Introduction
Supply chain management (SCM) is a dynamic field that determines business success in a globalized corporate realm. It deals with a holistic procurement system, marketing channels, logistics and operation, and inventory management. The success of SCM is determined by the system’s ability to create, connect, consolidate, customize, and coordinate the seamless flow of goods and services from one place to another.
Supply chain vulnerabilities expose the system to disturbance responsible for jeopardizing efficiency. The vulnerabilities result from all the supply chain risks that prevent the systems from effectively serving the customer networks. This research analyzes the holistic nature of SCM vulnerabilities and how artificial intelligence may be used to avert the challenges. It is relevant because the vulnerabilities cover a wide range of human perspectives, and the selected technology to enhance seamless SCM may disrupt it further.
The contemporary SCM vulnerabilities may include socio-political factors, financial risks, legal and environmental parameters, political and project organization, and human behavior risks. The solution introduced to avert the vulnerabilities must be carefully selected and analyzed to ensure it does not worsen the situation. Artificial intelligence (AI) is a branch of computer science where machines are programmed to monitor and make automated real-time decisions to ease goods management. Integrating AI in SCM is both a social and technical process and, therefore, requires careful consideration for operations to be streamlined (Hendriksen, 2023).
This literature review analyzes AI’s integration into SCM and assesses its ability to resolve the problems. It is divided into different sections to offer a logical flow of ideas. It begins with the historical perspective of supply chain vulnerabilities. The AI used real-world case studies, the function of AI, and the theoretical perspectives in research.
Historical Perspective of Supply Chain Vulnerability
Disruption in the seamless flow of goods and services in the SCM may be described as vulnerabilities. Research by Ganesh and Kalpana (2022) defined supply chain vulnerability as any potential risk that may lead to delay or customer dissatisfaction. Another research by Toorajipour et al. (2021) supported the findings by Riahi et al. (2021), which stated that the factors causing customer dissatisfaction may be transportation, supplier, demand, and production risks.
An effective flow of goods is achieved only when the risks are analyzed and resolved before jeopardizing the seamless flow of goods from suppliers to buyers. Azadegan et al. (2020) inferred that the vulnerabilities are dynamic and require an intelligent system to monitor and resolve the challenges. The SCM must, therefore, control all the elements contributing to the successful flow of goods.
The prerequisite for achieving an effective SCM is to understand the vulnerabilities that may affect the system. Research by Sharma et al. (2023) stated numerous vulnerabilities, but the most common ones are disruptions, quality control, geopolitical issues, supplier reliability, and cyber security. The disruptions may include natural or political calamities such as hurricanes and political instability.
Quality control may make the products affect the transactions, especially when there is a communication breakdown. Research by Gupta et al. (2020) inferred that quality control checks cause 43% of the delay in supply chain operations. Supplier reliability is an important issue when determining the outcome of a business transaction. Technological advancement has made numerous companies digitize their supply chain management. Consequently, cyber security is also a potential vulnerability involving hacking and diverting goods, leading to delays or theft.
The evolution of SCM vulnerabilities has posed a challenge in overcoming it. Numerous studies analyzed the diverse phases of the vulnerabilities to be able to predict the future and develop solutions that are tailored to the needs of the customers. Research by Shishodia et al. (2023) subdivides SCM vulnerabilities into the pre-industrial era, during the Industrial Revolution, the 20th century, and globalization and information age vulnerabilities.
In the pre-industrial era, the SCM was localized, and all the disruptions could be monitored physically and resolved. Sharma et al. (2023) also inferred that during the Industrial Revolution, SCM challenges were caused by labor disputes, transport delays, and equipment breakdown. The 20th-century vulnerability included geopolitical events like the oil crisis that occurred in the 1970s (Gupta et al., 2020). The information age disruption transcends the physical challenges and focuses on intellectual property and data protection issues. Based on the categorization of the vulnerabilities in supply chain management, it is, therefore, the role of the companies to monitor and understand them to customize the solution.
Case Studies in Supply Chain Vulnerability
The susceptibilities in supply chain management can be well understood when real-world examples are analyzed to understand what could have been done differently to avoid the crisis. Each vulnerability exposed the world to difficulties that could have been averted when the necessary actions would have been taken to avoid them. Some of the famous real-world SCM disruptions that led to losses include the oil crisis in the 1970s, the strikes in the West Coast Port in 2014, and the Suez Canal blockage in 2021 (Fan et al., 2022). Each case gave an example of challenges in the supply chain and how each could have been averted.
