AI and Big Data Impact on Tesco’s Supply Chain Management In UK Food Retail Sector

Chapter 1: Introduction

1.1 Introduction

This chapter begins the process of looking at how disruptive AI and Big Data empowerment on the Tesco supply chain management in the UK retail.

The dissertation will focus on the analysis of chosen research areas and their goals with the hope of providing a comprehensive picture of the range of factors that define the industry including market trends, regulatory influences, technological advancements, competitive dynamics, consumer behaviour and more.

Following, the next part shows a roadmap for understanding the important impacts of technology on the Tesco supply chain management in the UK retail business with the logic from the research study carefully explained.

1.2 Research background

The applications of AI and Big Data technologies together have been pivotal in generating a change in the Tesco supply chain management in the UK’s retail industry in the past. For example, it can help to change customer purchasing behaviour and social media trends.

AI is expected to deliver a very positive economy in the UK for a total of £232 billion as of 2030.

According to McKinsey, it would have a significant impact on the food retail industry (Mckinsey, 2023).

More retailers are apt to rely on AI-led data to personalize consumer experience, increase store efficiency and tailor marketing campaigns that directly resonate with the client’s desires (Deloitte, 2023).

Figure 1.1: Impact of AI in the retail business in the UK

(Source: statista.com, 2019)

In this dynamic era, the rapidly growing technology, AI technologies help UK retailers to deploy AI technologies in consumer services like chatbots. This study has found that around 57% of retailers benefit from AI technologies such as stock management areas, warehousing, logistics and more.

This tool can improve the operational efficiency, and inventory management process, enhance supply chain management and more. However, these issues remain, such as the concern for data privacy and also the employee’s need to be transferred to another job with a high level of skill (PwC, 2023).

AI is critical from an industry leader’s perspective. It is perceived by 85% of retailers while on the other hand, it faces some obstacles such as the high cost, privacy and cybersecurity concerns (Capgemini, 2020). Also, Big Data is not only a must-have but a necessity for the understanding of the interests and behaviour patterns of consumers (Liu, Wang and Jia, 2020).

Around 62% of marketers claim, after the survey conducted by IBM, that the key factor in customer centricity is the analytics of Big Data (IBM, 2024).

The technique of Big Data has been well adopted by the retailer, Tesco, for optimization in supply chain use which consequently leads to a fall in the store inventory by 30% (Tesco 2024).

On the other hand, only 49% of retailers report having had issues with data integration from different sources showing that these aspects are no easy tasks.

Lastly, despite the array of advantages, the full reaping ability of UK businesses with AI and Big Data analytics depends on resolving the data protection, skills of the workforce & data integration issues.

Figure 1.2: Delivered a very positive economy through AI

(Source: PwC, 2023)

1.3 Problem statements

The first problem statement has emphasised that along with the increasing adoption of AI and Big Data within the UK retail industry, issues related to privacy, integration problems, and overall lack of transparency contribute negatively (Dwivedi et al. (2021),

Despite this clarification, the reductions of these risks through staff training and other HR related activities is still highly questionable.

On the other hand, this second problem statement explained that the UK retail sector has been undergoing a substantial transformation in terms of operational efficiency and customer customisation by the widespread adoption of AI and Big Data technology.

For example, Sainsbury’s has used AI-powered analytics to personalize customer promotions regarding customer shopping records and preferences.

It is also optimising its supply chain operations by forecasting demand and controlling inventory. Furthermore, this approach can enhance the customer purchasing experience and operational efficiency (Guha et al., 2021)

In contrast, the third problem statement has pointed out that AI and Big Data integration into the UK retail market is limited due to technology transfer issues and privacy infringements.

Despite these challenges, the financial benefits are important which show promising growth and efficiency improvements.

However, it provides massive economic advantages and the industry’s survey projections are motivational for retailers, investors, and technology developers.

Numerous researches have demonstrated that there exist more opinions that may not be universally accepted on the most capable techniques to address these particular issues (Haddaway et al., 2020).

1.4 Research aim and objectives

To evaluate the impact of AI and Big Data on the supply chain management practices of Tesco in the UK food retail sector, focusing on the challenges, strategies, and insights derived from their implementation.

Research objectives

• To analyse the current state of AI and Big Data utilization in Tesco’s supply chain management.
• To identify the key challenges Tesco faces in implementing AI and Big Data in its supply chain management.
• To evaluate the strategies Tesco employs to overcome the challenges associated with AI and Big Data implementation.
• To assess the overall impact of AI and Big Data on Tesco’s supply chain efficiency and performance.

1.5 Research questions

• What is the current state of AI and Big Data utilization in Tesco’s supply chain management?
• What are the key challenges Tesco faces in implementing AI and Big Data in its supply chain management?
• How to evaluate the current strategies Tesco employs to overcome the challenges associated with AI and Big Data implementation?
• How to assess the overall impact of AI and Big Data on Tesco’s supply chain efficiency and performance?

1.6 Research rationale

The UK retail market has several problems to do with the AI adoption and Big Data at the moment such as transparency, ethical hazards, insecurity issues of this technology, integration problems, as well as privacy, among others.

Privacy seems to be a serious problem, aggravated by companies which underperform in several fields of their human resource strategies, especially with the employees’ skill development and training. This variety causes the complexity of operations and also takes into account privacy threats.

As highlighted by researchers, the subject of the gravity of these issues needs no emphasizing anymore but there is also a dearth of knowledge on how retail sectors deal with these challenges.

Hence, as the knowledge gap still exists, it is necessary to continue the research and provide data-based evidence of the issues that occur between AI, Big Data, and food retail.

The purpose of this work is to explain how brick-and-mortar businesses face unknown challenges resulting from such technologies and the apt theories since they play a crucial role in shaping their strategies.

In this scenario, research plays a pivotal role as the students have to bring the knowledge that they have gained from the theory classes and combine it with the insights and practices on how to manage risks and use AI and Big Data for the retail industry. 

1.7 Research structure

This study can be considerably enriched by integrating of interpretivism as a research philosophy statistical data and inductive as well as a secondary qualitative research design. 

Leading to the rapidly growing influence of artificial intelligence (AI) and Big Data on the food industry, along with the ability to discover and trace patterns and topics that exist in the data, an inductive method provides an opportunity to explore. 

For the contextual aspects as well as subjective comprehension are of great importance and this facilitates a more nuanced perception regarding the way things are done in the organizations, as well as the way we adapt to technological changes. 

Through learning from the body of current literature, the secondary qualitative research design will help to reveal fine elements and build a solid foundation for theory building and practical tactics for food demand sectors confronting difficulties due to AI and Big Data.

Figure 1.3: Research structure

(Source: By self-created)

1.8 Methodology

The researcher will use a qualitative research method to collect data regarding this research topic. This qualitative research provides insight into the impact of AI and Big Data on Tesco’s business by enlisting case studies as the research method.

This process can help the researcher to collect and find the contemporary challenges which have been raising the universality of comprehension.

On the other hand, this research method can help the researcher find comparative methods that can allow a broader approach to the general challenges of integrating these technologies into the food retail market.

1.9 Summary

A description of the disruptive effects of AI and Big Data throughout UK retail is given in this chapter. It sets the basis for a thorough investigation of the research field by highlighting the obstacles, tactics, and insights guiding the sector.

Chapter 2: Literature Review

2.1 Introduction

The Chapter demonstrates the way how the implementation of artificial intelligence (AI) and Big Data can determine the future of the food retail sector in the UK.

Here, complex issues that are created as a result of technological innovations, how they give edge to the companies, improve process efficiency, and redefine the market dynamics will be examined.

