Demand and Supply Risk: Sustainability Manufacturing Industries of Turkey Based on Artificial Intelligence

Authors

  • Nouri Ali Higher Institute of Medical Sciences and Technologies, Bani
  • Iman Namroud Collage of Electronics Engineering, Bani Walid, Libya
  • Mohsen Ibrahim Higher Institute of Engineering Technologies, Bani Walid, Libya

Keywords:

Supply Chain Risk Management, Artificial Intelligence (AI), Sustainability Performance, Manufacturing Industries, Demand Management

Abstract

The aim of this paper is to investigate the influence of artificial intelligence (AI) on improving the sustainability performance of supply chains in the manufacturing sectors of Turkey. The paper took a quantitative approach, with a sample size of 350 manufacturing entities. It combined both qualitative and quantitative approaches. Statistical tools were employed to analyse the data and identify patterns and linkages. The results highlighted the significant impact of AI in reducing supply chain risks, stressing its crucial role in managing demand. Moreover, the research emphasized that incorporating artificial intelligence (AI) into company operations goes beyond only technological progress and instead becomes a crucial necessity for enhancing resilience and sustainability. The paper implies that firms can derive significant advantages by incorporating AI technology to effectively manage unpredictable demand, optimize inventories, and improve overall sustainability. Nevertheless, the study's focus on Turkey's manufacturing industries implies the necessity for more extensive research, including other sectors and areas. To summarize, AI has great potential to transform supply chain risk management, which might have a major impact on the future of manufacturing in Turkey and potentially worldwide.

Dimensions

Published

2024-07-14

How to Cite

Nouri Ali, Iman Namroud, & Mohsen Ibrahim. (2024). Demand and Supply Risk: Sustainability Manufacturing Industries of Turkey Based on Artificial Intelligence. African Journal of Advanced Pure and Applied Sciences (AJAPAS), 3(3), 20–34. Retrieved from https://aaasjournals.com/index.php/ajapas/article/view/873