Oil Crisis in the 1970s
The energy crisis of the 1970s is a common example of disruption of the supply chain due to human activities. Most of the fuels used in the world are mined in the Middle and the Far East and are shipped to different parts of the world. The Organization of Petroleum Exporting Countries, commonly known as OPEC, imposed embargos on all nations that seemed to support Israel in the Yom Kippur War (Gong and Liu, 2020).
Some of the nations impacted by the crisis were the USA, Canada, and New Zealand. As a result of the oil shortage due to geopolitical factors, the cost of living in the affected countries increased because oil is the basic commodity for production. The Iranian revolution in 1979 also affected oil production due to the overthrowing of the Shah, the leader facilitating oil production (Hendriksen, 2023). The cases show that the geopolitical factors must be monitored to ensure that the SCM for essential goods is not disrupted.
The Strikes in the West Coast Port in 2014
In the globalized economy, most bulk goods enter nations through the ports since it is cheaper than air transport when carrying goods of equal value. In the USA, there was a significant crisis in the supply chain due to a labor dispute, which caused strikes in the major ports and slowed down the seamless flow of goods and services in the port. When the Pacific Maritime Association (PMA) demanded better pay for their staff, there was a backlog of container clearance in the port as most of the goods entering the country were not cleared on time (Gong and Liu, 2020). Consequently, the delay disrupted the supply chain, enabling customers to receive their goods on time.
The Suez Canal Blockage in 2021
The Suez Canal is an important route for transferring goods between Asia, Europe, and the USA. Since it is an artificial canal, blockage may occur hence making the most convenient trade route impassible. In 2021, the Suez Canal was jammed due to challenges in weather conditions and human error. The rescue efforts were futile because of the size of the ship.
As a result, the world’s busiest trade route was interrupted, and over 10% of the world trade was halted (Fan et al., 2022). The shipment of consumer goods, fuel, and industrial raw materials was delayed, leading to significant losses. The event prompted many supply chain companies to invest in mitigation strategies to ensure that the global supply chain is understood and mitigated before it leads to numerous losses worldwide.
Artificial Intelligence Technologies Used in Supply Chain Management
Artificial intelligence tools may track the supply chain systems in real time and provide predictive data for better decision-making. If the organizations and the nations affected by the crises had used predictive analytics, they would have understood the predicted trends and made decisions to ensure that the supply of the necessary goods was not affected.
An example of an AI technology used to enhance the seamless movement of goods is Roambee’s AI-powered platform, which connects and monitors vital information in ports, airlines, traffic, weather reports, and the Internet of Things (Ganesh and Kalpana, 2022). The tool is important as it helps managers make appropriate decisions to avoid unnecessary delays. For example, the delay in the Suez Canal due to blockage happened because the ship captain did not know about the weather conditions. As a result of missing vital weather information, the ship could not cross effectively at the canal.
AI helps in integrating information to make factual decisions to avoid any errors. AI also utilizes machine learning to enhance its predictive capabilities to forecast the future of the supply chain system. Introducing AI systems in the supply chain poses a significant threat to privacy and may share classified information. Riahi et al. (2021) inferred that sensors may expose an organization’s safety to terrorists and pose a greater existential threat.
However, research by Ganesh and Kalpana (2022) underscored the fact that using the technology will be regulated using cyberspace protection protocols. If machine learning and data collection had been used in the globalized supply chain, the OPEC embargo would have been understood on time and evaluated. Further, the automatic checks would ensure that the automated checks would reduce the risks of delays due to quality checks before packaging and tracking. Cascading the inferences of Riahi et al. (2021) and Ganesh and Kalpana (2022) forms the basis for concluding that when AI is implemented professionally and securely, it could be used to reduce more of the delays that are caused by the delays.
Function of the AI in Mitigating the Vulnerabilities
The use of AI offers numerous functionalities that can facilitate decision-making in SCM. Research by Riahi et al. (2021) proved that the Internet of Things is one of the most useful technologies that may be used to track the location of products in transit and be able to analyze traffic, and choose the shortest available routes. For products in transit, real-time monitoring can notice any change in traffic flow and instruct the driver to use an alternative route to ensure that goods are delivered on time.
However, research by Pournader et al. (2021) challenged the use of data collection and analysis tools, stating that it had legal implications based on security reasons. The findings by Pournader et al. (2021) on the legal issues were addressed by another research by Helo and Hao (2022), which stated that the Internet of Things might use information in the public domain, such as weather forecasts, to make decisions.
