Improving maintenance reliability: human-centric strategies for knowledge management and task assignment

A ranking method for maintenance strategy selection of airport mechanical equipment using an analytic hierarchy process

Despite the growing consciousness that the appropriate blends of maintenance strategies aid substantial reduction in plant downtimes, the extant literature fails to adequately explain how the critical maintenance criteria of mechanical equipment in airports could be ranked to impact the maintenance program positively. This paper offers the analytical hierarchical process (AHP) to explain how the significant maintenance criteria from a technical perspective (i.e., adaptability and maintainability) and in relationship with customer viewpoints (i.e., funds availability and customer trust) impact rank determination. Expandability and maintainability were equally ranked first with a criterion weight of 0.36 each. Customer trust was ranked second (criterion weight of 0.17) and the least important criterion of funds availability was ranked third (criterion weight of 0.10). Thus, the AHP proposes the choice of either adaptability or maintainability to yield satisfying results as inputs into maintenance program development. Consequently, the AHP provides explanations on how the choice of maintenance criteria for an airport's mechanical equipment helps to solve the maintenance program development dilemma for maintenance engineers and managers.

Synergizing automation and human capital: a strategic model for productivity enhancement in retail warehousing

The research addresses the critical need for operational efficiency and international competitiveness in the modern logistics sector through the perspective of warehouse automation. While global trends move toward smart warehousing, many companies face hesitancy due to high initial investment costs and concerns regarding workforce displacement. This research employs a mixed-methods approach, combining qualitative expert interviews with quantitative financial analysis, to develop a three-phased strategic model for retail companies in Latvia. The model integrates automated sorting systems with a primary focus on workforce reskilling. Key findings indicate that automation significantly enhances speed, accuracy, and labour productivity while reducing human error. The proposed model advocates for stage-wise implementation and collaborative training programs to bridge the digital skill gap. Financial evaluation of the reskilling program demonstrates a positive return on investment (ROI) of 10.03%, confirming the economic feasibility of human-centered automation. By bridging the digital skill gap through collaborative training, the model provides a scalable framework for retailers to maintain competitiveness in the dynamic Baltic and European logistics markets.

Knowledge-Based Strategic Cost Management

This study proposes a knowledge-based strategic cost management model integrating goal programming (GP) and accounting indicators to improve managerial decision-making in complex and competitive business environments. As global competition intensifies and cost structures become more intricate, organizations require decision support tools that treat cost not only as a control variable but also as a strategic performance driver. The model combines accounting indicators, including cost variances, activity-based costing, overhead allocation, and efficiency ratios, with GP to address multiple and conflicting cost-related objectives under operational and financial constraints. By integrating quantitative accounting data with qualitative managerial knowledge, organizational priorities, and experiential insights, the framework supports transparent and informed decision-making. Experimental findings indicate cost reductions of up to 18%, resource utilization exceeding 90%, and significantly lower deviations compared to traditional methods.

Interchangeability and cannibalization in industrial maintenance: perspectives for asset management with machine learning, big data and artificial intelligence

Applied Expert Systems and Knowledge Management

Identifying and Prioritizing the Role of Artificial Intelligence in Predictive Maintenance and Repairs in the Automotive Industry

This study aims to identify and prioritize the key roles of artificial intelligence in predictive maintenance and repair processes within the automotive industry from an expert-based decision-making perspective. The study adopts a positivist philosophy with a deductive approach and employs a mixed-methods case-survey design. Initially, a comprehensive review of the relevant literature was conducted to extract the principal roles of artificial intelligence in predictive maintenance and repairs. Based on this review, ten AI-related roles were identified and operationalized into evaluation criteria. Data were collected through structured pairwise-comparison questionnaires administered to a panel of 15 experts drawn from maintenance, research and development, and information technology departments in the automotive sector. To prioritize the identified roles, the Analytical Hierarchy Process (AHP) was applied. The consistency of expert judgments was assessed using the consistency ratio to ensure the reliability and logical coherence of the comparisons. The AHP results indicate that artificial intelligence plays a multidimensional role in predictive maintenance and repairs. Among the identified roles, saving time in the repair and maintenance process achieved the highest priority weight (0.196). This was followed by optimizing vehicle performance (0.185) and improving vehicle availability (0.169). Other significant roles included enhancing predictive accuracy, improving estimation of remaining useful life, reducing overall operational costs, optimizing maintenance schedules, strengthening security and privacy, and increasing customer satisfaction. The consistency ratio confirmed the acceptable reliability of the prioritization results. The findings demonstrate that artificial intelligence is a critical enabler of efficient, reliable, and proactive predictive maintenance in the automotive industry, with its greatest value perceived in reducing maintenance time and enhancing operational performance.