This chapter presents an exploratory qualitative study on the use of artificial intelligence (AI) in supply chain management (SCM). With the growing complexity and uncertainty of supply chains, the integration of AI has gained increased attention as a potential solution for enhancing SCM. Using semi-structured interviews with experts in the field, this study investigates the application of AI for supply chain optimization, as well as the challenges that firms face in implementing such technologies. The results of this study reveal that AI-based SCM systems have the potential to enhance supply chain efficiency, reduce costs, and improve overall performance. However, challenges such as data quality and compatibility, organizational resistance to change, and lack of trust in AI technology were identified as barriers to successful implementation. These findings contribute to the understanding of the current state and future direction of AI in SCM, and provide practical implications for managers seeking to use AI-based solutions in their supply chain operations.
TopIntroduction
The sectors of logistics, manufacturing, supply chains, and transportation are undergoing a rapid and exceptional period of change. These industries have a bright future thanks to innovation and technology. Recent years have seen the development of ideas like 3D printing, the Internet of Things (IoT), and drone deliveries, which were before unimaginable.
Lemoine (2014) has attempted to outline the historical trajectory of digitization, highlighting its extensive timeline that commenced in the 1930s with the inception of computers and continued well beyond 2008 when we entered the era of digitalization. Lemoine (2014) has attempted to outline the historical trajectory of digitization, highlighting its extensive timeline that commenced in the 1930s with the beginning of computers and continued well beyond 2008 when we entered the era of digitalization. Hence, it is evident that since 2008, the business world has transitioned into the realm of digitalization, where digital technology has become pervasive across all sectors of the economy (Rharoubi and Talmenssour, 2022). Nevertheless, service companies for instance are increasingly turning to digital practices to enhance their SCM, necessitating a need to identify the distinctive characteristics of these digital practices (Bentalha et al., 2019).
Artificial Intelligence (AI) has revolutionized various industries, including Supply Chain Management (SCM). Companies now have access to AI-powered SCM tools that can significantly improve their operational efficiency. Integrating AI in SCM has numerous benefits, such as reducing costs, improving forecasting accuracy, and enhancing supply chain visibility. AI-powered SCM tools can automate routine tasks, analyze large amounts of data, and provide real-time insights to help companies make informed decisions. With the advent of machine learning (ML), the future of SCM looks even more promising.
Nowadays, companies and service providers are strategically integrating these technologies to optimize their operations by enhancing speed, cost-efficiency and reliability and sustainability. AI has the potential to significantly enhance various aspects of logistics, such as distribution and transportation, logistics hub management, healthcare logistics, and logistics risk management (Toorajipour et al., 2021). These areas can benefit from AI's application potential, as well as the opportunity to address the current research gaps in the field. AI, identified as an Industry 4.0 technology1, holds transformative potential across numerous industries and domains, as noted by Kearney et al. (2018) and Townsend and Hunt (2019). Consequently, nearly all areas within SCM and its subfields are susceptible to the influence and impact of AI.
The objectives of this study are to identify the current status of AI implementations in SCM and to identify the extent of their impact on supply chain optimization. We aim to investigate the challenges and barriers faced by organizations when implementing AI-based solutions in SCM, particularly within the context of Moroccan firms. In addition to that, we also attempt to identify some of the critical success factors for a successful integration and implementation of AI-based solutions and assess their impact on supply chain performance. This research also tries to examine the ethical, legal, and social implications of using AI in SCM.
Therefore, we endeavor in this work to bring answers to the following research question:
How can the use of AI-based solutions improve supply chain optimization and performance in Moroccan companies?
Sub-questions are derived from this main problem, which explain the course of our study:
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What are the key success factors for implementing AI-based solutions in SCM, and how can these factors be applied in Moroccan companies?
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What are the potential benefits and limitations of using AI-based solutions for SCM?
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How do supply chain (logistics) managers perceive the use of AI-based solutions in their operations, and what factors influence their attitudes and beliefs?
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How can AI-based solutions be integrated with existing SCM systems and processes, and what challenges must be overcome to achieve successful integration?