Managing Risks in Supply Chain - Challenges, Impacts, and Mitigation Strategies When Integrating Artificial Intelligence and Machine Learning

Aug 5, 2026

This research examines how artificial intelligence (AI) and machine learning (ML) reshape supply chain management (SCM) while introducing material operational, governance, and trust risks. Survey and interview data collected from managers across multiple sectors are used to isolate three critical risk areas: data quality, cyber security, and transparency. The study concludes that robust data governance, proactive cybersecurity, and explainable AI frameworks strengthen confidence and efficiency. Sector-specific strategies, inter-department collaboration, and ethical guidelines support effective risk mitigation and responsible AI adoption in SCM.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages38

Reading Time1 h 36 min

AccessFree to download

Introduction

Literature Review

Explaination of Supply Chain

Major Risks in Supply Chain

Risks related to AI and ML in the SCM

Problem Statement

Research Questions

Research Methodology

Data Collection Methods

Data Analysis Techniques

Surveys results

Proposal

Interview results

Comparison of Results: Survey and Interviews

Discussion

References

Appendices

Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Management from the Nova School of Business and Economics. TITLE OF WORK PROJECT MANAGING RISKS IN SUPPLY CHAIN: CHALLENGES, IMPACTS, AND MITIGATION STRATEGIES WHEN INTEGRATING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ROSARIA BISCEGLIA 60480 Work project carried out under the supervision of: Paulo Faroleiro 06/01/2025 ABSTRACT This research explores the development of artificial intelligence (AI) and machine learning (ML) in supply chain management (SCM), taking into consideration their transformational potential and inherent risks. By analyzing data from surveys and interviews conducted with managers of various sectors, the research identifies three critical risks: data quality, cyber security and transparency. The findings provide the need for strong data governance, proactive cybersecurity measures and the adoption of AI frameworks that can be explained to improve confidence and efficiency. Recommendations include sector-specific risk management strategies, inter-departmental collaboration and ethical guidelines. This research provides insights to optimize AI and ML adoption in SCM, mitigating associated risks. Keywords: AI risks; Supply chain management; Machine learning transparency; Data governance; Cybersecurity in SCM. Acknowledgement A deep thanks to my supervisor, Paulo Faroleiro, for his constant support and valuable advice that has guided every step of this work. Your support has been an invaluable source of inspiration and motivation. This work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) . 1. Introduction......................................................................................................................3 2. Literature Review ............................................................................................................4 2.1 Explaination of Supply Chain ....................................................................................4 2.2 Major Risks in Supply Chain .....................................................................................6 2.3 Risks related to AI and ML in the SCM.....................................................................7 3. Problem Statement...........................................................................................................9 3.1 Identification of Risks in AI and ML adoption ..........................................................9 3.2 Research Gaps............................................................................................................9 4. Research Questions ........................................................................................................ 10 5. Research Methodology .................................................................................................. 11 5.1 Research design ....................................................................................................... 11 5.2 Data Collection Methods ......................................................................................... 11 5.2.1 Survey Methodology............................................................................................ 11 5.2.2 Interview Process ................................................................................................. 12 5.3 Data Analysis Techniques ........................................................................................ 12 6. Surveys results................................................................................................................ 12 6.1 Analysis of Survey Data ..........................................

FAQs

What risks does the research identify when integrating AI and ML into supply chain management?

The research identifies three critical risks: data quality, cyber security, and transparency. These risks affect reliability, protection, and stakeholder trust.

How do the findings suggest organizations should mitigate AI and ML risks in SCM?

Recommendations include strong data governance, proactive cybersecurity measures, and adopting explainable AI frameworks. These actions aim to improve confidence and operational efficiency.

What research methods were used to derive the results?

The study uses surveys and interviews with managers from various sectors. It combines quantitative survey analysis with qualitative interview analysis to derive key findings.

Aug 5, 2026

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