Innovative Logistics: Assessing AI’s Impact on Supply Chain Excellence

Abstract

Many facets of corporate operations could be revolutionized by artificial intelligence (AI). Artificial Intelligence (AI) has the potential to improve supply chain inefficiencies, optimize logistics and transportation routes, and forecast demand based on data analysis. This may result in shorter lead times, lower costs, and better response to variations in demand. Using the Scopus database, this study examines and evaluates the uses of artificial intelligence (AI) in supply chain management (SCM).

The goal is to close the existing research gap on the effects of AI on supply chain management (SCM) performance. This includes identifying AI techniques that can improve SCM performance, SCM subfields that have a high potential for AI enhancement, the effects of AI application on SCM performance, and how the performance can be explained from an agile-lean perspective. The Scopus database was used to list and categorize the current nations and areas involved in AI impact on SCM performance, document type, and subject area.

In addition to addressing the existing research gap, this study delves into the challenges and ethical considerations surrounding the integration of artificial intelligence (AI) in supply chain management (SCM). The lack of standardized application methods poses a challenge for cross-enterprise comparisons, making it crucial to explore the variations in outcomes and impacts across diverse industries. Furthermore, the study highlights the absence of standardized metrics for assessing the return on investment (ROI) of AI in SCM, hindering businesses in evaluating the true value of their AI investments.

An essential aspect examined in this research is the integration of AI systems with existing SCM frameworks, revealing potential limitations on data availability and accuracy. Ethical concerns, including issues of discrimination and the protection of sensitive data, emerge as critical considerations that demand greater attention in the context of AI-driven SCM solutions. This study aims to shed light on these ethical dimensions and emphasizes the necessity for a human-centric approach in developing AI solutions, with a focus on workforce development and training alongside process optimization and cost savings.