







































 

 

 
72 

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 Revolutionizing supply chains: The role of emerging technologies in digital transformation 

 

 

 Naimul Islam1 

 Tipon Tanchangya2 

 Kamron Naher3+ 

 Ummah Tafsirun4 

 Md Rakib Mia5 

 Shoaibur Rahman 
Sarker6 

 Fahad Rashid7 

 

1Department of Accounting, Finance and Economics, University of Greenwich, 
SE10 9LS, London, UK.  
Email: naimmgtdu75@gmail.com    
2Department of Finance, University of Chittagong, Chittagong 4331, 
Bangladesh.  
Email: tipon.tcg.edu@gmail.com    
3Department of Business Administration, Presidency University, Dhaka-1212, 
Bangladesh.  
Email: naherk@pu.edu.bd   
4Department of Business Administration, Sonargaon University, Dhaka, 1215, 
Bangladesh.  
Email: ummah.tafsirun@gmail.com    
5Department of Business Administration, Ahsanullah University of Science 
and Technology, Dhaka 1208, Bangladesh.  
Email:  mdrakibmia087@gmail.com 
6School of Business and Law, Northumbria University, 110-114 Middlesex 
Street, London, E1 7HT, UK.  
Email: shoaibur.fin.du@gmail.com 
7Centre for Islamic Finance, University of Bolton, Bolton BL3 5AB, UK.  
Email: fr7bbs@bolton.ac.uk   

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 30 January 2025 
Revised: 4 March 2025 
Accepted: 12 March 2025 
Published: 20 March 2025 
 

Keywords 
Digital transformation 
Emerging technologies 
Operational efficiency 
Supply chain management. 
 

 

 
The main objectives of the study are to provide a comprehensive overview of emerging 
technological solutions, their applications, and their impacts on supply chain digital 
transformation. It is qualitative research, and secondary data were collected. The study 
identified five effective applications of the individual solutions. AI provides effective 
insights, demand forecasting, warehouse automation, transportation and route 
optimization, supplier selection and management, and predictive maintenance. 
Blockchain enables tracking and transparency, enhancing traceability, cutting down on 
counterfeiting, encouraging sustainable and ethical sourcing, and facilitating smart 
payments. Business intelligence ensures improved communication, monitoring expenses, 
inventory management, tracking key performance indicators, and optimized 
visualization. Data science facilitates demand prediction, route enhancement, inventory 
management, hazard assessment, and supplier administration. IoT enables shipment and 
delivery tracking, warehouse capacity monitoring, inventory management, storage 
condition monitoring, and routine optimization and automation. RFID is effective for 
warehouse management, inventory management, freight transportation, supply chain 
visibility, and retail management. These emerging technologies collectively promote a 
more integrated, adaptable, and resilient supply chain landscape, address significant 
challenges, and open doors to future innovations. The results suggest that by adopting 
all emerging technologies within the supply chain context, business executives would 
increase their efficiency and enhance firm value as well. 
 

Contribution/Originality: The study highlights how the emerging technologies complementing each other in 

digital transformation. It also shows where and how these technologies are used so that the users can get an overview 

of the applications.  

 

 

Financial Risk and Management Reviews 
2025 Vol. 11, No. 1, pp. 72-102 
ISSN(e): 2411-6408 
ISSN(p): 2412-3404 
DOI: 10.18488/89.v11i1.4143 
© 2025 Conscientia Beam. All Rights Reserved. 

 
 
 

 
 
 

 

 
 
 
 

https://orcid.org/0009-0005-7001-1770
https://orcid.org/0009-0009-2365-4959
https://orcid.org/0009-0001-9663-5427
https://orcid.org/0009-0005-5373-4619
https://orcid.org/0009-0004-7267-9515
https://orcid.org/0009-0000-4196-682X
https://orcid.org/0009-0001-2482-3808
mailto:naimmgtdu75@gmail.com
mailto:tipon.tcg.edu@gmail.com
mailto:naherk@pu.edu.bd
mailto:ummah.tafsirun@gmail.com
mailto:mdrakibmia087@gmail.com
mailto:shoaibur.fin.du@gmail.com
mailto:fr7bbs@bolton.ac.uk
https://www.doi.org/10.18488/89.v11i1.4143


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1. INTRODUCTION 

Recent years have seen a dramatic shift in supply chain dynamics due to the advent of Industry 4.0 and its range 

of revolutionary technologies, including the Internet of Things (IoT), blockchain (BC), big data analytics, and artificial 

intelligence (AI). New paradigms, such as digital transformation and Industry 4.0, are transforming supply chain 

management operations in business sectors (Sahoo, Kumar, & Upadhyay, 2023). The seamless movement of products 

and services across long distances has always been made possible by supply chains, which have served as the basis of 

global trade. Industry 4.0 has facilitated improved supply chain management through the emergence of new digital 

technologies (Galati & Bigliardi, 2019). However, the rise of emergent technologies is not just revolutionizing the 

way supply chains operate but also promising a future of transformation and efficiency. These technologies hold the 

potential to reshape supply chains, inspiring a future where efficiency and transparency are the norm (Attaran, 2020). 

The supply chain can benefit significantly from digitalization, including better inventory management, real-time data 

collection, optimization of logistics practices, and enhanced information availability (Bigliardi, Filippelli, Petroni, & 

Tagliente, 2022). Blockchain, for example, can offer a safe and transparent medium for monitoring the origin of 

products, thereby guaranteeing the quality and safety of food. Additionally, blockchain can encourage the 

development of trust and mitigate the risk of fraud by enabling the seamless collaboration of supply chain stakeholders 

(Attaran, 2020). Emerging technologies are transforming the supply chain and ensuring a future of efficiency and 

transformation. These are merely a few instances of emerging technologies transforming the supply chain and 

indicating a future of efficiency and transformation.  

Along with the rise of new technologies, there is a greater focus on being environmentally friendly. To boost 

their efficiency, innovation management, and corporate growth, businesses have put in more effort to become more 

sustainable. Additionally, they wanted to improve their position in the marketplace and gain a significant edge over 

their competitors (Schmidt & Wagner, 2019). Organizations must be equipped to confront the obstacles and 

opportunities that these emergent technologies present as supply chains become increasingly digitized. The adoption 

of a digital transformation paradigm is not only essential but also mandatory, as it enables organizations to remain 

agile and responsive in the presence of market challenges (Hartley & Sawaya, 2019). Digitally driven organizations 

have transcended many conventional supply chains (Verhoef et al., 2021). Supply chains were changed after COVID-

19 broke out, which affected the whole world. During this circumstance, companies with more resilient supply chains 

had a more significant edge over their competitors (Li, Zhang, & Liu, 2022; Sarkis, 2020).  

Prior studies examined the impact of digital transformation on supply chain management. Lerman, Smith, and 

Zhang (2024) investigated how digital transformation in supply chains supports firms' social and economic 

performance in emerging markets. Akbari, Nguyen, and Le (2024) showed how digital technologies are changing the 

supply chain landscape in Vietnam by embracing industry 4.0 technologies. The adoption of specific technologies and 

the relationship between sustainable supply chain management and digital transformation were examined by 

Stroumpoulis and Kopanaki (2022) and a conceptual framework was developed to better explain how the combination 

could result in the development of sustainable performances. AlMulhim (2021) looked into how smart technologies 

are an important part of building the link between digital transformation and firm performance. Despite digital 

transformation's evident and substantial impact on companies, these advancements have received relatively little 

academic attention. In recent years, scholars have only recently begun investigating the domains of digitization, 

digitalization, and digital transformation (Gao, Li, & Wang, 2022). 

Therefore, an analysis of the existing research showed a significant gap that looked at the interaction between 

emerging technologies and digital transformation in the supply chain context. Thus, an in-depth study is needed to 

clearly show how important new technologies are in bringing automation to supply chain activities. Therefore, to 

address this need, this study aims to give an in-depth understanding of these technological advances, how they can 

be implemented, and how they influence the digital transformation of the supply chain. Thus, this study provides 

insight into the role of new technologies (AI, IoT, BC, BI) in supply chain digitalization and identifies the challenges 



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that companies must confront. It helps academics and practitioners grasp the potential of digitization for firms and 

management in supply chains. Thus, the study can serve as a foundation for using and analyzing emerging 

technologies and future research. 

This paper is organized as follows. The current discourse regarding emerging technologies that are presently in 

use is reviewed in Section 2. The application of these technologies in supply chain management is discussed in Section 

3, while the benefits of incorporating them into supply chain management are presented in Section 4. In Section 5, 

several successful case studies are presented and discussed. Section 6 concludes by emphasizing the implications and 

opportunities for future research and some of the study's limitations. 

 

2. EMERGING TECHNOLOGIES 

Industry 4.0 has fostered the development of innovative technologies that have created novel potential for 

business growth. Supply chains are altered by these technologies, which accelerate the development of unique value-

generating methods (Arenkov, Ivanov, & Pavlov, 2019). This section highlights some such emerging technologies. 

 

2.1. Artificial Intelligence (AI) 

Numerous sectors have recently shown a growing interest in the potential applications of artificial intelligence 

(AI) technology (Dubey, Gunasekaran, & Childe, 2020). Artificial Intelligence (AI) denotes the capacity of machines 

to acquire knowledge from experience and make decisions similar to human intelligence (Duan, Edwards, & Dwivedi, 

2019). Rapid advancements and increased attention to AI began in the early 2000s, and the field has since been 

reconsidered in both research and practical settings (Helo & Hao, 2022). Recently, AI has been growing and becoming 

more and more popular. This is due to several organizational and environmental factors, such as changing customer 

requirements, fierce global competition, companies going digital overall, and the fast-paced evolution of technology 

(Dubey et al., 2020).  

