Developing a Recommendation Model for the Smart Factory System
In Industry 4.0, the concept of a Smart Factory heralds a new phase in manufacturing; the Smart Factory System (SFS) will have a huge demand in Taiwan. However, the cost of constructing a factory system will be high, and the complexity processes and introduction time must be considered. Thus, it is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/18/8606 |
| _version_ | 1797520341618130944 |
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| author | Chun-Yang Chang Chun-Ai Tu Wei-Luen Huang |
| author_facet | Chun-Yang Chang Chun-Ai Tu Wei-Luen Huang |
| author_sort | Chun-Yang Chang |
| collection | DOAJ |
| description | In Industry 4.0, the concept of a Smart Factory heralds a new phase in manufacturing; the Smart Factory System (SFS) will have a huge demand in Taiwan. However, the cost of constructing a factory system will be high, and the complexity processes and introduction time must be considered. Thus, it is important to figure out how to grasp the key success factors for Smart Factories to reduce difficulties in the process, deal with the occurrence of problems, and improve the success rate of constructing Smart Factories. This research constructs an SFS recommendation model to make up for past research deficiencies in terms of recommendation. It combines the methodology of the Engel–Kollat–Blackwell Model (EKB Model) and the Modified Delphi Method to derive SFS recommendation indicators. Through analyzing weights, the ELECTRE II was used to obtain the importance of each dimension by calculating the Modified Compound Advantage Matrix. For prototype indicators, it reviewed the past literature to find out deficiencies and examined the world’s four largest manufactories or computer technology corporations to analyze their Smart Factory solutions regarding the SFS function characteristics. The survey ran for several rounds with a group of five experts to amend indicators until a consensus was obtained. It proposed 64 indicators of 8 primary dimensions in total, based on the Updated Information System Success Model, and then added the concepts of SFS Function characteristics, Information Security, Perceived Value, Perceived Risk, and UI Design. According to the indicators, the framework and prototype of this system will provide solutions and references for purchasing SFS, the functions of which include SFS purchase ability analysis, demand analysis of manufacture problems, and raking and scoring of recommendation indicators. It will provide real-time ranking and the best alternative recommendations to suppliers, and will not only be referred to for design and modification but also enable the requirements to be closer to the users’ demands. |
| first_indexed | 2024-03-10T07:55:23Z |
| format | Article |
| id | doaj.art-af7ddefeaab549eabc914d3cc79ea7b1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:55:23Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-af7ddefeaab549eabc914d3cc79ea7b12023-11-22T11:55:26ZengMDPI AGApplied Sciences2076-34172021-09-011118860610.3390/app11188606Developing a Recommendation Model for the Smart Factory SystemChun-Yang Chang0Chun-Ai Tu1Wei-Luen Huang2Department of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanBusiness Intelligence School, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanKaohsiung Veterans General Hospital, Kaohsiung 807618, TaiwanIn Industry 4.0, the concept of a Smart Factory heralds a new phase in manufacturing; the Smart Factory System (SFS) will have a huge demand in Taiwan. However, the cost of constructing a factory system will be high, and the complexity processes and introduction time must be considered. Thus, it is important to figure out how to grasp the key success factors for Smart Factories to reduce difficulties in the process, deal with the occurrence of problems, and improve the success rate of constructing Smart Factories. This research constructs an SFS recommendation model to make up for past research deficiencies in terms of recommendation. It combines the methodology of the Engel–Kollat–Blackwell Model (EKB Model) and the Modified Delphi Method to derive SFS recommendation indicators. Through analyzing weights, the ELECTRE II was used to obtain the importance of each dimension by calculating the Modified Compound Advantage Matrix. For prototype indicators, it reviewed the past literature to find out deficiencies and examined the world’s four largest manufactories or computer technology corporations to analyze their Smart Factory solutions regarding the SFS function characteristics. The survey ran for several rounds with a group of five experts to amend indicators until a consensus was obtained. It proposed 64 indicators of 8 primary dimensions in total, based on the Updated Information System Success Model, and then added the concepts of SFS Function characteristics, Information Security, Perceived Value, Perceived Risk, and UI Design. According to the indicators, the framework and prototype of this system will provide solutions and references for purchasing SFS, the functions of which include SFS purchase ability analysis, demand analysis of manufacture problems, and raking and scoring of recommendation indicators. It will provide real-time ranking and the best alternative recommendations to suppliers, and will not only be referred to for design and modification but also enable the requirements to be closer to the users’ demands.https://www.mdpi.com/2076-3417/11/18/8606Smart Factoryrecommendation modelrecommendation systemModified Delphi MethodELECTRE II Method |
| spellingShingle | Chun-Yang Chang Chun-Ai Tu Wei-Luen Huang Developing a Recommendation Model for the Smart Factory System Applied Sciences Smart Factory recommendation model recommendation system Modified Delphi Method ELECTRE II Method |
| title | Developing a Recommendation Model for the Smart Factory System |
| title_full | Developing a Recommendation Model for the Smart Factory System |
| title_fullStr | Developing a Recommendation Model for the Smart Factory System |
| title_full_unstemmed | Developing a Recommendation Model for the Smart Factory System |
| title_short | Developing a Recommendation Model for the Smart Factory System |
| title_sort | developing a recommendation model for the smart factory system |
| topic | Smart Factory recommendation model recommendation system Modified Delphi Method ELECTRE II Method |
| url | https://www.mdpi.com/2076-3417/11/18/8606 |
| work_keys_str_mv | AT chunyangchang developingarecommendationmodelforthesmartfactorysystem AT chunaitu developingarecommendationmodelforthesmartfactorysystem AT weiluenhuang developingarecommendationmodelforthesmartfactorysystem |