisations to keep up with the newest advances in these domains, raising worries about obsolescence and the need for continued investment in these technologies.

Key Findings

To summarise, while “Blockchain” and “AI” technologies have the potential to transform the way organisations run, there are significant hurdles that firms must overcome in order to properly implement them. Technical, legislative, data privacy and security, cost and investment, and organisational obstacles are among them. Businesses must invest in the appropriate technical skills, traverse complicated legal frameworks, prioritise data privacy and security, justify expenditures and investments, and handle cultural shifts and employee reluctance to effectively implement new technologies. But, with the proper strategies in place, firms may realise the full potential of these technologies and achieve a competitive advantage in the market.

Summary and Literature Gap

This component of the research includes literature studies on a variety of new technology subjects such as supply chain, “Blockchain”, and “AI”. The study of literature investigates the possible benefits of using “AI” in supply chain management via “Blockchain” technology, such as greater transparency, efficiency, and security. It also investigates and discusses the fundamental problems that organisations face when using “Blockchain” and “AI” technology, such as technical, regulatory, data privacy and security, cost and investment, organisational, interoperability, scalability, and ethical issues. In addition, the section focuses on the use of “AI” in “Blockchain” supply chain management, emphasising the potential for “AI” to automate supply chain activities, enhance efficiency, and reduce costs. While conducting the research, it is found that the topic contains very few papers that are dated recently. This goes on to show that the topic should be researched much more to find even more interesting findings and to proceed with more technological development in these fields.

Chapter 3: Research Methodology

Overview

The aims and objectives of the research paper are already stated in the first chapter, that is, the introduction chapter. Moreover, the chapter has also explained how the research is a feasible project and how it can contribute to increasing the knowledge of the readers. The second chapter of the research paper reviewed the existing scholarly papers, journals and other literature available which focuses on “Blockchain”, “Artificial Intelligence” and supply chain. The review of the existing literature was based on the papers available from 2013 to 2023, that is last ten years. This is the third chapter of the research paper, which provides the reader and the researcher outline of the methods that will be used to conduct research for the paper as well as provide knowledge of the various methods of research.

The section acts as an introduction to the methodology chapter, outlining the chapter’s purpose, breadth, and significance. The primary goal of this chapter is to evaluate the different techniques accessible and select the best method for the study. The approach will include “Blockchain”, “Artificial Intelligence”, supply chain, “Blockchain” management, and supply chain management. This chapter will provide an in-depth look at the study method. Both the reader and the researcher will benefit from the methodology part. It assists the reader in evaluating the addition and removal factors examined for the research and provides insight into the particular research methods used. This chapter assists the researcher in narrowing down the specifics of the study and gives justification for the methods selected. It also provides an idea of the various study methods that are accessible for future studies.

Research Philosophy

Figure 1: “Research Onion”

(Source: Saunders, Lewis and Thornhill, 2009)

The methods chapter’s Research Philosophy portion is an important part of the research process. The general position on the nature of knowledge and the research process is derived from the Research Onion paradigm in this study. Because of its practical and adaptable strategy, pragmatism was selected as the research philosophy for this project (Kelly and Cordeiro, 2020). The use of pragmatism as the research theory is justified by the fact that it enables the researcher to take a practical approach to the research process while recognising the complexities of the research subject. Pragmatism recognises that there are numerous methods to know and that the research process should represent this. A pragmatic strategy allows the researcher to be adaptable and flexible during the research process, while also ensuring that the research outputs are actually pertinent and applicable (Walther, Sochacka and Kellam, 2013). The consequences of employing pragmatism as the study philosophy are important. To accomplish the study goals, the researcher must be flexible and adaptable, as well as use a diversity of data gathering and analysis techniques. Pragmatism also promotes the use of mixed-methods research, which combines quantitative and qualitative data to provide a more thorough grasp of the study subject. Furthermore, this method enables the researcher to consider both theoretical and practical implications of the research, as well as to integrate stakeholder views in research planning and analysis.

Research Reasoning Approach

The research reasoning strategy for this research is inductive. Inductive thinking is a logical process that begins with particular observations and patterns and then progresses to the development of a theory or generalisation based on those observations (Zalaghi and Khazaei, 2016). The choice of an inductive method is justified because it enables the researcher to investigate and watch the research subject in a systematic and rigorous manner. The researcher can spot patterns and motifs that arise from data collection and analysis, which can then be used to create a theory or generalisation about the research subject (Lodico, Spaulding and Voegtle, 2010). When the study subject is complex or poorly known, inductive reasoning enables the researcher to create new insights and knowledge. The consequences of employing an inductive strategy are substantial. This strategy necessitates the researcher collect and analyse data in a methodical and rigorous manner, employing a variety of methods to ensure the data’s reliability and validity. Inductive reasoning also necessitates the researcher’s open-mindedness and flexibility, as well as the ability to adjust the research process as new patterns and themes surface. This method is time-consuming and resource-intensive, but it can result in new insights and understanding about the study subject.

Research Design

The research design for this research is an exploratory technique. This method is suitable for this research because the research’s goal is to examine different papers, infer a pattern, and develop a theory, as is characteristic of inductive research (Lodico, Spaulding and Voegtle, 2010). An exploratory research design is required to accomplish this objective. The exploratory study method has a number of benefits, including its adaptability and freedom (Dunn, Rochlen and O’Brien, 2013). The researcher can adjust the research plan as the study advances and new patterns appear. Furthermore, this design enables the researcher to obtain a better grasp of the study subject and generate new ideas and hypotheses. The exploratory study approach, however, has drawbacks. The main drawback is that the results are frequently tentative and unclear (Dirnagl, 2020). The exploratory research strategy is meant to produce hypotheses that can be evaluated in future research rather than to provide definitive solutions. The exploratory research strategy for this study has ramifications for the need for a fluid and adjustable approach to data gathering and analysis. Top of Form

Research Strategy

Secondary research was selected as the research strategy for this paper. This approach entails gathering and analysing existing data from a variety of sources, including scholarly papers, books, and internet platforms (Bell, Bryman and Harley, 2022). The use of supplementary research in this study is justified because it is a cost-effective and time-efficient method of data collection. Secondary research enables the researcher to swiftly and easily obtain a vast quantity of information without the need for time-consuming and expensive primary research.

Furthermore, secondary research is appropriate for this study because it gives access to a wide variety of books on the research subject. This enables the researcher to acquire a more thorough grasp of the research subject and find holes in the extant material that can be addressed in the study. The use of secondary research for this study has the consequence that the data analysed may be susceptible to constraints such as the quality and trustworthiness of the data sources (Cho and Lee, 2014). Furthermore, because the data is already accessible, the researcher may have limited control over it, and it may not completely match the research question. To overcome these constraints, the research must ensure that the sources used are trustworthy and credible and that the data gathered is pertinent to the research topic. The research will also need to closely assess current material in order to create new insights and theories.

Methodological choices

This research’s methodological choices are qualitative in character. Qualitative research is selected because of its ability to provide in-depth knowledge of complicated events as well as to examine participants’ views and experiences (Austin and Sutton, 2014). The analytical options are founded on the belief that the research should strive to understand the research participants’ psychological realities, as well as how they perceive and experience the research topic. The decision to conduct qualitative research stems from the research’s goal of analysing and interpreting extant literature on “Blockchain”, “Artificial Intelligence”, supply chain, “Blockchain” management, and supply chain management. Qualitative research equips the researcher with the tools needed to spot and analyse themes, patterns, and relationships in data, which are critical for developing a thorough grasp of the research issue.