AI has made the most essential progress in information technology through its unique ability to perceive and 

think. AI can be used for numerous purposes, such as computer vision, speech and voice recognition, robotic process 

automation, machine learning, and deep learning (Sharma, Singh, & Gupta, 2022). The COVID-19 pandemic 

demonstrated the vulnerability of global supply chains since they depend on many suppliers in different places and 

have longer physical flows. As a result, stakeholders' needs for resilience, agility, and flexibility have grown 

significantly. This calls for supply chain management systems that incorporate AI. It is used in almost every area of 

supply chain decision-making. For example, AI is used to predict supply chain risks (Baryannis, Validi, & Nivolianitou, 

2019) to predict fashion trends using logistic regression (Chakraborty, Choudhury, & Tiwari, 2020) to predict back-

order scenarios in the supply chain using gradient-boosting machine-learning techniques (Islam & Amin, 2020) to 

forecast (Nguyen, Phan, & Le, 2021) and to analyze how machines break down (Okabe & Otsuka, 2021). 

 

2.2. Blockchain Technology (BT) 

Crosby, Pattanayak, Verma, and Kalyanaraman (2016) define blockchain technology (BT) as a distributed 

database comprising records or shared public/private records of all digital transactions conducted and disseminated 

among participating agents in the blockchain network. Four primary qualities make BT different from other 

information systems: non-localization, security, auditability, and smart execution (Saberi, Kouhizadeh, Sarkis, & Shen, 

2019). BT could help move products and processes in the supply chain. Any data could be attached to the item to 

connect the real item to its digital character in the blockchain (Abeyratne & Monfared, 2016). By connecting these 

two things, blockchain can help supply chains find fraudulent suppliers and fake goods since only authorized partners 

can record data. BT could also make information secure and unalterable, which means that it cannot be changed 

without the permission of the right people. This would stop cheating. Besides these things, blockchain could help the 

environment by reducing and managing the need for recalls and rework in the supply chain (Saberi et al., 2019).  



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As a result, BT might improve the control of the supply chain. By simplifying the supply chains, it is possible to 

follow goods from the chain's beginning to the customer's end. This stops waste, fraud, and other improper conduct. 

It may also inform people more about how a product is made and shipped so they can choose products that are better 

for the environment (Stroumpoulis & Kopanaki, 2022). 

 

2.3. Business Intelligence (BI) 

The word "business intelligence" is usually used as a generic term for a system (Shollo & Kautz, 2010) or a set 

of ideas and methods (Sabherwal & Becerra-Fernandez, 2013) that help people make better decisions by using reality 

support networks. In research, terms like "business intelligence," "business analytics," and "big data" are often used 

to refer to the identical phenomenon. Some authors have called business intelligence "a process and a brand" (Jourdan, 

Rainer, & Klein, 2008) "a process, a brand and a combination of methods, or a mixture of such" (Shollo & Kautz, 2010) 

and "a good or service alone" (Seddon, Constantinides, & Tzuo, 2017).  

Furthermore, according to Wieder and Ossimitz (2015) BI is an analytical, technology-supported process that 

accumulates and analyses scattered business and market data to reveal an organization's goals, prospects, and 

positioning. BI tools are BI software that is deployed in an organization, and BI solution is a collection of tools and 

related technologies, applications, and processes used to support BI goals. Business intelligence integrates all news 

sources. It transforms operational data from the enterprise's resource planning system into actionable insight that 

supports strategic goals (Al-Mobaideen, 2014). Companies of all sizes use BI tools. They are getting more and more 

popular. They help businesses make all kinds of decisions, from short-term to long-term ones. BI can be seen as a 

shield that keeps companies safe and a set of best practices and tools that let top leaders access and analyze company 

data and turn it into information, they can use promptly to make decisions (Ragazou, Apostolou, & Karampinis, 2023).  

 

2.4. Data Science (DS) 

Data science is an area of knowledge that gives decision-makers predictive and statistical tools. It is also an 

effective way to run organizations from a data-driven viewpoint. DS needs a lot of different skills, like computer 

science, machine learning, predictive analytics, data-driven methods, and statistics (Kotu & Deshpande, 2019; Waller 

& Fawcett, 2013). In contrast to business data analytics, which is about gathering, storing, and analyzing data, data 

science is about more complicated data analytics. This mainly focuses on forecast analytics, like machine learning and 

deep learning algorithms. Methodologically, data science and business data analytics help supply chain managers 

make choices at the strategic, tactical, and daily levels. Businesses can gain a competitive edge using data science and 

business data analytics (Kamley, Prakash, & Karan, 2016). By lowering costs, making supply chains more sustainable, 

lowering risk, and making them more resilient (Baryannis et al., 2019) data science and business data analytics 

techniques also help companies better understand what customers want and predict market trends (Hribar, Pucihar, 

& Rajh, 2019). 

 

2.5. The Internet of Things (IoT) 

International literature has a lot of different meanings for the Internet of Things (IoT). Because IoT is a 

combination of two words and meanings, "Internet" and "Things," the scenario arises (Atzori, Iera, & Morabito, 

2010). The term and concept of the Internet of Things (IoT) encompass a variety of disciplines and aspects associated 

with extending the Internet and the web to physical devices. Miorandi, Sicari, De Pellegrini, and Chlamtac (2012) 

assert that the Internet of Things (IoT) aims to establish a future in which digital and physical entities can be 

connected to generate new business opportunities. Therefore, the Internet of Things (IoT) is a novel information 

technology that is currently in the process of being developed. The technology framework has not yet been fully 

developed, and an integrated, standard structure has yet to be developed (Wu, Ma, & Zhao, 2021).  



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Using standard communication, the Internet of Things (IoT) links physical objects to the digital world, like 

sensors, pumps, thermometers, and RFID tags (Wortmann & Flüchter, 2015). Sensor-based technology in the IoT 

makes it possible for all players in a supply chain to share information over the Internet. As suggested by Tu, Liu, 

and Li (2018) IoT should be implemented within supply chain transportation systems. The proposed system would 

be able to monitor products throughout the supply chain. Furthermore, the Internet of Things (IoT) has the potential 

to enable stakeholders to participate in the decision-making process by providing them with real-time information 

(Rezaei, Ortt, & Roodhooft, 2017). The IoT could also improve food supply chains by facilitating the exchange of 

information with stakeholders and providing a continuous monitoring system (Tagarakis, Andriani, & Sideris, 2021). 

 

2.6. Radio Frequency Identification (RFID) 

A key device that is widely used and seen as necessary for the IoT is radio frequency identification (RFID). It has 

three main parts: an RFID tag, which is made up of a chip and an antenna; a reader, which sends radio signals and 

gets responses from tags; and middleware, which connects RFID hardware to business applications (Sarac, 

Abolhasani, & Syntetos, 2010). As companies become more digital, RFID helps them do so by allowing a common 

framework across businesses, superior compatibility with IT systems, ease of use, and collaboration between 

functional areas (Kamble, Gunasekaran, & Sharma, 2019). RFID helps digitize supply chains because it creates data 

from sensors that can be analyzed to find ways to automate and improve processes. RFID technology works by 

detecting when an item is nearby, recording data, and then storing that data (Musa & Dabo, 2016). System data 

analysis produces data-driven ideas that help with optimization decision-making (Fanti, Gabbrielli, & Pappalardo, 

2017). RFID offers real-time data that boosts overall efficiency and accuracy, which can help increase inventory levels, 

shorten delivery routes, and improve the customer experience (Choi, Rogers, & Vakil, 2018). 

 

2.7. Robotics and Automation 

Due to the need for faster and more efficient supply chains, robotics and automation have become crucial supply 

chain management technologies. Robotics and automation increase supply chain management by lowering long-term 

costs, increasing work and usage strength, reducing errors, minimizing repetitive inventory checks, updating 

orchestration, managing times, and assembling induction to risky and problematic areas (Mohan Banur, Patle, & 

Pawar, 2024). Advanced robotics is a common way to improve efficiency in warehousing and manufacturing tasks 

like picking and packaging, welding, and inspection (Krueger, Sutherland, & Rohn, 2016). It eliminates the need for 

complex, repetitive work, lowers costs, saves energy, and creates a safer, healthier workplace (Ganesan, Gupta, & 

Kumar, 2017). As automation rises, fewer workers are needed to do routine duties, which lowers emissions and saves 

energy (Moglia, Alvarez, & Marquez, 2021). 

 

3. EMERGING TECHNOLOGIES IN SUPPLY CHAIN MANAGEMENT 

3.1. AI Application in Supply Chain Management 

Over the past few years, AI has revolutionized SCM by increasing the effectiveness and efficiency of supply chains 

at lower costs and with better information. The application of AI in various aspects of the supply chain enables 

organizations to handle the competitiveness of today’s approaches. Some of the areas where AI is proving highly 

beneficial include demand planning, warehouse management, transportation and routing solutions, supplier 

identification, and predictive maintenance. This paper will discuss these applications in detail, with real-life data and 

case studies to show how pragmatic AI is for SCM. 

 

3.1.1. Demand Forecasting 

Demand forecasting is one of the most crucial aspects of SCM and a key application of AI in the field. It is essential 

to accurately forecast future needs to ensure the company purchases the right inventory to meet clients’ demands. 



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Traditional forecasting methods rely on general trends and statistical models, but these approaches often fail to 

account for market fluctuations, social changes, global crises, or other disasters. 

 

 
Figure 1. AI enhances demand forecasting accuracy with real-time data integration. 

 

Figure 1 shows how AI criteria for demand forecasting are used to consider sales information, characteristics of 

potential buyers, and external conditions to enhance the reliability of the forecast, minimize lost sales, and minimize 

holding costs. Businesses can more readily forecast demand by leveraging algorithms, thus making the right decisions 

about inventory, all of which lead to better business operations and costs. 

