Drivers, Barriers and Social Considerations for AI Adoption in Supply Chain Management

Table of Contents

1 INTRODUCTION

1.1 THE INITIAL SITUATION, STATE OF RESEARCH

1.2 THE PROBLEM, RESEARCH GAP, AND RESEARCH QUESTION

1.3 AIM OF THE WORK AND METHODOLOGICAL APPROACH

2 LITERATURE REVIEW

2.1 SUPPLY CHAIN

2.1.1 Supply Chain

2.1.2 Supply Chain Management

2.1.3 Supply Chain Operational Reference Model

2.2 ARTIFICIAL INTELLIGENCE

2.2.1 Definition of AI

2.2.2 AI-based on technology

2.2.3 AI-based on Functionality

2.2.4 Topics and Sub-Topics of AI

2.2.5 Commonly used algorithms and AI concepts in Supply Chain

2.2.5.1 Adaptive-network-based fuzzy inference (ANFIS)

2.2.5.2 Ant Colony Optimisation (ACO)

2.2.5.3 Artificial Bee Algorithm (BA)

2.2.5.4 Artificial Bee Colony (ABC)

2.2.5.5 Artificial Immune System (AIS)

2.2.5.6 Artificial Neural Networks (ANN)

2.2.5.7 Auction-based algorithm (ABA)

2.2.5.8 Bayesian Networks (BN)

2.2.5.9 Benders Decomposition (BD)

2.2.5.10 Branch-and-Cut Algorithm (BCA)

2.2.5.10.1 Case-Based Reasoning (CBR)

2.2.5.10.2 Column Generation

2.2.5.11 Constraint Programming (CP)

2.2.5.12 Cutting Plane Algorithm (CPA)

2.2.5.13 Decision Support System (DSS)

2.2.5.14 Decision tree algorithms (DT)

2.2.5.15 Deep Reinforcement Learning (DRL)

2.2.5.16 Differential Evolution (DE)

2.2.5.17 Dual Decomposition (DD)

2.2.5.18 Evolutionary Algorithm (EA)

2.2.5.19 Fuzzy logic algorithm

2.2.5.20 Genetic Algorithms (GAs)

2.2.5.21 Gradient Boosting Algorithm (GB)

2.2.5.22 Hidden Markov Models (HMM)

2.2.5.23 Integer Linear Programming (ILP)

2.2.5.24 K-Nearest Neighbors (KNN)

2.2.5.25 Linear Programming (LP)

2.2.5.26 Logistic Regression (LR)

2.2.5.27 Markov Decision Process (MDP)

2.2.5.28 Memetic Algorithm (MA)

2.2.5.29 Mixed Integer Linear Programming (MILP)

2.2.5.30 Monte Carlo Tree Search (MCTS)

2.2.5.31 Naive Bayes (NB)

2.2.5.32 Nonlinear Programming Algorithm (NLPA)

2.2.5.33 Particle Swarm Optimisation (PSO)

2.2.5.34 Random Forest (RF)

2.2.5.35 Reinforcement Learning (RL)

2.2.5.36 Rule-Based System (RBS)

2.2.5.37 Support Vector Machines (SVM)

2.2.6 Categorisation algorithms

2.2.6.1 Most used algorithms in supply chain

2.2.6.2 Categorisation by algorithm type

2.2.6.3 Categorisation by Supply Chain usage

2.2.7 What is the quality of the SLRs?

2.2.8 What are the research areas on AI addressed by SLRs in SCM?

2.2.9 Which individuals, organisations, and publication venues are the most active in research on AI in SCM?

2.2.10 What are the barriers to and drivers of AI adoption in SCM?

2.2.11 What is the importance placed on human and social factors in AI applications in SCM?

2.2.12 What recommendations should be made for future research on AI in SCM?

2.2.13 What role did the SCOR model play, and which area of the SCOR model was used for the research?

2.3 THE DIGITALISATION OF THE SUPPLY CHAIN

2.4 THE NEED FOR ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT

2.5 SUMMARY OF THE LITERATURE REVIEWS

3 METHODOLOGY

3.1 LITERATURE REVIEW

3.1.1 Research Question for Literature Review

3.1.2 Search Strategy

3.1.3 Selection Process

3.1.4 Quality Assignment

3.2 SURVEY

3.2.1 Questionnaire Design

3.2.2 Questionnaire administration

3.2.3 Data Set

