Bo Qin, Peng Peng, Jian Zhang, Hongwei Wang, Ke Ma
{"title":"A framework and prototype system in support of workflow collaboration and knowledge mining for manufacturing value chains","authors":"Bo Qin, Peng Peng, Jian Zhang, Hongwei Wang, Ke Ma","doi":"10.1049/cim2.12073","DOIUrl":null,"url":null,"abstract":"<p>In the field of industrial design and manufacture, computer-supported collaborative work (CSCW) systems have been widely deployed for better teamwork. However, the traditional CSCW systems have a main drawback in effectively processing and utilising knowledge across different industrial workflows. To bridge this gap, we propose a framework for collaboration between members across the manufacturing value chains to increase efficiency and reduce duplication in team cooperation. The framework contains three parts, namely workflow, knowledge mining, and services. Specifically, the workflow part provides a collaborative environment for multiple users. The knowledge mining part, as the core of the framework, extracts in-context knowledge from workflows. The part of services can interact with users with different users in each workflow, including information recommendation they need in the future or information retrieval they want to know from other workflows. Furthermore, we develop a prototype system for supporting multiple value chains collaboration to verify the effectiveness and efficiency of the framework.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"5 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12073","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12073","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
In the field of industrial design and manufacture, computer-supported collaborative work (CSCW) systems have been widely deployed for better teamwork. However, the traditional CSCW systems have a main drawback in effectively processing and utilising knowledge across different industrial workflows. To bridge this gap, we propose a framework for collaboration between members across the manufacturing value chains to increase efficiency and reduce duplication in team cooperation. The framework contains three parts, namely workflow, knowledge mining, and services. Specifically, the workflow part provides a collaborative environment for multiple users. The knowledge mining part, as the core of the framework, extracts in-context knowledge from workflows. The part of services can interact with users with different users in each workflow, including information recommendation they need in the future or information retrieval they want to know from other workflows. Furthermore, we develop a prototype system for supporting multiple value chains collaboration to verify the effectiveness and efficiency of the framework.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).