Enhancing Occupational Safety in AI-Driven Supply Chains: Challenges and Solutions

Authors

  • Rohit Raman PwC Advisory Services LLC
  • Rashmi Shrivastava Amazon Inc.

DOI:

https://doi.org/10.47941/ijscl.2490

Keywords:

Robotics, Artificial Intelligence, Occupational Safety, Robot-Human Collision, AI Regulations, Occupational hazards

Abstract

Purpose: This paper explores the transformative impact of Artificial Intelligence (AI) and robotics in the fifth industrial revolution, particularly post-pandemic, where their adoption has significantly streamlined operations, reduced costs, and enhanced product development and distribution. Despite these advantages, their widespread use raises concern about occupational safety, including worker injuries and psychological harm.

Methodology: A systematic literature review was conducted to trace the evolution of workplace hazards and identify emerging risks associated with AI and robotics. The study also assessed strategies for mitigating these risks and evaluated the effectiveness of current regulatory frameworks in promoting occupational safety.

Findings: The research highlights that while AI and robotics reduce certain traditional workplace hazards, they introduce new risks such as human-robot collisions, algorithmic bias, and unintended consequences. Regulatory bodies are pivotal in developing and enforcing policies to safeguard workers. Additionally, organizations, AI developers, and individuals must collaborate to create safer workplaces.

Unique Contribution to Theory, Policy and Practice: This study contributes to the understanding of occupational safety in AI and robotics by identifying emerging risks, suggesting strategies for human-robot collaboration, and offering regulatory recommendations. It emphasizes a multi-stakeholder approach to ensure safe and effective integration of these technologies in the workplace.

Downloads

Download data is not yet available.

Author Biographies

Rohit Raman, PwC Advisory Services LLC

Senior Associate

Rashmi Shrivastava, Amazon Inc.

Medical Care Quality Lead

References

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion, 58(1), 82–115. https://doi.org/10.1016/j.inffus.2019.12.012

Belenguer, L. (2022). AI bias: exploring discriminatory algorithmic decision-making models and applying possible machine-centric solutions adapted from the pharmaceutical industry. AI and Ethics, 2(2). https://doi.org/10.1007/s43681-022-00138-8

Berx, N., Decré, W., Morag, I., Chemweno, P., & Pintelon, L. (2022). Identification and classification of risk factors for human-robot collaboration from a system-wide perspective. Computers & Industrial Engineering, 163, 107827. https://doi.org/10.1016/j.cie.2021.107827

Daly, A., & Segate, R. V. (2023). Encoding the Enforcement of Safety Standards into Smart Robots to Harness Their Computing Sophistication and Collaborative Potential: A Legal Risk Assessment for European Union Policymakers. European Journal of Risk Regulation, pp. 1–40. https://doi.org/10.1017/err.2023.72

Farina, M., Yu, X., & Lavazza, A. (2024). Ethical considerations and policy interventions concerning the impact of generative AI tools in the economy and society. AI and Ethics. https://doi.org/10.1007/s43681-023-00405-2

Fisher, E., Flynn, M. A., Pratap, P., & Vietas, J. A. (2023). Occupational Safety and Health Equity Impacts of Artificial Intelligence: A Scoping Review. International Journal of Environmental Research and Public Health, 20(13), 6221–6221. https://doi.org/10.3390/ijerph20136221

Huck, T. P., Münch, N., Hornung, L., Ledermann, C., & Wurll, C. (2021). Risk assessment tools for industrial human-robot collaboration: Novel approaches and practical needs. Safety Science, 141, 105288. https://doi.org/10.1016/j.ssci.2021.105288

Lopez-de-Ipina, K., Iradi, J., Fernandez, E., Calvo, P. M., Salle, D., Poologaindran, A., Villaverde, I., Daelman, P., Sanchez, E., Requejo, C., & Suckling, J. (2023). HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics. Sensors, 23(3), 1170. https://doi.org/10.3390/s23031170

Martinetti, A., Chemweno, P. K., Nizamis, K., & Fosch-Villaronga, E. (2021). Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations. Frontiers in Chemical Engineering, 3. https://doi.org/10.3389/fceng.2021.666237

Mohamed Shaffril, H. A., Samsuddin, S. F., & Abu Samah, A. (2020). The ABC of Systematic Literature review: the Basic Methodological Guidance for Beginners. Quality & Quantity, 55(1), 1319–1346. https://doi.org/10.1007/s11135-020-01059-6

Mollaei, N., Fujao, C., Silva, L., Rodrigues, J., Cepeda, C., & Gamboa, H. (2022). Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms. International Journal of Environmental Research and Public Health, 19(15), 9552. https://doi.org/10.3390/ijerph19159552

Pishgar, M., Issa, S. F., Sietsema, M., Pratap, P., & Darabi, H. (2021). REDECA: A Novel Framework to Review Artificial Intelligence and Its Occupational Safety and Health Applications. International Journal of Environmental Research and Public Health, 18(13), 6705. https://doi.org/10.3390/ijerph18136705

Rantala, M., Lindholm, M., & Tappura, S. (2022). Supporting Occupational Health and Safety Risk Assessment Skills: A Case Study of Five Companies. International Journal of Environmental Research and Public Health, 19(3), 1720. https://www.mdpi.com/1660-4601/19/3/1720

Robla-Gomez, S., Becerra, V. M., Llata, J. R., Gonzalez-Sarabia, E., Torre-Ferrero, C., & Perez-Oria, J. (2017). Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments. IEEE Access, 5, 26754–26773. https://doi.org/10.1109/access.2017.2773127

Shah, I. A., & Mishra, S. (2024). Artificial Intelligence in advancing the Occupational Health and Safety: An encapsulation of developments. Journal of Occupational Health, 66(1). https://doi.org/10.1093/joccuh/uiad017

Simkute, A., Luger, E., Jones, B., Evans, M., & Jones, R. (2021). Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable. Journal of Responsible Technology, 7-8, 100017. https://doi.org/10.1016/j.jrt.2021.100017

Staneva, M., & Elliott, S. (2023). Measuring the Impact of Artificial Intelligence and Robotics on the Workplace. In New Digital Work (pp. 16–30). https://doi.org/10.1007/978-3-031-26490-0_2

Wang, X., Chen, M., & Chen, N. (2024). How artificial intelligence affects the labor employment structure from the perspective of industrial structure optimization. Neliyon, 10(5), e26686–e26686. https://doi.org/10.1016/j.heliyon.2024.e26686

Yaacoub, J.-P. A., Noura, H. N., Salman, O., & Chehab, A. (2021). Robotics Cyber security: Vulnerabilities, attacks, countermeasures, and Recommendations. International Journal of Information Security, 21(21). https://doi.org/10.1007/s10207-021-00545-8

Zhang, B., Anderljung, M., Kahn, L., Dreksler, N., Horowitz, M. C., & Dafoe, A. (2021). Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers. Journal of Artificial Intelligence Research, 71(71). https://doi.org/10.1613/jair.1.12895

Downloads

Published

2025-02-02

How to Cite

Raman, R., & Shrivastava, R. (2025). Enhancing Occupational Safety in AI-Driven Supply Chains: Challenges and Solutions. International Journal of Supply Chain and Logistics, 9(2), 1–17. https://doi.org/10.47941/ijscl.2490

Issue

Section

Articles