The consequences of conducting a qualitative study are numerous. It is indicated by Tolley et al. (2016) Qualitative research provides for a thorough examination of the research subject, yielding insights that quantitative research techniques cannot provide. Furthermore, the methodology is adaptable and fluid, enabling the researcher to modify the research process as necessary to ensure that the research goals are met. However, the technique is time-consuming and resource-intensive, requiring the scholar to devote significant time and effort to data collection and analysis.

Data Collection Techniques

The analysis of the pertinent literature will be conducted using a secondary data-gathering method in this research article. Scholarly papers, articles, magazines, books, and any company-specific data that is accessible will be used as data sources. The use of secondary data collection techniques has several benefits, including giving a thorough and in-depth study of the subject matter, lowering data collection costs and time, and ensuring data uniformity and dependability. Furthermore, secondary data gathering allows researchers to access a broad variety of available data, allowing them to cover a larger sample size and enhance the veracity of the study results (Hair, Page and Brunsveld, 2019). However, it is also essential to recognise the limitations of the secondary data gathering method, such as possible bias and data quality problems. The researcher will use a critical method to assess the relevance and reliability of the sources chosen and will ensure that the research results are backed by the available evidence.

Data Analysis

Content analysis was selected as the data analysis method for this study. This method entails analysing data from various sources, such as academic papers, articles, journals, books, and company-specific data, in order to find similar themes and patterns (Veile et al., 2021). The use of content analysis is justified because it is a methodical and impartial strategy that allows the scholar to effectively analyse large quantities of data. Furthermore, it is a versatile method that can be tailored to a variety of research topics and is appropriate for qualitative data analysis. According to Neuendorf (2017), content analysis is an effective method for this study because it provides a thorough and impartial examination of the available data. This method will allow the researcher to create a complete grasp of the connection between “Blockchain” technology, “Artificial Intelligence”, and supply chain management by finding patterns and themes within the data. The research results will be based on a comprehensive and methodical analysis of the available data as content analysis is used as the data analysis methodology. This will give the researcher a thorough grasp of the research issue and allow him or her to spot important themes and patterns that arise from the data. Furthermore, content analysis enables the discovery of voids in the extant literature, which can then be used to guide future research paths.

Ethical Consideration

Ethical concerns are essential in all research, and this research paper is no exception. The use of secondary data-gathering methods raises several ethical concerns, including the possibility of plagiarism and data tampering (Pupovac and Fanelli, 2015). To resolve these issues, appropriate citation and referencing will be used to guarantee proper credit is given to the original authors. Furthermore, the research will be based on openly accessible data, with no confidential or proprietary data being used. In addition, the researcher will take proper steps to protect the privacy and anonymity of subjects in any referenced studies or research. The ethical concerns of this study are warranted in order to guarantee the integrity and correctness of the research and to avoid any damage to the subjects or society as a whole.

Summary

This is the third chapter of a research paper, which outlines the research methodology. The chapter starts with a summary of the research’s goals, objectives, and potential contributions to the research. The chapter is organised around a survey of extant literature from 2013 to 2023. The methodology chapter’s goal is to assess different techniques and choose the best method for the research. The study’s research philosophy is pragmatism, which acknowledges different ways of knowing and enables the researcher to be fluid and malleable during the research process. The inductive research reasoning method allows the researcher to create a theory or generalisation based on data and trends. The paper’s research method is exploratory, which enables the researcher to investigate various papers, deduce a pattern, and create a theory. The paper’s research method is secondary research, which involves gathering and analysing current data from different sources. The research paper will also consider the ethical factors stated and will perform a thorough study of existing scholarly articles, journals, and books, as well as any company-specific information.

Chapter 4: Findings and Discussion

Overview

Interest in using cutting-edge technology like “Blockchain” and “Artificial Intelligence” (AI) in supply chain management has grown over the past few years. Researchers and industry professionals have looked into how these technologies might improve supply chain efficiency, transparency, and sustainability. A increasing corpus of literature addressing the use of “Blockchain” and “AI” in supply chain management has resulted from this. It is clear from the analysed sources that these technologies have the potential to completely transform the supply chain sector by raising customer trust, enhancing traceability, and minimising fraud. The important topics and conclusions from the sources under evaluation are briefly summarised in this introduction, which also draws attention to the growing interest in the potential of emerging technologies in supply chain management.

Analysis

Sources 1, 2, 3 and 4

Vendrell-Herrero et al. (2017) seek to investigate the effects of supply chain interdependency, servitization, and digitization on business performance. Servitization is the trend of delivering services alongside products. They also look into how these relationships are moderated by the level of industry maturity. In order to do this, the authors surveyed 252 Spanish manufacturing companies, then utilised structural equation modelling to evaluate the data.

According to their findings, supply chain interdependency is positively impacted by servitization and digitization, which in turn improves corporate performance. However, they also discovered that in sectors with higher levels of maturity, the beneficial effects of servitization and digitization on supply chain interdependency are less pronounced.

The objectives of Jabbar et al. (2021) are to analyse “Blockchain” technology in the context of supply chain management, identify the prospects and limitations of its application, and suggest future research trajectories. The authors reviewed 101 publications on supply chain management and “Blockchain” that were published between 2008 and 2020 as part of their review of the literature.

According to their analysis, “Blockchain” can help with a variety of supply chain issues, including inefficiency, lack of transparency, and security worries. The implementation of “Blockchain” technology in supply chain management, however, is hampered by a number of obstacles, including technical limitations, legal restrictions, and a lack of standards. The authors suggest various lines of further research, including examining how “Blockchain” affects the sustainability and resilience of supply chains.

In conclusion, while Jabbar et al. (2021) conducted a literature review to analyse the challenges and opportunities of “Blockchain” technology in supply chain management and suggest future research directions, Vendrell-Herrero et al. (2017) used a survey and structural equation modelling to examine the impact of servitization, digitization, and supply chain interdependency on business performance.

The article by Khan and Manzoor, which was published in the International Journal of Economics & Business Administration in 2021, sought to determine how new technologies might be used to lessen the effects of supply chain disruptions brought on by the COVID-19 pandemic. In addition, the authors sought to define the uses and effects of these technologies in supply chain management. The writers gathered and analysed data from a variety of sources, including scholarly publications, books, and reports, using a systematic literature review approach. A thorough search across many databases turned up 30 papers that satisfied their inclusion requirements.

The study’s conclusions showed that different phases of the supply chain, including procurement, production, distribution, and customer support, used these technologies in diverse ways. For instance, IoT was utilised to track the condition and location of goods in transit, and “Blockchain” technology was employed to guarantee the validity and traceability of products.

The study concluded that these technologies significantly improved supply chain performance, including enhanced efficiency, agility, and resilience. The study also emphasised how crucial organisational preparation is to successfully integrating these technologies into supply chain management, including the availability of resources and knowledge. In terms of methodology, the study employed a systematic literature review approach, which allowed the authors to locate pertinent publications and conduct a methodical analysis of their results. The inclusion criteria for the study made sure that only high-quality studies that complied with certain requirements were included, delivering accurate and reliable data.

The study by Khan and Manzoor offers insightful information about how new technologies can be used to lessen the COVID-19 pandemic’s effects on supply chain management. According to the study’s findings, these technologies have a lot of potential for better inventory management, lowering lead times, and improving supply chain visibility. However, their successful implementation requires organisational readiness and adequate resources and expertise.

The article by Ray et al., was published in the International Journal of Management Studies in 2019 and looked into the potential advantages and difficulties of doing so. The authors also sought to determine how “Blockchain” technology might be incorporated into the food supply chain and what effect it might have on the sector. In-depth interviews with stakeholders from various organisations involved in the food supply chain, such as farmers, retailers, and regulators, were conducted by the authors as part of their qualitative research methodology. Also, they carried out a thorough literature study of pertinent studies on supply chain management using “Blockchain” technology.