It focuses on the way the companies use data and AI to create more tailored marketing strategies that also leave prospects with a desire to purchase more in the future.

Moreover, the report focuses on the problems faced by food retailers in the implementation of these technologies and proposes workable solutions to these issues.

Ethical issues, such as data protection and regulatory compliance, related to the stream of the time-changing industry are essential to the examination.

2.2 Key literature themes

Role of AI and Big Data in Food Retail Transformation

AI and data analytics technologies are disrupting the retail food industry in the UK and influencing how people shop and also how business operation is conducted as retailers get insights from these technologies. Firstly, personalized suggestions that are AI-driven serve the purpose of increasing consumer engagement and revenue.

The report from McKinsey states that the tailored recommendations based on the machine learning algorithms are a key contributor to 35% sales of Amazon. As cited by Misra et al. (2020), British retailer Tesco, in its Clubcard loyalty program, uses AI to identify behavioural patterns and then provides personalized suggestions.

Moreover, it is Big Data analytics that can transform demand forecasts. Retailing on analytics for demand forecasting lists their supply chain efficiency about 75% more, findings reveal from the research by Forbes Insights.

Figure 2.1: How-AI Transforming-the-Future-of-the-Industry

(Source: Agarwal et al. 2022)

Sainsbury’s has been able to achieve that by combining AI algorithms to be able to assess past sales data and outliers like weather conditions reducing out-of-stock situations by 30% and then maximizing inventory turnover rates. The functioning efficiency of these systems is enhanced by AI-optimized supply chain management.

The AI and Big Data make a huge impact on the Tesco Supply chain. It helps data-driven warehouse systems, Ocadopromises an additional 15% in picking productivity.

Finally, Ocado enhances consumer satisfaction in many ways, including minimization of operations and cancellations, decreasing delivery times, and automating pick procedures.

These, while difficult to implement, are not all impossible to accomplish. First of all, privacy issues and regulatory compliance may act as the most important challenges.

The PWC report has stated that 85% of UK consumers were concerned about safeguarding their personal information (Pwc, 2024).

However, along with considerable infrastructure and manpower expenditure for the deployment of AI and Big Data platforms, there would be a need for integration.

Deloitte research states half of UK businesses are experiencing difficulties finding sufficient numbers of data scientists who are proficient in Big Data technologies.

The role of AI, in effect, has changed how the in-store experience of food retail was perceived and therefore transformed completely the traditional retail operations with an increased customer engagement level.

Among many factors, putting smart shelf displays with personalized recommendations into effect is a notable example.

Scientific data shows that smart AI screen displays, fully resourced with sensors and surveillance cameras, can detect client behaviour accurately in real-time to deliver customized items.

As per the statement of Misra et al. (2020), the global market for smart shelves will rise above 25% CAGR in the period between 2021 and 2026, showing the accelerated use of AI-related resolutions in shops.

Besides, retailers are experiencing that AI-powered chatbots have profoundly increased the quality of service to their customers in a physical store setup.

As stated by Montgomery et al. (2019), it is estimated that employing chatbots in business would help businesses save over $8 billion each year by 2022 alone.

In the food retail sector, AI chatbots make it possible to deliver much faster counsel and tailored advice to consumers, which in effect better the in-store experience.

For instance, Whole Foods supermarket applies AI technology in its chatbots to answer consumers’ queries, provide product information and offer recipe suggestions which enable the company to engage customers promptly and create customer satisfaction.

Another game-changing AI application in food retailing is systems that do not require cashiers to make final purchases.

Adobe Analytics-based research indicates that 28% of customers want to have cashierless checkouts (Agarwal et al. 2022).

A new trend in retailing is epitomized by Amazon Go stores where AI-powered technologies are employed to follow customers’ choices without a cashier and a checkout process where customers can pay by themselves.

Using such an original system the customer can save time during shopping and add comfort with it.

Customer Insights and Retention

Concerning the global food retail industry, AI and Big Data are playing a crucial role in retaining customers through leveraging data-driven insights to enhance customer experience, loyalty programs and precise marketing campaigns focusing on the right target audience.

A customer experience which is tailored to the individual with the help of AI and Big Data leads to better retention rates of customers. As mentioned by Rejeb et al. (2022), around 91% of consumers from brands will buy options that are relevant to their needs and are more likely to take appropriate offers, which has been revealed by Accenture.

The retail business related to food such as Walmart and Kroger use AI algorithms for analyzing consumer behavior and also showing the appropriate product recommendation for each customer.

This increases the retention percentage and improves the satisfaction level. Besides, the utilization of Big Data analytics underlines the role of loyalty programs and simplifies the loyalty process.

Research published by Sharma et al. (2021) indicates that a 5% increase in customer retention may have some influence worth a 25–95% profit increment.

AI is used by retailers like Tesco and Waitrose to check client buying patterns and adjust loyalty awards to increase customer engagement and it gives rise to customer retention as well.

Targeted marketing tactics build customer loyalty that arises mainly out of AI-generated information. Approximately 15-20% uplift in marketing ROI is seen in those firms that are using AI in their marketing effort (Harkness et al. 2023).

Local food sellers can create targeted marketing techniques that bond with the audience and get more conversions and frequent purchases using customer behaviour and choices on their information.

From the context of the UK grocery business over the last two years, there has been significant growth in sampling customers, which is due to Big Data analysis applications.

According to the industry estimates, Tesco and Sainsbury’s large food retailers have up to a 10-25% increase in consumer engagement indicators like the average order value and frequency of repeat purchases.

AI-based personalization of customers by dividing them into various categories matching with their tastes has changed the marketing pattern of the retail sector.

Through the scientific results, AI-driven customer segmentation assists retailers in detecting prominent consumer segments possessing similar interests, conducts, and demographics.

As stated by Ascarza et al. (2018), Machine learning algorithms are advanced tools which can process a huge amount of customer data into customer segments that have a practical meaning, such as purchase history, browsing patterns, and social media engagement.

It is exactly those customized campaigns when retailers can promote items based on every particular customer’s preference and need.

The studies have however shown that when customized promo deals are based on segmentation of customers by AI, they have produced high conversion rates and enhanced customer engagement.

Another point is its AI-driven customer segmentation mechanism ensures real tailored product suggestions.

As mentioned by Lamrhari et al. (2022), It becomes easy for retailers to know exactly what the customers want as well as to suggest goods and services that are meant for that group.

This in turn benefits customers and retailers with an improved shopping experience and increased sales.

On the other hand, Food retail has recently started to experience the need for integrating AI into customer journeys while finding the optimal Big Data usage for better customer satisfaction and business growth.

A major tactic followed by signifying culinary retailers is agglomerating customer data to locate the pain points during the shopper experience, both online and offline.

AI algorithms are capable of handling significant quantities of data, which allows retailers to identify problematic areas in customers’ experience and to come up with solutions to these areas.

A study done by Larsson and Broström (2020) on the applicability and effect of AI to customers shows revenue of companies which use AI to give a personalized customer experience goes up by 6-10%.

AI- enabled technologies to allow retailers to give footprint-customized propositions as well as make purchasing easier while predicting clients’ needs from the available data.

For example, Tesco and Sainsbury’s supermarkets utilize AI to analyze shoppers’ behaviour and preferences through which the physical store layout and online interfaces are optimized, and ultimately, customer experience facilitated.

In addition, AI-based replies are of great help to customers in real-time, with quick responses to their particular challenges. As per Deloitte research, 53% of consumers demand serial replies from businesses (Deloitte, 2024).

