Aligning Supply Chain Functions with Emerging Technologies: A Strategic Approach ^†

Abstract

This paper delves into the changing landscape of supply chain management, exploring the influence of advanced technologies like blockchain, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML). Using a comprehensive PRISMA-based approach, we sift through literature, real-world applications, and success stories to uncover key insights. From established technologies like AI and ML to emerging players like IoT and blockchain, we highlight their continuous evolution. The paper details practical applications across various supply chain functions and acknowledges research gaps, emphasizing challenges like scalability and interoperability. Ultimately, it underscores the transformative potential of these technologies, positioning supply chains as intelligent entities shaping the future of global commerce in Industry 4.0.

1. Introduction

In an era defined by relentless technological advancement, the landscape of supply chain management stands at the precipice of a profound transformation. This study investigates the relationship between supply chain complexities and emerging technologies. This technological revolution is being propelled by four major technologies: artificial intelligence (AI), machine learning (ML), blockchain, and the Internet of Things (IoT). The incorporation of these technologies is strategically significant for effectively managing the complexities of modern supply chains, given the growing demand for efficiency, transparency, and flexibility across various industries [1].

Blockchain, often synonymous with its origin in cryptocurrency, has evolved into a system for establishing trust and transparency within supply chains. Its decentralized and immutable ledger offers a singular version of truth, mitigating risks associated with fraud and counterfeiting. Simultaneously, the Internet of Things (IoT) amplifies the potential for data-driven decision-making by orchestrating a symphony of interconnected devices. Real-time monitoring of assets, inventory, and environmental conditions facilitates agile and responsive supply chain management [2]

Artificial intelligence (AI) emerges as a game-changer, augmenting decision-making capabilities through machine learning algorithms. These algorithms, fueled by vast datasets, empower predictive analytics for demand forecasting, inventory optimization, and route planning. AI-driven insights enhance operational efficiency and enable proactive responses to disruptions [3]. Complementing AI, machine learning (ML) propels intelligent automation within supply chain ecosystems. Continuous learning algorithms refine predictive capabilities, allowing organizations to automate routine tasks, optimize resource allocation, and enhance overall resilience [4].

This exploration delves beyond the isolated prowess of each technology, aiming to understand the synergies that emerge when they come together. Real-world applications and success stories serve as waypoints, guiding the journey toward understanding the transformative potential that blockchain, IoT, AI, and ML collectively hold for the future of supply chain management. As we navigate this technological landscape, this research paper contributes to the evolving storyline that portrays supply chains as more than just logistical entities but as dynamic, intelligent organizations positioned to influence the dynamics of Industry 4.0.

RQ1:

Which emerging technology to use in which function of a supply chain?

2. Methodology

This research paper utilizes a comprehensive methodology, incorporating the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique to explore the revolutionary potential of four major technologies in supply chain management: blockchain, machine learning (ML), artificial intelligence (AI), and the Internet of Things (IoT) [5].

The study paper’s selection of the PRISMA methodology is based on its thorough and resilient approach to investigating the transformative possibilities of cutting-edge technology in supply chain management. The use of the PRISMA approach offers several benefits that enhance the dependability, comprehensiveness, structured eligibility, and clarity of the research [1].

One particularly well-known and respected framework in the field of systematic reviews and meta-analyses is the PRISMA technique as in Figure 1. Its methodical and structured approach guarantees an in-depth and well-organized investigation of the topic, enabling a careful analysis of the application and consequences of cutting-edge technologies—specifically, blockchain, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) in supply chain management [6].

Through the application of the PRISMA technique, the following key steps were taken:

• Identification: A systematic search and identification of relevant literature through selective keywords, databases, academic journals, and industry publications related to the application and impact of these technologies to supply chain management.
• Screening: Selection of eligible studies (maximum over four years old) by going through the abstract of each work. The decision to limit the selection to studies published within the last four years is driven by the aim to capture the most recent advancements in supply chain management technologies. This timeframe ensures the research prioritizes current insights, aligning with the rapidly evolving nature of our field.
• Eligibility: Assessment of the selected studies to determine their suitability for inclusion in the review based on predefined criteria, which include the relevance and quality of the research objectives and the quality of evidence.
• Inclusion: Synthesis and analysis of the included studies to extract key findings, insights, and trends related to the application of blockchain, ML, AI, and IoT in SCM.
• Synthesis: Integration of the findings from the selected studies to provide a comprehensive overview of the transformative potential of these technologies in reshaping modern supply chains.

