AI integration in supply chains significantly boosts environmental and economic sustainability, but social aspects require further development.
Category: Sustainability · Effect: Strong effect · Year: 2024
Artificial Intelligence offers powerful tools for optimizing supply chains to be more environmentally and economically sustainable, though its application in addressing social sustainability challenges remains underdeveloped.
Design Takeaway
Prioritize the development and integration of AI solutions that address the social dimensions of sustainability in supply chains, alongside environmental and economic optimizations.
Why It Matters
Designers and engineers can leverage AI to create more efficient and less wasteful supply chain systems. Understanding the current limitations, particularly in social impact, allows for targeted innovation to develop solutions that address ethical and human-centric concerns within these complex networks.
Key Finding
AI is effective for making supply chains greener and more cost-efficient, but it's not yet well-equipped to handle the social aspects of sustainability.
Key Findings
- AI-integrated technologies are capable of enabling SSCM across various sectors.
- Current AI applications in SSCM primarily focus on environmental and economic sustainability.
- There is a significant technological gap in using AI to address social sustainability issues, such as working conditions and fair labor practices.
Research Evidence
Aim: What are the current research trends and future directions for integrating AI technologies into sustainable supply chain management?
Method: Bibliometric and text analysis
Procedure: A systematic review of 170 articles published between 2004 and 2023 from the Scopus database was conducted using the PRISMA protocol. Bibliometric and Latent Dirichlet Allocation (LDA) methods were employed to identify research trends and generate future research topics and propositions.
Sample Size: 170 articles
Context: Sustainable Supply Chain Management (SSCM)
Design Principle
Holistic sustainability in supply chain design requires balancing environmental, economic, and social considerations, with AI as a tool to achieve this balance.
How to Apply
When designing or redesigning supply chain processes, explore how AI can be used not only for efficiency and environmental impact reduction but also to monitor and improve social metrics like worker well-being and ethical sourcing.
Limitations
The review focuses on published research, potentially missing emerging or proprietary AI applications. The analysis of social sustainability may be limited by the availability and reporting of relevant data in the reviewed literature.
Student Guide (IB Design Technology)
Simple Explanation: AI can make supply chains better for the planet and for business profits, but it's not great yet at making sure workers are treated fairly or that businesses are ethical.
Why This Matters: Understanding how AI impacts different aspects of sustainability helps in designing more responsible and effective supply chains for future projects.
Critical Thinking: Given AI's current limitations in addressing social sustainability, what alternative or complementary design strategies should be employed to ensure ethical and equitable supply chains?
IA-Ready Paragraph: This research highlights that while AI-driven technologies are effective in enhancing the environmental and economic dimensions of sustainable supply chain management (SSCM), there remains a significant gap in their application to social sustainability aspects, such as ensuring fair working conditions and ethical practices. This suggests a critical area for future design innovation to develop AI solutions that holistically address all facets of SSCM.
Project Tips
- When researching AI in supply chains, look for studies that specifically mention 'social sustainability' or 'ethical sourcing'.
- Consider how you could use AI to track or improve social aspects, even if current research is limited.
How to Use in IA
- Use this research to justify the importance of considering social sustainability when designing AI-driven supply chain solutions.
- Cite this paper to highlight the current gap in AI's application to social sustainability and propose your own innovative solutions.
Examiner Tips
- Demonstrate an understanding of the multifaceted nature of sustainability (environmental, economic, social) when discussing AI applications.
- Critically evaluate the extent to which proposed AI solutions address all three pillars of sustainability.
Independent Variable: ["AI-integrated technologies","Supply chain management strategies"]
Dependent Variable: ["Environmental sustainability performance","Economic sustainability performance","Social sustainability performance"]
Controlled Variables: ["Industry sector","Geographical region of supply chain","Time period of research"]
Strengths
- Comprehensive review of a large body of literature.
- Systematic methodology (PRISMA protocol).
- Identification of specific research gaps and future directions.
Critical Questions
- How can AI be specifically designed or adapted to better monitor and improve social sustainability metrics in supply chains?
- What are the ethical considerations and potential biases associated with using AI to manage social aspects of supply chains?
Extended Essay Application
- Investigate the potential for AI to automate ethical sourcing verification within a specific product supply chain.
- Design a framework for using AI to monitor worker well-being in manufacturing facilities, considering data privacy and ethical implications.
Source
Reviewing the Roles of AI-Integrated Technologies in Sustainable Supply Chain Management: Research Propositions and a Framework for Future Directions · Sustainability · 2024 · 10.3390/su16146186