The study’s concludes, organisations may follow the transportation of food goods from farm to table using “Blockchain” technology, ensuring their safety and authenticity. The study also demonstrated how “Blockchain” technology may boost the efficacy and efficiency of the food supply chain while lowering costs and fostering greater cooperation among supply chain participants. The study also outlined a number of difficulties with implementing “Blockchain” technology in the food supply chain, including the necessity for standardisation, the difficulty of implementation, and the scarcity of technical resources. The authors suggested that these challenges could be addressed through regulatory and industry-wide initiatives that promote the adoption of “Blockchain” technology.

The study employed a qualitative research methodology, which allowed the researchers to investigate the perspectives and experiences of stakeholders from diverse organisations involved in the food supply chain.

In conclusion, the Ray et al. study offers insightful information about the possible advantages and difficulties of applying “Blockchain” technology to the food supply chain. The study’s findings indicate that “Blockchain” technology has a lot to offer the food industry, but for it to be successfully implemented, its technological, organisational, and regulatory aspects must be carefully taken into account. The study’s suggestions emphasise the necessity for legislative and sector-wide actions to encourage the use of “Blockchain” technology in the food supply chain.

Sources 5, 6 and 7

The purpose of Tsolakis et al. (2022) is to investigate the possible advantages of integrating “Blockchain” and “Artificial Intelligence” (AI) in supply chains, notably in terms of sustainability and data monetization. The risks and difficulties involved in implementing these technologies in supply chain management are also examined by the writers. The authors used a systematic review framework to interpret their findings after conducting a literature analysis of 108 articles on supply chains, “Blockchain”, and “Artificial Intelligence”.

According to their research, the integration of “AI” and “Blockchain” can increase supply chain sustainability by boosting productivity, cutting waste, and increasing transparency. Also, they suggest a number of potential strategies for data monetization, including the use of “Blockchain”-enabled smart contracts for safe and automated data exchange. The authors do, however, also draw attention to a number of difficulties and dangers related to the application of these technologies, such as interoperability problems and legal restrictions.

Aiming to present case studies on the evolving application of “Blockchain” technology in supply chain logistics, Subramanian et al. (2020). The acceptance and use of “Blockchain” technology across four distinct supply chains—pharmaceuticals, food, diamonds, and luxury goods—is examined by the writers. The writers interviewed business professionals and used a qualitative analysis to interpret their findings. According to their research, the use of “Blockchain” technology in supply chain logistics may have a vast number of advantages improved transparency, traceability, and security. The authors do, however, also draw attention to a number of difficulties and constraints, such as the necessity of collaboration, trust, and interoperability across supply chain stakeholders.

The article by Subramanian, Chaudhuri, and Kaykc, which was published in Springer Nature in 2020, sought to examine the uses of “Blockchain” technology in supply chain logistics through a number of case studies. Also, the authors sought to define the advantages, difficulties, and restrictions of applying “Blockchain” technology to supply chain logistics.

In-depth interviews were conducted with stakeholders from various firms that incorporated “Blockchain” technology in their supply chain logistics as part of the authors’ qualitative study technique. Four businesses representing four distinct industries were the subjects of the case studies: a pharmaceutical company, a food processing and distribution business, a logistics service provider, and a transportation business.

The study’s conclusions showed that “Blockchain” technology can improve efficiency, security, traceability, transparency, and other aspects of supply chain logistics. “Blockchain” technology can help businesses cut costs, improve partner collaboration, and streamline operations throughout their supply chains. The case studies also demonstrated how “Blockchain” technology may be used to specific supply chain logistics issues, including product fraud, legal compliance, and supply chain interruptions. The study also outlined a number of restrictions and difficulties related to applying “Blockchain” technology to supply chain operations. These difficulties include the complexity of putting “Blockchain” into practise, the requirement for standards, and the dearth of technological resources and know-how.

Regarding methodological techniques, the study made use of a numerous case study design that allowed the researchers to investigate how “Blockchain” technology is being applied in various sectors of business and supply chain settings. In-depth interviews with participants from the firms’ stakeholder groups yielded insightful information about the real-world effects of applying “Blockchain” technology to supply chain logistics.

The study by Subramanian, Chaudhuri, and Kay, in conclusion, sheds light on the possible applications of “Blockchain” technology in supply chain logistics and offers insightful information about the advantages, difficulties, and restrictions of its application. The study’s findings indicate that “Blockchain” technology can significantly improve supply chain logistics, but for this to happen, its technological, organisational, and regulatory components must be carefully considered.

Sources 8 and 9

Gohil and Thakker (2021) want to investigate how “Blockchain”-integrated technology may be used to address various supply chain management problems. The authors also give a general overview of the numerous ways that supply chains might use “Blockchain” technology for traceability, transparency, and safe data sharing. The authors used a systematic review framework to examine their findings after conducting a literature analysis of 45 articles on supply chains and “Blockchain” to accomplish their goals.

According to their research, “Blockchain”-integrated solutions can address a number of supply chain management problems, including ineffective procedures, a lack of transparency, and data security concerns. The authors suggest a number of potential uses for these technologies, including employing “Blockchain”-enabled smart contracts to automate supply chain transactions and using “Blockchain”-based tools to facilitate safe data exchange and collaboration between supply chain participants. The authors do draw attention to the need for additional study and development, though, in order to solve the difficulties and constraints presented by these technologies.

A summary of the numerous paradigm shifts and transitions related to the deployment of “Blockchain” technology in transportation and logistics is what Koh et al. (2020) set out to do. The authors analyse the possible advantages and difficulties of implementing “Blockchain” in logistics and transportation, and they suggest many future research trajectories in this field. The authors used a theme analysis approach to examine their findings after conducting a study of the literature with 72 publications on “Blockchain” and transportation logistics in order to accomplish their goals.

According to their research, “Blockchain” technology has the potential to improve a number of transport and logistics-related factors, including supply chain efficiency, transparency, and traceability. The authors put up a number of paradigms for the use of “Blockchain” in logistics and transportation, including cargo tracking systems based on “Blockchain” and vehicle-to-vehicle communication facilitated by it. The authors also point out a number of obstacles and restrictions to the use of “Blockchain” in logistics and transportation, including the requirement for interoperability, scalability, and data protection.

In conclusion, Koh et al. (2020) provided an overview of the potential advantages, challenges, and future directions of “Blockchain” adoption in transport and logistics through a literature review and thematic analysis approach. Gohil and Thakker (2021) conducted a literature review to put forward the core aspects of “Blockchain”-integrated technologies in addressing various supply chain management challenges.

Sources 10 and 11

In their study published in 2022, Bolesnikov et al. seek to better understand how customers perceive creative uses of “Artificial Intelligence” (AI) to enhance their shopping experiences in the sustainable fashion sector. The authors look at how “AI”-enabled solutions might increase the fashion industry’s sustainability while simultaneously raising consumer happiness. The writers surveyed 326 members of the sustainable fashion sector in order to accomplish their goals, and then they used descriptive statistical analysis to examine the results.

According to their research, “AI”-enabled solutions can improve the apparel industry’s sustainability by decreasing waste, increasing efficiency, and encouraging sustainable production methods. The authors suggest a number of potential uses for “AI” in the sustainable fashion sector, including the use of recommendation systems, customised sizing options, and “AI”-powered virtual try-ons. The authors also draw attention to the need for more study and advancement to address the difficulties and constraints of these technologies, including algorithmic bias and data privacy issues.