AI-based demand forecasting models surpass traditional methods by analyzing large amounts of data, including 

current sales data, customer behavior, and external factors like macroeconomic conditions and weather (see Figure 

1). These models use machine learning techniques to detect patterns and trends that human or traditional statistical 

models may not recognize. 

As stated in a McKinsey report, AI-driven demand forecasting models can reduce absolute forecast error by as 

much as 50%, leading to significant cost benefits in operations. Specifically, companies can reduce lost sales by 65% 

and cut inventory holding costs by 10-15%, avoiding both stockouts and overstock situations (McKinsey & Company, 

2021). For instance, Amazon is a prime example of a company that uses AI to manage its stock and predict product 

demand across its numerous outlets. With AI, Amazon can predict network load during peak periods, such as the end 

of the year, ensuring that hubs with popular products are prepared for increased sales   (McKinsey & Company, 2021). 

 

3.1.2. Warehouse Automation 

Technology applications in warehousing have also seen a major boost with the integration of artificial intelligence 

in automated warehousing. In most conventional warehousing processes, like order picking, packing, and order 

moving, activities are done through human effort and thus are characterized by physical complexities, high costs, and 

many handling errors. Autonomous mobile robots, AGVs, mechanization, or a combination have radically changed 

warehouse operations as several activities are becoming automated. 

The application of AI technology in the warehouse has dramatically improved efficiency and reduced operational 

costs. For instance, in its report, Deloitte highlighted that firms implementing AI-based automation systems had 

reduced labor costs by 70% (Deloitte, 2020a). AI-run robots can work around the clock, which means order deliveries 

will be completed ahead of time compared to manned workers. 

Ocado is the best example of how AI is yielding incredible efficiency in the warehousing sector of online selling 

companies. For example, Ocado deploys AI to deliver over 222,000 orders weekly without the participation of 

employees. These robots are mainly used for picking and packing groceries within a system that is highly integrated 



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and mechanically engineered for efficiency in space and time within a warehouse (Meticulous Research, 2024). 

Moreover, through AI-operated drones, real-time stock checks and monitoring of the storage conditions of the 

products are done to ensure proper conditions before sale. 

 

3.1.3. Transportation and Route Optimization 

Transportation and logistics are some of the critical areas within the supply chain that have benefited from the 

use of artificial intelligence. AI can be used to adjust transportation routes based on factors such as traffic congestion, 

weather conditions, fuel prices, and delivery times. By applying predictive analytics, AI can help decrease fuel 

expenditure, optimize delivery times, and reduce transportation expenses. 

According to Capgemini, companies that have integrated AI for route optimization saved between 10 to 30% on 

logistics costs and experienced delivery times that were 15 to 25% faster (Capgemini Research Institute, 2020). A 

prime example of AI’s application in transportation is UPS’s On-Road Integrated Optimization and Navigation 

(ORION) system. ORION uses AI to calculate millions of data points, including GPS data and specifics related to 

parcel deliveries, in order to find the most efficient routes for delivery trucks. This system has helped UPS reduce 

more than 100,000 metric tons of carbon emissions and save over 10 million gallons of fuel annually. 

Not only does improving transportation routes lead to increased efficiency and reduced costs, but it also makes 

organizations more environmentally friendly. Real-time data processing prevents companies from suffering delays 

and disruptions, making the supply chain more responsive. 

 

3.1.4. Supplier Selection and Management 

Another area in SCM where AI has been most pertinent is the supplier selection process. Traditionally, supplier 

selection was a function that was not very integrated with its counterparts, and the primary inputs at its disposal 

were cost lead time, and performance. AI is disrupting this process by enabling firms to analyze multiple information 

vectors on suppliers, their performance, financial standing, sustainable operations, and risks. 

The absorption of information about the chosen suppliers is facilitated using advanced intelligent management 

systems to enhance decision-making. A study by Gartner revealed that firms that implement AI in supply chain 

procurement for supplier identification and handling achieved a 30% improvement in supplier performance and a 40% 

reduction in supply chain disruptions (Gartner, 2022a). For instance, Siemens has installed a system that determines 

supplier credentials for sustainability compliance with the company’s high environmental and ethical requirements. 

With the use of AI, Siemens can effectively monitor the performance of its suppliers and identify risks, including if 

the supplier has become insolvent or is unwilling or unable to meet environmental standards. 

Supplier management is another function that the utilization of AI significantly improves, with unique new 

standards of cooperation between purchasing organizations and their suppliers. Supply chain visibility leads to better 

supplier relations because the company can often get real-time data on the supplier, making it easier to negotiate with 

them. 

 

3.1.5. Predictive Maintenance 

Predictive maintenance is one of the most effective AI applications in supply chain management, particularly for 

companies with large and complex manufacturing and distribution systems. Traditionally, maintenance has been 

classified as 'breakdown' maintenance, where equipment is only repaired when it fails. This approach often leads to 

high downtime, production delays, and unplanned repairs. 

AI-powered predictive maintenance involves real-time monitoring of equipment conditions using sensors to 

predict performance. By analyzing this data, AI can determine when equipment is likely to fail, allowing maintenance 

to be performed before a failure occurs. This highly efficient maintenance strategy reduces repair costs and prolongs 

the useful life of the equipment. 



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IBM (2021) revealed that companies that implemented AI-based predictive maintenance reduced unplanned 

downtime by half and cut maintenance costs by one-third. For example, GE uses AI to control its industrial systems, 

such as turbines and engines. Predictive analytics help GE identify potential failures and schedule maintenance at 

times when it will cause the least impact on business operations (IBM, 2021). 

Additionally, AI-powered predictive maintenance helps conserve energy demand and enhances the operational 

life of equipment, playing a crucial role in corporate sustainability initiatives. By applying the concept of relative loss, 

organizations that adopt predictive maintenance can run their operations in a more sustainable way while using fewer 

resources. 

 

3.2. Blockchain in Supply Chain Management 

Blockchain is an open distributed database that stores transactions so that any changes cannot be made afterward. 

Due to the properties of immutability, transparency, and decentralization, blockchain can become effective in solving 

a large number of issues in supply chain management (SCM). Blockchain in SCM is even said to offer improved 

tracking and control over products, more excellent product provenance, limited counterfeiting, increased 

sustainability, ethical sourcing, innovative contract payments, and more (see Figure 2). This section then provides a 

critical review of the use of blockchain in the above-discussed areas of supply chain management. 

 

3.2.1. Tracking and Transparency 

The traditional element questioned in SCM is the opacity observed in most supply chain networks. Blockchain 

solves this problem by creating a private ledger that records all transactions and processes in a blockchain. The 

consensus among all the parties in the chain is that each link in the supply chain, from raw materials and processing 

to distribution and delivery of the final product, is documented on blockchain technology for every stakeholder 

interested in the supply chain to verify. This enhances responsibility and enables different companies in the chain to 

trace merchandise as it flows through the channel in real-time. 

 

 
Figure 2. Blockchain enhances supply chain transparency, visibility, and ethical compliance across industries. 



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The Figure 2 shows how blockchain improves supply chain transparency by enabling real-time tracking, creating 

immutable records, and ensuring regulatory compliance. This enhances accountability and visibility from raw 

materials to the final product, with examples like Walmart's food safety traceability and Ford's ethical cobalt sourcing. 

A Deloitte (2020b) report pointed out that since blockchain has a decentralized, shared ledger that records every 

transaction, it dramatically improves supply chain transparency. For instance, blockchain has been applied in the food 

industry to handle produce and identify its origin and safety standards before reaching the consumer’s table. Walmart 

Inc., in collaboration with IBM, incorporated blockchain in tracing their suppliers of leafy greens, and it took only 

seconds to trace the source of contaminated production compared with weeks (Deloitte, 2020b). 

In car production, Ford has adopted blockchain to monitor cobalt flow, a material crucial to rechargeable 

batteries. Blockchain also helps Ford trace that cobalt is mined from specific regions free from conflict, thus passing 

the ethical standards accrued (Ford Motor Company, 2021). These use cases demonstrate how blockchain can increase 

the clarity of the process, speed up operations, and meet legislative requirements. 

 

3.2.2. Enhancing Traceability 

Blockchain enhances Traceability foundationally and principally, allowing companies to track products or raw 

materials from manufacturers to consumers. This is especially relevant in industries where product security is a 

concern, such as drug and food supply chains. 

A McKinsey & Company (2021b) suggests that applying Traceability through blockchain in the pharmaceutical 

sector also prevents the distribution of counterfeit drugs, which cost about $200 billion worldwide. Using blockchain 

to track pharmaceuticals keeps fake products from reaching consumers while authentic products are granted easier 

access. With the food supply chain being among the most essential, blockchain blocks every aspect of food. Nestlé 

and Unilever, among other firms, have implemented it to enhance food safety. 

Blockchain also facilitates traceability solutions for luxury goods such as diamonds. To address the issues of 

conflict-free and ethically sourced diamonds, the De Beers group has created the Tracr blockchain platform (De Beers, 

2020). It helps maintain customer confidence and curbs the cycles of so-called conflict diamonds. 

 

3.2.3. Cutting Down on Counterfeiting 

Piracy has become a significant problem in supply chains worldwide, specifically in fashion accessories, medicines, 

and technology products. Blockchain’s permanency means that it can't be altered once a trade has been made, 

confirming its genuineness. 

According to the World Economic Forum (2021), global counterfeiting costs about 2.8 trillion US dollars 

annually. Counterfeiting is eliminated through the help of blockchain since every single product can be given a unique 

number. This identifier, stored in the blockchain, enables consumers and businesses to check the product’s 

genuineness throughout its lifecycle. 

Specifically, within the luxury products sector, Aura, developed by the luxury goods giant LVMH (Moët 

Hennessy Louis Vuitton), allows consumers to clearly check the origins of luxury goods using blockchain solutions. 

