Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

Abstract

The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.

Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management

“…The integration of meaningful AI applications into international logistics teaching models highlights the importance of orienting education towards the competencies required for 21st century citizens. (17,18,19,20,21) During the research and data collection phase, several reiterative evidences were highlighted as a result of a thorough analysis of the existing literature on AI learning. (22) This evidence highlights key areas of opportunity and integration, focusing on understanding the fundamentals of machine learning and its practical applications in logistics optimization.…”

Developing an entrepreneurial mindset in supply chain managers: Exposing a powerful potential

“…Literature reviews summarize the supply chain applications and AI techniques used to date (e.g., Toorajipour et al, 2021), underscoring considerable focus on applications directed to supply chain risk (e.g., Baryannis et al, 2019) and resilience (e.g., Modgil et al, 2022), but with little examination to date on the business growth opportunities found in SCM. A recent editorial by Richey et al (2023) offers the most comprehensive view to date on both the up-and downside potential of AI in SCM with a helpful navigational framework for yielding impactful theoretical and practical research on the topic, recognizing the societal implications of the technology, as many of the leading developers of the technology, themselves, are warning about the potentially devastating impacts associated with unfettered, unconstrained application. We, therefore, echo the call of Richey et al (2023) for the exploration of not only the potential upsides found in the use of these technologies but also the plausible hazards and limitations of their use, or in the eloquent prose of Richey et al (2023): the pursuit of Dr. Jekyll and containment of Mr. Hyde.…”

“…A recent editorial by Richey et al. (2023) offers the most comprehensive view to date on both the up‐ and downside potential of AI in SCM with a helpful navigational framework for yielding impactful theoretical and practical research on the topic, recognizing the societal implications of the technology, as many of the leading developers of the technology, themselves, are warning about the potentially devastating impacts associated with unfettered, unconstrained application. We, therefore, echo the call of Richey et al.…”

“…(2023) for the exploration of not only the potential upsides found in the use of these technologies but also the plausible hazards and limitations of their use, or in the eloquent prose of Richey et al. (2023): the pursuit of Dr. Jekyll and containment of Mr. Hyde.…”

Editorial: Navigating excellence: understanding and overcoming common causes of manuscript rejections in logistics and supply chain management research

“…We are highly receptive to manuscripts that showcase advancements in decision-making, productivity, supply chain structure reconfiguration, innovative processes and practices, sustainability initiatives, worker well-being considerations, regulatory policy considerations and ethical considerations (i.e. Richey et al ., 2023; Fosso Wamba et al ., 2023). However, pursuing these advancements should be accompanied by a heightened level of theoretical explanation closely aligned with the research design, advanced methodologies and meticulous data collection.…”