With a focus on the UK environment, Chowdhury et al. (2022) seek to explore the adoption of “Blockchain” technology for managing risks in operations and supply chain management. In order to address numerous operational and supply chain risks like fraud, counterfeiting, and data breaches, the authors investigate the possibilities of “Blockchain”-enabled solutions. The writers interviewed 30 UK business professionals in semi-structured interviews to accomplish their goals. They then used a thematic approach to evaluate the data they collected.

According to their research, “Blockchain” technology can reduce operational and supply chain risks by enabling transparency, traceability, and data security. The authors suggest a number of possible uses for “Blockchain” in operations and supply chain management, including the use of smart contracts built on “Blockchain” technology to automate supply chain transactions and “Blockchain”-enabled tools for secure data sharing and collaboration between supply chain stakeholders. The authors also stress the need for additional research and development to solve the difficulties and restrictions associated with the implementation of “Blockchain” technology, such as legal restrictions and problems with technical compatibility.

In conclusion, Bolesnikov et al. (2022) conducted a survey to examine perceptions of creative “AI” usage in enhancing customer purchasing experience within the sustainable fashion industry, while Chowdhury et al. (2022) used semi-structured interviews and thematic analysis to examine the adoption of “Blockchain” technology for managing risks in operations and supply chain management.

Sources 12

In the article introduced by Vyas et al., the authors seek to give readers a thorough grasp of “Blockchain” technology in the context of supply chain management. The authors’ goal is to examine the limitations and promise of “Blockchain” technology for supply chain management. The goal of the book is to serve as a reference for managers, practitioners, and researchers who are interested in learning about “Blockchain” technology and its supply chain applications.

The book’s writers use case studies and secondary data analysis along with a qualitative research methodology to examine the research issues. Following a review of the literature on “Blockchain” technology and supply chain management, they give an outline of the ways in which “Blockchain” technology can be used to improve various supply chain components. Additionally, they offer case studies showing how “Blockchain” technology has been used in supply chains across different sectors, such as those for food, drugs, and luxury items.

According to the book’s results, “Blockchain” technology can enhance transparency, traceability, and accountability in the supply chain management process. The authors emphasise how “Blockchain” technology has the potential to lower fraud and counterfeiting, increase supply chain effectiveness, and boost customer trust. They also point out that, in order to fully achieve its potential, “Blockchain” technology is not a panacea and requires careful design and implementation.

In terms of methodology, the book draws on case studies, secondary data analysis, and an extensive assessment of the literature to offer insights into the use of “Blockchain” technology in the supply chain. The authors also offer a framework for determining whether adopting “Blockchain” technology in the supply chain is feasible.

Overall, the book by Vyas et al. is an invaluable tool for learning about the potential of “Blockchain” technology in the context of supply chain management. It gives a thorough explanation of “Blockchain” technology and its uses, as well as its drawbacks and implementation considerations. Researchers and practitioners can use the case studies and framework given to investigate the application of “Blockchain” technology in the supply chain.

Summary

As a conclusion, it can be said that the sources under study show the growing interest in supply chain management applications of cutting-edge technology like “Blockchain” and “Artificial Intelligence”. The results indicate that through boosting transparency, efficiency, and sustainability, the use of these technologies has the potential to change the supply chain industry. The different ways in which these technologies are being employed in supply chain management are highlighted by the methodological approaches used in these research, which range from literature reviews to case studies. It is obvious that the integration of “Blockchain” and “Artificial Intelligence” into supply chain management has the potential to provide enormous value for companies, and it is probable that interest in these technologies will increase in the years to come.

Discussion

The cited sources offer light on the condition of digital supply chain management in the UK retail industry as well as the difficulties organisations confront when implementing “Blockchain” and “Artificial Intelligence” (AI) technologies. The interdependence of supply chains in the context of servitization and digitization is explored by Vendrell-Herrero et al. (2017). They contend that these two developments have boosted cooperation and communication among supply chain participants. However, they also point out that such interdependence can result in risks and vulnerabilities, such as supply chain disruptions and cyberattacks.

“Blockchain”-enabled supply chains, which have grown in popularity recently because to their promise to boost transparency, efficiency, and security, are the subject of Jabbar et al. (2021)’s study. The authors list a number of obstacles that enterprises must overcome in order to utilise this technology, including scalability, interoperability, and legal concerns. They also point out that stakeholders frequently do not completely comprehend the advantages of “Blockchain”, which can result in resistance to change and delayed adoption rates.

In addition to highlighting the value of digital technology for supply chain management, these sources also draw attention to the difficulties that firms confront in implementing these technologies. In this context, it is crucial to analyse the state of digital supply chain management in the UK retail industry and pinpoint the major obstacles to implementing “Blockchain” and “AI” technology.

With rising competition and shifting consumer preferences, the UK retail industry has seen considerable changes recently. Through increasing productivity, lowering expenses, and improving the customer experience, digital technologies have the potential to assist organisations in overcoming these difficulties. Yet, these technologies also introduce new hazards and vulnerabilities that need to be addressed, as noted by Vendrell-Herrero et al. (2017).

“Blockchain” technology has the probability to bring in improvement in the supply chain security and transparency, according to Jabbar et al. (2021). Nevertheless, they also point out that implementing this technology presents a number of difficulties for enterprises, including the requirement for interoperability and legal compliance. To fully enjoy the advantages of “Blockchain”, these difficulties must be overcome. The sources mentioned offer insightful information about the level of digital supply chain management in the UK retail industry as well as the difficulties businesses face when implementing “Blockchain” and “Artificial Intelligence” (AI) technologies. In light of the shifting retail scene and the potential for digital technology to help businesses overcome their obstacles, the goal of assessing these issues is quite pertinent.

Tsolakis et al. (2022) examine how these technologies are used in supply chains with an emphasis on how they could improve sustainability and data monetization. The authors contend that while “Blockchain” can improve transparency and trust in supply chain operations, “AI” can be used to examine massive volumes of data created by supply chains and find chances for optimisation. The advantages of more transparency, lower costs, and higher security are highlighted in case studies of “Blockchain” application in supply chain logistics presented by Subramanian et al. (2020). They contend that “Blockchain” can enhance visibility throughout the supply chain and foster more collaboration between partners.

These sources show the potential advantages of “Blockchain” and “AI” for managing the digital supply chain in the UK retail industry. In order to meet consumer needs, the retail industry must practise efficient and effective supply chain management. Retailers may be able to improve consumer satisfaction and supply chain efficiency by implementing these technologies.

Tsolakis et al. (2022) show how “AI” has been upgraded to improve the process of supply chain sustainability by spotting areas that can be optimised, such cutting waste and raising energy effectiveness. Additionally, they contend that the usage of “Blockchain” can assist to improve supply chain accountability and transparency, which will benefit businesses’ reputations and win over more loyal customers.

The advantages of “Blockchain” in supply chain logistics, such as enhanced efficiency, decreased costs, and improved security, are illustrated by case studies provided by Subramanian et al. (2020). Retailers who have to manage complicated and dynamic supply networks to satisfy customer needs may find these advantages to be of special value. Strong support for the potential advantages of “Blockchain” and “AI” in digital supply chain management in the retail sector of the UK is provided by the sources discussed. The application of these technologies can boost supply chain operations’ efficiency, transparency, and sustainability. Given the competitive retail environment and the requirement for efficient and effective supply chain management, the goal of researching the possible advantages of these technologies is quite pertinent.

The above sources offer insightful information on how “Blockchain” and “AI” affect the various phases of the supply chain process, as well as best practises for using these technologies. This is the investigation’s goal, and the sources give a number of results that are pertinent to it. Gohil and Thakker (2021) talk about the difficulties that supply chain management faces and how “Blockchain” and “AI” could be able to help. The authors highlight the various steps in the supply chain process, such as purchasing, production, storage, transportation, and delivery, and they examine the advantages that “Blockchain” and “AI” might have at each step. To show how these technologies have been successfully incorporated into supply chain management, they also provide a number of case stories.