Aura captures each interaction a product has through each phase of its lifecycle, from material acquisition to end-use, 

guaranteeing the authenticity of product supplies and stopping counterfeit products from reaching the end user 

(LVMH, 2020). 

In the pharmaceutical industry, blockchain makes it possible to effectively stop counterfeit drugs from circulating 

through the market by documenting transactions and movement through the supply chain. Pfizer and Merck recently 

partnered to utilize blockchain to verify medicines and prevent counterfeiting that poses financial and reputational 

damage to companies like Pfizer (Pfizer, 2021a). 

 

 



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3.2.4. Encouraging Sustainable and Ethical Sourcing 

Environmentalism and social responsibility remain high among consumers, shareholders, and governments. 

Blockchain makes it possible to track products' environmental and social effects practically in real time as they journey 

through the supply chain. 

Consequently, the Gartner (2022b) study revealed that sustainability was a key concern in supply chain 

management, with 90% of consumers demanding sustainably produced products. Blockchain guarantees that 

organizations can validate assertions about sustainability and sourcing concerning products and services by 

establishing credibility. 

It applies blockchain technology to the supply tracking of palm oil to promote sustainability and socially 

responsible production. It sources palm oil from the plantations by asking blockchain technology providers to track 

its journey from the plantation to the finished product while discouraging deforestation and ensuring fair labor 

practices for employees (Unilever, 2021). 

Blockchain also enhances the circular economy by tracking and tracing products through recycling and disposal 

across various value chains. For example, companies like Coca-Cola are already using blockchain to explore the 

possibilities of increasing their efficiency in recycling bottles and packaging materials (Coca-Cola, 2020). It also helps 

in waste management and makes the company look environmentally friendly in the eyes of the customer. 

 

3.2.5. Smart Payments 

Smart contracts use blockchain to automate the payment process. According to a PwC (2021) estimate, smart 

payments related to blockchain decrease the cost of transactions by 30-50%, especially in cross-border payments 

where multiple parties like banks and payment processors complicate the flow and charge additional fees (see Figure 

3). 

 

 
Figure 3. How smart contracts revolutionize payments with blockchain for cost efficiency’. 

 

In the Figure 3, intelligent contracts facilitate and reduce them by 30-50% while eliminating potential buyer 

characteristics effectiveness, especially in cross-border forecasts' reliability. The advantages include faster payments 

and lower charges, besides enabling suppliers to gain much-needed trust. 

Smart contracts—digital contracts written in code that automatically execute based on parameters set out in the 

code—make intermediaries obsolete by cutting out the time and cost of a basic payment system. In supply chains, 

intelligent payments guarantee that suppliers receive payment once predefined conditions are fulfilled, such as the 

delivery of goods or the achievement of a specific target. 



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Maersk, one of the world’s biggest international shipping companies, currently uses blockchain in the payment 

process for its shipment services. With smart contracts, Maersk guarantees payments to be made immediately upon 

product delivery, thus increasing reliability and efficiency between buyers and sellers (Maersk, 2020). This also 

reduces fraud and human error likely to disrupt the general supply chain, promoting innovative blockchain payments. 

 

3.3. Business Intelligence in Supply Chain Management 

BI is one of the most essential tools in SCM since it provides timely information to enhance decision-making and 

facilitate operations. In the contemporary world, where data is a precious resource, firms use BI tools to gain a 

competitive advantage, improve their performance, and realize the optimum use of their resources. Managing a supply 

chain is enhanced through BI with the benefits of communication improvements, tracking of costs, inventory control, 

key performance indicators (KPIs), and visual display improvement (see Figure 4). This section evaluates BI in SCM 

based on empirical data and real-life experiences of organizations. 

 

3.3.1. Communication 

It is essential to have proper information flows between different levels of the supply chain to support the goal 

of this concept. Advanced BI tools generate real-time data that optimize interactions between suppliers, 

manufacturers, and retailers. In organizations with centralized data systems, BI allows increased collaboration, breaks 

down silos, and ensures that the right people are getting the correct information. 

Langlois and Chauvel (2017) have identified that companies that apply BI to enhance communication with their 

supply chains recorded a 20% decrease in operation delays. The automotive industry has adopted BI tools, especially 

in communication between suppliers and manufacturing plants. In addition to the above, Toyota has minimized 

production delays through real-time working updates on the quantity of available parts and delivery schedules 

(Langlois & Chauvel, 2017). 

BI tools like Tableau and MS Power BI offer dashboard features through which users and their teams can grab 

information in the form of stories and make decisions. As Davenport and Harris (2017) rightly pointed out, it has 

been estimated that organizations that use BI for communication can solve problems 40% faster than organizations 

that don’t use business intelligence for communication. Most importantly, it helps reveal inter-departmental 

dependencies and improve collaboration, thus delivering a flexible supply chain. 

 

3.3.2. Monitoring Expenses 

Most functions of SCM involve cost control, as most expenses will influence the firm's profit. Business 

Intelligence offers features to monitor expenses in real time and help detect flaws, avoiding unnecessary expenditures. 

Some areas where an organization can implement it are transportation costs, procurement expenses, warehousing 

fees, and so on. 

Accenture (2020a) report states that businesses that conduct BI on expenses have noted cost-cutting of up to 

25%. For example, Procter & Gamble uses BI to manage spending within its global supply chain operations. By 

tracking the costs of transport and logistics solutions, P&G saw the options for choosing optimal routes when it came 

to fuel expenditure that was closely trimmed down (Accenture, 2020b). 

Furthermore, new BI tools like SAP HANA enable the analysis of operational and financial costs within a single 

system. They also help improve the efficiency of budget preparation, leading to better financial control in these firms' 

supply chain operations. A study by Caserio and Trucco (2018) identifies that organizations employing BI for 

controlling expenses are 30% more likely to meet their cost-cutting objectives than the average. 

 

 

 



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3.3.3. Inventory Management 

Inventory management is one of the areas that undergoes a massive transformation under the influence of 

Business Intelligence. BI tools allow companies to monitor and track their inventory levels, ensuring they get the 

right stock at the right time and avoid overstocking and stock shortages. The right positioning of stored stock must 

be done efficiently to avoid high holding costs while ensuring a constant flow of goods. 

As noted by Fawcett, Magnan, and McCarter (2008) organizations that have adopted BI for order management 

can decrease inventories by as much as 15–30 percent and increase order satisfaction by up to 25 percent. For instance, 

BI and predictive analyses help the world’s largest online retailer, Amazon, track and organize its inventory. To do 

this, Amazon organizes and analyzes customers’ perceived demand and other past data to fashion its supply so that 

there will be no stock-out situations or excessively high inventories in its warehouses. 

QlikView is one of the BI tools that helps determine how and when a particular product's inventory is sold, which 

product is selling very fast, or which is actually stagnant. This helps supply chain managers decide when to reorder, 

reduce prices for certain products, or stop offering them altogether. As Sherman (2014) stated, many firms that 

adopted BI for inventory management decreased their holding costs by 20 percent, enhancing the business’s viability. 

 

3.3.4. Monitoring Organization’s Key Performance Indicators 

KPIs (Key Performance Indicators) are imperative to track performance efficiency in supply chain management. 

Business Intelligence tools keep all the KPI parameters in a real-time application, allowing companies to monitor 

performance and take corrective action if necessary. Typical measures for evaluating SCM performance include order 

fulfillment percentage, transportation expenses, time needed to complete an order, and supplier quality. 

According to a Gartner (2021b) companies that used BI to monitor supply chain KPIs experienced a 15% 

enhancement in operational efficiency. Coca-Cola uses BI to manage its key performance indicators affecting its supply 

chain operations globally, improving efficiency, delivery time, and customer satisfaction. This has helped maintain 

high service levels while reducing operating expenses (Gartner, 2021b). 

Enterprise BI systems like IBM Cognos Analytics enable firms to define personalized views of KPIs, showing 

them in real-time on specific dashboards. These metrics can be used to determine critical elements of workflow, such 

as constraints, problems, and opportunities for improvement. Jafari, Zarei, Azar, and Moghaddam (2023) state that 

enterprises that monitor KPIs with the help of BI are 35% more likely to reach their performance targets than those 

that track performance metrics using traditional tools. 

 

3.3.5. Optimized Visualization 

The use of Business Intelligence tools has been found to enhance the optimal presentation of big data. Different 

visualization tools help supply chain managers make sense of large datasets, which can be helpful when making 

different decisions. Data visualization makes it easier for businesses to identify complex patterns that may not easily 

be seen through simple numbers and figures. 

 

 
Figure 4. Business intelligence tools enhance supply chain decision making with data. 



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The Figure 4 shows how business intelligence tools such as Power BI and Tableau, in aspects such as supplier 

performance data, transport costs, and customer demand data, are helpful in decision-making, accurate, and cost-

effective in supply chain management using data visualization. 

A study by Khakpour, Colomo-Palacios, and Martini (2021) identified that data visualization tools enhanced 

decision-making by approximately 28% in supply chain management. Current data used by Unilever includes supplier 

performance, transportation costs, and customer demand, which are analyzed by Business Intelligence visualization 

tools. Through visualizations of this data in various dashboards, Unilever's supply chain managers can make beneficial 

decisions, such as minimizing costs (Khakpour et al., 2021).  

Tools like Tableau and Power BI offer powerful visualization capabilities, allowing users to create customized 

reports and dashboards that cater to their specific needs. These visualizations can be shared across the organization, 

ensuring that all stakeholders have access to the same data and insights. According to Kirtane et al. (2024) companies 

that use BI for data visualization see a 20% improvement in decision-making accuracy, as visual insights enable better 

interpretation of complex data. 

