Industrialisation has greatly changed people's lives since the eighteenth century. At that time, the focus of activities shifted from the physical to the cognitive. We are now in the fourth industrial revolution. Through the development of ever better artificial systems, more and more cognitive activities are being carried out by machines.
But what exactly does artificial intelligence mean? What changes has it already created in supply chain management and what is still possible in the future? What are the risks associated with advancing digitalisation?
In his publication, Johannes Hangl creates a comprehensive picture of the status of current and future developments as well as trends in the use of artificial intelligence. He shows what possibilities, effects, opportunities and risks it has for supply chain management.
From the contents:
- AI;
- Internet of Things;
- Logistics;
- Cyber-Physical Systems
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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Bullwhip effect
The bullwhip effect, or in German the whip impact effect, goes back to Forrester's investigations from 1958. Forrester found that when demand increases, players in a value chain (raw material supplier, manufacturer, distributor, distributor, and customer) overreact. It has been shown that even an unplanned increase in demand of 10% causes the manufacturer to increase production by up to 40%. This can also be seen in Figure 3.
For example, the end consumer has a short-term increase in demand of 10% due to an offer or a bottleneck situation (discount campaign, bottleneck, hamster purchases) by the retailer. The retailer orders an increased quantity from the up-supplier. Across the stages, the demand forecast increases because the individual market partners can only see the needs of the respective upstream stage. In addition to the lack of transparency of demand, the reasons for this are the distortion of information, price changes and changes in inventory levels.
To mitigate or combat the bullwhip effect, there are certain tools. Above all, an improved exchange of information along the entire supply chain is needed in order to be able to determine the actual demand of the end customer.
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.
- Citar trabajo
- Johannes Hangl (Autor), 2020, Artificial Intelligence in Supply Chain Management. Opportunities and risks of digitalisation, Múnich, GRIN Verlag, https://www.grin.com/document/1187321