Koh et al. (2020) narrow their attention to the usage of “Blockchain” in logistics and transportation while recognising certain paradigm shifts and transitions that are pertinent to the adoption of this technology. The authors talk about the potential advantages of “Blockchain” in terms of trust, security, and transparency and provide various case studies to show how it has been successfully incorporated into logistics and transportation.

Both publications include examples of how “Blockchain” and “Artificial Intelligence” (AI) might affect various supply chain stages as well as best practises for integrating these technologies. Enhancing supply chain transparency, security, and trust through the integration of “Blockchain” and “AI” can also increase supply chain management’s efficacy and efficiency.

The potential advantages of “Blockchain” and “AI” at each stage of the supply chain process are highlighted by Gohil and Thakker (2021). “Blockchain”, for instance, can enable safe and open procurement procedures, while “AI” can be applied to improve production and warehouse management. The writers also offer a number of integration best practises, such as creating a clear plan and picking suitable partners.

By emphasising the potential advantages in terms of transparency, security, and trust, Koh et al. (2020) concentrate primarily on the application of “Blockchain” in transportation and logistics. The authors contend that cooperation between supply chain partners and a thorough understanding of the technology are essential for the successful integration of “Blockchain”.

The sources offer insightful information on how “Blockchain” and “AI” affect the various phases of the supply chain process as well as the best practises for incorporating these technologies. Collaboration between supply chain partners and a thorough knowledge of these technologies’ potential applications are essential for their successful integration. In light of the competitive retail environment and the requirement for effective supply chain management, the goal of assessing the impact of “Blockchain” and “AI” on the supply chain process and establishing best practises for integrating these technologies is particularly significant.

The cited sources shed light on stakeholders’ views and perceptions of the application of “AI” and “Blockchain” technology in digital supply chain management as well as adoption constraints in the UK retail sector. These results are extremely pertinent to the goal of examining stakeholders in the UK retail sector’s attitudes and perceptions regarding “AI” in digital supply chain management and identifying adoption hurdles.

In their study published in 2022, Bolesnikov et al. explore the perspective of novel “AI” applications in enhancing client purchase experiences in the sustainable fashion sector. The authors poll key players in the fashion business to learn how they feel about using “AI” to improve the customer experience. The results indicate that stakeholders are generally supportive of “AI” use and are aware of its potential advantages, such as enhanced productivity and higher customer satisfaction. The authors do point out that there are worries regarding AI’s possible influence on employment and its ethical ramifications.

The implementation of “Blockchain” technology for risk management in operations and supply chain management in the UK is the main topic of Chowdhury et al(2022) .’s study. The authors poll supply chain experts in the UK to learn more about their views on applying “Blockchain” technology. According to the findings, although there is knowledge of the potential advantages of “Blockchain” technology, such as greater transparency and improved security, there are also worries about the difficulty and expense of adoption as well as the absence of standards and regulations.

The attitudes and perspectives of stakeholders concerning the employment of developing technologies in supply chain management are highlighted in both sources. Although the potential advantages of these technologies are acknowledged, there are also worries about their ethical ramifications, complexity, expense, and lack of standardisation. These results imply that although there are some adoption hurdles that need to be overcome, stakeholders in the UK retail sector are largely supportive of the use of “AI” and “Blockchain” technology in supply chain management. Thus, the sources shed light on stakeholders’ views and opinions of the use of “AI” and “Blockchain” technology in digital supply chain management as well as adoption constraints in the UK retail sector. The results indicate that while stakeholders are largely supportive of using these technologies, there are some reservations about their potential ethical ramifications, complexity, expense, and lack of standardisation. In light of the competitive retail environment and the requirement for efficient and effective supply chain management, the goal of exploring the attitudes and perceptions of UK retail sector stakeholders towards “AI” in digital supply chain management and identifying adoption barriers is highly pertinent.

The two sources offer perspectives on digital supply chain management for various businesses, but they are nonetheless helpful for building a framework to evaluate the efficacy of “Blockchain” and “AI” in the UK retail industry. The potential of digitization for agro-food supply chains, which can be applied to other industries, is examined by Amentae and Gebresenbet in 2021. They talk about how supply chain management can be improved by integrating diverse technologies, such as “Blockchain” and “AI”. The usage of “Blockchain”, according to the authors, can increase supply chain traceability and transparency, allowing for more accurate tracking of products from farm to customer. They also stress how by studying data and forecasting demand, “AI” may be used to optimise supply chain operations. These results can be used to assess how well supply chain management and customer satisfaction are improved by “Blockchain” and “AI” technology in the UK retail industry.

A thorough description of “Blockchain” technology and supply chain management applications is given by Vyas et al. (2019). The authors talk about how “Blockchain” technology might improve supply chain efficiency, security, and transparency. Also, they go over how “AI” is used in supply chain management, including route optimisation, demand forecasting, and predictive analytics. The authors give various instances of how “Blockchain” and “AI” have been successfully applied, including Walmart’s use of “Blockchain” to track food supplies and Maersk’s use of “Blockchain” and “AI” to optimise shipping routes. These illustrations can be used as a benchmark for evaluating the efficacy of “Blockchain” and “AI” in the UK retail industry. All things considered, these sources offer insightful information for creating a framework to evaluate the efficacy of “Blockchain” and “AI” in digital supply chain management in the UK retail industry.

These sources collectively imply that the application of “Blockchain” and “AI” technology can enhance efficiency, transparency, and traceability in supply chain management. It is crucial to take into account these technologies’ possible effects on different supply chain process stages and to assess the effectiveness of prior deployments in other industries in order to create a framework for judging their efficacy. Examining the attitudes and views of stakeholders in the UK retail sector towards these technologies is also essential in order to spot potential adoption hurdles.

Chapter 5: Conclusion & Recommendation

Conclusion

The aim of this research was to assess how “Blockchain” and “AI” are affecting digital supply chain management in the UK retail industry. To achieve the study goals, the extant literature from 2013 to 2023 was analysed, with a focus on papers published in the last five years as much as feasible. The study’s goal is to determine the possible benefits and challenges of implementing “Blockchain” and “AI” technologies in digital supply chain management in the UK retail sector, as well as to develop a framework for assessing the efficacy of these technologies.

Pragmatism is the research philosophy used in this research. The study thinking is inductive, allowing the researcher to spot a pattern in different papers and develop a theory. The exploratory research technique allows the scholar to examine different papers, deduce a pattern, and develop a theory. Secondary research is used, and it entails collecting and analysing current data from various places. The research paper also takes ethical considerations into account and conducts a comprehensive review of current academic papers, journals, and books, as well as company-specific information.

The result of the research provides insights into the present status of digital supply chain management in the UK retail sector, as well as the challenges that organisations encounter when adopting “Blockchain” and “AI” technologies. The result of the research emphasises supply chain dependency in the context of servitization and digitization, which has increased collaboration and communication among supply chain players but also presents risks such as supply chain interruptions and hacking. On the other hand, some research result also addresses the barriers that firms must surmount in order to use “Blockchain” technology, such as scalability, interoperability, regulatory concerns, and stakeholder opposition to change. While digital technologies have the ability to help retailers increase productivity, cut costs, and improve the consumer experience, they also bring new risks and vulnerabilities that must be addressed. The result from the research entails the fulfilment of the objective of analysing the current state of digital supply chain management in the UK retail sector as well as the paper identified the key challenges that business faces in the adoption of “Blockchain” and “AI” technologies.

