The Impact of Artificial Intelligence in Logistics and Supply Chain in the USA – Focusing on Leading Industries in the 21st Century

The Impact of Artificial Intelligence in Logistics and Supply Chain in the USA – Focusing on Leading Industries in the 21st Century

Benjamin Kwame Amponsah, Paul Boadu Asamoah and Manso Frimpong

Westcliff University, United States of America (USA)

DOI: https://doi.org/10.51244/IJRSI.2024.1111003

Received: 21 October 2024; Accepted: 26 October 2024; Published: 25 November 2024

ABSTRACT

Artificial Intelligence plays a crucial role in global supply chain management and logistics. It builds opportunities for cost reduction in purchase requirement planning, demand forecasting, sales/marketing, transportation, packaging, warehousing, inventory, production planning, finance, customer services, and information services. It also offers a competitive edge to the firms utilizing it. Artificial intelligence will likely make insightful decisions and improve efficiency via exceptional capabilities.

This research aims to highlight the contributions of AI to supply chain management and logistics by using a thorough literature review and systematic review of the current literature from the internet. This will focus on AI applications within top American industries, such as e-commerce, retail, pharmaceuticals, and automotive, by leveraging artificial intelligence tools such as machine learning, route optimization, predictive analytics, and robotics. This paper evaluates the benefits and challenges associated with artificial intelligence adoption, providing an extensive overview of how AI tools are rebuilding SCM performance while also spotlighting the challenges linked to implementation and data security. By supplementing the literature review with case studies and data analysis, this research highlights the pivotal role of AI in shaping the future of logistics and supply chain management in the 21^st Century.

This research shows that although AI provides significant opportunities for innovation and growth, it presents damaging challenges; hence, strategic planning and investment are needed to overcome these intertwined challenges. This study also offers insight and recommendations for industry stakeholders and policymakers aiming to leverage the full potential of AI in logistics and supply chain management operations.

INTRODUCTION

Background Information

The logistics and supply chain sector is a crucial part of the global economy that ensures the smooth flow of goods worldwide. In today’s complicated and intertwined marketplace, this sector experiences many challenges, from ensuring overall operational efficiency to inventory management (Khadem et al., 2023). This calls for smooth coordination across customer service, scheduling, and transportation domains.

Artificial intelligence has become a significant difference maker in logistics and supply chain management, providing solutions that fortify security, automate mundane tasks, reduce costs, optimize routes, and enhance customer experiences (Tomas et al., 2023). According to a quote made in 1965 by Herbert Simons, “Machines will be able to do any work a man can do.” Today, AI has come a long way and is close to what Simon forecasted fifty years ago. According to Balfaqih (2022), AI applications are saturating supply chain management and logistics since the sustained advancements in computing power blended with the widespread availability of data offer new chances to enhance supply chain decision-making. Data can initially come from digital logistics apps or the interconnection of assets via the Internet of Things technologies (Rosenberg, 2020). Artificial intelligence can also fuel the automation of well-defined workflows.

 By utilizing AI, firms can attain improved profitability, operational efficiency, and customer satisfaction. According to Verified Market Research, the market size for artificial intelligence in SCM and logistics in 2022 was $3037.98 million (“In-depth industry outlook: AI, 2024). Predictions show that by 2030, this number might increase to $64459.38, looking at the compound yearly growth rate of 46.50 percent from 2023 to 2030.

Purpose of this Research

This study proposes to analyze the influence of artificial intelligence on SCM and logistics in the US, focusing on top industries. This paper will evaluate how AI tools are being implemented, the advantages and challenges they bear, and their overall impact on industry competitiveness and efficiency.

Significance of the Research

The knowledge of AI’s role in SCM and logistics is essential for organizations that want to stay competitive in the ever-changing market. As companies experience the hiking demand for more quick and efficient supply chains, AI tools offer the potential to rebuild these procedures for the better (Nandi et al., 2024). This research will provide insights into how AI can enhance supply chain performance and generate recommendations for successful implementation.

Research Questions

How are artificial intelligence tools being implemented in logistics and supply chain management?

What are the primary benefits and challenges related to artificial intelligence in logistics and supply chain management?

Which industries are at the forefront of the adoption of artificial intelligence tools?

