Artificial Intelligence in Supply Chain Management. Opportunities and risks of digitalisation

Table of Contents

1 Introduction

1.1 Problem statement

1.2 Objective

1.3 Working methodology

1.4 Course of work

2 Theoretical foundations

2.1 Supply Chain Management

2.2 artificial intelligence

3 Areas of application and current research on artificial intelligence in the supply chain

3.1 Process Overview Supply Chain

3.2 Procurement

3.3 Production

3.4 Intralogistics and warehousing

3.5 Distribution

3.6 Compliance, Customs, Import and Export

3.7 Disposal and recycling

4 Opportunities and risks of artificial intelligence in the supply chain

4.1 Odds

4.2 Risks

4.3 Summary of the use of artificial intelligence in the supply chain

5 Conclusion

Objectives and Research Themes

The primary objective of this work is to provide a comprehensive analysis of the current state and future trends of artificial intelligence within supply chain management, specifically examining the associated possibilities, effects, and risks to determine how these technologies can be utilized to generate prosperity and operational efficiency. The research investigates how AI can transform traditional supply chain processes while balancing potential benefits against challenges such as ethical considerations, data security, and market disruption.

• Theoretical foundations of Supply Chain Management and Artificial Intelligence.
• Practical applications of AI across various stages of the supply chain including procurement, production, and distribution.
• Analysis of the opportunities for efficiency gains and optimization through intelligent systems.
• Evaluation of risks including data privacy, ethical decision-making, and labor market impacts.
• The concept of the "Smart Factory" and the future role of AI as a competitive factor.

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Summary of Chapters

1 Introduction: Defines the problem, research objective, and the methodological approach used to analyze the influence of AI on supply chains.

2 Theoretical foundations: Establishes the necessary knowledge base regarding SCM definitions, AI technical terms, machine learning types, and hardware requirements.

3 Areas of application and current research on artificial intelligence in the supply chain: Examines specific AI implementations across different supply chain segments like procurement, production, logistics, and recycling.

4 Opportunities and risks of artificial intelligence in the supply chain: Discusses the dual-sided nature of AI, evaluating both the potential for economic growth and the risks regarding ethics, data security, and societal impact.

5 Conclusion: Synthesizes the findings, confirming that the potential of AI outweighs the risks, and highlights that the future focus should be on ethical integration for long-term prosperity.

Keywords

Artificial Intelligence, Supply Chain Management, Machine Learning, Industry 4.0, Big Data, Predictive Maintenance, Smart Factory, Logistics, Blockchain, Robotics, Natural Language Processing, Digital Twin, Sustainability, Circular Economy, Risk Management.

Frequently Asked Questions

What is the fundamental focus of this research paper?

The research focuses on the intersection of artificial intelligence and supply chain management, aiming to identify how AI technologies can optimize processes and what opportunities and risks accompany this digital transformation.

What are the central thematic areas covered in the work?

The work covers theoretical basics of SCM and AI, practical applications in various supply chain sectors (procurement to recycling), and a comprehensive discussion of risks like data protection and labor market shifts.

What is the primary objective of the study?

The objective is to provide a comprehensive picture of the current state of AI development and its trends, while demonstrating the possibilities, effects, and risks that these technologies present for supply chain management.

Which scientific methods were employed?

The author conducted a combined quantitative and qualitative study, including an extensive literature search and a systematic content analysis of 678 sources, which were coded and evaluated for relevance.

What topics are addressed in the main part of the text?

The main part analyzes specific areas of application such as procurement, production, intralogistics, and distribution, highlighting technologies like predictive maintenance, digital twins, and autonomous robots.

Which keywords characterize this work?

Key terms include Artificial Intelligence, Supply Chain Management, Machine Learning, Industry 4.0, Big Data, and Risk Management.

How does the "Bullwhip effect" impact the supply chain?

The Bullwhip effect describes how small demand increases at the retail level result in significant, inefficient production surges at the manufacturing level due to a lack of transparency and information distortion.

What role do "digital twins" play in modern production?

Digital twins serve as digital models used to map and simulate future components or assemblies, allowing for optimization and collaboration before physical production takes place.