Digital Twins: A Maturity Model for Their Classification and Evaluation
Цифровые двойники: модель зрелости для их классификации и оценки
2022-01-01
SCID: 54.1/h2qm4w76
Discuss with AI
Digital TwinsIndustry 4.0PRISMA systematic literature reviewmaturity modelproduction and logistics
Figures from the paper
Abstract (AI)
Digital Twins represent a powerful tool for transforming production and logistics towards Industry 4.0. They mirror physical assets in the digital world, enriching them with additional capabilities and features such as decision-making or lifecycle management. Due to the diverse possibilities associated with the Digital Twin, their design and implementation are also wide-ranging. This paper aims to contribute to the formalization and standardization of the description of Digital Twins. It presents a method for evaluating them through their lifecycle, from design to operation. The paper is based on an overview of their potential functionalities and properties with ranked stages of development. This method allows for an application-specific evaluation of Digital Twins and describes how they can be improved to suit the application better. The maturity model development follows the procedure for developing maturity models for IT management. Relevant capabilities and features were identified with a systematic literature review following the PRISMA guidelines. The results of this review were ranked and categorized and constitute the core of the maturity model, which was validated on five use-cases from different domains in production and logistics. The maturity model assesses Digita Twins in seven categories (context, data, computing capabilities, model, integration, control, human-machine interface) with 31 ranked characteristics. It evaluates existing solutions for potential improvements for a given application or the transfer to a new use-case. The resulting method and a supplementary web service present a generalized model for the evaluation of Digital Twins. Based on a description of a potential application, this is the first step towards a systematic evaluation, improving the structured development of such applications
Key Findings
1
A PRISMA-based systematic literature review identified, ranked, and categorized Digital Twin capabilities and features underlying the maturity model.
2
The framework contains 31 ranked characteristics and supports application-specific assessment, improvement planning, and transfer of existing Digital Twins to new use cases.
3
The model evaluates Digital Twins across seven categories: context, data, computing capabilities, model, integration, control, and human-machine interface.
4
The paper introduces a maturity model for formalizing, classifying, and evaluating Digital Twins throughout their lifecycle from design to operation.
5
Validation on five production and logistics use cases, together with a supplementary web service, demonstrates a generalized approach for systematic Digital Twin evaluation.
Research Object
Digital Twins for production and logistics applications
Research Subject
Their maturity-based classification and lifecycle evaluation across context, data, computing capabilities, models, integration, control, and human-machine interfaces
Publication Details
Publication Date
2022-01-01
Journal
Publisher
ISSN
Cited by
128
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest
References available in scid.ai8
Digital Twin in Industry: State-of-the-Art2019
Digital Twin: Enabling Technologies, Challenges and Open Research2020
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective2020
A Review of the Roles of Digital Twin in CPS-based Production Systems2017
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison2019
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications2019
Leveraging Digital Twin Technology in Model-Based Systems Engineering2019
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities2021