The next objective of the research is based on the investigation of the benefits that may be gained by the usage of “Blockchain” and “AI” in digital supply chain management in the UK retail industry. This objective is also satisfied in the research. The result of the research states that despite “Blockchain” technology being able to enhance supply chain security and transparency, adopting it poses challenges for companies, such as interoperability and regulatory compliance. These difficulties must be overcome in order to completely appreciate its advantages. “AI” can also be used to analyse data generated by supply chains and identify chances for improvement. “Blockchain” and “Artificial Intelligence” (AI) have the potential to enhance supply chain efficiency, openness, and sustainability, thereby helping companies’ reputations and consumer loyalty. Implementing these technologies can help retailers increase supply chain productivity and customer satisfaction. The sources reviewed to provide insights into best practices for implementing these technologies, and case studies demonstrate how they have been effectively integrated into supply chain management. Given the competitive retail climate and the need for efficient and effective supply chain management, the aim of studying the potential benefits of these technologies is relevant.

The next objective of the research was to evaluate the impact that “Blockchain” and “AI” can have in the different stages of supply chain management as well as to identify the best practices for integrating the technologies. The result of the research indicates the fulfilment of the objective as the research’s discussion part also discusses the effect of “Blockchain” and “Artificial Intelligence” (AI) on different stages of the supply chain process, as well as best practices for their integration. The sources mentioned providing information on how these technologies can improve supply chain openness, security, and confidence, resulting in greater effectiveness and efficiency. Successful integration requires collaboration and a comprehensive grasp of the technology. The results also found that the use of “AI” improves consumer encounters in the sustainable fashion sector, whereas some results concentrate on the use of “Blockchain” technology for risk management in operations and supply chain management in the United Kingdom. Both studies emphasise both the benefits and drawbacks of adoption.

The next objective of the research was to explore the attitudes and perceptions of the UK retail sector stakeholders to the change of using “Blockchain” and “AI” in their supply chain management. The result of the research identifies the fulfilment of this objective as well. The research paper’s discussion part mentions two sources that emphasise stakeholders’ views and viewpoints on the use of emerging technologies, such as “AI” and “Blockchain”, in supply chain management in the UK retail sector. While stakeholders recognise these technologies’ possible benefits, they are concerned about their ethical consequences, complexity, expense, and absence of standardisation. Despite these concerns, stakeholders generally support the use of “Artificial Intelligence” and “Blockchain” technology in supply chain management. To enhance supply chain management in the competitive retail industry, the book emphasises the significance of investigating stakeholders’ perspectives and finding acceptance obstacles. The second source addressed in the text looks at how integrating various technologies, such as “Blockchain” and “Artificial Intelligence”, can enhance supply chain management and consumer satisfaction in agro-food supply networks. Both sources’ findings can be used to evaluate the effectiveness of “Blockchain” and “AI” technology in enhancing supply chain management in the UK retail sector.

Finally, the last objective of the research was to develop a framework, which can be useful for assessing the effectiveness of “Blockchain” and “AI” in digital supply chain management in the UK retail industry. The result of the research implies the fulfilment of this objective as the result of the research paper discusses the possible advantages of using “Blockchain” and “AI” technologies in supply chain management, as well as the obstacles and concerns connected with their adoption. The researcher notes some of these technologies’ success stories, such as Walmart’s use of “Blockchain” to monitor food inventories and Maersk’s use of “Blockchain” and “AI” to improve cargo routes. It does, however, express worries about the ethical consequences, intricacy, cost, and absence of standardisation. To assess the usefulness of these technologies in the UK retail industry, consider their influence on various phases of the supply chain as well as their success in other industries. It is also critical to examine the opinions and perspectives of stakeholders in the UK retail industry towards these technologies in order to find possible adoption barriers. Overall, the application of “Blockchain” and “Artificial Intelligence” technologies has the potential to improve supply chain effectiveness, openness, and traceability.

Recommendations

The results of the research leave room for improvement. Based on the result of the research, the following recommendations can be provided:

• Address the Risks and Vulnerabilities: Digital technologies have the ability to increase efficiency, reduce costs, and improve the customer experience. However, it also introduces new risks and weaknesses that must be addressed. Organisations must take the required precautions to mitigate risks and safeguard the supply chain from possible disruptions.
• Overcome Adoption Challenges: “Blockchain” and “Artificial Intelligence” (AI) technologies have the ability to improve supply chain security, openness, and effectiveness. However, companies must handle the issues that arise as a result of their usages, such as interoperability, legal conformance, and stakeholder resistance to change. To completely reap the benefits of these technologies, organisations must strive to overcome these obstacles.
• Explore Best Practices: The research emphasises the significance of investigating best practices for adopting “Blockchain” and “AI” technologies in supply chain management. Successful case studies can help organisations learn and adjust their practices. Collaboration and a thorough grasp of the technology are essential for effective integration.
• Evaluate Stakeholder Perceptions: While most parties support the use of “Blockchain” and “AI” technology in supply chain management, they are concerned about the ethical implications, intricacy, expense, and lack of standardisation. It is critical for companies to assess stakeholder views and impressions and resolve any barriers to adoption.
• Develop an Assessment Framework: Creating a structure to evaluate the efficacy of “Blockchain” and “AI” technologies in supply chain management can be beneficial. It is critical to assess the impact of these technologies on different stages of the supply chain, as well as their success in other sectors. Examining stakeholders’ views and perspectives can also aid in identifying possible usage obstacles.

(Source: Bolton et al., 2018; Wong et al., 2020; Javaid et al., 2021; Dwivedi et al., 2021; Gohil and Thakker, 2021)

In conclusion, the research indicates that the use of “Blockchain” and “AI” technologies have the potential to enhance supply chain efficiency, transparency, and accountability in the UK retail sector. However, in order to completely realise the benefits of their adoption, organisations must face the challenges connected with their adoption and strive to overcome them.Top of Form

Future Research Scope

The rapid advancement of technology has had a major effect on many sectors, including supply chain management. With the advent of new technologies such as “Blockchain” and “Artificial Intelligence” (AI), companies have an enormous opportunity to better their supply chain processes. However, the successful application of these tools in supply chain management is still a work in progress. As technology advances, it is critical to investigate the ramifications of future inventions in the area of supply chain management. Researchers must weigh the advantages and disadvantages of using new technologies to improve supply chain transparency, productivity, and security. Researchers must also examine the viability of implementing these technologies, as well as the effect on current business strategies and possible barriers to adoption.

Alternative Research Methods

If the researchers had used primary study techniques, they could have obtained a more current and comprehensive knowledge of the topic. This would have resulted in the supply of up-to-date and precise data on the subject, assuring the study’s results’ veracity and dependability.

References

Chapter 1

• Amentae, T.K. and Gebresenbet, G., 2021. Digitalization and future agro-food supply chain management: A literature-based implications. Sustainability, 13(21), p.12181.
• Gohil, D. and Thakker, S.V., 2021. Blockchain-integrated technologies for solving supply chain challenges. Modern Supply Chain Research and Applications, 3(2), pp.78-97.
• Koh, L., Dolgui, A. and Sarkis, J., 2020. Blockchain in transport and logistics–paradigms and transitions. International Journal of Production Research, 58(7), pp.2054-2062.
• Kshetri, N., 2021. Blockchain and supply chain management. Elsevier.
• Martin?evi?, I., Sesar, V. and Žunac, A.G., 2021, October. Artificial Intelligence and Blockchain—New Challenges for Digital Supply Chain Management. In Proceedings of the 21st International Scientific Conference on Business Logistics in Modern Management, Osijek, Croatia (pp. 7-8).
• Paksoy, T., Kochan, C.G. and Ali, S.S. eds., 2020. Logistics 4.0: Digital transformation of supply chain management. CRC Press.
• Ray, P., Harsh, H.O., Daniel, A. and Ray, A., 2019. Incorporating block chain technology in food supply chain. International Journal of Management Studies, 6(1), p.5.
• Sinha, A., Bernardes, E., Calderon, R. and Wuest, T., 2021. Digital supply networks. London, UK: McGraw Hill-Ascent Audio.
• Tsolakis, N., Schumacher, R., Dora, M. and Kumar, M., 2022. Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?. Annals of Operations Research, pp.1-54.
• Zhang, H., 2021, September. Blockchain facilitates a resilient supply chain in steel manufacturing under covid-19. In 22nd European conference on knowledge management, ECKM 2021. Academic Conferences and Publishing International Limited (pp. 964-972).