 

3.4. Data Science in Supply Chain Management 

In recent years, Data Science has emerged as a critical insight for supply chain management (SCM). Through 

better data use, managers can forecast demand, optimize carrier selection and inventory management, evaluate risks, 

and improve supply vendor management. Intelligent techniques help to improve supply chain responsiveness, 

effectiveness, and ability to recover from disruption. This section overviews the transformations introduced by data 

science into each of these areas and provides case evidence and statistical data. 

3.4.1. Demand Prediction 

One key idea is demanding prediction to achieve supply chain responsiveness and flexibility in operations. 

Historically, forecasting consumer behavior and market trends was highly basic, using statistical models to identify 

future trends based on past data. Recent advancements in big data analysis, including the utilization of machine 

learning (ML), have helped optimize demand forecasting by processing large datasets in real time. 

As reported by Feizabadi (2022) the application of machine learning models in demand forecasting increased 

predictive accuracy by 20–30 percent in the retail industry. These models consider temporal and spatial 

characteristics, leading customer behaviors, and systematic events (e.g., economic fluctuations or sudden epidemics). 

Amazon, for example, leverages ML to predict demand to restock goods in the right place and at the right time 

(Feizabadi, 2022). 

Moreover, organizations, including PepsiCo, leverage data science to forecast high sales periods during holidays 

or events. PepsiCo's application of big data in determining stockout rates involved analyzing social media data, 

historical sales data, and even weather, which resulted in a 15% reduction in stockout problems (Harvard Business 

Review, 2020). Demand forecasting models help companies identify over or under-demand, reducing the impact of 

demand variability. 

 

3.4.2. Route Enhancement 

Transportation management is one of the most important functions of supply chain management, especially for 

organizations with a vast distribution network. In this case, data science provides methods to calculate traffic flow, 

weather conditions, and delivery timetables so that routes can be designed and fuel expenses minimized. 

Anitha and Patil (2018) assessed that transportation costs may decrease by 12–20% through data-driven route 

optimization. For example, UPS applies analytical methods to optimize delivery routes. Through its ORION (On-

Road Integrated Optimization and Navigation) system, UPS identifies over 250 million data points daily, saving 10 

million gallons of fuel every year (Anitha & Patil, 2018). Applying data science to improve routes is critical to 

optimizing costs and addressing environmental concerns by eliminating unnecessary movement and emissions. 



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DHL applies real-time information to switch routes during operations depending on traffic density, barriers, and 

unfavorable conditions. This has resulted in a 15% improvement in the timely delivery of goods and services and a 

major decrease in their carbon footprint (DHL, 2022). Data science allows organizations to adapt to new conditions 

on the ground so that goods are delivered to required locations on time and at reduced costs. 

 

3.4.3. Inventory Management 

Another clear example of the use of data science is in inventory management techniques. Therefore, it is 

important to engage predictive analytics to help companies manage their inventories effectively and in the right way. 

This results in lower holding costs eliminates stockout incidences and improves customer satisfaction. 

According to a report by McKinsey & Company (2021c) business organizations that applied data science in 

inventory management realized 15–25% reductions while achieving or surpassing service levels. For instance, 

Walmart employs live analytics to monitor the quantity of products in its stores and estimate demand, leading to 

better restocking and minimal stock loss (McKinsey & Company, 2021c). 

Decision-support tools such as ARIMA for demand forecasting and machine learning models enable firms to 

adapt their inventory holding to demand variability. This is especially crucial for manufacturing firms that deal with 

perishable commodities like food and drugs since overstocking can result in losses. The big supermarket chain Kroger 

uses prescriptive analytics for fresher food and has reported a 10–15% reduction in food waste while adequately 

stocking its stores (Gartner, 2020b). 

 

3.4.4. Hazard Assessment 

Conducting a hazard assessment for supply chain risk management is important in industries where disruptions 

can result in catastrophic consequences. Risk management is another area where data science delivers value by 

showing businesses what threats might arise, such as hurricanes, failed suppliers, or geopolitical events, and how to 

avoid them. 

Keurulainen (2024) argues that risk analytics and other data science tools can be used to forecast disruptions in 

their infancy, providing companies with ways to reduce such incidents. For instance, General Motors (GM) employs 

big data systems to evaluate the risks of failed suppliers and natural calamities. This way, GM has adequate data to 

alter sourcing strategies and prevent process disruptions, as Keurulainen (2024) recommends. 

Additionally, organizations such as IBM have designed predictors that enable them to determine and evaluate 

the vulnerability of supply chains to climate change. These models assess parameters like weather conditions, 

infrastructure, and supplier locations to trace risky regions. Implementing this data into companies’ supply chain 

management means that plans can be enhanced, even when some dangers are worth avoiding (IBM, 2020). 

 

3.4.5. Supplier Administration 

Supplier management is a sophisticated process that involves managing supplier relationships, supervising 

contracts, and minimizing risks associated with such partnerships. This is where data science comes in handy: when 

supplier performance data is analyzed and evaluated for risk, the supplier selection process is improved. 

Stefanovic (2014) reported that embracing data science models can enhance supplier performance by 10–15 

percent by observing reliability, quality of supply, and delivery performance in real time. For example, Siemens 

(2021b) deploys data mining in real time to evaluate suppliers' efficiency and reliability. Highlighting delivery times, 

product quality, and environmental compliance gives Siemens the necessary information to decide which suppliers to 

work with (Thirumalai, 2014). 

Furthermore, data science techniques allow firms to minimize the time spent evaluating potential suppliers. 

Companies like Intel, for example, apply machine learning approaches to ranking their suppliers according to cost, 



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quality, and risk. This has enhanced the company’s supplier evaluation process by 20%, allowing it to work with the 

right suppliers (Intel, 2021). 

 

3.5. IoT in Supply Chain Management 

IoT is one of the pillars that have dramatically impacted SCM through the observation, optimization, and 

automation of the supply chain. Examples of IoT applications in SCM include shipment tracking, delivery tracking, 

capacity tracking throughout the warehouse, inventory tracking, storage environment tracking, and routine tracking 

and automation. With the help of IoT devices and sensors, businesses can gather and analyze data, significantly 

improving planning and controlling processes. This section discusses IoT contributions based on selected areas of 

SCM, supported by empirical evidence and case studies. 

 

3.5.1. Shipment and Delivery Tracking 

Thanks to advanced IoT, shipments and deliveries can be controlled in real-time, allowing for a better 

understanding of the entire supply chain process (see Figure 5). Smart tags embedded in the load, trucks, and 

containers allow continuous tracking of the shipment's location, temperature, humidity, and other aspects. 

 

 
Figure 5. IoT revolutionizes shipment tracking, enhances efficiency and reduces costs. 

 

The Figure 5 demonstrates how IoT-enabled shipment tracking using smart tags and sensors provides real-time 

monitoring of location, condition, humidity, and temperature. This technology improves timely deliveries by 20% and 

reduces costs by 15%, as seen in examples from BMW and DH. 

A report published by Deloitte in 2021 established that enterprises that embraced the technology with the 

application of IoT for shipment tracking witnessed a 20% increase in timely deliveries and a 15% decrease in 

transportation costs. This becomes equally important when time factors, such as health-related products and 

perishable goods, are critical in production and delivery to customers. For instance, DHL leverages IoT by 

incorporating tracking devices that monitor consignments' physical condition and location to ensure they arrive in 

the best condition (Deloitte, 2021b). 

Another example of IoT success is the automotive and industrial company BMW, which has installed IoT sensors 

in its supply chain to monitor the flow of specific auto parts across the globe. Real-time information about the location 

and status of shipments has helped BMW reduce its supply chain risks and increase efficiency (BMW, 2020). The 

application of IoT technology helps to make forecasts in shipments to avoid delays resulting from other factors. 

 

3.5.2. Capacity Analysis for Warehouses 

One of IoT's most noteworthy applications is in warehouse capacity monitoring. Internet of Things sensors in 

warehouses include space occupancy, inventory, and product movement within the warehouse. Such information 

assists firms in managing warehouses by making the best use of space. 



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McKinsey & Company (2022) showed that IoT utilization for warehouse capacity monitoring reduced 

warehousing costs by 25% and upgraded inventory turnover by 30%. For instance, Amazon incorporates IoT sensors 

and robotics in its fulfillment centers to identify the number and location of products in available spaces and optimize 

storage (McKinsey & Company, 2022). IoT has improved picking and packing performance, making Amazon's order 

fulfillment faster. 

Furthermore, IoT sensors can identify open spaces in warehouses, allowing organizations to enhance space 

utilization and potentially eliminate the need for new warehouse construction. This is not only cost-effective but also 

helps control the environmental impact of warehousing. 

 

3.5.3. Inventory Management 

As one of the supply chain segments, inventory management is an essential element in which IoT can be helpful. 

The implementation of IoT sensors facilitates automated measurement of inventories by regularly capturing the 

quantity and availability of stocks, usage rates, and demand. This, in turn, offers organizations an alternative to 

holding too much or too little stock, saving on holding costs while enhancing customer satisfaction. 

When Accenture (2021) studied an IoT use case for inventory management, businesses reported a decrease in 

inventory by 15–30 percent without compromising or even potentially improving service quality. Walmart uses IoT 

sensors and RFID technology to track inventory without physically handling the products, which helps with better 

restocking and demand estimation (Accenture, 2021). This has helped reduce stockout occurrences, especially during 

the holiday seasons, negatively affecting customer satisfaction. 

IoT devices also support information that can be used to enhance inventory ordering mechanisms. If the amount 

of stock reaches a low level, the system can order the product to refill the stock and avoid running out of products. 

Such automation enhances productivity and enables employees to focus on higher value-added roles. 

