PREDICTIVE MAINTENANACE AND ITS ROLE IN ENERGY EFFICIENCY IN THE HOSPITALITY INDUSTRY

Abstract

Energy efficiency has emerged as a critical priority in the hospitality industry, driven by the need for sustainable operations and long-term cost optimization. Predictive Maintenance (PdM), an innovative strategy that integrates advanced analytics, machine learning, and real-time monitoring, offers a proactive approach to equipment management. Despite its potential, many hospitality establishments remain reliant on reactive maintenance models, resulting in frequent equipment failures, excessive energy consumption, and shortened asset lifespans. This study examines the application of PdM in enhancing energy efficiency across hospitality facilities and seeks to establish a framework for anticipating equipment failure, reducing operational disruptions, and promoting sustainable resource use. Using a desk research approach, the study draws insights from a comprehensive review of contemporary literature on smart sensor technologies, machine learning applications, and energy-efficient practices in facility management in Nigeria. The findings underscore PdM’s capacity to extend equipment lifespan, curtail energy waste, and reduce operating costs. However, barriers such as high implementation expenses, insufficient technical expertise, and algorithmic biases continue to impede widespread adoption. Small- and medium-sized enterprises (SMEs), in particular, face significant challenges due to limited financial and human capital, often defaulting to reactive or minimal preventive maintenance approaches. To overcome these constraints, the study advocates for a strategic blend of ethical AI deployment and workforce upskilling. By addressing structural and skill-related gaps, the hospitality sector can unlock the full potential of predictive maintenance, positioning it as a transformative pathway toward greater operational resilience, energy efficiency, and sustainable growth.