Chapter 2

• Aday, S. and Aday, M.S., 2020. Impact of COVID-19 on the food supply chain. Food Quality and Safety, 4(4), pp.167-180.
• Allam, Z. and Dhunny, Z.A., 2019. On big data, artificial intelligence and smart cities. Cities, 89, pp.80-91.
• Alzoubi, H.M., Elrehail, H., Hanaysha, J.R., Al-Gasaymeh, A. and Al-Adaileh, R., 2022. The role of supply chain integration and agile practices in improving lead time during the COVID-19 crisis. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 13(1), pp.1-11.
• Angelis, J. and Da Silva, E.R., 2019. Blockchain adoption: A value driver perspective. Business Horizons, 62(3), pp.307-314.
• Atlam, H.F., Azad, M.A., Alzahrani, A.G. and Wills, G., 2020. A Review of Blockchain in Internet of Things and AI. Big Data and Cognitive Computing, 4(4), p.28.
• Attinasi, M.G., Balatti, M., Mancini, M. and Metelli, L., 2022. Supply chain disruptions and the effects on the global economy. Economic Bulletin Boxes, 8.
• Attkan, A. and Ranga, V., 2022. Cyber-physical security for IoT networks: a comprehensive review on traditional, blockchain and artificial intelligence based key-security. Complex & Intelligent Systems, 8(4), pp.3559-3591.
• AXSOM, T., 2022. 10 Trends Shaping the Future of Supply Chain Management. [online] Available at: https://www.fictiv.com/articles/10-trends-shaping-the-future-of-supply-chain-management [Accessed on: 18 March 2023]
• Azmi, I., Hamid, N.A., Hussin, M.N.M. and Ibrahim, N.I., 2017. Logistics and supply chain management: The importance of integration for business processes. Journal of emerging economies and Islamic Research, 5(4), pp.73-80.
• Benzaid, C. and Taleb, T., 2020. AI-driven zero touch network and service management in 5G and beyond: Challenges and research directions. Ieee Network, 34(2), pp.186-194.
• Berente, N., Gu, B., Recker, J. and Santhanam, R., 2019. Managing Ai. MIS Quarterly, pp.1-5.
• Blanco, C., Caro, F. and Corbett, C.J., 2016. The state of supply chain carbon footprinting: analysis of CDP disclosures by US firms. Journal of Cleaner Production, 135, pp.1189-1197.
• Chang, S.E., Chen, Y.C. and Lu, M.F., 2019. Supply chain re-engineering using blockchain technology: A case of smart contract based tracking process. Technological Forecasting and Social Change, 144, pp.1-11.
• Chang, V., Baudier, P., Zhang, H., Xu, Q., Zhang, J. and Arami, M., 2020. How Blockchain can impact financial services–The overview, challenges and recommendations from expert interviewees. Technological forecasting and social change, 158, p.120166.
• Dagher, G.G., Mohler, J., Milojkovic, M. and Marella, P.B., 2018. Ancile: Privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. Sustainable cities and society, 39, pp.283-297.
• Dash, R., McMurtrey, M., Rebman, C. and Kar, U.K., 2019. Application of artificial intelligence in automation of supply chain management. Journal of Strategic Innovation and Sustainability, 14(3), pp.43-53.
• Devarajan, Y., 2018. A study of robotic process automation use cases today for tomorrow’s business. International Journal of Computer Techniques, 5(6), pp.12-18.
• Duan, J., Zhang, C., Gong, Y., Brown, S. and Li, Z., 2020. A content-analysis based literature review in blockchain adoption within food supply chain. International journal of environmental research and public health, 17(5), p.1784.
• El Khatib, M., Al Mulla, A. and Al Ketbi, W., 2022. The Role of Blockchain in E-Governance and Decision-Making in Project and Program Management. Advances in Internet of Things, 12(3), pp.88-109.
• Frederico, G.F., 2021. From supply chain 4.0 to supply chain 5.0: Findings from a systematic literature review and research directions. Logistics, 5(3), p.49.
• Gosling, J., Jia, F., Gong, Y. and Brown, S., 2016. The role of supply chain leadership in the learning of sustainable practice: toward an integrated framework. Journal of Cleaner Production, 137, pp.1458-1469.
• Han, H., Shiwakoti, R.K., Jarvis, R., Mordi, C. and Botchie, D., 2023. Accounting and auditing with blockchain technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems, 48, p.100598.
• Hosseinnia Shavaki, F. and Ebrahimi Ghahnavieh, A., 2022. Applications of deep learning into supply chain management: a systematic literature review and a framework for future research. Artificial Intelligence Review, pp.1-43.
• Jun, M., 2018. Blockchain government-a next form of infrastructure for the twenty-first century. Journal of Open Innovation: Technology, Market, and Complexity, 4(1), p.7.
• Khan, A.A., Laghari, A.A., Gadekallu, T.R., Shaikh, Z.A., Javed, A.R., Rashid, M., Estrela, V.V. and Mikhaylov, A., 2022. A drone-based data management and optimization using metaheuristic algorithms and blockchain smart contracts in a secure fog environment. Computers and Electrical Engineering, 102, p.108234.
• Kiron, D. and Schrage, M., 2019. Strategy for and with AI. MIT Sloan Management Review, 60(4).
• Llopis-Albert, C., Rubio, F. and Valero, F., 2021. Impact of digital transformation on the automotive industry. Technological forecasting and social change, 162, p.120343.
• Maddikunta, P.K.R., Pham, Q.V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R. and Liyanage, M., 2022. Industry 5.0: A survey on enabling technologies and potential applications. Journal of Industrial Information Integration, 26, p.100257.
• Mäntymäki, M., Minkkinen, M., Birkstedt, T. and Viljanen, M., 2022. Defining organizational AI governance. AI and Ethics, 2(4), pp.603-609.
• Modgil, S., Singh, R.K. and Hannibal, C., 2022. Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, 33(4), pp.1246-1268.
• Moore, J., 2019. AI for not bad. Frontiers in Big Data, 2, p.32.
• Mou, X., 2019. Artificial intelligence: investment trends and selected industry uses. International Finance Corporation, 8.
• Mukhamedjanova, K.A., 2020. Concept of supply chain management. Journal of Critical Reviews, 7(2), pp.759-766.
• Nair, M.M. and Tyagi, A.K., 2021. Privacy: History, statistics, policy, laws, preservation and threat analysis. Journal of Information Assurance & Security, 16(1).
• Organisation for Economic Co-operation and Development, 2020. Food supply chains and COVID-19: impacts and policy lessons. OECD Publishing.
• Paul, J. and Criado, A.R., 2020. The art of writing literature review: What do we know and what do we need to know?. International Business Review, 29(4), p.101717.
• Pournader, M., Kach, A. and Talluri, S., 2020. A review of the existing and emerging topics in the supply chain risk management literature. Decision Sciences, 51(4), pp.867-919.
• Raj, A., Mukherjee, A.A., de Sousa Jabbour, A.B.L. and Srivastava, S.K., 2022. Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of business research, 142, pp.1125-1139.
• Ransbotham, S., Khodabandeh, S., Fehling, R., LaFountain, B. and Kiron, D., 2019. Winning with AI. MIT Sloan management review.
• Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A. and Albores, P., 2020. The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing. International Journal of Production Research, 58(15), pp.4610-4630.
• Salah, K., Rehman, M.H.U., Nizamuddin, N. and Al-Fuqaha, A., 2019. Blockchain for AI: Review and open research challenges. IEEE Access, 7, pp.10127-10149.
• Simonetto, M., Sgarbossa, F., Battini, D. and Govindan, K., 2022. Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda. International Journal of Production Economics, p.108582.
• Sweeney, E., 2022. The big challenges for supply chains in 2022. Logistics and Transport Focus, 24(2), pp.48-49.
• Thrall, J.H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K. and Brink, J., 2018. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), pp.504-508.
• Tien, N.H., Anh, D.B.H. and Thuc, T.D., 2019. Global supply chain and logistics management. Dehli: Academic Publications.
• Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P. and Fischl, M., 2021. Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, pp.502-517.
• Wagner, G., Lukyanenko, R. and Paré, G., 2022. Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), pp.209-226.
• Wang, S., Ouyang, L., Yuan, Y., Ni, X., Han, X. and Wang, F.Y., 2019. Blockchain-enabled smart contracts: architecture, applications, and future trends. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(11), pp.2266-2277.
• Wu, J. and Tran, N.K., 2018. Application of blockchain technology in sustainable energy systems: An overview. Sustainability, 10(9), p.3067.
• Wynstra, F., Suurmond, R. and Nullmeier, F., 2019. Purchasing and supply management as a multidisciplinary research field: Unity in diversity?. Journal of Purchasing and Supply Management, 25(5), p.100578.
• Yapo, A. and Weiss, J., 2018. Ethical implications of bias in machine learning.
• Yli-Huumo, J., Ko, D., Choi, S., Park, S. and Smolander, K., 2016. Where is current research on blockchain technology?—a systematic review. PloS one, 11(10), p.e0163477.