METHODOLOGY

The empirical approach used for this study is a systematic literature review, a popular research method in academic research to single out and analyze literature on a topic. To draft this paper, peer-reviewed academic articles were collected from the internet via reliable databases such as Sprinker, Google Scholar, Science Direct, and the JSTOR website. The search terms are all related to AI tools and their use in logistics and supply chain management for leading firms such as retailers and pharmacies. A multistep screening process was followed to double-check the relevance of the articles. At the start, 300 articles were obtained online, only guided by their titles. After deleting 50 duplicates, 250 papers were evaluated based on their abstract sections. Of the 250, 216 papers were eliminated based on content, leaving 34 papers utilized in this study. Various tests were not conducted for this research as it is a literature review emphasizing synthesizing and evaluating existing research and knowledge on the role of AI in logistics and SCM. However, the selected sources were thoroughly evaluated to identify the critical research gaps, themes, and trends correlated to the applications of AI in logistics and SCM. The articles were also analyzed based on their research relevance, quality, and rigor to the goals of this study. This study utilizes the systematic review method as it is an objective and robust approach to evaluating existing knowledge on a chosen topic. It also permits an extensive evaluation of the research opportunities and gaps in artificial intelligence, logistics, and SCM. For instance, this process reviewed that future research in L&SCM can focus on comprehending ways in which AI change practices both downstream and upstream, surpassing possible pitfalls that should be considered before process, structural, and policy implementation adjustments are made solid.

LITERATURE REVIEW

Supply chain management (SCM) is a procedure of planning, organizing, and controlling the movement of information, goods, and services from suppliers to customers (Pournader et al., 2021). It is a challenging and intricate task, and firms are always on their edges trying to find ways to enhance their supply chain (Ghouati et al., 2024). On the other hand, according to Rosenberg (2020), logistics is the management of how goods are moved. Rosenberg (2020) further spotlights the relationship between logistics and supply chain management by saying that logistics is a segment of the supply chain, insinuating that a person managing the supply chain will be accountable for managing customs brokers, shipping companies, freight forwards, parcel delivery firms and third-party logistics organization. Therefore, to adapt to the flexibility of SCM, businesses must build and adopt a formal logistics approach.

A study conducted by Tungsten Network In 2017 estimated that organizations lose around 6500 hours annually doing busy work that could be more effectively and efficiently done by digital automation, including answering supplier inquiries, tweaking purchase orders, and processing papers (Material et al., 2019). This is why many businesses have started adopting AI technology for supply chain and logistics tasks. As a result, the human workforce is freed up to do more intricate tasks that computers cannot handle—still waiting.

According to Toorajipour et al. (2021), artificial intelligence is believed to have a high capability in logistics and supply management (L&SCM) linked technologies. Several artificial intelligence manifestations, such as robotic process automation (for instance, cobots or collaborative robots), speech recognition, techniques in computer vision, natural language process, deep learning, and machine learning, have opened a new door to effectively and efficiently managing intricate operations and decision making (Min, 2019; Gunasekaran & Ngai, 2019). According to research by Richey et al. (2023), these resources can establish dynamic abilities, helping firms reinvent structures, innovate procedures, flex policies, and provide multiple improvisations to create value. Research by Min (2019) shows that L&SCM managers are now hoping for remarkably rapid objective and data-fueled decision-making.

Richey et al. (2023), however, have a different thought: (”haven’t we all experienced this hype before?) According to their article, a few years ago, the stock prices were exploding at overvalued firms gaining substantial investments in blockchain technology, a thing that is going to happen soon. Hype-laden speculations have been common in L&SCM and research since technology is crucial to transporting people, goods, information, and finances (Richey et al., 2023; Hellingrath & Lechtenberg, 2019). Nevertheless, Enholm et al. (2021) hold that AI feels different from previous tech developments. Artificial intelligence has already shown differing levels of success, and people such as editors are taking quick notice of AI’s capability to accumulate remarkable written text.

AI in SCM has countless applications (Toorajipour et al., 2021). This makes AI a promising technology tool that can improve any business’s supply chain performance (Nandi et al., 2024). Fields of impact within L&SCM include but are not limited to operational procurement using chatbots and intelligent data, supply chain planning to predict supply and demand, more accurate and quicker shipping to minimize transportation expense and lead times, optimal supplier selection via the use of real-time data and warehouse management to organize stock (Richey et al., 2023; Boute & Udenio, 2022).