Furthermore, the methodology involves the exploration of real-world applications and success stories to illustrate the synergies that materialize when these technologies converge. By examining practical implementations and industry case studies, this research seeks to provide insights into the practical implications and benefits of integrating these technologies within supply chain management. Overall, the research methodology is designed to contribute to the evolving narrative that positions supply chains as dynamic, intelligent entities poised to shape the contours of Industry 4.0. Through a rigorous literature review approach, this research paper aims to offer valuable insights into the strategic assimilation of cutting-edge technologies in the constantly evolving field of supply chain management.

3. Literature Review

Emerging technologies in the supply chain landscape continuously reshape operations, enhancing efficiency, transparency, and adaptability. Cutting-edge technologies such as IoT, AI, robotics, blockchain, automation, machine learning, augmented reality, and 3D printing are changing the game in supply chains. While some, like robotics, automation, augmented reality, 3D printing, and big data, might not be brand new, their impact keeps growing.

3.1. Established Use Cases

Many of these technologies have already demonstrated their value and have established use cases within supply chains. For instance, robotics and automation are widely used in warehouses for picking and packing, while AI and big data analytics are employed for demand forecasting and optimization [1].

3.2. Integration and Optimization

Companies have been actively integrating these technologies into their supply chains, fine-tuning their applications, and focusing on optimizing their use for specific challenges and requirements.

3.3. Focus on Other Emerging Technologies

The spotlight often shifts to newer technologies that are in the earlier stages of adoption or that promise groundbreaking disruptions. Hence, while these established technologies are still crucial, attention might shift to highlight newer entrants to the tech landscape.

For these reasons, we chose to work on the following technologies: IoT, blockchain, machine learning, and AI. Their prominence and impact in the supply chain industry should not be understated, but they might not be considered as “new” emerging technologies due to their established roles and widespread adoption in various aspects of supply chain management.

3.4. Continuous Evolution

While they are not new, these technologies continue to evolve. AI, for example, is progressing into more sophisticated forms like explainable AI and reinforcement learning. Augmented reality applications are becoming more refined for training and maintenance tasks [7].

Industry 4.0 techniques will improve production and engineering systems, product and service quality, customer–organization relationships, business opportunities, economic advantages, academic standards, and working conditions across the value chain [8].

Machine learning is crucial for optimizing decision-making in supply chain management and the flow of products and services [9]. Regarding IoT, the study discusses IoT evolution, scrutinizing the relationship between IoT and global logistics and supply chain dynamics. It also delves into future challenges and risks associated with the adoption of IoT, exploring its broader integration within the global supply chain to sustain competitiveness by meeting evolving customer demands in the current market landscape [10]. Blockchain technology is used in various sectors, including finance, supply chain management, healthcare, and more; several advantages can be achieved, including improved sustainability, fewer errors and delays, lower transport expenses, quicker issue detection, enhanced trust with consumers and partners, and better management of product transport and inventory [11].

4. Application

4.1. Inventory Management

Effective inventory management leads to higher customer satisfaction and lower supply chain and logistical expenses. Numerous AI-based inventory management methods have been suggested, as mentioned below. A back-propagation neural network was created using sales data from an enterprise’s ERP system. The neural network was trained using historical sales data and evaluated using fictional simulations [12].

Another study optimized completed product inventory levels using an artificial neural network (ANN) model. The optimal network design used a feed-forward back-propagation ANN with four inputs (setup, holding, product demand, and material prices), ten hidden layer neurons, and one output [13]. IoT also helps in inventory management—IoT devices such as smart shelves, RFID tags, and sensors can be deployed to monitor product levels in real-time, providing retailers with actionable insights into their inventory.

