Role of artificial intelligence in optimizing automotive supply chain planning and decision making
Authors
Kumar Venugopal, Saravana
Issue Date
2024-05
Degree
MBA in Project Management
Publisher
Dublin Business School
Rights holder
Rights
Abstract
The automotive industry operates within a complex and dynamic global supply chain ecosystem, where efficient planning and decision-making are paramount to ensure competitiveness and sustainability. This dissertation embarks on a comprehensive investigation into the role of artificial intelligence (AI) in optimizing automotive supply chain planning and decision-making processes, with a particular focus on integrating insights from random sampling strategy and quantitative data analysis techniques.
Challenges in the Automotive Industry: The dissertation initiates by delving into the current state of the automotive industry and elucidates the key challenges confronted by supply chain stakeholders. These challenges include demand volatility, supply chain disruptions, and escalating customer expectations, all of which underscore the necessity for innovative approaches to supply chain management.
Exploration of AI Technologies: Building upon this foundation, the study navigates through various AI technologies such as machine learning, predictive analytics, and optimization algorithms, probing their potential applications in mitigating the challenges. By leveraging these advanced technologies, automotive companies can bolster their capacity to forecast demand accurately, optimize inventory levels, enhance supplier management, streamline production scheduling, optimize transportation logistics, manage risks, and advance sustainability initiatives within their supply chains. Furthermore, the integration of insights from random sampling strategies ensures a rigorous approach to data collection and representation, thereby enhancing the robustness and validity of the research findings.
Analysis of AI Applications: A critical analysis of AI applications within the automotive supply chain domain is meticulously presented, highlighting the multifaceted roles AI can play across various operational domains. From demand forecasting to sustainability initiatives, each aspect is scrutinized to delineate the potential benefits and challenges associated with AI adoption. Through empirical evidence, case studies, and theoretical frameworks, the dissertation paints a comprehensive picture of how AI can revolutionize automotive supply chain management practices.
Implications and Considerations: Moreover, the dissertation delves into the implications of AI adoption on organizational structures, workforce skills, and ethical considerations within the automotive industry. As AI permeates various aspects of supply chain operations, organizational structures may evolve, necessitating shifts in workforce skillsets and competencies. Additionally, ethical considerations surrounding AI implementation, such as data privacy, algorithmic bias, and transparency, are scrutinized to ensure responsible and ethical deployment of AI technologies in the automotive supply chain context.
In conclusion, this dissertation contributes significantly to the burgeoning body of knowledge on AI-enabled supply chain optimization in the automotive industry. By synthesizing insights from diverse sources and methodologies, the study provides practical recommendations for policymakers, industry practitioners, and academics alike. By embracing AI technologies strategically, automotive companies can augment efficiency, agility, and resilience in their supply chain operations, thereby driving sustainable growth and competitive advantage in an increasingly digitalized and interconnected world.