Maintenance resource allocation – the business analytics usage in Industry 4.0 conditions

Abstract

Purpose: The purpose of this publication is to present the applications of usage of business analytics in maintenance resources allocation. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The integration of business analytics with condition monitoring technologies heralds a transformative shift in maintenance resource allocation. Through the utilization of real-time data from sensors and monitoring devices, organizations can craft dynamic and adaptive maintenance strategies. These strategies, grounded in the current equipment condition, strike a nuanced balance between preventive and corrective actions. Business analytics enriches decision-making by factoring in critical elements such as equipment importance, operational impact, resource availability, and budget constraints. This integration instigates a paradigm shift, fostering proactive, adaptive, and efficient resource allocation, resulting in heightened asset reliability, diminished downtime, and amplified operational performance. The evolving synergy between business analytics and maintenance practices is becoming integral to the future of asset management and industrial operations. The diverse applications of business analytics in maintenance, as outlined in Table 1, underscore its versatility, encompassing predictive maintenance, condition monitoring, asset performance management, work order prioritization, resource optimization, cost-benefit analysis, inventory management, and performance metrics monitoring. Concurrently, the adoption of advanced software solutions in Industry 4.0 conditions, exemplified by IBM Maximo Asset Management, SAP Intelligent Asset Management, and Fiix CMMS, reflects a commitment to efficiency and innovation in maintenance resource allocation. Despite the substantial advantages, addressing challenges outlined in Table 4, including data quality, integration complexities, implementation costs, and skill development, is crucial. These challenges underscore the necessity for a strategic and holistic implementation approach that considers technology, personnel training, and organizational readiness. In essence, the evolution of maintenance resource allocation through business analytics signifies a data-driven revolution poised to optimize current operations and position organizations for sustained success amid rapid technological advancements and the transformations of Industry 4.0. Originality/value: Detailed analysis of business analytics in the case of maintenance resource allocation. Keywords: business analytics, Industry 4.0, digitalization, artificial intelligence, real-time monitoring; maintenance resource allocation. Category of the paper: literature review.