Shuo Su , Aydin Nassehi , Adam McClenaghan , Andrew Langridge , Ben Hicks
{"title":"一种估算数字孪生体成本的方法","authors":"Shuo Su , Aydin Nassehi , Adam McClenaghan , Andrew Langridge , Ben Hicks","doi":"10.1016/j.jmsy.2025.04.004","DOIUrl":null,"url":null,"abstract":"<div><div>This paper proposes a methodology for estimating the cost of developing digital twins (DTs) in manufacturing processes. It formulates a cost model that identifies main cost elements and presents the estimation process for establishing an acceptable cost reference based on a given set of physical information entities as DT inputs. To achieve this, six core data activities are derived from the ISO 23247 DT reference framework and the Digital Twin Data concept to characterize the functioning of DTs from a data perspective. These activities are data gathering, data interaction, data storage, data processing, data servitization, and data maintenance. The activity-based costing (ABC) method is applied to allocate six resources in the development of DTs (personnel, machine, equipment, material, facility, and service) to these data-intensive activities. The resultant cost structure comprises 40 cost activities, along with associated quantitative metrics. This work presents a case study on developing a DT for estimating the dimensional accuracy in the MEX process, where thermal and acceleration measurements are considered. For the information set with extruder and build plate temperatures as well as X- and Y-axis acceleration, developing a DT is estimated to cost between £4780 (for one temperature signal) and £39,285 (for two temperature signals and two acceleration signals) for two years of service and one year of data archiving. In addition, the cost distribution across four categories (IT infrastructure, resource, data activity, and investment) are analysed. The derived insights can support cost-related analysis, physical information entity selection, budget control, and standard open databases for DT costs.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 841-858"},"PeriodicalIF":12.2000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A methodology for estimating the cost of a digital twin\",\"authors\":\"Shuo Su , Aydin Nassehi , Adam McClenaghan , Andrew Langridge , Ben Hicks\",\"doi\":\"10.1016/j.jmsy.2025.04.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper proposes a methodology for estimating the cost of developing digital twins (DTs) in manufacturing processes. It formulates a cost model that identifies main cost elements and presents the estimation process for establishing an acceptable cost reference based on a given set of physical information entities as DT inputs. To achieve this, six core data activities are derived from the ISO 23247 DT reference framework and the Digital Twin Data concept to characterize the functioning of DTs from a data perspective. These activities are data gathering, data interaction, data storage, data processing, data servitization, and data maintenance. The activity-based costing (ABC) method is applied to allocate six resources in the development of DTs (personnel, machine, equipment, material, facility, and service) to these data-intensive activities. The resultant cost structure comprises 40 cost activities, along with associated quantitative metrics. This work presents a case study on developing a DT for estimating the dimensional accuracy in the MEX process, where thermal and acceleration measurements are considered. For the information set with extruder and build plate temperatures as well as X- and Y-axis acceleration, developing a DT is estimated to cost between £4780 (for one temperature signal) and £39,285 (for two temperature signals and two acceleration signals) for two years of service and one year of data archiving. In addition, the cost distribution across four categories (IT infrastructure, resource, data activity, and investment) are analysed. The derived insights can support cost-related analysis, physical information entity selection, budget control, and standard open databases for DT costs.</div></div>\",\"PeriodicalId\":16227,\"journal\":{\"name\":\"Journal of Manufacturing Systems\",\"volume\":\"80 \",\"pages\":\"Pages 841-858\"},\"PeriodicalIF\":12.2000,\"publicationDate\":\"2025-04-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Manufacturing Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0278612525000937\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0278612525000937","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
A methodology for estimating the cost of a digital twin
This paper proposes a methodology for estimating the cost of developing digital twins (DTs) in manufacturing processes. It formulates a cost model that identifies main cost elements and presents the estimation process for establishing an acceptable cost reference based on a given set of physical information entities as DT inputs. To achieve this, six core data activities are derived from the ISO 23247 DT reference framework and the Digital Twin Data concept to characterize the functioning of DTs from a data perspective. These activities are data gathering, data interaction, data storage, data processing, data servitization, and data maintenance. The activity-based costing (ABC) method is applied to allocate six resources in the development of DTs (personnel, machine, equipment, material, facility, and service) to these data-intensive activities. The resultant cost structure comprises 40 cost activities, along with associated quantitative metrics. This work presents a case study on developing a DT for estimating the dimensional accuracy in the MEX process, where thermal and acceleration measurements are considered. For the information set with extruder and build plate temperatures as well as X- and Y-axis acceleration, developing a DT is estimated to cost between £4780 (for one temperature signal) and £39,285 (for two temperature signals and two acceleration signals) for two years of service and one year of data archiving. In addition, the cost distribution across four categories (IT infrastructure, resource, data activity, and investment) are analysed. The derived insights can support cost-related analysis, physical information entity selection, budget control, and standard open databases for DT costs.
期刊介绍:
The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs.
With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.