These AI-driven chatbots and virtual assistants allow food retailers to touch the customers the moment they need help and guide them through personalized shopping, giving the customers a better shopping experience.

Comparative Analysis of Tesco’s Success Strategies

Tesco’s marketplace management inside the UK grocery sector can be attributed to numerous key techniques that set it other from its competition.

One exceptional advantage is Tesco’s significant shop network, which incorporates over 3,400 locations across the UK (Yaiprasert, and Hidayanto, 2024). It surpasses the reach of opponents like Sainsbury’s, which operates around 1,400 shops.

This vast geographical presence ensures that Tesco is without problems on hand to a larger patron base (Shi, et al. 2020). Additionally, Tesco’s loyalty application, with over 19 million energetic customers, presents massive records on purchaser preferences and shopping behaviour.

However, Tesco optimized stock for competitive aspects over friends which includes Asda, which no longer has a comparable loyalty scheme.

For example, Clubcard statistics became instrumental in Tesco’s hit “Aldi Price Match” campaign, which provided price discounts on products in comparison to Aldi, directly addressing the aggressive threat from the discount retailer (Upadhyay, 2020).

Another area where Tesco excels is its digital innovation. Tesco’s online grocery income reached £8.1 billion in 2023, showing a 6% growth from the preceding year, as compared to Sainsbury’s £7.5 billion.

Tesco’s funding in a seamless omnichannel revel, such as equal-day shipping and a user-pleasant app, complements consumer pride and comfort.

Future Trends and Innovations

The development of AI alongside the Big Data era with their innovations is leading to the transformation of the food retail industry and making consumers happy, increasing the efficiency of operations and making business processes less time-consuming by using state-of-the-art technologies.

One application is the use of predictive analytics to predict demand and shopper attitude.

Meanwhile, the market projection for the retail predictive analytics market, which implies a CAGR of more than 20% between 2021 and 2026, is one of the positive indicators (Johnson et al. 2021).

Food retail operations use predictive analytics to evaluate previously gathered sales data, information about weather trends, and social media sentiment to build accurate sales forecasts and control timely inventory management that helps to avoid spoilage and increase profits.

However, with the help of improved AI, it helps to reduce the disruptor of the supply chain. The world market for warehouses is forecast to be estimated at more than $20 billion in 2027 according to Global Market Insights which has researched the matter (Arora and Sharma, 2023).

However, AI-driven robots are now being used by retailers like Ocado and Amazon to automate picking, packing and inventory management which now uses the previous traditional model’s workforce.

This way, it either leads a to reduction in operating costs as well as results in speedy order processing.

The combination of IOT devices is also assisting in changing how food retailers deal with things, as actual-time data collection and surveillance are happening. Statista asserts that not less than 30 billion gadgets will be joined to the IoT by 2025 (Rejeb et al. 2021).

Using sensors is the way for food retail systems to optimize supply chain logistics, track the freshness of goods, and keep the right storage conditions.

For example, Walmart uses the Internet of Things with temperature sensors that sense whether there is a violation of food quality and safety in the supply chain.

The intersection between AI and Big Data results in the emergence of innovative approaches in the field of food retailing which proves that this industry is constantly changing.

Ciccullo et al. (2022) have stated that retailers will be able to make adjustments depending on consumer behaviour and succeed in optimizing the levels of item inventory by leveraging predictive analytics.

AI-powered robots facilitate logistics efficiency and make logistics processing faster so that the orders of customers will be processed and delivered soon. IoT devices have brought to life real-time collection and monitoring of data that guarantees supply chain transparency and product quality due to the instant supply.

Personalized dietary recommendations and audio-activated shopping represent innovative utilization of AI in the food industry, which provides empowering and specialized experiences to end-users.

Machine learning algorithms, which are embedded in AI-powered recipes, are capable of analyzing individual dietary requirements and instead that they are catering to the available ingredients and also user preferences.

The evidence given in recent findings that personalized recipe recommendations significantly impact consumer behaviour and the engagement level displays this. 62% of consumers are going to rear a retailer that offers personalized recommendations.

For example, food delivery services such as HelloFresh and Blue Apron use AI to develop tailored meal plans that match customers’ preferences, ultimately leading to enhanced loyalty and satisfaction.

In addition, AI offers voice recognition shopping that permits customers to converse with food stores and make orders using voice commands.

The study from Boon and Edler (2018), has shown that USD 40 billion was achieved through voice shopping by 2022. AI-serviced artificial virtual assistants like Amazon’s Alexa and Google Assistant advance users’ capability to integrate their products into their shopping lists, order goods and obtain personalized recommendations through their voice interactions.

This innovation does the job of shortening the process of shopping and making it easier, in as much as it removes the irritations and raises the general level of availability for the consumers. Additionally, AI-powered voice interactions not only apply in in-store displays but also on digital and social platforms, greatly reinforcing engagement and customer support.

The research of Lee and Trimi (2018), indicates that vocal displays explicitly attract consumers and, therefore, help keep them in stores longer. For example, the AI bots in grocery stores can ask for product alternatives, nutrition labels for products and cooking tips using Voice Commands.

This sense of personal touch allows the clients to be part of the whole experience and, in the end, to get the desired result, which makes them satisfied and enhances their loyalty. While they offer benefits, there are also challenges and issues connected with personalized recipe recommendations and voice-enabled grocery purchases in grocery shops.

Even with privacy concerns associated with data collection and use, more people are increasingly comfortable with online shopping because of the convenience it offers. This research by Deloitte states that 47% of market respondents consider protecting data privacy while using personalized recommendations as their major worry.

Challenges of Implementing AI and Big Data

Concerning the application of AI and Big Data technology in the processes of food retail companies, obstacles can be numerous and create an impediment to further progress. To start with the issue of privacy and data security, there are a lot of challenges surrounding it. Data protection and privacy appear to be the leading problem for 39% of the respondents, according to a Cisco study.

As opined by Misra et al. (2020), the implementation of laws like GDPR in the EU requires retailers to ensure that data is protected as well as compliant with the law and that leads to the enhancement of cybersecurity operations and protection of consumers’ privacy.

Another reason for the possibility of huge expenditure in infrastructure is that with the introduction of AI and Big Data, some infrastructure money should be spent. According to the research conducted by Deloitte, 58% of firms face more than the normal cost of implementing new solutions. This encompasses cloud usage, hardware, software, and data storage that makes it possible to host AI algorithms and data-intensive analysis.

Lastly, the education and training of workers are full of hardship and need to be made. A survey by Gartner shows that 56% of businesses state that the lack of skilled human resources is a limitation to AI implementation, while others cite mysterious fears regarding the technology.

As argued by Bhat, S.A. and Huang (2021), Data science capability either through hiring new data scientists or training available staff in these areas of Big Data and AI technology is the only way to make sure efficient use of these advanced tools. Therefore, either financial and or human resources needed for integrating AI and Big Data analytic technologies with current protocols might lead to a financial burden.

According to Kumar et al. (2021), one in two business leaders declare there is a challenge to integrate their classical systems. Similarly, the seamless connecting of new tech functionalities with the existing network layers generates variability in the data flows, disintegration of information and shareability issues.

These difficulties give us some hints as to why it is important to put the preparation, funding, and readiness on the organizational level, such as when deciding to implement AI and Big Data in the food retail industry. Stakeholders support, ensuring consistent strategy alignment, continuous investment in talent training, infrastructure, privacy, and smooth integration processes.

2.3 Literature gap

Along with the AI and Big Data impact on the food retail sector some knowledge clusters have still emerged from the previous literature.

