Published July 28, 2026 | Version v1
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Human-AI collaboration in supply chain decision-making.

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This study examines the role of Human–AI collaboration in improving decision-making effectiveness in supply chain management. The objective of the study is to understand how artificial intelligence capabilities and human expertise work together to support better decisions in areas such as demand forecasting, inventory management, logistics, and risk management. A quantitative research approach was adopted, and primary data was collected through a structured questionnaire from 120 respondents familiar with supply chain activities. The collected data was analyzed using percentage analysis, reliability testing, descriptive statistics, correlation analysis, regression analysis, t-test, and ANOVA. The findings indicate that Human–AI collaboration, trust in AI, transparency, and organizational support have a positive influence on decision-making effectiveness. The results highlight that AI improves data analysis and prediction capabilities, while human involvement provides experience, judgment, and contextual understanding. This study emphasizes that successful supply chain decisions can be achieved through effective collaboration between humans and AI rather than replacing human decision-makers.

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2026-07-28
Lai et al. (2021) highlighted that trust, transparency, and understanding AI recommendations are essential for effective Human–AI decision-making. Gomez et al. (2025) found that AI systems often provide recommendations rather than true collaboration, emphasizing the need for communication and user control. Leitão et al. (2022) suggested that adaptive Human–AI systems are required to support changing decision environments. Aman et al. (2025) identified trust, explainability, and user acceptance as key factors for successful AI adoption. Li and Tian (2026) proposed that effective AI–human collaboration depends on transparency, communication, trust, and the integration of human judgment with AI capabilities.

References

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