Development of a CMMS-Integrated Predictive Maintenance Framework for Industrial PLC and Drive Systems

Abstract

Industrial organizations depend on programmable logic controllers, drives, sensors, and computerized maintenance management systems, yet these technologies often remain fragmented, limiting the conversion of condition data into maintenance action. This study aimed to develop and evaluate a CMMS-integrated predictive maintenance framework for PLC and drive systems. A quantitative, cross-sectional, multiple-case-based design was applied across 12 enterprise cases representing textile, food, pharmaceutical, cement, steel, power, chemical, automotive, packaging, water-treatment, and electronics operations. Of 360 questionnaires distributed, 330 were returned and 312 valid responses were retained, producing an 86.67 percent valid response rate. Respondents included maintenance, automation, electrical, reliability, instrumentation, production, CMMS, and management professionals. The key variables were CMMS Integration Capability, Condition-Monitoring Data Quality, Predictive Analytics Capability, Maintenance Workforce Competency, Organizational Readiness, Predictive Maintenance Effectiveness, and PLC and Drive System Maintenance Performance. Data were analyzed using descriptive statistics, Cronbach’s alpha, factor-based validity assessment, Pearson correlation, multiple regression, and diagnostics in SPSS. The instrument demonstrated overall reliability, α = .93, with construct alphas ranging from .82 to .90, while KMO = .918 and Bartlett’s test was significant, χ² (861) = 7,426.31, p < .001. Condition-Monitoring Data Quality achieved the highest predictor mean of 4.16. The five predictors explained 64.2 percent of the variance in Predictive Maintenance Effectiveness, R² = .642, F (5, 306) = 109.75, p < .001. Data quality was the strongest predictor, β = .29, followed by predictive analytics, β = .27, CMMS integration, β = .21, workforce competency, β = .17, and organizational readiness, β = .14. Predictive Maintenance Effectiveness explained 57.1 percent of maintenance-performance variance, β = .76, F (1, 310) = 412.60, p < .001. The findings imply that reliable data, integrated systems, analytical capability, skilled personnel, and organizational support must be developed together to reduce downtime, repeated faults, repair duration, and production interruption while improving availability, reliability, and continuity.