One of the drawbacks is that AI-powered initiatives have not proved to be sustainable over a long period in the food retail industry, despite the apparent advantages in the short run.

Some pre-existing research targets the immediate effects of AI on enhancing customer experience, as well as operational efficiency, but there are scarcely any such long-term studies that feature a succession of outcomes.

The next step should be to conduct extensive research that reveals how AI influences business performance over the long run, customer satisfaction, and stakeholder competitiveness.

Another knowledge that could be filled is the ethics of AI and Big Data apps in food retail.

As cited by Lu (2019), Only a few ethical issues have been considered and investigated in the previous research works.

There needs to be more in-depth studies that also explore data privacy ethical dilemmas, technological biases and consumer autonomy issues.

Recognizing the ethical concerns and inducing guidelines on the rational use of AI in food retailing helps to establish trust and is a good way to guarantee the ethical application of technology.

Moreover, it has also required that research papers in the same area be investigated to find solutions for AI applications in small and medium-sized food retailers. As cited by Sheng et al. (2021), Current studies are mostly geared towards big-scale enterprises and fail to uncover the pain points and prospects of SMEs in the implementation and adaptation of such tools.

Researching AI options that suit the precise needs and constraints of small businesses to reduce the gap and provide practical recommendations for success and innovation in the sector may be a scalable solution. Importantly, there is an issue in recognizing a type of employment affected by the expansion in artificial intelligence and Big Data within the food industry.

As mentioned by Hu et al. (2019), While some researchers suppose that AI adoption may cause some job losses attributed to automation, the strong evidence that AI technologies reshape the food retail sector and the skills required are absent. The impact of AI adoption on the social and economic sector needs to be examined in-depth in future studies. For instance, this includes workforce reskilling, job creation, and how employers will respond to these technological changes.

2.4 Theoretical framework

TAM (technology acceptance model)

The TAM (technology acceptance model) is one of the most important theories that can be tied to the research on the potential impact of AI and Big Data on the UK grocery industry. TAM represents a theory that was introduced by Fred Davis in the early 1980s and was designed for studying and predicting the level of technology user acceptance and integration within an organization.

When these technologies are applied in the food retailing industry by TAM, a detailed approach is used and all variables that positively or negatively affect both how customers use them and how retailers use them are observed (Kamal et al. 2020), The model conceives usefulness (perceived value) and usability (perceived ease of use) as critical intertwined elements that affect an individual’s intention to implement and adapt to technology.

Concerning perceived usefulness, food businesses should be cognizant of the fact that AI and Big Data analytics may improve customer insights, increase operational efficiency, and therefore boost profitability through supply chain optimization and marketing with targeted consumers.

For real-life applications, user perception of ease refers to how convenient the available tools are to use and how they interact with existing systems concerning structural complexity.

This study will determine the behavioural and attitudinal elements modifying the incorporation of AI and Big Data into food retail using TAM. Having an opportunity to grasp this theoretical assumption provides useful lessons for succeeding adoption policies and overcoming implementation challenges by pinpointing the reason some store reception is welcomed and others opposed.

2.5 Conceptual framework

Figure 2.2: Conceptual framework

(Source: Self-created)

2.6 Summary

To conclude, this chapter has shown how AI and Big Data are changing the UK food retail sector.

Discussion is going about key themes for example supply chain optimization, demand forecasting and personalized suggestions.

The themes may be seen as a way of enhancing customer experience and operational efficiency. However, IoT and predictive analytics are some of the trends that revealed themselves out of the enhanced statistics.

These areas, including personnel training, costs of infrastructure development, data protection and system configuration have been put through Simulation.

Despite the above-mentioned disadvantages, AI and Big Data are also riddled with many benefits for the food retail industry. Essential for effectively exploiting these opportunities is strategic planning as well as foresight.

Chapter 3: Research Methodology

3.1 Introduction

The approach used to look into how AI and Big Data are affecting the UK food retail industry is described in this chapter. We will talk about the research philosophy, approach, strategy, and method selection. There will also be information on Big Data gathering, analysis techniques, and sample selection. To guarantee the validity and trustworthiness of the research findings, ethical issues and study constraints will also be discussed.

3.2 Research philosophy

The research process through which we explored the impact of AI and Big Data in the UK food retail sector. It has simplified the complex relationships and motives which are represented in this scenario. Researchers have been able to examine the varying subjective experiences, perceptions, and social realities of important stakeholders, such as consumers, retailers, and industry experts, by utilizing interpretivism. As cited by Braun (2018), this technique brings context and understanding that knowledge is not only made by direct observation but also by a human being’s mind.

The adoption of the interpretivists’ approach by researchers is an indication of the utilization of a secondary qualitative method, which brings into light a thorough Big Data analysis and interpretation. According to Sharma et al. (2021), with the help of existing literature, including academic papers, industry reports, case studies researchers have a lot of knowledge in the area of how these technologies are conceived, understood and used by the food retail industry.

Application of the secondary qualitative data collection methods has shown an increase in getting a variety of views and discovering complex factors that influence both the adoption and the outcome of the said technology. Besides that, interpretivism is more aligned with the purpose of the present study which aims to reveal different dimensions of the impact of Big Data and AI (Agarwal, et al. 2022). It has turned out to be much easier now to discover the motivation behind the decisions, the meanings those decisions carry and the societal dynamics that drive retail food sector management and the results of their operations.

3.3 Research approach

The research approach used is predominantly inductive, focusing on developing insights and theories based on the analysis of secondary qualitative data. Inductive reasoning helps identify patterns and themes from specific instances of AI and Big Data application in Tesco’s supply chain management, allowing for a deeper understanding of these technologies’ impacts.

As stated by Rodríguez et al. (2022), Through the use of inductive reasoning methods scientists have been able to draw general conclusions and notice patterns from specific instances from the use of AI and Big Data of huge volumes in food retail (Guha, et al. 2021). This approach has played a major role in advancing generation of novel concepts and theories in the light of observations, questionnaires and interviews.

As cited by Kerins et al. (2020), Researchers can use an inductive approach to get to its roots and also make discoveries and generate themes that resemble the real-world events in their field. Due to the research work in the area of better understanding the outcome of AI and Big Data related technologies scholars refer to the process of discovering common patterns, themes and interconnections.

This proves that statistical Big Data are right and guarantees that the findings published are justified by the facts arising from the base. According to Lagorio and Pinto (2021), Concerning this position, reflexivity and adaptability have been fostered through the research approach being inductive.

Scholars have been involved in regular processes of acquisition, analysis, and interpretation, with each discovery leading to a more precise understanding (Dwivedi, et al. 2021). Moreover, they have succeeded in gaining the knowledge which has embodied its contextuality. Thereafter, they have been able to comprehend the diversity of experiences that take place in the food retail industry.

3.4 Research strategy/methodology

This research employs a secondary qualitative method. The qualitative data will be gathered from existing literature, including academic papers, industry reports and case studies. This method allows for a comprehensive understanding of the impact of AI and Big Data on Tesco’s supply chain management by analyzing detailed descriptions and contextual insights from various sources (Bhat, and Huang, 2021). These data provide insights into trends in the market, rates of diffusion of technology, and financial performance metrics allowing for statistical analysis of the influence of AI and Big Data on operational efficiency and market dynamics.

This would include detailed case studies on the implementation of AI technologies in supply chain management and consumer analytics, providing an in-depth look at actual applications and challenges (Brennen, 2021).

