WorkDifferentWithAI.com Academic Paper Alert!
Written by Robert Glenn Richey Jr., Soumyadeb Chowdhury, Beth Davis-Sramek, Mihalis Giannakis, Yogesh K. Dwivedi
Category: “Enterprise AI”
Article Section: Supply chain optimization with AI
Publication Date: 2023
SEO Description: “Exploring AI’s transformative potential and challenges in logistics and supply chain management research.”
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Richey Jr., Robert Glenn, et al. “Artificial Intelligence in Logistics and Supply Chain Management: A Primer and Roadmap for Research.”
Journal of Business Logistics, vol. 44, no. 4, 2023, pp. 532–49,
https://doi.org/10.1111/jbl.12364.
Claude.ai-Generated Paper Summary
Based on my analysis, here are some key points about the novelty and commercial applications of the second paper:
Novelty:
- Provides an overview of how generative AI like ChatGPT can transform the logistics and supply chain management (L&SCM) field. This emerging technology has high potential but research exploring its intersection with L&SCM remains limited.
- Proposes a comprehensive research framework to guide future work on integrating generative AI in L&SCM. The framework spans factors influencing adoption, impact on L&SCM practices, workforce changes, business model innovation, and desired outcomes.
- Discusses a wide range of potential applications of generative AI in L&SCM including procurement, risk management, forecasting, distribution, customer service, sustainability etc.
- Highlights key challenges of training, bias, transparency, cybersecurity, legal implications that need to be addressed for successful AI adoption in L&SCM.
- Provides an extensive research agenda with questions to examine AI’s capabilities, limitations, and impact on processes, jobs, business models etc. in L&SCM.
Commercial Applications:
- Generative AI has significant potential to enhance efficiency, visibility, and innovation in supply chain operations. This can lead to commercial solutions and enterprise AI tools for L&SCM organizations.
- Startups are emerging that focus on leveraging generative AI to provide supply chain analytics, risk assessment, optimization, forecasting as a service to clients.
- Large technology firms like Microsoft, IBM are introducing AI-powered features into their existing L&SCM software suites to improve resilience, sustainability, customer service.
- Logistics companies can utilize generative AI internally to optimize routes, loading, inventory etc. and externally to improve customer experience via chatbots.
- Retailers and e-commerce firms can apply generative AI for demand forecasting, inventory planning, return predictions to increase revenue and reduce costs.
- Generative AI’s ability to process and learn from vast, heterogeneous data makes L&SCM an ideal application domain for AI-based products and services.
In summary, the paper lays a strong foundation and research agenda for exploring the integration of cutting-edge generative AI capabilities into the L&SCM field. This can pave the way for both research advancements and commercial opportunities.
Keywords
Artificial intelligence, logistics and supply chain management, research integrity, implementation challenges, AI integration
Author’s 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.
Read the full paper here
