{"title":"开发珠宝生产过程的数字模型,用于资源优化和预测","authors":"Fei Lin, M. C. Wong, Ming Ge","doi":"10.1080/1023697X.2018.1535284","DOIUrl":null,"url":null,"abstract":"ABSTRACT Smart manufacturing is becoming one of the core tendencies in manufacturing nowadays. In the conventional production process mode, the varieties in the production process, manpower processing time, and production scheduling lead to a big challenge in production process management. In order to keep pace with the rapid technology development and market demand diversification, digital and intelligent transformation became extremely essential for manufacturing industries. It is able to evaluate and manage the production process performance in a timely and scientific manner. With the digital model, the production efficiency can be improved and the resources management can be optimised based on the prediction. In this study, the traditional labour-intensive jewellery manufacturing is used and analysed to evaluate its digital model for the resource optimisation and prediction. By evaluating the production process by functional groups separately with the manpower level classification, the digital model could provide an automatic and efficient solution to its production process management system and logistic flow. It eliminates the unnecessary time-consuming working process and enhances the working process efficiency, which is capable of optimising the entire production efficiency as well as performing the resource prediction.","PeriodicalId":35587,"journal":{"name":"Transactions Hong Kong Institution of Engineers","volume":"25 1","pages":"229 - 236"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/1023697X.2018.1535284","citationCount":"7","resultStr":"{\"title\":\"Development of the digital model of the jewellery production process for resource optimisation and prediction\",\"authors\":\"Fei Lin, M. C. Wong, Ming Ge\",\"doi\":\"10.1080/1023697X.2018.1535284\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Smart manufacturing is becoming one of the core tendencies in manufacturing nowadays. In the conventional production process mode, the varieties in the production process, manpower processing time, and production scheduling lead to a big challenge in production process management. In order to keep pace with the rapid technology development and market demand diversification, digital and intelligent transformation became extremely essential for manufacturing industries. It is able to evaluate and manage the production process performance in a timely and scientific manner. With the digital model, the production efficiency can be improved and the resources management can be optimised based on the prediction. In this study, the traditional labour-intensive jewellery manufacturing is used and analysed to evaluate its digital model for the resource optimisation and prediction. By evaluating the production process by functional groups separately with the manpower level classification, the digital model could provide an automatic and efficient solution to its production process management system and logistic flow. It eliminates the unnecessary time-consuming working process and enhances the working process efficiency, which is capable of optimising the entire production efficiency as well as performing the resource prediction.\",\"PeriodicalId\":35587,\"journal\":{\"name\":\"Transactions Hong Kong Institution of Engineers\",\"volume\":\"25 1\",\"pages\":\"229 - 236\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/1023697X.2018.1535284\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions Hong Kong Institution of Engineers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/1023697X.2018.1535284\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions Hong Kong Institution of Engineers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/1023697X.2018.1535284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Development of the digital model of the jewellery production process for resource optimisation and prediction
ABSTRACT Smart manufacturing is becoming one of the core tendencies in manufacturing nowadays. In the conventional production process mode, the varieties in the production process, manpower processing time, and production scheduling lead to a big challenge in production process management. In order to keep pace with the rapid technology development and market demand diversification, digital and intelligent transformation became extremely essential for manufacturing industries. It is able to evaluate and manage the production process performance in a timely and scientific manner. With the digital model, the production efficiency can be improved and the resources management can be optimised based on the prediction. In this study, the traditional labour-intensive jewellery manufacturing is used and analysed to evaluate its digital model for the resource optimisation and prediction. By evaluating the production process by functional groups separately with the manpower level classification, the digital model could provide an automatic and efficient solution to its production process management system and logistic flow. It eliminates the unnecessary time-consuming working process and enhances the working process efficiency, which is capable of optimising the entire production efficiency as well as performing the resource prediction.