Chapter 3

• Austin, Z. and Sutton, J., 2014. Qualitative research: Getting started. The Canadian journal of hospital pharmacy, 67(6), p.436.
• Bell, E., Bryman, A. and Harley, B., 2022. Business research methods. Oxford university press.
• Cho, J.Y. and Lee, E.H., 2014. Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences. Qualitative report, 19(32).
• Dirnagl, U., 2020. Preregistration of exploratory research: Learning from the golden age of discovery. PLoS biology, 18(3), p.e3000690.
• Dunn, M.G., Rochlen, A.B. and O’Brien, K.M., 2013. Employee, mother, and partner: An exploratory investigation of working women with stay-at-home fathers. Journal of Career Development, 40(1), pp.3-22.
• Hair, J.F., Page, M. and Brunsveld, N., 2019. Essentials of business research methods. Routledge.
• Kelly, L.M. and Cordeiro, M., 2020. Three principles of pragmatism for research on organizational processes. Methodological innovations, 13(2), p.2059799120937242.
• Lodico, M.G., Spaulding, D.T. and Voegtle, K.H., 2010. Methods in educational research: From theory to practice. John Wiley & Sons.
• Neuendorf, K.A., 2017. The content analysis guidebook. sage.
• Pupovac, V. and Fanelli, D., 2015. Scientists admitting to plagiarism: a meta-analysis of surveys. Science and engineering ethics, 21, pp.1331-1352.
• Saunders, M., Lewis, P. and Thornhill, A., 2009. Research methods for business students. Pearson education.
• Tolley, E.E., Ulin, P.R., Mack, N., Robinson, E.T. and Succop, S.M., 2016. Qualitative methods in public health: a field guide for applied research. John Wiley & Sons.
• Veile, J.W., Schmidt, M.C., Mueller, J.M. and Voigt, K.I., 2021. Relationship follows technology! How Industry 4.0 reshapes future buyer-supplier relationships. Journal of Manufacturing Technology Management, 32(6), pp.1245-1266.
• Walther, J., Sochacka, N.W. and Kellam, N.N., 2013. Quality in interpretive engineering education research: Reflections on an example study. Journal of engineering education, 102(4), pp.626-659.
• Zalaghi, H. and Khazaei, M., 2016. The role of deductive and inductive reasoning in accounting research and standard setting. Asian Journal of Finance & Accounting, 8(1), pp.23-37.

Chapter 4

• Amentae, T.K. and Gebresenbet, G., 2021. Digitalization and future agro-food supply chain management: A literature-based implications. Sustainability, 13(21), p.12181.
• Bolesnikov, M., Popovi? Stija?i?, M., Keswani, A.B. and Brklja?, N., 2022. Perception of Innovative Usage of AI in Optimizing Customer Purchasing Experience within the Sustainable Fashion Industry. Sustainability, 14(16), p.10082
• Chowdhury, S., Rodriguez-Espindola, O., Dey, P. and Budhwar, P., 2022. Blockchain technology adoption for managing risks in operations and supply chain management: evidence from the UK. Annals of operations research, pp.1-36.”
• Gohil, D. and Thakker, S.V., 2021. Blockchain-integrated technologies for solving supply chain challenges. Modern Supply Chain Research and Applications, 3(2), pp.78-97
• Jabbar, S., Lloyd, H., Hammoudeh, M., Adebisi, B. and Raza, U., 2021. Blockchain-enabled supply chain: analysis, challenges, and future directions. Multimedia Systems, 27, pp.787-806.
• Khan, M.R. and Manzoor, A., 2021. Application and impact of new technologies in the supply chain management during COVID-19 pandemic: a systematic literature review. International Journal of Economics & Business Administration (IJEBA), 9(2), pp.277-292.
• Koh, L., Dolgui, A. and Sarkis, J., 2020. Blockchain in transport and logistics–paradigms and transitions. International Journal of Production Research, 58(7), pp.2054-2062
• Ray, P., Harsh, H.O., Daniel, A. and Ray, A., 2019. Incorporating block chain technology in food supply chain. International Journal of Management Studies, 6(1), p.5.
• Subramanian, N., Chaudhuri, A. and Kay?kc?, Y., 2020. Blockchain and supply chain logistics: evolutionary case studies. Springer Nature.
• Tsolakis, N., Schumacher, R., Dora, M. and Kumar, M., 2022. Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?. Annals of Operations Research, pp.1-54.
• Vendrell-Herrero, F., Bustinza, O.F., Parry, G. and Georgantzis, N., 2017. Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60, pp.69-81.
• Vyas, N., Beije, A. and Krishnamachari, B., 2019. Blockchain and the supply chain: concepts, strategies and practical applications. Kogan Page Publishers.

Chapter 5

• Bolton, R.N., McColl-Kennedy, J.R., Cheung, L., Gallan, A., Orsingher, C., Witell, L. and Zaki, M., 2018. Customer experience challenges: bringing together digital, physical and social realms. Journal of service management, 29(5), pp.776-808.
• Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A. and Galanos, V., 2021. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, p.101994.
• Gohil, D. and Thakker, S.V., 2021. Blockchain-integrated technologies for solving supply chain challenges. Modern Supply Chain Research and Applications, 3(2), pp.78-97.
• Javaid, M., Haleem, A., Singh, R.P., Khan, S. and Suman, R., 2021. Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain: Research and Applications, 2(4), p.100027.
• Wong, L.W., Leong, L.Y., Hew, J.J., Tan, G.W.H. and Ooi, K.B., 2020. Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, p.101997.