3.3 EXPERT INTERVIEWS

3.3.1 Inclusion Criteria

3.3.2 Exclusion Criteria

3.3.3 Data Analysis

3.3.4 Methodology of the Interview Data Anaylsis

3.3.5 Interview Guide

3.4 METHODOLOGICAL LIMITATIONS

4 RESULTS

4.1 SURVEY

4.1.1 Usage of AI inside the organisation

4.1.2 Areas of Usage of AI

4.1.3 Usage of AI Types

4.1.4 Drivers for the Use of AI in the Supply Chain

4.1.5 What are the barriers to the usage of AI

4.1.6 Social Considerations of AI

4.1.7 In which areas do you think more research is needed

4.1.8 Spend and budget on AI

4.2 EXPERT INTERVIEWS

4.2.1 Overall Interview Summary

4.2.2 Expert View on Drivers and Barriers of AI

4.3 DRIVERS AND BARRIERS WHO FORCE THE USE OF AI IN SCM

4.3.1 Barrier: Lack of Understanding About AI

4.3.2 Barrier: Data Availability Issues

4.3.3 Barrier: High Initial Investment Costs

4.3.4 Barrier: Technical Challenges

4.3.5 Barrier: Lack of Expertise

4.3.6 Barrier: Ethical Concerns

4.3.7 Driver: Enhanced Efficiency

4.3.8 Driver: Competitive Edge

4.3.9 Driver: Customer Satisfaction

4.3.10 Driver: Data-Driven Decision Making

4.3.11 Driver: Increase in Automation

4.3.12 Driver: Internal Motivation Over External Pressures

5 RECOMMENDATION

5.1 OVERALL RECOMMENDATIONS

5.2 RECOMMENDATIONS FOR DEVELOPING AI SOLUTIONS FOR SUPPLY CHAIN

5.3 RECOMMENDATIONS FOR BUYING AI SOLUTIONS FOR SUPPLY CHAIN

5.4 HOW TO AVOID BARRIERS OF SUPPLY CHAIN

5.4.1 Initial Steps and Scaling Up

5.4.2 Building Partnerships and Fostering Innovation

5.4.3 Data Optimisation and Comprehensive Understanding

5.4.4 Terminology and Team Composition

5.4.5 Data Governance and Ethical Considerations

5.4.6 Change Management and Integration

5.4.7 Change Management and Integration

5.4.8 Focus on ROI and Business Value

5.4.9 Continuous Monitoring and Improvement

5.4.10 Employee Engagement and Participation

5.4.11 Risk Management

5.5 SOCIAL RECOMMENDATIONS FOR AI IN SUPPLY CHAIN

5.5.1 Engage with stakeholders

5.5.2 Establish clear data governance policies

5.5.3 Invest in talent and resources

5.5.4 Focus on ethics and transparency

5.5.5 Continuously improve and adapt

5.6 RECOMMENDATIONS FOR COMPANIES ON HOW TO IMPLEMENT AI IN SCM

5.7 RECOMMENDATIONS WITH POINTS TO AVOID IMPLEMENTING AI

6 DISCUSSION

6.1 SUMMARY

6.2 SCIENTIFIC RECOMMENDATIONS

6.3 BUSINESS RECOMMENDATIONS

6.4 LIMITATIONS

6.4.1 Limitations Literature Review

6.4.2 Limitations Survey

6.4.3 Limitations Expert Interview

7 CONCLUSION

7.1 LITERATURE REVIEW CONCLUSION

7.2 SURVEY CONCLUSION

7.3 EXPERT INTERVIEW CONCLUSION

7.4 OVERALL CONCLUSION

7.5 REGULATORY CHANGES

7.6 FUTURE RESEARCH DIRECTION

Research Objectives & Main Themes

The core objective of this dissertation is to investigate the drivers and barriers influencing the adoption of artificial intelligence (AI) within supply chain management (SCM). By utilizing a mixed-methods approach—including a systematic literature review, an expert survey, and expert interviews—the research aims to bridge the gap between theoretical AI capabilities and practical industrial implementation, ultimately providing actionable guidelines for businesses to navigate the complexities, ethical considerations, and integration challenges of AI in their supply chains.

