Details

Autor(en) / Beteiligte

• Chang, Chun-Yang
• Tu, Chun-Ai
• Huang, Wei-Luen

Titel

Developing a Recommendation Model for the Smart Factory System

Ist Teil von

• Applied sciences, 2021-09, Vol.11 (18), p.8606

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2021

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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.