By employing secondary method, one can make a robust analysis of the measurable evidence arising from qualitative data and the contextual and interpretive insights. In this respect, this approach would ensure that a comprehensive understanding of how AI and Big Data are changing the UK food retail sector is attained, together with empirical trends showing strategic impacts (Hancock, et al. 2021). This methodology will effectively capture both the measurable outcomes and strategic nuances of AI and Big Data in offering a comprehensive view of their influence on the industry.

3.5 Big Data collection methods

This look at the impacts of AI and Big Data on the United Kingdom food retail enterprise had acquired top assistance from the utility at the secondary records collection approach. It can be suitable to know how the impact of AI and massive information in the UK meals retail region. The study utilizes secondary qualitative data collection methods. Data sources include academic journals, industry reports, case studies, and expert interviews published in business magazines and journals.

These sources provide detailed information on the implementation and impact of AI and Big Data on Tesco’s supply chain management. For instance, case studies on AI applications in supply chain optimization and expert interviews offer in-depth insights into the challenges and strategies employed by Tesco (Cavallo, et al. 2020). This consists of facts from marketplace research corporations and monetary statements from leading shops, as well as industry guides.

For instance, monetary reviews from organizations like Tesco and Sainsbury’s consist of facts on how AI and huge records affect operational efficiencies and sales overall performance (da Costa Peres, et al. 2022). Industry reports from businesses inclusive of Nielsen and Euromonitor encompass statistical information associated with marketplace tendencies, client conduct, and adoption charges of the era.

This is complemented by qualitative secondary Big Data, which presents contextual perception and a detailed description of ways AI and big statistics are carried out in the region. Case research, white enterprise papers, and expert interviews posted in journals and business magazines can contribute to this (Costa Peres, et al. 2020). Thus, this method is able to look at the strengths of each information kind and offer extra detailed information on ways AI and massive information make an effect (Burgos and Ivanov 2021).

3.6 Big Data analysis methods

The data will be analyzed using conceptual analysis under content analysis. This involves:

Coding: Identifying key concepts and assigning codes to them.

Categorization: Grouping related codes into categories to identify patterns and themes.

Pattern Analysis: Analyzing patterns and relationships within the data to understand the impact of AI and Big Data on Tesco’s supply chain management.

Interpretation: Interpreting the findings in relation to the research questions and objectives.

For qualitative Big Data analysis the thematic analysis process had been adopted. Initially, conceptions, ideas, and categories are perceived in the secondary literature source reviews of researchers through the systemic process of Big Data coding.

Big Data collected via the codes is then scrutinized by our team to search for landmark ideas which seem to be popular in the context of the other secondary studies.

This includes the process of arranging associated terms and discovering the links among various ideas as well as the location of the points of converging information.

Over time, the issues that were identified have been studied by researchers, and they have explored this by the research vision and agenda.

Scientists have drawn well-known and basic consequences related to the humanization of nature.

3.7 Ethical considerations

Ethical considerations are paramount in this research. The study ensures confidentiality and data security by anonymizing personal information and obtaining informed consent where necessary.

Transparency in publishing results and disclosing any potential conflicts of interest is maintained to uphold ethical integrity (Ciccullo, et al. 2022).

To preserve ethical reliability throughout the process of research, transparency was maintained when it came to publishing results and revealing any possible conflicts of interest.

3.8 Limitations

The study has several limitations. It focuses primarily on the UK food retail sector, specifically Tesco, which may limit the generalizability of the findings to other regions or sectors. Additionally, reliance on secondary data may introduce bias or overlook recent technological advancements.

Efforts will be made to mitigate these limitations by triangulating data from multiple sources and continually updating the literature review to include the most recent developments (Burgos, and Ivanov, 2021).

Chapter 4: Data Analysis and Findings

4.1 Introduction

This chapter presents the in-depth analysis and resultant findings from the secondary qualitative data relating to the impact of AI and Big Data on Tesco’s supply chain management.

Conceptual content analysis is employed for this analysis since it provides a structured way to slice and interpret complex data in an orderly fashion.

This will be done through establishing the use, challenges, strategies, and overall impact that AI and Big Data technologies have on Tesco’s supply chain. The approach sets up relevant research objectives and questions for the study.

First, key concepts of AI, Big Data, and supply chain management are identified and coded.

This coding is supportive in breaking down the information into further chunks so that it becomes convenient to find out any trend and infer meaningful insights from those trends (Braun, 2020).

These concepts are then grouped under more general categories like AI and Big Data Utilization, Challenges, and Strategies and Solutions.

Now, this categorization is more of a question about how data will be systematically organized for further analysis.

Subsequently, in-depth pattern analysis is done to unearth common themes and trends in the data. Such analyses are important in telling the big stories and how these connect with the research questions (Bhat and Huang, 2021).

Under this close scrutiny of frequency and interlinkages from the many codes, some important patterns crop up indicative of the status of the AI and Big Data utilizations at present, key challenges their owners or users are facing, and strategies they are using to keep such challenges at bay.

Finally, the interpretation of these patterns links back to the research questions and the Technology Acceptance Model.

The interpretation not only offers answers to research questions but also situates findings within a clear theoretical framework, therefore adding rigor and depth to analyses.

This chapter will, therefore, be concluded based on a firm understanding of the interplay of AI and Big Data on Tesco’s supply chain management evidenced by detailed qualitative analysis and theory.

4.2 Coding

4.2.1 Identification of Key Concepts

First, in the conceptual analysis, it is necessary to detect the main emerging concepts from the secondary data sources.

Those concepts represent the essence that brings clarity regarding how AI and Big Data empower Tesco in managing its supply chain. The primary concepts identified from the literature and the secondary data sources are as follows:

PA: This is the use of AI in predicting future trends and behaviours, a very key cornerstone of demand forecasting and inventory management. Predictive analytics helps improvise informed decision-making processes based on historical data and trends, hence largely improving the preciseness in varied supply chain processes (Braun, 2020).

IM: AI and Big Data Applications—Reconcile stock levels to levels that forestall wastage but ensure availability of products. Effective inventory management helps Tesco meet customer demand by avoiding overstocking of products in the warehouse, hence cutting down on costs and reducing wastage (Brennan, 2021).

LO: Logistics Optimization—using AI for route optimization, consequent reduction in delivery times, and improved supply chain efficiency. Optimized logistics will ensure on-time deliveries with cost savings and improved customer satisfaction.

Demand Forecasting (DF): A method of using big data to predict consumer demand and then adjust supply chain operations accordingly. It helps a business to plan its inventory and logistics effectively, hence avoiding both stockouts and overstock situations (Agarwal, Gupta and Kraut, 2021).

SCE: This will provide the extent to which AI and big data improve overall supply chain performance. Improvement in efficiency means better resource use, reduced costs of operation, and enhanced performance of operations.

Data Integration: It’s about putting different sources of data into one view to aid decision-making. With proper integration of data, Tesco will have an all-inclusive view of the supply chain, hence better coordination and decision-making (Burgos and Ivanov, 2021).

AIC refers to challenges in the adoption of AI technologies; some of the problems related to technical, organizational, and cultural barriers. The challenges must be understood to devise strategies for overcoming them.

BDUC simply means Big Data Utilization Challenges, which touch on data quality, data governance, and data volume. Meeting these challenges ensures that the data used in breaking a decision is reliable and actionable.

Strategic Solutions (SS): Approaches and strategies taken by Tesco regarding AI and Big Data to defeat the challenges. These are imperative for successful implementation and getting the most benefits out of these technologies.

