{"title":"制造业中数字孪生优先发展的方法论方法","authors":"Sara Blasco Román, Till Böttjer","doi":"10.1002/asmb.2889","DOIUrl":null,"url":null,"abstract":"<p>The digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision-making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. This real-world application demonstrates the effectiveness of our approach in identifying and prioritizing Digital Twin applications in a complex industrial context. This research contributes to the growing body of knowledge surrounding Digital Twins, providing organizations with a structured approach to leverage the potential of this transformative technology.</p>","PeriodicalId":55495,"journal":{"name":"Applied Stochastic Models in Business and Industry","volume":"41 3","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/asmb.2889","citationCount":"0","resultStr":"{\"title\":\"A Methodological Approach to Prioritize Digital Twin Development in Manufacturing\",\"authors\":\"Sara Blasco Román, Till Böttjer\",\"doi\":\"10.1002/asmb.2889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision-making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. 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A Methodological Approach to Prioritize Digital Twin Development in Manufacturing
The digital age has brought about a need for organizations to utilize Digital Twins to improve operational efficiency and decision-making. However, it is difficult for companies to identify and prioritize Digital Twin initiatives that meet the needs of their stakeholders and align with the capabilities of the company and its strategic plans. This paper proposes a methodology for the systematic identification and prioritization of Digital Twin applications in complex industrial settings. The methodology begins by documenting business requirements, current processes, and challenges, and subsequently identifying areas with potential benefits from Digital Twins through the use of an opportunity scoring system. To refine the portfolio of Digital Twin applications to include only those that are impactful and viable, the feasibility of Digital Twin is quantified by evaluating technological (technical capacity and digital skills), organizational, and project risk factors. To validate the proposed methodology, a case study was conducted in collaboration with an industrial partner specializing in injection molding. This real-world application demonstrates the effectiveness of our approach in identifying and prioritizing Digital Twin applications in a complex industrial context. This research contributes to the growing body of knowledge surrounding Digital Twins, providing organizations with a structured approach to leverage the potential of this transformative technology.
期刊介绍:
ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process.
The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.