The natural language processing features may be used to analyze the communication between the parties and predict the relationships. If any conflict issue is flagged, such as the strike notices in the port staff, the AI tool issues a red flag alert, and the management may use alternative ports. However, research by Riahi et al. (2021) inferred that ethical principles prohibit data access without prior consent from the people sharing the information. An inference made by Fan et al. (2022) further clarified that AI-powered Chatbots may be used to analyze only the feedback and communication from the stakeholders to be able to understand potential conflict.
Another research by Gong and Liu, (2020) proved that the AI has a predictive tool that may be able to use machine learning to predict the relationship between the stakeholders in the supply chain management. Research by Sharma et al. (2023) inferred that machine learning offers support in the system through three prongs: forecasting demand, predicting time for maintenance, and quality control and cognition.
Industrial automation is an important parameter that helps SCM overcome some of the vulnerabilities that would have improved the quality of production and distribution. Research by Fan et al. (2022) showed that errors caused by human monotony may be reduced by using robots and drones to conduct inventory management. Helo and Hao (2022) argued that increased automation will lead to job losses among people. However, an assertion by Gong and Liu (2020) underscored the fact that AI brings more opportunities, like maintaining and calibrating the sensors. Despite the challenges associated with AI, it leads to improved efficiency, making the SCM achieve a better flow of goods and services.
Benefits of AI in Overcoming Supply Chain Vulnerability
The SCM vulnerabilities can be reduced or eliminated by applying strategic AI tools. The foremost benefit of artificial intelligence systems is optimizing the operations and ensuring that all the SCM system processes operate as expected. When the production facilities are automated using AI, any defect will be discovered and resolved, reducing all the quality control delays. Further, the real-time monitoring of the routes will likely cause additional information for decisions, such as weather forecasts and traffic updates, which may be used to make route decisions. The tool is, therefore, useful in ensuring real-time monitoring and delivering data for effective and efficient decision-making.
An efficient management of inventory is made possible by AI tools. Most of the vulnerability in the SCM systems is caused by inventory errors. The AI techniques offer a more robust and reliable supply chain management, which eliminates all the errors that are key in reducing the challenges. A study by Helo and Hao (2022) further inferred that AI tools could improve routing and dynamic transport optimization to ensure that it does delay regardless of the traffic and weather conditions in any form of transport.
Research by Ganesh and Kalpana (2022) concluded that the AI system can overcome all the challenges in supply chain management and offer a better solution. The popular world cases of SCM disruption could have been averted if AI monitored the system, routes, and communication. AI integration is considered the antidote to all the challenges that affect supply chain management. Risk analysis and mitigation are primary features of AI tools, and their integration in supply chain management may be useful in overcoming the challenges.
Theoretical Framework
Theoretical frameworks provide an overview of how the integration of AI in SCM affects the general outcome of the organization. The two theories that form the building blocks for this research are the resource-based view (RBV) and the diffusion of innovation theory.
The Resource-Based View
The resource-based view states that when an organization has a strategic resource, it can gain a competitive advantage and thrive in the competitive market. The theory was first proposed by Birger Wernerfelt in 1984 and later researched and confirmed by Barney Jay in 1991 (Dubey et al., 2019). For this research, AI tools and their integration into supply chain management are the strategic resources (Dubey et al., 2019). Since the SCM vulnerability is caused by diverse factors, AI tools may be used to collect data and help the organization make real-time decisions. The dynamic capabilities of the resources may be leveraged to enhance efficiency.
The Diffusion of Innovation Theory
The theory focuses on how firms can effectively incorporate innovations in the business realm. For example, AI is an innovative technology that can transform operations and, hence, improve the organization’s outcomes. The theory was developed by E. M. Rogers in 1962 and underscored the factors to consider when introducing an innovation to business transactions (Elmghaamez et al., 2022). It stated that the adoption process may differ, and the people who are the first to use it are likely to gain more benefits.
The five categorizations include the innovators, early adopters, early majority, late minority, and laggards who do not leverage the benefits of the innovation (Elmghaamez et al., 2022). According to the theory, AI is an emerging innovation, and the early adopters will likely benefit more. Although there may be social and ethical issues related to innovation, supply chain managers must evaluate its feasibility and use it to their advantage.