4.2. Demand Forecasting

By anticipating demand, enterprises may reduce the bullwhip impact in their supply chains and produce or acquire goods to suit specific needs. Significant research has been conducted to construct AI-based demand forecasting models with higher accuracy compared to conventional approaches to create a neural network model for predicting future demand. The neural network toolbox in MATLAB was used to evaluate various feed-forward back-propagation neural network combinations [14]. Furthermore, as blockchain regulatory frameworks develop, organizations are increasingly able to use decentralized finance (DeFi) solutions for financial forecasting and planning. Industry-wide cooperation to establish interoperability standards and identify new applications may shape the future environment, enabling a smooth and superior incorporation of the blockchain era into planning and forecasting procedures [15].

4.3. Logistics Management

Logistics and transport use AI and ML to analyze massive volumes of data to detect trends and forecast demand. More efficient and effective routing and resource allocation may result from this. The integration of IoT technologies is revolutionizing fleet management and transportation operations. The transformative impact of IoT in logistics extends beyond efficiency gains; it fosters a responsive and adaptive logistics ecosystem that can swiftly address changes in demand, unforeseen disruptions, and dynamic market conditions. By embracing IoT in logistics, organizations position themselves at the forefront of innovation, ensuring agility and resilience in an increasingly complex and interconnected supply chain landscape. When the flow of commodities is fully visible, logistics operators can make more informed decisions because of this visibility [16].

4.4. Sourcing

AI can optimize procurement processes by examining supplier and other data to find patterns and trends to assist supply chain managers in making better choices. An ANN framework has been proposed for supplier selection to identify long-term, suitable suppliers using MLP (multi-layer perception) classification [17]. AI can analyze sensors and other data to predict supply chain difficulties and mitigate interruptions. Also, it can monitor weather and traffic to forecast cargo delays and adapt delivery timetables [9]. The advent of IoT is also revolutionizing sourcing practices by fostering a connected and transparent relationship with suppliers. Real-time visibility into supplier operations, facilitated by IoT devices, is transforming procurement processes. This connectivity reduces lead times, enhances communication, and cultivates collaborative partnerships between buyers and suppliers.

4.5. Manufacturing and Retail

In manufacturing, AI can monitor the production chain in real time to find inefficiencies and bottlenecks. This allows producers to swiftly detect and resolve faults, improving manufacturing efficiency. It can evaluate sensor and other data to forecast machine breakdowns, helping manufacturers plan maintenance and minimize expensive downtime. AI may also evaluate quality control data to find patterns and trends that may signal a product or process issue [9]. In retail, blockchain technology can be used to track counterfeit goods and for supply chain tracking, transforming payments, loyalty programs and customer data management, security, and sharing. It offers a fresh platform for connecting customer service providers with efficient distribution channels and introduces a new form of transaction for retail market participants [18].

4.6. Distribution

By leveraging IoT in distribution, organizations can identify potential bottlenecks, optimize routes, and make informed decisions to streamline the flow of goods. This not only improves operational efficiency but also enhances customer satisfaction through timely and accurate deliveries. AI and ML can improve delivery routes, anticipate vehicle and equipment maintenance, and detect supply chain interruptions. AI and ML can automate scheduling and dispatching, reducing costs and improving accuracy [19].

4.7. Reverse Logistics

Machine learning can be used in reverse logistics and supply chain management. Product returns management uses AI and ML to find trends and causes for returns. Organizations may increase product quality and minimize returns using this. In inventory management, artificial intelligence and machine learning systems may examine returned product data to optimize inventory levels and eliminate waste [20]. In the realm of product returns, IoT devices play a crucial role in providing visibility into the journey of returned items. By leveraging blockchain technology, companies can create a tamper-proof record of every transaction and movement of goods in the reverse logistics process, from the point of collection to the point of disposal or recycling.

4.8. Information Systems

IoT technologies facilitate the seamless integration of real-time data from diverse sources, creating a holistic and dynamic view of the entire supply chain ecosystem. These integrated data empower organizations with comprehensive insights into various facets of their operations, from inventory levels and production processes to transportation routes and customer demand. It can anticipate product demand using historical and real-time data. Predictive maintenance uses AI and ML to monitor equipment and forecast maintenance needs, decreasing downtime and expenses. Moreover, AI and ML algorithms can also detect and mitigate risks by analyzing supplier performance, market trends, and other data [21].

