Chinwendu Onuegbu - Faculty of Ocean Engineering Technology, Universiti Malaysia Terengganu, ۲۱۰۳۰ Terengganu, Malaysia
Hamza Idriss - Multidisciplinary Faculty of Nador, University of Mohamed۱, ۶۰۷۰۰ Nador, Morocco

The objective of this study is to explore the influence of predictive maintenance technologies on operational efficiency in manufacturing startups, focusing on implementation processes, operational impacts, and the challenges encountered. This qualitative study employed semi-structured interviews to gather data from key stakeholders in manufacturing startups, including founders, operations managers, and maintenance engineers. A total of ۲۲ participants were interviewed, with the sample size determined by theoretical saturation. The interviews were transcribed verbatim and analyzed using NVivo software. Thematic analysis was conducted to identify and categorize key themes and subthemes related to the implementation and impact of predictive maintenance technologies. The analysis revealed three main themes: Implementation Process, Operational Impact, and Challenges and Barriers. Within these themes, several categories and concepts emerged. The Implementation Process theme highlighted the importance of planning, technology selection, system integration, employee involvement, pilot testing, change management, and post-implementation review. The Operational Impact theme identified efficiency gains, predictive analytics, maintenance scheduling, resource optimization, and quality improvement as significant outcomes. The Challenges and Barriers theme underscored technological challenges, financial constraints, organizational resistance, skill gaps, data management issues, and the necessity of vendor support. The findings indicate that predictive maintenance technologies significantly enhance operational efficiency in manufacturing startups by reducing downtime, increasing productivity, and optimizing resource utilization.

Predictive Maintenance, Operational efficiency, manufacturing startups, data Analytics, Machine Learning, Internet of Things