 

3.5.4. Periodical Check of Storage Conditions 

In some industries, the quality of the product is highly influenced by the environment in which it is stored (such 

as pharmaceuticals, food, and chemicals). IoT sensors monitor the storage environment, including temperature, 

humidity, and light exposure. Research has established that proper product storage enhances a firm’s ability to avoid 

spoilage while adhering to regulatory requirements. 

For instance, Pfizer deploys IoT intelligent temperature sensors in its supply chain to track the conditions to 

which vaccines are exposed. These sensors provide instant notifications if temperatures exceed the set range necessary 

for the vaccines to remain potent during transportation and storage (Pfizer, 2021b). Gartner (2020a) identified 

research stating that firms employing IoT in SCM realized a 20% reduction in spoiled products and a 15% 

improvement in regulatory compliance. 

In the food industry, IoT sensors are used by Nestlé to monitor storage conditions. By ensuring that perishable 

products are correctly stored, Nestlé has minimized wastage and improved product quality (Gartner, 2020a). This 

benefits the organization in terms of increased customer satisfaction and helps minimize the environmental impact of 

supply chain management. 

 

3.5.5. Routine Optimization and Automation 

IoT makes it easier to perform routine optimization and automation in SCM by acquiring real-time data on 

various aspects of the supply chain that can be used to improve processes. Remote equipment can be used to assess 

the performance of machinery, monitor employee activity, and track the movement of products within the supply 

chain, identifying potential areas for improvement. 

Capgemini (2021) showed that industries leveraging IoT for routine process improvements saw efficiency gains 

of 20–30%. IoT makes P&G’s factories smart by determining equipment parameters and correlating them with the 



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likelihood of failure shortly. This has minimized downtime by 25%, while enhancing overall performance (Capgemini, 

2021). 

Moreover, IoT enables the automation of repetitive tasks like order selection, packaging, and delivery. Zara has 

adopted IoT in its depots to increase automation, reduce labor expenses, and expedite order processing (Zara, 2021). 

With such tasks being automated, Zara has quickly responded to changes in consumer demand, enhancing its 

competitiveness in the international fast fashion industry. 

 

3.6. RFID in Supply Chain Management 

RFID as an application has significantly impacted today’s supply chain management (SCM). This technology 

creates new opportunities for a company to manage its assets, monitor stock, and improve the efficiency of 

warehouses, logistics, supply chain, and retailing. Drawing on empirical data and pertinent examples, this section 

critically discusses how RFID can be used in SCM. 

 

3.6.1. Warehouse Management 

In warehouse management, RFID technology has significantly changed how organizations manage the flow of 

goods. With RFID tags and readers applied in warehouses, it is convenient for a company to monitor the position of 

products in real-time, increasing efficiency and requiring less workforce. Compared to conventional barcode 

technology, RFID does not require an object to be aimed at directly to be scanned, making object scanning faster and 

more precise. 

Specifically, Accenture (2020b) argued that warehouses employing RFID reduce labor costs by 15–30% while 

gaining a 25% enhancement in inventory reliability. Amazon uses RFID in its fulfillment centers to track stocks, 

ensuring faster order processing and minimal picking errors (Accenture, 2020b). The integration of a tracking system 

to monitor the flow of goods without the need to involve personnel speeds up warehouse operations. 

Additionally, RFID systems can easily interface with warehouse management software (WMS) to further 

enhance inventory tracking activities and arrange the goods in proper storage. For instance, Siemens adopted RFID 

technology to automate warehouse procedures, reducing the time for product identification and palletizing by 30% 

(Siemens, 2021a). 

 

3.6.2. Inventory Management 

One of the most practical uses of RFID technology is in inventory management. RFID technology makes real-

time tracking of products possible, helping businesses achieve efficient stock management and reduce stock surplus. 

RFID tags differ from barcodes because they can hold significant information and provide the product’s location, 

status, and movement at any given time. 

Zebra Technologies (2021) identified that RFID has helped firms improve inventory visibility by 25% while 

reducing shrinkage by 20% among those that adopted it for inventory control. Walmart has led the way in 

implementing RFID in its stores and retail distribution centers. Walmart realized that RFID helped cut its inventory 

costs by 15% and increased shelf inventory accuracy or availability since products were reordered before they ran out 

of stock (Zebra Technologies, 2021). 

Moreover, RFID technology adopted in stores helps perform automatic stock-taking, eliminating the need for 

physical counts. These audits give the Company real-time information required to update stock periodically. 

Carrefour, a European retailer, reduces inventory through stock counts over RFID, which cuts the time taken to 

perform audits by 50% and increases data accuracy (Carrefour, 2020). 

 

 

 



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3.6.3. Freight Transportation 

In freight transportation, RFID increases efficiency by enabling better shipment tracking. RFID tags attached 

to shipping containers or affixed to each product enable companies to track the status of assets in transit. They also 

foster high visibility of business operations, facilitate logistics management, reduce transportation costs, and ensure 

timely delivery. 

DHL (2022) reported that RFID technology reduced shipment processing time by 20%, and lost shipments 

decreased by less than 10%. RFID technology is used by Maersk, a global shipping company that tracks containers 

in real time. An effective container tracking system provides location updates for items being shipped with Maersk 

and helps the firm enhance asset turnover, reduce container idle time, and lower shipping costs (DHL, 2022). 

RFID also improves the security of freight transport by tracking the state of goods throughout transportation. 

With the help of RFID sensors, companies can recognize changes in temperature, humidity, or vibration, ensuring 

that goods are delivered in appropriate conditions. This is especially crucial for industries dealing with products that 

must meet certain standards, such as pharmaceuticals or food. For example, Pfizer implements RFID technology to 

track vaccine temperatures during transportation, ensuring proper conditions (Pfizer, 2021a). 

 

3.6.4. Supply Chain Visibility 

Supply chain visibility is one of the most important factors in today’s complex supply chains. RFID enables real-

time tracking of materials and products from raw material suppliers through distributors to customers. This increased 

visibility allows timely responses to disruptions, optimization of inventory, and improved customer service. 

McKinsey & Company (2021d) established that companies applying RFID to increase supply chain visibility saw 

lead time reduced by 15–20% and on-time delivery increased by 10%. Ford uses RFID systems across its supply 

chains to monitor the position of strategic components in real-time. This has eliminated production delays and 

enhanced the firm’s capacity to meet client needs (McKinsey & Company, 2021a). 

It also helps companies track the performance of suppliers they conduct business with by tracking their suppliers’ 

data in real time. Monitoring the flow of goods from suppliers enables firms to predict bottlenecks in the system 

accurately and take action to minimize lead time. Unilever applies RFID technology to monitor its suppliers' 

shipments and avoid potential supply chain disruptions (Unilever, 2020). 

 

3.6.5. Retail Management 

RFID has also transformed retail management by providing retailers with real-time visibility into inventory 

levels, reducing shrinkage, and improving customer service. In retail environments, RFID tags can be used to track 

individual products, ensuring that shelves are stocked and that customers can easily find what they are looking for. 

A report by Gartner (2022c) found that retailers using RFID experienced a 15-30% reduction in shrinkage and 

a 10% improvement in sales due to better stock visibility. Zara, a global fashion retailer, has implemented RFID in 

its stores to track inventory in real-time. By providing accurate data on stock levels, Zara ensures that its shelves are 

always stocked with the right products, reducing stockouts and improving customer satisfaction (Gartner, 2022c). 

Additionally, RFID enables faster checkout processes by allowing retailers to scan multiple items at once, 

reducing wait times for customers. Decathlon, a leading sporting goods retailer, uses RFID to enable self-checkout 

in its stores. Customers can scan their items with an RFID reader, and the system automatically detects the products 

and processes the payment. This has improved the customer experience and reduced the time spent in line by 30% 

(Decathlon, 2021). 

 

3.7. Robotics and Automation in Supply Chain Management 

Robotics and automation are essential strategic tools in today’s SCM, offering increased efficiency, accuracy, and 

decreased costs. With technologies like AS/RS, AGVs, collaborative robots, and controls, organizations can reduce 



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worker intervention and increase efficiency. This section critically evaluates the use of robotics and automation in the 

context of supply chain management, with special consideration given to warehouse management, automation, and 

supervisory control. 

 

3.7.1. Warehouse Management 

There has been tremendous change in warehouse management brought about by robotics and automation. 

Warehouse management needed manual effort from people in the past, and this is quite a disadvantage since it is 

time-consuming and prone to errors, thus increasing the cost of running the whole process. Today, robotics has 

enabled companies to enhance processes, including picking, packing, and inventory control. 

Dhaliwal (2020) states that the use of robots in warehouses has helped to act as robots to increase productivity 

by 30 % while, at the same time, cutting down the costs of human labor by 20%. Amazon is a clear example of this, 

having implemented the use of robotic arms, as well as mobile robots within its fulfillment centers. These operation 

robots are companions with human employees to minimize mistakes and enhance order fulfillment (Dhaliwal, 2020). 

Awareness can also be constant because robots do not require any rest, thus making the operations in the 

warehouse efficient. McKinsey & Company (2021e) revealed that firms implementing robotic systems in warehousing 

cut the processing time by 25% while achieving 15% warehouse efficiency. This level of automation assists businesses 

in working with increased numbers of products without compromising accuracy and speed (McKinsey & Company, 

2021b). 

 

3.7.2. Automated Storage and Retrieval Systems (AS/RS) 

Automated Storage and Retrieval Systems are one of the significant categories of warehouse automation systems 

that find applications in the appropriate picking of stored products, especially those with high cube utilization but 

requiring low order selection variety. AS/RS systems involve the storage and retrieval of goods and products by 

manual handling equipment without the intervention of man and, hence, the use of robotic systems to move goods in 

a particular warehouse. This saves time, helps avoid contact with people, and ensures the compactness of the storage 

space. 

