Abstract
Transparency encompasses the potential to monitor operations instantaneously so that required corrective actions can be taken as needed. Transparency entails the ability to track processes in real-time, enhance the visibility of the operations, and require a seamless network for improved communication for smart manufacturing systems. However, there is a lack of proper metrics to assess the transparency of smart manufacturing environments. This paper contributes to the assessment of transparency by proposing a metric for its evaluation. In doing so, we found that the assessment of transparency takes the quantification of traceability into account. Hence, a step-in assessment is conducted by initially developing a mathematical model for traceability, followed by a model for transparency. The model is validated by analysing the sensitivity and applicability through simulation-based experimentation. The results demonstrate the level of traceability followed by transparency with the implementation of smart manufacturing systems. A point of inflexion that determines the variability in the offerings of traceability at a given set of inputs was found. This is one of the few works that focus on the development of a metric for quantifying transparency through the traceability of smart manufacturing systems. Furthermore, it investigates the behaviour by analyzing the sensitivity of the model through simulation-based approaches, which is a unique addition to the realm of the smart manufacturing literature. Managers can refer to this study's findings to design the deployment of smart manufacturing systems with informative trade-offs to maintain their required traceability and transparency capabilities.
Author supplied keywords
Cite
CITATION STYLE
Parhi, S., Joshi, K., Garza-Reyes, J. A., & Akarte, M. (2025). Evaluating the transparency capability of smart manufacturing systems. Operations Management Research, 18(3), 849–869. https://doi.org/10.1007/s12063-025-00547-y
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.