Artificial Intelligence: Supply Chain Application and Perspectives Essay

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.

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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.