Deloitte (2021a) reported that, companies that employed AS/RS garnered a 40% reduction in storage area 

footprint and a 30% improvement in throughput. For example, Walmart utilized this technology in its warehouses 

to enhance stock control. Such systems help Walmart increase the density of specific products while improving 

efficiency in picking and space search (Deloitte, 2021a). 

The use of AS/RS systems also has another advantage: the time it takes to fulfill an order is also reduced. When 

using automated retrieval systems, Karpova (2022) showed that picking times were reduced to half, thus enabling 

companies to respond effectively to increased customer demand for expeditious deliveries. Besides, these systems can 

work in various hostile environments, for example, in refrigerated warehouses where people can work with some 

constraints (Karpova, 2022). 

 

3.7.3. Automated Guided Vehicles (AGVs) 

Automated Guided Vehicles (AGVs) are a standard supply chain technology used to transport materials within 

a warehouse, factory, and distribution center. Partially automated guided vehicles, or AGVs for short, move along 

specific tracks that may be marked using sensors, lasers, or magnetic strips, thus reducing the need for human control. 

These vehicles can lift large loads, which will help eliminate some of the handling equipment, such as forklifts. 

Guru, Khan, and Deshmukh (2018) have indicated that companies using AGVs have experienced a 20% decrease 

in material handling costs and an increase in operational efficiency by 15%, as supported by Bechtsis, Tsolakis, 

Vlachos, and Iakovou (2017). Toyota employs AGVs in all its manufacturing facilities to move parts from assembly 



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lines to stores. By implementing AGV systems, Toyota has increased the manufacturing productivity of materials 

and overall workplace safety by automatically handling material movement (Bechtsis et al., 2017). 

AGVs also help minimize downtime and enhance safety, which he believes has benefits. Kiva Systems, now a 

subsidiary of Amazon Robotics, has come up with vehicles that transport shelves of products to employees for packing 

and sorting. It also cuts the number of hours the workers are required to walk, something that would not be possible 

with human-operated forklifts (Amazon Robotics, 2021). The AGVs can also be used in highly complicated and 

dynamic environments. With the increased technological features, they can avoid the obstacles in their path within 

their working area. 

 

3.7.4. Collaborative Robots (Cobots) 

Collaborative robots, or cobots, are designed to work alongside human workers, enhancing productivity without 

replacing human labor entirely. Unlike traditional industrial robots, which are typically isolated for safety reasons, 

cobots are equipped with advanced sensors and AI systems that allow them to operate safely in close proximity to 

people. 

A study by Boston Consulting Group (2020) found that the adoption of cobots in supply chains can increase 

productivity by 20-40%. Cobots can handle repetitive tasks, such as packaging, labeling, or quality control, freeing 

up human workers to focus on more complex and value-added activities. ABB Robotics has developed cobots that 

work in automotive supply chains, performing tasks such as assembling small components and checking product 

quality (Boston Consulting Group, 2020). 

Cobots also offer flexibility and scalability. Universal Robots has developed cobots that can be easily programmed 

to perform a wide range of tasks, making them ideal for small and medium-sized enterprises (SMEs) with varying 

production needs (Universal Robots, 2021). The collaborative nature of these robots allows companies to scale their 

operations quickly and adapt to changes in demand without the need for extensive reconfiguration. 

 

3.7.5. Control and Supervision 

Robotics and automation in supply chains require sophisticated control systems to ensure that all processes run 

smoothly and efficiently. Advanced control systems use artificial intelligence (AI) and machine learning (ML) to 

monitor robotic operations, optimize workflows, and prevent equipment failures. These systems provide real-time 

data on the performance of robots, enabling companies to make data-driven decisions and improve operational 

efficiency. 

A report by Gartner (2021a) revealed that companies using AI-driven control systems in their supply chains saw 

a 15-25% improvement in efficiency and a 20% reduction in downtime. Siemens, for example, uses AI-powered control 

systems to monitor its automated manufacturing plants. These systems analyze data from sensors in real-time, 

predicting equipment failures before they occur and scheduling maintenance proactively (Gartner, 2021a). 

Moreover, AI-driven control systems enable supply chain managers to optimize robotic workflows, ensuring that 

resources are allocated efficiently. GE Healthcare uses control systems to monitor its robotic systems in real-time, 

allowing for adjustments to be made dynamically based on demand fluctuations. This has led to a 20% increase in 

production efficiency and a 15% reduction in lead times (GE Healthcare, 2021). 

 

4. ADVANTAGES OF ADOPTING EMERGING TECHNOLOGIES IN SUPPLY CHAINS 

4.1. An Integrated and Resilient Supply Chains 

In recent times, there has been a noticeable surge in interest in supply chain resilience, with practitioners and 

scholars concentrating on creating a supply chain that can withstand unfavourable circumstances (Chatterjee, 

Chaudhuri, & Vrontis, 2024). Organizations can implement emerging technologies like blockchain (Bayramova, 

Edwards, & Roberts, 2021; Kurpjuweit, Neumann, & Müller, 2021) Industry 4.0, and artificial intelligence (Birkel & 



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Müller, 2021) to further enhance the effectiveness and resilience of their sustainable supply chain management 

(SSCM) concept (Karmaker et al., 2021). To increase the robustness of their SSCs, many businesses would rather go 

beyond remote monitoring to control, optimization, and sophisticated autonomous AI-based systems. While AI 

technologies have applications in marketing, logistics, and production, they are also applicable in nearly every other 

area and subfield within supply chain management (SSCM). These applications include high accuracy, high 

throughput, and fast issue-solving (Kazancoglu, Ozbiltekin-Pala, Mangla, Kumar, & Kazancoglu, 2023). Adopting 

I4.0 technologies, often known as IDT of I4.0, offers significant technical advancements that make it possible to 

integrate real-time supply chain partners and gather and analyze massive amounts of data automatically. Making 

more precise judgments and enhancing supply chain integration may be facilitated by using more IDT of I4.0, such 

as cloud computing, big data, and the Internet of Things (IoT) (Oliveira-Dias, Maqueira-Marín, & Moyano-Fuentes, 

2022). Additionally, it is predicted that Industry 4.0 technologies powered by ICT would improve process integration, 

leading to long-term organizational performance. BT is an organizational capacity that unifies all of the resources 

and assets of SC, enhancing tasks like information sharing, product monitoring, and transaction transparency. In 

addition to its fundamental advantages, blockchain provides a platform for integrating cutting-edge technology like 

AI and IoT. To demonstrate how blockchain technology has not only streamlined current procedures but also opened 

the door for innovative business models and cooperative ecosystems, case studies and experimental projects are 

investigated (Oriekhoe et al., 2024). BT adds an extra degree of protection against intrusions and data breaches, which 

often happen through network-level attack vectors, by encrypting all data shared inside a network. Decentralization 

reduces the risk of a single point of failure, while the traceability feature of a permanent record of all transactions 

carried out by authorized users in the permissioned network eliminates the threat of insider attacks (from people as 

an attack vector) (Bayramova et al., 2021). The creation of a digital SC twin, or computerized digital SC model that 

represents the network state in real-time and enables complete end-to-end SC visibility to strengthen resilience and 

test contingency plans, is possible with the use of data analytics to enhance the current decision-support tools. For 

planning and making choices about control in real-time, a digital twin may be utilized to simulate the physical SC by 

using real transportation, inventory, demand, and capacity data (Ivanov & Dolgui, 2021). Utilizing supplier IT for 

exploitation streamlines upstream structured activities such as material shipment, inventory management, invoicing, 

and buying. Businesses may swiftly find alternative materials by using standardized and institutionalized information 

when the upstream is harmed by disruptions. This enables businesses to address material shortages and bounce back 

from disruptions quickly, which improves supplier resilience (Gu, Yang, & Huo, 2021). 

 

4.2. Addressing Significant Challenges 

The primary obstacles to AI adoption in supply chain management include change management, current 

technological constraints, human acceptance of these approaches, comprehension and usefulness of these techniques, 

and people's existing expertise, in addition to the high implementation costs of such solutions. Other obstacles include 

a lack of openness, problems with security and privacy, a lack of technological principles and abilities, and deficiencies 

in data, documentation, and the resilience of these solutions (Hangl, Behrens, & Krause, 2022). It is not easy to 

integrate blockchain into supply chain management. It is necessary to carefully negotiate regulatory constraints, 

scalability problems, and interoperability issues. Unlocking blockchain's full potential and guaranteeing its smooth 

incorporation into various supply chain contexts require industry-wide cooperation and standardization initiatives 

(Oriekhoe et al., 2024). Even though the developing IDT of I4.0 has attracted attention recently, research on the 

advantages and difficulties of adopting these IDT and their role in fostering an agile supply chain is still in its infancy. 

