Analysis of energy efficiency investments in smart manufacturing via double hierarchy linguistic structure-based hybrid decision-making model

This study examines energy efficiency investments in smart manufacturing. For this purpose, a hybrid decision framework is put forward within the double hierarchy linguistic context. The hybrid model encompasses methods for rational determination of decision parameters, viz., experts' weights, criteria weights, and ranks. The research gap in methodically determining experts' weights, along with considering the interdependencies among experts, is addressed by DHHFL-CRITIC, which utilizes correlation and variance measures to overcome this challenge. Likewise, the gap of fluctuations or spikes in preferences owing to extreme scale values is nullified through DHHFL-LOPCOW, which uses log functions to smooth spikes. Finally, the computational complexity gap during rank determination is circumvented by DHHFL-OPARA, which eliminates the normalization procedure, thereby reducing the complexity during rank determination. These methods together form the framework, and the framework analyzes investment options in energy efficiency. The findings of this study have important implications for both theory and practice. In the theoretical sense, a new model is proposed to contribute to the literature. Thanks to the latest model, reliable results can be obtained. In practical terms, a set of criteria affecting energy efficiency in smart manufacturing processes is defined, and the most optimal strategies affecting energy efficiency in these processes are identified. The analysis results with the new model show that the most influential criteria are maintenance and idle time, along with the green building design and energy monitoring system being the key options for investments to support energy efficiency with smart manufacturing.