Integrated Queue Management and Service Composition for Enhancing Manufacturing-as-a-Service Performance
Keywords:
Manufacturing-as-a-Service, Queue Management, Service Composition, Service-Oriented ManufacturingAbstract
This study aimed to develop and empirically evaluate an integrated framework examining the effects of integrated queue management and service composition capability on Manufacturing-as-a-Service (MaaS) performance, while assessing the enabling role of technological support factors within service-oriented manufacturing environments. This applied quantitative study employed a descriptive-correlational design using structural equation modeling. The statistical population consisted of manufacturing managers, industrial engineers, production planners, and digital manufacturing specialists working in manufacturing organizations in Tehran, Iran. A total of 384 participants were selected through stratified random sampling. Data were collected using a researcher-developed questionnaire measuring integrated queue management, service composition capability, technological support factors, and MaaS performance. Content validity was confirmed by an expert panel, while construct validity was assessed through confirmatory factor analysis. Reliability was established using Cronbach’s alpha, composite reliability, and average variance extracted indicators. Data analysis was performed using SPSS 27 and SmartPLS 4. The measurement model demonstrated satisfactory reliability and validity, with Cronbach’s alpha values ranging from 0.887 to 0.925, composite reliability values between 0.909 and 0.938, and average variance extracted values exceeding 0.50 for all constructs. Structural equation modeling revealed that integrated queue management had a significant positive effect on MaaS performance (β = 0.421, t = 8.974, p < 0.001). Service composition capability also exerted a significant positive effect on MaaS performance (β = 0.366, t = 7.852, p < 0.001). Technological support factors significantly influenced MaaS performance directly (β = 0.214, t = 4.986, p < 0.001) and indirectly through their effects on integrated queue management (β = 0.492, t = 9.843, p < 0.001) and service composition capability (β = 0.547, t = 11.308, p < 0.001). The proposed model explained 68.7% of the variance in MaaS performance (R² = 0.687), indicating substantial explanatory power and predictive relevance. The findings demonstrate that integrated queue management and service composition capability are critical drivers of Manufacturing-as-a-Service performance, while technological support factors provide the essential infrastructure enabling their effectiveness.
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Copyright (c) 2026 Mina Ravanesh; Emad Roghanian (Author)
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