4.9. Risk Management

Blockchain is a powerful tool that can do some incredible things in risk management. It is not just about creating smart contracts that automate tasks like processing insurance claims; it is also about boosting security and privacy for users. By using decentralized applications, it can make data breaches and cyber-attacks less likely. It is a game-changer in how risk is handled, promising more transparency, lower costs, and stronger trust among everyone involved.

In the complex landscape of supply chain management, effective risk management is paramount, and IoT technologies play a crucial role in bolstering resilience. The real-time data insights provided by IoT devices enable organizations to proactively identify and mitigate potential risks. Whether monitoring geopolitical events, weather conditions, or supplier performance, IoT-driven risk management strategies empower organizations to stay ahead of disruptions. Predictive analytics, fueled by IoT data, allow for the identification of patterns and trends that may signal emerging risks, facilitating timely interventions [22].

4.10. Sustainability

AI and ML have several supply chain sustainability applications. They can assess transportation options, routes, and energy use to reduce carbon emissions. Also, they may examine supplier environmental and social performance to find sustainable sourcing solutions. In energy management, AI and ML algorithms can evaluate energy consumption data to find ways to save expenses and usage [19].

The integration of IoT technologies in supply chain operations is a driving force behind sustainable practices. Through the provision of real-time data on energy use, emissions, and environmental effects, IoT makes resource utilization optimization easier. In general, blockchain is very effective in making every function of supply chain operations sustainable. It can effectively change the atmosphere to a greater extent and can make supply chains greener through these functions [20].

5. Research Gaps and Limitations

There are many research gaps in planning and forecasting functions. Firstly, scalability remains a challenge, as blockchain networks can face barriers in dealing with massive volumes of statistics and transactions at excessive speeds. Addressing scalability problems is critical for sizable adoption in industries with enormous planning and forecasting needs. Secondly, interoperability among specific blockchain systems and present structures is a subject that needs attention. Seamless integration with present technology is important for a clean transition to planning and forecasting solutions based primarily on blockchain. Lastly, the improvement of standardized frameworks and protocols for blockchain implementation in making plans and strategies is important to ensure consistency, security, and simplicity of adoption throughout numerous industries [23]. Closing those research gaps will make a contribution to unlocking the total capacity of blockchain generation in reshaping planning and forecasting practices. The limitations of blockchain technology include the lack of established laws and regulations in the blockchain industry, which might cause consumer confusion. Additionally, blockchain is not a cheap solution, and the costs related to operating and implementing blockchain systems are significant and should not be overlooked [24]. Significant investments in technology, infrastructure, and human resources may be necessary for AI approaches. If data are inadequate or faulty, algorithms may produce false predictions or suggestions. AI and ML may need large technological and infrastructure investments, making them unsuitable for certain enterprises.

Heavy reliance on technology might reduce human competence and decision-making. It raises ethical considerations, notably with privacy and security. Furthermore, AI systems may unintentionally reinforce biases in the data they are trained on, resulting in unfair or discriminatory results. Finally, using AI approaches may require major changes in company cultures and practices, which might be difficult. Weighing the benefits and drawbacks of supply chain management with artificial intelligence is essential before making any decisions. One obstacle or restriction of IoT in supply chain management is the complexity and time required for the integration of IoT devices with current supply chain systems [25].

6. Conclusions

In conclusion, supply chain management as we know it is changing due to the incorporation of cutting-edge technologies like blockchain, machine learning, artificial intelligence, and the Internet of Things. When combined, these technologies provide special benefits like self-optimization, transparency, cognitive computing, and real-time monitoring. By strategically aligning these technologies, companies can enhance their operational efficiency, optimize resource allocation, and improve overall resilience. This paper provides valuable insights into the transformative potential of these technologies in reshaping modern supply chains, contributing to the evolving narrative that positions supply chains as dynamic, intelligent entities poised to shape the contours of Industry 4.0.