• Analysis of the current drivers and obstacles for AI adoption in SCM.
• Categorization and categorization of commonly used AI algorithms in supply chain contexts.
• Exploration of social and human-centric implications, such as upskilling and job role evolution.
• Development of a systematic checklist and implementation framework for businesses.
• Evaluation of data governance, cybersecurity, and regulatory compliance practices in AI-integrated supply chains.

Extract from the Book

Summary of Chapters

1 INTRODUCTION: Outlines the historical evolution of industrial revolutions and the shift toward digitalization in supply chains to establish the necessity of AI.

2 LITERATURE REVIEW: Explores existing frameworks, AI definitions, and provides a categorization of various AI algorithms used in supply chain management contexts.

3 METHODOLOGY: Details the multi-method approach, including a systematic literature review (SLR), expert surveys, and qualitative expert interviews used to collect data.

4 RESULTS: Presents findings from the survey and expert interviews regarding the primary drivers, barriers, and technical challenges of integrating AI into supply chain systems.

5 RECOMMENDATION: Offers a comprehensive blueprint for businesses to implement AI, including strategies for talent management, data governance, and ethical practice development.

6 DISCUSSION: Synthesizes research findings and provides scientific and business recommendations, while explicitly acknowledging the limitations of the current study.

7 CONCLUSION: Summarizes the study’s core insights and suggests future research directions regarding regulatory shifts and technological advancements in AI-driven supply chains.

Keywords

Supply Chain Management, SCM, Artificial Intelligence, AI, Supply Chain Operational Reference Model, SCOR, Digital Transformation, AI Adoption, Optimization, Algorithms, Machine Learning, Robotics, Expert Systems, Data Governance, Sustainability.

Frequently Asked Questions

What is the core purpose of this research?

The research is designed to examine the factors that drive the adoption of artificial intelligence within supply chain management and the barriers that hinder its effective implementation, providing a guiding framework for successful integration.

What are the primary themes discussed in the work?

The work focuses on five key areas: identifying drivers and obstacles for AI, categorizing appropriate AI algorithms, addressing human and social factors, establishing implementation strategies, and navigating data governance and regulatory compliance.

What is the main goal or research question?

The primary research aim is to understand how barriers and drivers for AI adoption evolve over time and to translate this understanding into forward-looking suggestions that both scientists and professionals can utilize for better technology application.

Which specific scientific methods are employed?

The study utilizes a mixed-method research design, consisting of a systematic literature review to analyze existing research, a survey of subject matter experts, and semi-structured expert interviews for deep qualitative insights.

What topics are covered in the main section of the dissertation?

The main sections cover current industrial trends, detailed categorization of AI algorithms used in supply chains (e.g., machine learning, robotics), an analysis of survey results on company-specific experiences, and strategic implementation recommendations.

Which keywords best describe this study?

Key terms include Supply Chain Management (SCM), artificial intelligence, SCOR model, digital transformation, data governance, predictive analytics, algorithm categorization, and supply chain resilience.

How does the work address the issue of human labor and AI?

The study explores the shift from manual labor to human-AI synergy, emphasizing the necessity of reskilling and upskilling workers, while also discussing concerns related to job displacement and the critical role of human judgment in AI implementations.

What specific practical recommendations are provided for companies?

Recommendations include adopting a phased implementation strategy starting with pilot projects, fostering a multidisciplinary team (including ethicists and data scientists), prioritizing data integrity and transparency, and aligning AI deployment with specific, measurable business goals.