4.2.2 Coding Process

Following the stage of identification of key concepts, the concepts have to be assigned codes. The stage involves a structured review of secondary data where appropriate sections are tagged using relevant codes. A detailed breakdown of the entire coding process is provided as follows:

PA: This includes those sections that discuss the uses of AI in forecasting trends, customer behaviour, and market conditions. It also comprises references to specific AI tools or techniques used to achieve predictive purposes. For example, algorithms and machine learning models mentioned as deployed toward predicting demand spikes and seasonal variations would fall under this code (Varriale et al., 2021).

Inventory Management: These are data related to AI applications in the management of stock levels to reduce instances of overstock and stock-outs and improve inventory turnover rates. This includes discussions concerning how AI-driven inventory systems track real-time data to maintain optimal stock levels, reducing wastage.

Logistics Optimization (LO): Information on AI-driven route planning, delivery scheduling, and transportation management falls under the LO code. Examples include the use of AI to optimize delivery routes, minimizing fuel consumption, and ensuring timely deliveries

DF Demand Forecasting: Any mention of AI or Big Data techniques that make use of customer demand forecasting, followed by the adjustment of supply chain activities in response to these projections, is coded as DF. It ranges from the use of historical sales data to market trends and consumer behaviour analysis for projecting future demand with accuracy (Dey et al., 2023).

SCE stands for Supply Chain Efficiency. Discussions on how AI and Big Data enhance overall supply chain performance, reducing costs while improving service levels, are tagged with SCE. This could involve case studies or reports of metrics before and after the implementation of such technologies.

Data Integration (DI): All sections that deal with the integration of different sources of data, data warehousing, and creation of a unified view of data are coded as DI.

AIC: The challenges in implementing AI technologies, like change resistance, lack of technical expertise, and integration issues—are recognized as AIC. Discussion on barriers in detail that Tesco has faced to infuse AI into their supply chain processes is covered in the sections.

Big Data Utilization Challenges (BDUC): Issues related to handling large volumes of data, ensuring data quality, and establishing effective data governance frameworks are coded as

Challenges to BDUC include data accuracy and consistency but also pertain to infrastructure requirements for processing and analysing extensive sets of data.

SS—Strategic Solutions: Strategies and solutions that Tesco may implement to handle the problems associated with the inbuilt setting of AI and Big Data are tagged with SS (Darbanian et al., 2024).

These strategies underline Tesco’s approach towards getting over the implementation barriers to maximizing the benefits from artificially intelligent robots or computers and Big Data.

It is the process of coding the raw data into structured segments, which helps in the emergence of patterns and themes. By systematically going through each segment, an attempt is made to unravel the multifarious impact that AI and Big Data have exerted on Tesco’s supply chain management. This provides a robust base for subsequent stages like categorization, pattern analysis, and interpretation.

4.3 Categorization

4.3.1 Thematic Grouping of Coded Data

This is followed by the grouping of these codes into broader thematic categories. This step helps in organizing data under coherent clusters, and through such clusters their analyses and interpretations. Key themes identified from the coded data are:

AI and Big Data Utilization: This is the theme covering all codes that speak to how AI and Big Data are utilized within Tesco’s supply chain today. It comprises predictive analytics, inventory management, logistics optimization, demand forecasting, etc.

Implementation challenges: these are the different challenges that Tesco faces in the implementation of AI and Big Data technologies, including problems of implementation in AI and problems using Big Data.

Strategies and Solutions: This is a theme dealing with approaches Tesco uses to meet the challenges associated with AI and Big Data. This includes the strategic solutions that give dimension to Tesco’s effort in addressing these challenges.

Supply Chain Efficiency Impact: This dimension looks at the overall influence of AI and Big Data on Tesco’s performance in the supply chain. It includes metrics for efficiency in the supply chain and data integration efforts.

These thematic categories provide a structured framework for further analysis, allowing for a more focused examination of the data.

4.3.2 Detailed Categorization Process

Reviewing each code, then grouping them under their relevant thematic category, involves the examination of the relationships between different codes and the understanding of how each contributes towards the broader theme. For example:

AI and Big Data Utilization: Codes on predictive analytics, inventory management, logistics optimization, and demand forecasting are all put together. This grouping explains clearly how these technologies are being used throughout most of Tesco’s activities regarding the supply chain (Darbanian et al., 2024).

Implementation Challenges: Under this theme are codes related to challenges of AI implementation and challenges of Big Data utilization. These point out the difficulties Tesco is experiencing in implementing these technologies, giving a reflection of the barriers that have to be addressed.

Strategies and Solutions: Codes regarding strategic solutions are focused together. This categorization concentrates on the approaches Tesco uses to overcome the identified challenges, highlighting how the strategies have been effective.

Impact on Supply Chain Efficiency: Codes associated with supply chain efficiency and integration of data are put under this theme. This grouping will be done in order to find out the holistic impact that AI and Big Data have on Tesco’s performance regarding the supply chains involved, hence indicating improvement in efficiency and decision-making.

Organizing the data in such a way into these thematic categories makes for a clear and structured framework where we could work through our analysis of the impact that AI and Big Data have had on Tesco’s supply chain. This provides a degree of consistency in going through all relevant data, making sure everything gets covered with respect to key issues and insights.

4.4 Pattern Analysis

4.4.1 Identification of Recurring Themes

Pattern analysis involves the identification of themes recurrent in the data that has been categorized. This forms one of the most critical steps in gaining an overview of the general stories and how they relate to the research questions. Crucial patterns can be uncovered by performing frequency analysis in addition to exploring relationships between diverse codes. The following themes have been identified as emergent:

Probably the most prominent theme is how AI and Big Data can integrate and optimize supply chain processes. It keeps returning in the discussion of predictive analytics, inventory management, and logistics optimization, which indicates that these technologies are core enablers for enhancing supply chain efficiency.

Challenges of data quality and governance are another recurring theme, viewed as the challenge to ensure that one has good data quality and effective data governance (Cadden et al., 2022). This clearly comes out in big data utilization challenge codes and points to how integral reliable data are to successful implementation.

Strategic Adaptation and Investment: The final point that lies in the category of strategic solutions is, once again, a recurring one: to consider strategic adaptation and investment in technology and training. This further defines how organizational processes need to be tailored and infrastructure and skills need to be invested in to overcome such challenges.

Operational Performance Impact: This is a recurring theme that evaluates the influence of AI and Big Data on operational performance within the supply chain efficiency category. This theme indicates that the use of technologies has considerable positive impacts on different performance metrics, including cost reduction, decision enhancement, and improved customer satisfaction (Talwar et al., 2021).

These recurring themes give a fully holistic view of the overall issues that are most important and leading trends in the data, emphasizing those critical factors whose influence shall be impacted on Tesco’s supply chain as AI and Big Data begin to make their mark.

4.4.2 Analysis of Patterns and Trends

Such trends and patterns are identified, and meaningful insights and conclusions are drawn from the data. Only the trends of these themes shall be analysed; the relationships between them need to be understood with respect to Tesco’s supply chain management.

Integration and Optimization: One can easily infer from the frequency of integration and optimization that business efficiency improvement in the supply chain is basically brought about by AI and Big Data. The recurrence of predictive analytics, inventory management, and logistics optimization reveals the high importance these technologies have in smoothing operations and generally improving effectiveness (Hancock, 2021). This identifies a pattern suggesting that Tesco’s focus on integrating AI and Big Data into their supply chain processes is paying off tremendously.

Data Quality and Governance Challenges: This is a recurring pattern in the data quality and governance challenges, which could be the critical barrier that Tesco will need to overcome so that AI and Big Data get implemented successfully. Clearly, this is indication that ensuring the data provided is accurate, consistent, and reliable is a major issue to be handled by Tesco. The identified challenges are very key if full potential in these technologies is to be realized in order to reap the expected benefits (Jagatheesaperumal, 2021).