Simultaneously, AI brings a dilemma as it minimizes human involvement in smoothening events. At the same time, it burdens humans with new decision-making duties coming from the information it produces (Ghouati et al., 2022). In the concept of L&SCM, the adoption of artificial intelligence guarantees a seismic transition in operational paradigms, resulting in redesigning practices both upstream, including the acquisition of raw materials, supplier relations, and embodying distribution techniques, and downstream, including after-sales services, distribution strategies, and customer engagement (Richey et al., 2023; Min, 2019). As firms strive to keep up with these transformations, it becomes crucial to comprehend how AI can drive innovation without succumbing to potential challenges (Boute & Udenio, 2022; Richey et al., 2023).

In the upstream supplier chain, a thorough evaluation of data security and privacy concerns is necessary to ensure information confidentiality and integrity. Furthermore, Balasubramaniam et al. (2023) hold that an over-dependence on artificial intelligence algorithms can likely obscure human skills, building a balance that would be maintained.

Simultaneously, the downstream supply chain benefits from the enhanced customer experiences brought in by AI-fueled insight and personalization (Tomas et al., 2023). However, it should be noted that the supply chain should apply these using a conscientious approach to avoid getting into any possible dilemmas, including biased algorithms and misinformation, which can arise during the deployment process of AI. As businesses plan to adopt artificial intelligence, it is essential to establish a proactive and open approach to process information and policy formation. The inclusive of well-rounded structural stipulations that emphasize innovations resulting from AI preventatively dealing with possible hardships establish a sustainable and ethical trajectory of logistics and supply chain management (Richey et al., 2023; Boute & Udenio, 2022). Establishing a multidimensional strategy engages strategies at all levels, calling for collaboration efforts to get through the intricate landscape artificial intelligence brings in.

Influence of AI on Logistics and Supply Chain Management

Automation and Efficiency

Operation efficiency in logistics and supply chain is significantly influenced by AI ( Boute & Udenio, 2022). Automation technologies such as AI-driven robotics and automated guided vehicles reduce the need for the workforce for activities such as sorting and delivering (Khadem et al., 2023). This reduces the chances of errors and eases operations. AI-driven systems can work 24/7, delivering and elevating efficiencies.

 El Haoud and Bachiri (2019) have shown that the transformative force of AI in L&SCM has offered solutions that improve security and automate manual tasks. Using artificial intelligence, organizations can achieve enhanced profitability and operational efficiency (Khadem et al., 2021). Referencing Verified Market Research in 2022, Future Data Stats notes that the AI market size in L&SCM was 3,037,98.38 million dollars in 2022. According to their report, this value is predicted to rise to 64,459.38 million dollars by 2030, mirroring an excellent compound growth of 46.5% (2023-2030) (Future Stats, 2022).

In his exceptional article, Rosenberg (2020) highlights that logistic firms will significantly grow in the future years. Artificial intelligence has driven automation tools by offering greater agility, efficiency, and scalability pathways. Rosenberg also mentions that automation enables responsiveness to market demands, enabling firms to quickly capitalize and adapt to new brand opportunities in the dynamic logistics ecosystem. Solidly, Khadem et al. (2023) mention that automation has become an essential aspect of logistic procedures, helping businesses reduce costs, enhance efficiency, and improve customer experience. Logistics firms can now streamline procedures, optimize routes, and deal with big data sets by leveraging AI tools. These reduce errors and shorten delivery time.

As Rosenberg (2020) mentions, the application of AI in inventory management has shown to be transformative in ensuring the efficiency of warehouse processes. AI-fueled inventory management assists logistics organizations in precisely observing inventory levels, preventing the threat of stock shortages, and automating replenishment protocols. The order-picking procedures have also been automated and made simple by applying AI algorithms, which evaluate data containing features such as demand patterns and inventory levels.

Demand Forecasting and Predictive Analytics

The power of artificial intelligence to analyze vast amounts of data allows for more accurate demand prediction (Toorajipur et al., 2021). By predicting customer demand more effectively, businesses can reduce excess inventory stockouts and optimize inventory levels (Niranjan et al., 2021; Shoushtar et al., 2021). Predictive analytics also help single out trends and patterns that can be invisible on traditional analysis tools, assisting firms to make more informed decisions.

