ARTIFICIAL INTELLIGENCE -POWERED CHATBOTS AND LOGISTICS PRICING AT MAERSK LINE COMPANY
Abstract
Purpose of the Study: In the dynamic landscape of global logistics and commerce, the integration of Artificial Intelligence (AI) is reshaping various facets of the industry. Shipping lines, pivotal components of the logistics ecosystem, encounter challenges in efficiently negotiating freight prices. Traditional methods, like email inquiries, impose logistical burdens, leading some companies to adopt online portals. However, these portals often rely on static pricing models, potentially overlooking complexities influencing customer willingness to pay. The repetitive nature of email negotiations consumes substantial time and resources, often favoring larger clients. AI-powered technology emerges as a solution to streamline these operations.
Brief Introduction of the Problem Statement: This article delves into freight rate negotiations within Maersk Line, aiming to explore the impact of AI-powered chatbots on logistics pricing. The study examines AI-driven chatbots as the independent variable, gauged through indicators such as technology utilization and responsiveness. Logistics pricing, the dependent variable, is assessed using indicators like pricing accuracy and responsiveness to customer demand. The study also delves into moderating variables influencing this relationship, including customer preferences and prevailing market conditions.
Methodology: The theoretical framework draws from Competitive Advantage Theory and the Technology Acceptance Model (TAM). Competitive Advantage Theory underscores AI's potential to augment cost leadership and differentiation in logistics pricing. TAM scrutinizes user adoption of AI chatbots, considering perceived usefulness and ease of use, thus guiding decisions regarding technology integration.
Study Results: Our investigation into Maersk Line Company's adoption of AI-powered chatbots and their impact on logistics pricing operations has yielded valuable insights aligned with our research objectives. The integration of AI-powered chatbots represents a strategic step towards harnessing advanced technologies to enhance logistics operations, fostering improved communication, efficiency, and responsiveness in addressing pricing-related inquiries.
Conclusion and Policy Recommendations: The adoption of AI-powered chatbots has delivered tangible benefits for Maersk Line, including heightened pricing accuracy, efficiency, and customer satisfaction. Noteworthy cost reductions have also been realized through diminished manual intervention and optimized resource allocation, underscoring the positive impact of chatbots on logistics pricing operations.
Looking ahead, we propose several recommendations to Maersk Line to further optimize its utilization of AI-powered chatbots in logistics pricing operations. Continued investment in AI technology, enhancement of data integration capabilities, and ongoing evaluation and refinement of chatbot algorithms are crucial steps to adapt to evolving market dynamics and meet evolving customer preferences.
Keywords: AI-powered chatbots, Logistics pricing and Technology utilization
References
Barua, L., Zou, B., & Zhou, Y. (2020). Machine learning for international freight transportation management: A comprehensive review. Research in transportation business and management, 34, 100453. https://doi.org/10.1016/j.rtbm.2020.100453.
Chen, J., Le, T., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail & Distribution Management, ahead-of-print. https://doi.org/10.1108/IJRDM-08-2020-0312.
Li, X., Zhang, M., Zhang, X., & Zhu, G. (2023). A Resilient Shipping Industry in the Wave of the Digital Economy during the Post Pandemic Era: Based on Maersk's Approach. Highlights in Business, Economics and Management. https://doi.org/10.54097/hbem.v14i.8973.
Lv, J. (2021). Optimization of E-Commerce Logistics System Based on Artificial Intelligence Technology. Journal of Physics: Conference Series, 1881. https://doi.org/10.1088/1742-6596/1881/3/032062.
Panigrahi, R., Shrivastava, A., Qureshi, K., Mewada, B., Alghamdi, S., Almakayeel, N., Almuflih, A., & Qureshi, M. (2023). AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country. Sustainability. https://doi.org/10.3390/su151813743.
Porter, M. E. (1985). The information revolution is transforming. Harvard Business Review, 17(3-4), 1-24.