Gaps in Research
AI provides an alternative way of overcoming vulnerabilities in SCM. However, research by Ganesh and Kalpana (2022) stated that it poses security and ethical threats that may jeopardize its operation. The technology may be successfully integrated into SCM management if the significant gaps in research are addressed. The integration’s legal and ethical concerns must be addressed before its application. The robustness of AI and its security implication is a significant gap that must be addressed for appropriate incorporation only after the security issues have been effectively addressed (Hendriksen, 2023). Further, the regulation issues and best practices must be analyzed in detail to enable the SCM to adopt technology that is compliant and promotes best practices for support.
Conclusion
Technological advancement and globalization have significantly impacted global supply chain management. The vulnerabilities in SCM have contributed to massive losses, delays, and disruption of businesses. One of the proposed solutions to overcome supply chain vulnerabilities is the adoption of AI. The technology offers diverse benefits, such as monitoring real-time reporting and inventory management. Although security questions are raised about the technology, ethical and security systems are likely to improve the operations and ensure that the adopted AI resolves the vulnerabilities detected in the SCM.
Reference List
Azadegan, A., Syed, T.A., Blome, C. and Tajeddini, K., (2020) ‘Supply chain involvement in business continuity management: Effects on reputational and operational damage containment from supply chain disruptions.’ Supply Chain Management: An International Journal, 25(6), pp.747-772. Web.
Bergmann, E. and Bergmann, E., (2020) ‘The first wave: The oil crisis and the new nationalists.’ Neo-Nationalism: The Rise of Nativist Populism, pp.53-82. Web.
Dubey, R., Gunasekaran, A., Childe, S.J., Blome, C. and Papadopoulos, T., (2019) ‘Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource‐based view and big data culture.’ British Journal of Management, 30(2), pp.341-361. Web.
Elmghaamez, I.K., Attah‐Boakye, R., Adams, K. and Agyemang, J., (2022) ‘The diffusion of innovation theory and the effects of IFRS adoption by multinational corporations on capital market performance: A cross‐countryanalysis.’ Thunderbird International Business Review, 64(1), pp.81-108. Web.
Fan, S., Yang, Z., Wang, J. and Marsland, J., (2022) ‘Shipping accident analysis in restricted waters: Lesson from the Suez Canal blockage in 2021.’ Ocean Engineering, 266, p.113119. Web.
Ganesh, A.D. and Kalpana, P., (2022) ‘Future of artificial intelligence and its influence on supply chain risk management–A systematic review.’ Computers & Industrial Engineering, 169, p.108206. Web.
Gong, Z. and Liu, N. (2020) ‘Mitigative and adaptive investments for natural disasters and labor strikes in a seaport–dry port inland logistics network.’ Maritime Policy & Management, 47(1), pp.92-108. Web.
Gupta, N., Tiwari, A., Bukkapatnam, S.T. and Karri, R., (2020) ‘Additive manufacturing cyber-physical system: Supply chain cybersecurity and risks.’ IEEE Access, 8, pp.47322-47333. Web.
Helo, P. and Hao, Y., (2022) ‘Artificial intelligence in operations and supply chain management: An exploratory case study.’ Production Planning & Control, 33(16), pp.1573-1590. Web.
Hendriksen, C., (2023) ‘AI for supply chain management: Disruptive innovation or innovative disruption?’. Journal of Supply Chain Management. Web.
Pournader, M., Ghaderi, H., Hassanzadegan, A. and Fahimnia, B., (2021) ‘Artificial intelligence applications in supply chain management.’ International Journal of Production Economics, 241, p.108250. Web.
Riahi, Y., Saikouk, T., Gunasekaran, A. and Badraoui, I., (2021) ‘Artificial intelligence applications in the supply chain: A descriptive bibliometric analysis and future research directions.’ Expert Systems with Applications, 173, p.114702. Web.
Sharma, S.K., Srivastava, P.R., Kumar, A., Jindal, A. and Gupta, S., (2023) ‘Supply chain vulnerability assessment for the manufacturing industry.’ Annals of Operations Research, 326(2), pp.653-683. Web.
Shishodia, A., Sharma, R., Rajesh, R., and Munim, Z.H., (2023) ‘Supply chain resilience: A review, conceptual framework, and future research.’ The International Journal of Logistics Management, 34(4), pp.879-908. Web.
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P. and Fischl, M., (2021) ‘Artificial intelligence in supply chain management: A systematic literature review.’ Journal of Business Research, 122, pp.502-517. Web.