Drawing from Toorajipur et al.’s (2021) article, machine learning in supply chain management can utilize comprehensive knowledge bases to make more accurate predictions via its exceptional abilities. Machine learning algorithms are highly skilled at identifying crucial patterns and factors impacting supply chain performance, helping employees make well-insight warehouse and inventory management decisions, and enhancing efficiency significantly (Niranjan et al., 2021; Shoushtar et al., 2021). According to Zhang (2020), the efficiency and accuracy of machine learning far surpasses manual data processing, which uses more time and valuable resources that could be used in other processes. AI has been enhancing accuracy in supply chain operations, with applications in fields such as demand forecast, both local and global, preventing the bullwhip effect and modifying inventory levels through technology-driven predictive analytics.

A practical example of AI applications in predictive analytics is IKEA’s innovative approach. The organization uses an artificial intelligence model designed to enhance the precision of demand forecasting significantly. This solution shows the power of AI to use new and existing data resources, generating accurate insights and permitting firms to align their inventory with market demand more effectively.

Earlier this year, Repetto and fellow scholars sport lighted that implanting demand forecast changes traditional strategies by using larger language models (LLMs) and blending them with businesses’ expansive data sets. This innovative strategy produces deep insights and improves data analysis, fueling precise predictions and assisting firms to match their production with demand trends, optimizing inventory, and managing supply chain operations. Drawing from Repettos et al. (2024), AI can evaluate historical data, recognizing factors such as seasonality, holidays, and promotions to identify trends and patterns. Retailers can use this analysis to understand past buyer behavior and make more insightful predictions for future demands.

Transportation and Route Optimization

Furthermore, according to Paul et al. (2022), supply chain management industries have been utilizing AI to improve their risk management and fraud detection systems. According to this researcher, machine learning has shown the power to evaluate data and identify potential risks, such as market fluctuations and supplier distributions, and recommended mitigation strategies. Additionally, AI can detect fraudulent operations, including payment fraud and counterfeit goods, by revealing transactional sequences, among other indicators.

In their exceptional article, Richey et al. (2023) highlight that machine learning and AI algorithms power AI systems to detect fraud and theft in the supply chain. According to Hassan et al. (2023), these AI tools are highly skilled in analyzing big data sets that relate to SCM transactions; they are tailored to identify irregularities patterns and anomalies that can indicate fraudulent activities such as fictitious orders, invoice manipulations, and unauthorized transfer of funds. An example is the popular digital freight networks, which have brought real-time models that directly oversee and prevent fraudulent activities from happening.

Fraud Detection and Risk Management

Moreover, according to Paul et al. (2022), AI is also used to enhance fraud detection and risk management in the supply chain. Machine learning can analyze data to determine possible risks, such as supplier distributions and market fluctuations, and suggest mitigation strategies. In addition, AI can sense fraudulent activities, such as counterfeit goods and payment fraud, by analyzing transaction patterns, among other indicators.

Richey et al. (2023) note that artificial intelligence algorithms and machine learning assist AI in sense theft and fraud in the supply chain. According to Hassan et al. (2023), these artificial intelligence tools are highly skilled in evaluating massive datasets linked to supply chain transactions. They are designed to spot intricate patterns, anomalies, and irregularities that can signify fictitious orders, unauthorized transfer of funds, and invoice manipulations, among other fraudulent incidences. For example, the popular digital freight network has brought in a real-time licensing model that tirelessly oversees carrier risks and initiates proactive measures to prevent these frauds from happening.

Artificial intelligence in logistics plays a vital role in preventing risks and possible hazards that can affect employee’s productivity and safety. Via developed machine learning-fueled analytics tools and models, artificial intelligence solutions evaluate supply chain preparedness for unforecasted incidences, such as force majeure cases (Niranjan et al., 2021). Logistic companies can proactively manage risks and maintain operational continuity by singling out and handling emerging issues. Utilizing AI-driven solutions safeguards productivity and ensures employees’ safety in case of disruptions, among other incidences (Paul et al., 2022). This proactive approach to risk management assists firms in enhancing resilience and adaptability, preparing them to fight future challenges of the same caliber effectively.

Case Studies of Leading Industries

E-Commerce

The e-commerce industry has been leading in adopting AI in logistics and supply chain management (Boute & Udenio, 2022). Firms like Amazon have adopted AI tools into their fulfillment stations, utilizing robotics to automate the packing and picking of orders (Anh, 2019). For instance, in an interview in 2021, Jeff Benzos- Founder and Executive Chairman of Amazon, mentioned that Amazon provides customized product recommendations as a marketing approach that helps the firms to have a competitive edge and continue to satisfy their customers by accurately predicting their needs via personalized product recommendations. A report by researchers who worked on this recommendation algorithm mentions that the algorithm operates by aligning each buyer’s previous purchases to similar products. AI-fueled inventory management and demand forecasting systems have permitted e-commerce, leading firms to maintain high customer satisfaction levels while reducing costs.