In essence, it means that the theme of strategic adaptation and investment underscores preparedness within an organization by way of planning for resource allocation towards successful implementation. The trend indicates that Tesco will need to adapt their processes to AI and Big Data by investing in technology training and hence be able to overcome all the challenges associated with them (Lagorio and Pinto, 2021). These strategic initiatives are critical in ensuring that the organization is empowered to work effectively with these technologies.

Operational Performance Impact: The recurring theme of the positive impact on operational performance suggests that AI and Big Data utilization underpin measurable improvements in supply chain metrics. What this would mean for Tesco is suffering less from these technologies, holding out a number of benefits such as cost reduction, enhanced decision-making, and improved customer satisfaction. These results show the value of AI and Big Data in driving operational excellence.

These patterns and trends may be further analysed to come up with meaningful conclusions on what the impact of AI and Big Data on Tesco’s supply chain management is. The insights say more than the research questions themselves and offer deeper knowledge on the critical factors that determine the successful implementation or failure of the applied technologies. This analysis supports strategic adaptation, data quality, and the integration of AI and Big Data as necessary conditions for the determination of efficiency and performance improvement within the supply chain.

4.5 Interpretation and Insights

4.5.1 Interpretative Analysis of Findings

After categorization and patterning, the data is subsequently analysed further through interpretation, tending to offer deeper insights into how AI and big data impact Tesco’s supply chain management. This section discusses the implications of the findings through relating the data to a broader context in the UK food retail sector.

Current State of AI and Big Data Utilization

The figures evidently indicate that Tesco has highly integrated AI and Big Data into its supply chain operations. One of the most easily observed applications used is predictive analytics and demand forecasting to predict customer demand and get the inventory levels right. This usage reduces cases of stockout and overstock, making sure that ‘the right products’ are there at the right time (Shahzadi et al., 2024). Besides, optimization in logistics due to AI algorithms has made the delivery routes and schedules more effective, and this optimization has resulted in cutting down the transportation costs and improved delivery times.

The high frequency of mentioning these technologies designates that Tesco supply chain management is still very data-driven, acting by the real-time information. This reliance on AI and Big Data to replace more traditional methods with highly advanced technology-driven approaches presents growth that is aligned with the wider trends across the industry toward digital transformation.

Challenges in Implementation

In spite of the benefits, some challenges that Tesco faces in the implementation of AI and Big Data can be noted in the very setting. These data quality and governance issues recur very frequently, suggesting that getting good, consistent, and secure data is actually a big problem. Bad data is certain to mean bad predictions and worse decisions that upset all probable advantages offered by AI and Big Data (Maghsoudi et al., 2023).

Furthermore, AI technologies are complex and very hard to implement. Huge investments in infrastructure and highly trained human resources are needed for their integration into existing systems. It can be noticed from provided data that Tesco has managed to make important progress in overcoming these challenges by making appropriate and timely means of investments and training programs. Nevertheless, the continuity of difficulties suggests the permanent effort and adaptation needed to let AI and Big Data bear full potential.

Strategies to Overcome Challenges

Such challenges would require multi-dimensional strategies to be overcome. Investment in advanced data management systems is one of the key ways through which Tesco can address its inadequate data quality and poor governance (Charles et al., 2023). Such a system ensures efficient collection, storage, and processing of data while maintaining integrity and reliability. In addition, Tesco has put in strong measures aimed at securing its data against breaches and other cyber threats.

The other important strategies are the training and development programs for employees. By upskilling their workforce, Tesco makes its employees capable of using AI and Big Data effectively. Since human capital is going to use these technologies effectively, proper investment in them is necessary to execute their working with state-of-the-art tools and techniques to bring improvement in supply chains.

Effects on supply chain efficiency

The data clearly attests to the fact that there has been a considerable impact of AI and Big Data on Tesco’s supply chain efficiency. With better predictability analytics and demand forecasting, inventory optimization reduces the costs associated with overstocking and stockouts. Proper logistics operations have also helped reduce the costs further and increased delivery performance (Lagorio and Pinto, 2022).

These efficiency improvements have had a direct impact on Tesco’s operational performance. Its lower cost and higher service levels make it more competitive in the marketplace. In addition, because it can respond to changes in customer demand and/or market conditions from analysis in real time, this develops Tesco’s strength for resilience and adaptability.

4.5.2 Insights and Implications: Linking to the Technology Acceptance Model (TAM)

TAM would be a useful framework through which perceived acceptance of AI and Big Data Technologies could be understood among Tesco employees. According to TAM, perceived usefulness and perceived ease of use are the two factors impacting technology acceptance.

Perceived Usefulness (PU)

These results mean that Tesco employees find AI and big data technologies very useful for improving supply chain efficiency. The benefits, which have been located with enhanced predictive analytics, improved inventory management, and finally optimized logistics, demonstrate practical advantages of such technologies (Cadden et al., 2022). This perceived usefulness is a critical factor driving the acceptance and adoption of AI and big data within Tesco. Employees are very much aware of the fact that these technologies can assist them in performing better, hence making better decisions and yielding output.

Perceived Ease of Use (PEOU)

This also means that Tesco has therefore put in place certain initiatives on training and development to make these technologies user-friendly to the employees. In doing so, Tesco, with the needed skills and knowledge, is able to assemble the puzzle of AI and Big Data technologies in simplistic terms for employees to understand and use. The focus on the ease of use through user-friendly interfaces and comprehensive training increases perceived ease of use, hence encouraging technology acceptance. When employees are confident about using any technology, they tend to adopt it more (Bhat and Huang, 2022).

External Variables

TAM also takes into consideration the role of external variables that may affect PU and PEOU. Evidence of strategic investments in data management systems and in security measures for AI and Big Data prove that Tesco is going extra miles to supply robust support (Talwar et al., 2021). This organizational support will strengthen both perceived usefulness and perceived ease of use, building a positive attitude toward technology adoption.

Furthermore, the continuous efforts to resolve data quality and governance challenges reflect how Tesco is proactive on ensuring that the information it deals with is reliable and secure. These are, therefore, some of the most important external factors in setting employees’ perception and acceptance of AI and Big Data technologies.

Behavioural Intention and Actual Use

Moreover, these indicators—perceived usefulness, perceived ease of use, and organizational support—are highly perceived as leading to a positive behavioural intention of using AI and Big Data technologies (Agarwal, Gupta and Kraut, 2021). That is, employees are motivated to accept these technologies since they perceive that they would enable better supply chain management. The evidence on the actual use of technologies gets concretized by the significant improvements in supply chain efficiency, thus portraying the successful implementation and acceptance of AI and Big Data within Tesco.

It is in regard to such insights that a TAM perspective bears on the differences between considerations around what influences acceptance and adoption of AI and Big Data technologies within Tesco’s supply chain. Perceived usefulness, ease of use, and external support all point to focused attention on both technological and organizational facets as prerequisites for successful technology adoption (Lagorio and Pinto, 2022).

4.6 Conclusion

It is finally conceptualized as gaining insights into how the intervention of AI and Big Data would affect Tesco’s management of the supply chain through an analysis of secondary qualitative data. The findings underpinned important benefits accruable from these technologies in enhancing supply chain efficiency and performance but pointed at problems related to data quality, governance, and continuous infrastructure and training investment.

