Introduction
The artificial intelligence (AI) development process has a direct exposure to supply chain regulation. With regard to globalization and multiplication of the possible delivery possibilities, human resources or thoroughly developed programs cannot compete with the analyzing power of artificial intelligence. Even though AI technologies began their history during World War II with the establishment of the Turing test, they began to be directly implied in supply chain processes only in the last decade of the 21st century (Baryannis et al., 2018). The analysis is aimed to measure the current impact of artificial intelligence presence in supply chain processes and ponder the perspectives of AI development in terms of the leading power of supply chain regulation.
Current Situation
The supply chain is one of the most important factors of the world economy operation since it links valuable parts of almost all business processes in the world and delivers them to the selling markets, where the final products are realized. As a result, without the cutting-edge level of supply chain functioning, the economy would experience significant stagnation due to the inability to assemble or furnish the final product to the customer.
One of the most notable characteristics of AI efficiency is a statistically proven increase in a company’s profitability due to the organic transition from human resources to new analyzing instruments. To improve communication between counterparties, new communication methods are being employed. For instance, installing virtual chatbots that customize and reflect the preferences of their customers produce a 10% greater return on equity and 10 percent more revenue than other companies from the survey pool (Modgil et al., 2021). Another important trend for AI integration emphasizes that due to the digitalization of supply chains as a result of the industry 4.0 strategy, they are evolving into supply chain ecosystems, which are made up of interconnected businesses that coordinate operations and face similar adaptive difficulties.
A precise value proposition and a specified, dynamic collection of agents with various responsibilities describe this governance paradigm with regard to different roles, such as producer, supplier, orchestrator, and complementor. As a result, AI developing companies and intermediates offering any form of the new position in such supply chain ecosystem has an increasingly high demand for industry 4.0 solutions that would effectively integrate into businesses’ operating ecosystems (Hofmann et al., 2019). From a data harvesting perspective, artificial intelligence helps to observe not only the individual characteristics, which is a common process for programmed applications and behavioral analytics but also the pricing proprieties of different companies, such as packaging and delivering sector corporations.
For instance, Modgil et al. revealed that last-mile delivery is the costliest logistics step, accounting for roughly half of the total package delivery cost (2021). Last but not least, today’s businesses are becoming more globalized but also less vertically integrated, which develops the complexity of distribution networks and exposes them to far more risks (Calatauyd et al., 2019). As a result, AI systems possess and apply their technical advantage in scanning a large pool of possibilities to address goods from ‘point A to point B’ with the lowest duration time and regulatory or natural risks.
The Future Implementation of Artificial Intelligence into Supply Chain Functionality
When it comes to the possibilities of future implementation of AI technologies into supply chain operating activities, it is critical to focus on those aspects that have significant exposure to the industry’s development. In fact, there are five of the most effective methods of organic AI application into supply chain functionality. First and foremost, a major part of the technological potential might be realized through the implication of inventory planning utilities. More specifically, customization has perspectives to be fully performed through AI technologies, which significantly helps determine future buying patterns, whether large purchases are required to retain stock in advance or whether the current inventory capacity percentage is maintained properly (Modgil et al., 2021). Secondly, internet commerce made a significant impact in experiencing supply chain disruptions during the global pandemic in 2020 and 2021. Despite the partial stagnation of global production, customers began ordering packages on internet marketplaces, which has disclosed another important niche for supply chain functioning (Modgil et al., 2021). More specifically, AI might be utilized to discover local suppliers, as many things do not require the use of worldwide vendors.
In addition, artificial intelligence assists in creating an effective and robust supply chain from local suppliers through vendor management solutions, such as credit management or vendor evaluation. As a result, this operating function could be attached to software analysis ecosystems to enforce the synergy effect of efficiency increase. Thirdly, artificial intelligence might be utilized to increase routine operation execution, such as package tracking. In fact, during the high-intense periods of deliveries, many companies face significant issues with achieving to execute the final package distribution before the previewed time. In many cases, customers experience deliveries’ delays and begin tracking their orders to understand their current situation. In this case, standard programmable software cannot demonstrate constant operating success due to unstable information updates.
However, artificial intelligence technologies might execute this routine operation with a relatively higher efficiency percentage. The cutting-edge technology would estimate shipment delivery time variations and preview the amount of risk connected with a cargo based on trend research. Fourthly, artificial intelligence technologies would certainly advance the quality of risk management in supply chain functioning. The adoption of AI developments is based on its future capacity to evaluate data, find the exact source of risk, and promote transparency among supply chain partners. These functions might be very useful in detecting the risk associated with various intersections of the distribution network and providing effective and timely remedies (Modgil et al., 2021). Last but not least, AI’s capacity to execute numerous experiments and calculate the possible outcomes would influence the process of market analysis.
For example, sophisticated analytics using AI may be used to anticipate future outcomes and market tendencies. At the same time, data may be examined utilizing the unrivaled computational capacity to forecast future demand or better understand client purchasing habits. In today’s world, it is crucial to not only anticipate the changes but also define the specific applications of AI technology in the supply chain functioning to benefit from its development.
Conclusion
To summarize, artificial intelligence is one of the most powerful tools for supply chain development, and its opportunities are already partially realized. From the current perspective, artificial intelligence helps to understand the functioning of standard operations and collaboratively define certain quantitative measures based on customers’ expected behavior. However, the whole potential is currently unrealized, which makes artificial intelligence a perspective domain for developing risk management in supply chain, routine operations execution, vendor performance analysis, packages tracking, and estimating market behavior.
References
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). Supply chain risk management and artificial intelligence: state of the art and future research directions.International Journal of Production Research, 57(7), 2179–2202. Web.
Calatayud, A., Mangan, J., & Christopher, M. (2019). The self-thinking supply chain.Supply Chain Management: An International Journal, 24(1), 22–38. Web.
Hofmann, E., Sternberg, H., Chen, H., Pflaum, A., & Prockl, G. (2019). Supply chain management and Industry 4.0: conducting research in the digital age.International Journal of Physical Distribution & Logistics Management, 49(10), 945–955. Web.
Modgil, S., Singh, R. K., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management, of. Web.