Automotive

In the automotive landscape, artificial intelligence has been utilized to enhance manufacturing processes and supply chain procedures. For instance, Tesla utilized artificial intelligence tools to manage its SCM, guaranteeing that parts are delivered right on time for production. According to its site, Tesla comprehensively utilizes AI and automation throughout its supply chain (AI & Robotics | Tesla, 2024). From advanced AI models powering production schedules to autonomous robots in Gigafactories, Tesla utilizes advanced technology to ensure efficiency. For instance, the firm uses machine learning to enhance the positioning of robots on its assembly lines and single out and rectify defects in its manufacturing. The firm also utilizes predictive management systems driven by artificial intelligence to oversee the state of its manufacturing tools and predict when maintenance is needed. Automation fastens the manufacturing processes and reduces the likelihood of errors, ensuring the unceasing quality of their products. Artificial intelligence also plays a life-changing role in predicting motor vehicle maintenance requirements improving manufacturing efficiency and customer satisfaction.

Pharmaceuticals

In addition, the pharmaceutical industry has embraced AI’s ability to enhance the success of its supply chain management. AI is used in drug development, distribution, and supply chain optimization (Patel, 2024). For example, AI-fueled analytics help pharmaceutical companies successfully manage their inventory, ensuring that waste is reduced and essential drugs are available when needed. Furthermore, pharmaceutical firms have also utilized artificial intelligence to make decisions, find the right therapy for patients, including personalized drugs, and manage clinical data generated and use it for future developments.

Retail

Still, on the same, retail firms are increasingly using AI to operate their inventory and supply chain management. Models driven by AI can analyze sales to predict which products will have increased demand, allowing retailers to adjust their inventory to match the predictions (Pasupuleti, 2024). This reduces the possibility of stockouts and overstocking, leading to cost savings and enhanced customer satisfaction.

Future Directions

Emerging AI Technologies

According to Boute Udenio (2022), the future of AI in logistics and supply chain management looks promising, with several emerging technologies in the range. These emerging technologies include drones for last-mile delivery, advanced machine-learning algorithms, and AI-fueled autonomous vehicles. Min (2019) expects these technologies to improve logistics and supply management firms further, as they can smoothen the operations, for example, by foreseeing supply disruptions before their occurrence. As these technologies evolve, they will possibly have an even more significant influence on the industry.

Recommendations for Industry Adoption

Firms anticipating the adoption of AI into their L&SCM operations must start with a straightforward approach and emphasize particular fields where artificial intelligence can add productivity (Boute & Udenio, 2022). Firms must invest in development and training to guarantee that their workforce is well-prepared to use AI tools. In addition, forming partnerships with technology providers can assist firms in getting through the intricacies of implementation.

Implementing artificial intelligence in this ecosystem uses a conscientious approach to prevent ethical dilemmas resulting from deploying AI models. As organizations integrate artificial intelligence, it is essential to promote an open and proactive approach to policy formulation and process adoption (Shrivastav, 2022). This includes building well-rounded structural adaptations that focus on efficiencies and innovations brought in by artificial intelligence and address possible challenges, building a resilient, ethical, and sustainable trajectory for the transformation of L&SCM.

CONCLUSION

This study found that AI in logistics and supply chain management brings a substantial opportunity for businesses to enhance their customer satisfaction and operational efficiency. This research also found that logistics and supply chain industries use AI-fueled models to automate and optimize rot planning, predict demand, manage inventory, and track real-time procedures. Resultantly, this has yielded improved customer experience, faster deliveries, enhanced resource distribution, and cost savings for these industries.

Moreover, AI in SCM can be utilized to prevent fraudulent activities and personalize customer experiences, which are critical aspects of the industry. The power of AI tools to transform the L&SCM industries is undisputable, and its influence is anticipated to grow. As new trends and developments emerge in technology, businesses should embrace AI’s ability and invest in deploying AI-fueled tools to earn a competitive advantage in the market. However, enhancing AI-powered solutions calls for expertise, insinuating that businesses should consider partnering with AI development institutions for successful operations.

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