Cybersecurity, sophisticated and collaborative robots (CObots), virtual or augmented reality, and other technologies 

are not well understood (Oliveira-Dias et al., 2022). The fundamental issues with blockchain technology, such as 

security, usability, and technological immaturity, comprise the technological hurdles. Policies, culture, and 

managerial commitment are examples of organizational aspects. The supply chain (inter-organizational) perspective 



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encompasses issues such as lack of knowledge, difficulty with collaboration, and information disclosure (Kouhizadeh, 

Saberi, & Sarkis, 2021). One of the biggest obstacles to the effective adoption of blockchain technology is the business 

owner's reluctance to try out novel technologies. A significant obstacle to striking a balance between the benefits 

offered and the possibility of any unexpected repercussions that may follow is regulatory ambiguity (Mathivathanan, 

Mathiyazhagan, Rana, Khorana, & Dwivedi, 2021). The most significant obstacles are transaction-level uncertainty 

(B1), usage in the underground economy (B2), management commitment (B5), scalability issues (B3), and privacy 

threats (B4), in that order of significance (Vafadarnikjoo, Badri Ahmadi, Liou, Botelho, & Chalvatzis, 2023). IoT has 

a lot of potential uses in supply chains, however there are a lot of implementation issues with the technology. The 

supply chain faces major obstacles in utilizing IoT to its full potential, including security, privacy, and scalability. IoT 

is a wireless technology, and numerous sensor nodes provide the foundation for applications. As a result, it raises 

several possible security issues for users with relation to data storage, data breach during wireless transmission, and 

storage site security. Certain IoT data are extremely sensitive, may have significant societal repercussions, and are 

legally protected. RFID technology has the potential to violate civil rights and harm consumer privacy if protections 

are not put in place (Attaran, 2020). Given that supply chains are intricate Systems of Systems (SoS), cyberattacks 

may have an impact at the corporate level, particularly if supply chain components depend on data from the Internet 

of Things. Their effective security is challenged by the integration of infrastructure, technology, and supply chain 

subnetworks into broader military ecosystems. Implementing Enterprise Architecture (EA) strategies is one possible 

way to lessen the hazards that system integration within complex supply chain systems poses (Sobb, Turnbull, & 

Moustafa, 2020). 

4.3. Opening Doors to Future Innovations 

New technology developments in the SC field prove to be game-changers for many firms. Several writers claim 

that AI in supply chain management (SCM) would enable businesses to see everything from the raw material to the 

final customer, giving them more time to make choices and take remedial action (Hangl et al., 2022). Blockchain will 

become an essential instrument for the optimization and transformation of global supply chains as a result of cross-

industry collaboration and the development of decentralized autonomous organizations (DAOs). Businesses who 

adapt to this changing environment by taking proactive measures to overcome obstacles, valuing teamwork, and 

utilizing all of block chain's possibilities will not only streamline their supply chain processes but also set themselves 

up for success as a leader in the coming era of international trade. As the voyage proceeds, block chain serves as a 

light, pointing the way in the direction of supply chain management's more inventive, transparent, and efficient future 

(Oriekhoe et al., 2024). SCRM is being revolutionized by AI and ML, which make predictive modeling possible for 

more precise risk assessment (Coker, Uzougbo, Oguejiofor, & Akagha, 2023). The growing integration of robots, 

automation, machine learning, and artificial intelligence will define future developments in SCRM and technology. 

These developments enable businesses to automate decision-making procedures, streamline warehousing and 

logistics, and proactively detect and address hazards. Organizations who adopt and strategically apply these 

technologies will be better able to handle the complexity of today's supply chain environment as these trends continue 

to develop (Odimarha, Ayodeji, & Abaku, 2024). Proactive risk mitigation techniques are made possible by early 

detection of possible threats. Decision-making processes are improved by increased forecasting and risk assessment 

accuracy. In order to forecast the possibility of disruptions, AI algorithms may examine past supplier performance, 

market trends, and geopolitical variables. This capability enables businesses to take proactive risk management 

measures and make well-informed decisions. One of the main trends in future SCRM is the automation of decision-

making processes with AI and ML (Ganesh & Kalpana, 2022). In summary, there is a rising awareness of blockchain's 

ability to improve efficiency, transparency, and collaboration in supply chains, which characterizes the present stage 

of its adoption.  

These studies offer important insights into the major success factors, difficulties, and directions for future study 

related to the effective application of blockchain in supply chains. 



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5. SUCCESSFUL CASE STUDIES 

Essentially, a blockchain is an extensive network of IT systems that functions as a digital record of transaction 

volume spread throughout the network. In the development of e-commerce, this technology serves as a dependable 

layer. To digitize the food supply chain process and bring transparency to the decentralized food supply ecosystem, 

Walmart and IBM have been collaborating on a food safety blockchain solution. The success of Walmart's blockchain 

experiment depended on departmental collaboration. Because blockchain technology aligned with the regulators' 

mission, they were intrigued by its potential (Sharma & Kumar, 2021). Four major players in the grocery and food 

industries—Walmart Stores Inc., Nestlé S.A., International Business Machines Corporation, and Dole Food Company 

Inc.—decided to collaborate to address issues facing the global food supply chain. This decision is detailed in the case 

study, Applications of Blockchain Technology in Business and Information Systems. Their goal is to accomplish this 

by tackling the issue of food safety by collaborating with several partners to create a blockchain-based traceability 

architecture. To explore the blockchain's potential for tracking food product origin, either individually or to obtain 

industry-wide insights, Walmart has conducted two tests thus far (Eze, Ugwu Chinyere, & Ogenyi Fabian, 2024).  

The success of Amazon.com is a result of its significant commitment to automation innovation. The business 

started developing and implementing a variety of autonomous robots after acquiring Kiva System in 2012, including 

the Palletizer, Robo-Stow, and several drive unit variations. The acquisition of CANVAS Technology, a technology 

that would be utilized to develop new drive robots with enhanced vision systems, demonstrated the company's 

ongoing commitment to innovation. Additionally, Amazon.com collaborates with businesses like SmartPac and 

CartonWrap to automate the packaging and wrapping of goods for delivery. The Upskilling 2025 effort was started 

by Amazon.com and offers a variety of programs to help employees learn and advance their abilities in fields including 

software engineering, IT, machine learning, and cloud computing (Laber, Thamma, & Kirby, 2020). Amazon adopted 

the concept of F-Warehouses a new type of fulfillment warehouse that solely handles online orders. This allowed for 

an investigation, which revealed that the corporation operated over 175 of these types of centers, able to fulfill up to 

0.5 million units daily (Onal, Zhu, & Das, 2023). 

Online grocery delivery is Ocado's business, and it boasts the largest and most automated warehouse in the world. 

In its automated warehouses in London, Ocado employs a fleet of 3,000 robots that get to work as soon as an order 

is placed and received. These robots head straight to the containers holding the necessary items and start the fulfilling 

procedure there. They shift apart by around five millimeters, illustrating how well-organized and fluid the codes and 

algorithms are, ultimately enabling Ocado to optimize operational efficiency and overall company success. Ocado's 

satisfaction the corporation can deliver 50% of client orders within 4 hours, compared to 10% if no warehouse 

automation was implemented, thanks to the 26 centers' architecture, which includes thousands of bots capable of 

picking 50 products in a matter of minutes (Savushkin, 2024). 

With the might of a global corporation and the inventiveness of a start-up, DHL. DHL picks, sorts, and tags 

items in the warehouse using Industry 4.0 technologies including AR (Augmented Reality) and IoT (Internet of 

Things). With the usage of Vuzix smart glasses, employees were able to operate in warehouses without using their 

hands thanks to the benefits of augmented reality (Patil, 2020). Furthermore, DHL Logistics Company investigates 

a wide range of augmented reality applications in several supply chain activities, including transportation 

optimization, last-mile delivery, warehouse operations, and improved value-added services (Kamau & Murori, 2024). 

 

6. CONCLUSION AND IMPLICATIONS 

The aim is to analyse how in the ecosystem of supply chain management, digital transformation is essential for 

business sectors to preserve their competitive edge and achieve operational efficiency. In the ecosystem of supply 

chain management, digital transformation is essential for business sectors to preserve their competitive edge and 

achieve operational efficiency. Five more efficient uses for the single answer were found by the investigation. Demand 

forecasting, warehouse automation, transportation and route optimization, supplier selection and management, and 



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predictive maintenance are all made possible by artificial intelligence. Blockchain makes monitoring and transparency 

possible, improving traceability, reducing counterfeiting, promoting ethical and sustainable sourcing, and facilitating 

intelligent payments. Better communication, cost monitoring, inventory control, tracking key performance indicators, 

and optimized visualization are all guaranteed by business intelligence. Demand forecasting, route optimization, 

inventory control, risk assessment, and supplier management are all made easier by data science. Tracking shipments 

and deliveries, inventory management, warehouse capacity monitoring, storage condition monitoring, routine 

optimization, and automation are all made possible by IoT. RFID works well for supply chain visibility, retail 

management, freight transportation, warehouse management, and inventory management. Automation and robotics 

are used in collaborative robots, automated guided vehicles, automated storage and retrieval systems, warehouse 

management, control, and monitoring.  

Leaders in the industry and policymakers are urged to take these suggestions into account to fully utilize 

blockchain technology and improve SCM processes' resilience, sustainability, and efficiency. SCM's future is in 

utilizing these cutting-edge technologies, and blockchain is at the vanguard of this revolutionary voyage. For SCRM 

to be effective, industry stakeholders must work together. Organizations may communicate pertinent information 

about industry trends, best practices, and possible hazards by forming partnerships for data sharing. By establishing 

cooperative platforms, partners and rivals may both add to a shared knowledge of the supply chain environment and 

enable better-informed risk management tactics. The idea behind blockchain technology is to eliminate the supply 

chain's conventional division. Blockchain offers a new value creation based on automotive supply chain theories, which 

is a basis for additional empirical research, from the perspectives of extensibility, degrees of freedom, redesign of 

automotive supply chain visibility, operational efficiency, and new business model. Managers and staff members need 

to be more open-minded for digital technology to be used successfully. The process of digital transformation is mostly 

driven by the executives. They must embrace technology and give it top attention. They must critically examine their 

company and its goals; they must also establish business cases, solutions, strategies, and roadmaps. 

 

Funding: This study received no specific financial support.    
Institutional Review Board Statement: Not applicable. 
Transparency: The authors declare that the manuscript is honest, truthful and transparent, that no important 
aspects of the study have been omitted and that all deviations from the planned study have been made clear. 
This study followed all rules of writing ethics. 
Competing Interests: The authors declare that they have no competing interests. 
Authors’ Contributions: All authors contributed equally to the conception and design of the study. All 
authors have read and agreed to the published version of the manuscript. 

 

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