The clear strategic implications for Tesco and the broader UK food retailing sector are that digitally driven transformation through AI and Big Data is central to any strategy capable of retaining competitiveness. Only after addressing the challenges identified and acting on insights gathered from this analysis will Tesco and other retailers seeks to continue their progress in supply chain operations, drive operational excellence, and undertake activities oriented towards enhanced value creation for customers. Future research in this area will further enlighten the evolving impact of these technologies, detail deeper understanding, and guide the industry.

Informed by this, the Technology Acceptance Model allows a greater depth of understanding as to how such technologies are perceived and adopted in Tesco. Stressing perceived usefulness, perceived ease of use, and organizational support underscores that both technological and human factors should be considered if there is any hope for any technology to be effectively adopted.

This places adequate measures for both technological and human considerations, which can ensure that AI and Big Data technologies become fully integrated into supply chain operations, resulting in immense efficiencies and improvement in performance.

Chapter 5: Conclusion and Recommendations

5.1 Introduction

The assessment of the analysis of the potentials of AI and Big Data in Tesco’s supply chain evaluation is critical in outlining the innovations realized together with the hitches that the UK food retail industry experienced.

The study also highlights the kind of strategies explained above through the evaluation of how these technologies are already being applied in demand forecasting, inventory management, and supply chain logistics, to show how they play a vital function in boosting operational effectiveness (Nair and Shams, 2024).

The research also reveals critical issues concerning data quality, technical support or drivers, and organizational end user resistance that influence AI and Big Data integration in Tesco’s supply chain management system. The paper presents a literature review in order to identify these challenges and review Tesco’s action plan to address them, as well as evaluate their impact on the performance of the supply chain.

5.2 Linking with Objectives

Objective 1: To analyse the current state of AI and Big Data utilization in Tesco’s supply chain management.

Identification of Key Concepts

Currently, Tesco supply chain has benefited from the incorporation of Artificial Intelligence and big data in its operations as a manifestation of organizational changes of retailers to adopt technological efficiency. There are several important notions that define this process; one of them is called Predictive Analytics (PA), which prescribes using AI applications for predicting clients’ behaviors and trends in the future.

This capability makes it easy for Tesco to make the right choices on demand forecasting and stock that is required to meet customers’ demand without stock-outs or places where there is so much excess stock that it takes a long while to sell it (Kaya, 2024).

In addition to PA, IM is enriched by AI and Big Data since it controls the stocks to avoid losses and at the same time ensure the availability of products. It also increases cost effectiveness and efficiency of resources used, thus enhancing Tesco’s operations.

Objective 2: To identify the key challenges Tesco faces in implementing AI and Big Data in its supply chain management.

The fourth quadrant emerging as the key area, which encompass Logistics Optimization (LO) is another area where AI even sees its importance. The effective use of AI in determining the delivery routes and schedules leads to reduction of delivery time in addition to improving supply chain processes in Tesco.

Such optimizations translate into evaluation of costs together with enhanced satisfaction of customers (Kaur et al. 2024). Likewise, Big Data integrated Demand Forecasting (DF) let Tesco foresee the rate of consumer demand, which the company can adapt in the supply chain to avoid stock shortages and circumstances where there are excessive inventories.

Objective 3: To evaluate the strategies Tesco employs to overcome the challenges associated with AI and Big Data implementation.

From such creations, the theme of Supply Chain Efficiency (SCE) can be derived because AI and big data alike have boosted Tesco’s overall supply chain. The enhancements in the effective and efficient management of resources together with the gain or operational cost reductions, and outcomes in general efficiency all relate to the application of these technologies in the company’s supply chain (Rodgers et al. 2024).

However, together with these opportunities, there are certain threats which Tesco has to deal with, namely Implementation of AI Challenges, and Big Data Utilization Challenges. There are challenges of technical, organizational, and cultural nature when it comes to implementing AI technologies, while data quality, governance, and volume aggravate the challenges involved in Big Data utilization. Solving these issues is pertinent to use AI and Big Data optimally for Tesco.

Objective 4: To assess the overall impact of AI and Big Data on Tesco’s supply chain efficiency and performance.

In order to overcome these barriers, Tesco has created the Strategic Solutions (SS) in the area of technology enhancement, efficient personnel training, and organizational change. Nonetheless, by effectively resolving the aforementioned challenges, Tesco will be in a better position to optimize on the benefits of AI and Big Data throughout the firm’s value chain (Jiang et al. 2024).

It also helps Tesco to address not only current issues but, at the same time, develop long-term organizational changes for the company’s operations and growth while providing better decision-making and higher customer satisfaction. It can be stated that the case of Tesco’s usage of AI and Big Data in supply chain management is not an isolated phenomenon in the contemporary context of retailing.

Hence, the emphasis placed on data integration, optimization and strategic adaptation explores the company’s tangible determination to achieving effective operation and competitive technological prowess, in the operation performance.

5.3 Recommendations

Enhance Data Quality and Governance:

There is a need for Tesco to invest in the proper data governance policies to make sure that the data collected is impeccable (Kaur and Watson, 2024). This involves ensuring that there are appropriate measures to prevent the input of wrong data that would eventually compromise the decision making in the organization.

Focus on Strategic Adaptation and Investment:

Thus, based on the identified challenges related to the implementation of AI and Big Data, Tesco should focus on targeted investments in technology and people. This entails having better systems, effective artificial intelligent tools, and meaningful procedures for enlisting ambitious development programs and enriching its personnel’s technological knowledge.

Improve Integration and Optimization:

The supply chain industry should focus on the further improvement of the AI and Big Data applications to the supply chain processes (Beck et al. 2024). Lastly, Tesco should provide attention to the improvement of the data integration, where the firm should collect all data from the various sources and create one workable network to ease the process of information flow within the supply chain.

Strengthen Predictive Analytics and Demand Forecasting:

Thus, it is possible for Tesco to understand the various trends within the market and the related customer behavior by improving the outcomes of the predictive analytics and demand forecast models. This will assist in achieving the required inventory control so as to minimize cases of stock-out and over-stock and consequently enhancing the supply chain performance.

Address Implementation Challenges Proactively:

To overcome the T/O/C challenges of implementing and adopting AI and big data, Tesco should be more proactive (Kuikka et al. 2024). This comprises establishing an innovation culture, cross-pollination of ideas, and managing change on ideas that people initially resist.

Leverage AI for Logistics Optimization:

Applying artificial intelligence to logistics can also have a positive impact on shortening the delivery time and depending on the distances, can also help to lower delivery expenses. Tesco should think about the further improvement of route planning and scheduling algorithms to optimize the company’s logistics activities.

Monitor and Evaluate Performance Metrics:

Therefore, there is a need to monitor and assess the performance indicators concerning AI and Big Data projects and their efficiency (Gupta et al. 2024). Key performance indicators KPI’s should be set to monitor the effects of these technologies so that the objectives of efficiency, cost reduction and improved customer satisfaction for Teso are met.

5.4 Summary

In their particular case of study, the authors have highlighted that the integration of AI and Big Data has significantly contributed to Tesco’s supply chain improvement and the firm’s performance, in general. Employing the application of predictive analytics and real-time inventory management has allowed Tesco to reduce the financial impacts of stock-related issues and enhance the customers’ satisfaction due to the effective supply chain.

However, the data quality issue, the technical integration issue, and the organizational issue become keys to strategic intervention (Panigrahi et al. 2024). Other best practices are, adopting better data control, proposing more investment in data tools and personnel, making better data connections, proposing stronger predictions analysis. Implementation, prevention of barriers and enhancement of logistics will introduce measures that will take Tesco’s supply chain to new heights thus enabling it to continuously serve the retail market with exceptional competence.

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