Work

An Intelligent Multi‐Criteria Framework for Digital Twin Adoption Assessment in Smart Manufacturing via Circular q‐Rung Orthopair Fuzzy CoCoFISO Dynamics

Year: 2026

Type: article

Abstract: ABSTRACT Digital twin technologies are gaining importance in modern intelligent manufacturing to facilitate increased efficiency, smartness in manufacturing, and sustainability. Nevertheless, the choice of a suitable digital twin solution is a complicated long‐term decision because of the high rate of technological development, integration issues, and financial limitations. To solve these problems, this research proposes a multi‐criteria decision‐making model based on the circular q‐rung orthopair fuzzy compromise of ideal solution (CqROF‐CoCoFISO). The proposed model uses circular q‐rung orthopair fuzzy sets, which is an effective model for capturing the uncertainty, hesitation and ambiguity that exists in smart manufacturing environments. The most important assessment criteria will be the technological flexibility, compatibility with the system, the cost of implementation, functionality of the system, intellectual property protection, adherence to security and sustainability. The CoCoFISO mechanism will allow ranking competing digital twin alternatives reliably based on conflicting criteria, which will improve the strength and stability of decision‐making results. The usefulness of the proposed framework can be proved through a comparative analysis. The findings offer useful information to manufacturing engineers, decision‐makers and policymakers aiming at informed and credible digital twin adoption policies in changing industrial conditions.

Source: IET Collaborative Intelligent Manufacturing

DOI: 10.1049/cim2.70076

Authors: Asma Farhad, Kifayat Ullah, Zeeshan Ali, Yilun Shang

Institutions: Riphah International University, Saveetha University, National Yunlin University of Science and Technology, Northumbria University

Language: English

Topic: Multi-Criteria Decision Making

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,520