The expectations of applying AI solutions for material localization process in Production Life Cycle Management
Jin, Pin (2022)
Jin, Pin
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2022100120710
https://urn.fi/URN:NBN:fi:amk-2022100120710
Tiivistelmä
The paper machinery industry is composed of professional equipment including pulp & energy, paper machines, and finishing machines with high-automatic technology. The state-of-the-art technology was mastered in the developed country over a hundred years of development. The emerging paper industry market in the developing country stimulated the giants in the paper machinery industry to expand their business globally. The case company was one of them that adopted the multi-domestic production model as a strategy to enter China market. The material translation was the barrier during the production. The company pursues a smart systematic way to assist the material localization process to meet business growth.
The objective of this research was to examine the expectations of stakeholders who were involved in the material localization process to get an understanding of how those expectations were categorized in current AI themes and distributed in the product lifecycle management stage. The results could be used as development proposals for further system development projects. The works of literature have been reviewed in the area of production localization concepts, the material definition in Product Life management, and Artificial intelligence application in the material selection to understand the factors which could impact the material localization process. The research has adopted the current AI thematic study area (Dwivedi, 2019) and product lifecycle management stages (Vila et al., 2015) as the primary theoretical framework in the empirical study along with the subcategory which is summarized by the ground theory to be the final research framework. The case study of the qualitative research approach was chosen in this research with the implementation of collecting qualitative data with the content analysis method.
The research found, firstly in the Decision-making topic, there were smart suggestions for local material, local manufacturing process, requirements from other stakeholders, and the smart localization process guidance. Secondly, the following expectations were detected in the Application domains area: the smart replacement function for local material code, the Nest local material & components information to the expert system, localization validation tools, and smart coding systems for material and component. Thirdly Data and information areas, are covered by the local material information acquisition, data analysis, and local data sharing. The expectations distribution in PLM stages covered mainly in embodiment design and detail design phases in design-development, as well in the procurement and supply phases in the manufacturing stage, and the client service phase in the service stage was observed with expectations.
The objective of this research was to examine the expectations of stakeholders who were involved in the material localization process to get an understanding of how those expectations were categorized in current AI themes and distributed in the product lifecycle management stage. The results could be used as development proposals for further system development projects. The works of literature have been reviewed in the area of production localization concepts, the material definition in Product Life management, and Artificial intelligence application in the material selection to understand the factors which could impact the material localization process. The research has adopted the current AI thematic study area (Dwivedi, 2019) and product lifecycle management stages (Vila et al., 2015) as the primary theoretical framework in the empirical study along with the subcategory which is summarized by the ground theory to be the final research framework. The case study of the qualitative research approach was chosen in this research with the implementation of collecting qualitative data with the content analysis method.
The research found, firstly in the Decision-making topic, there were smart suggestions for local material, local manufacturing process, requirements from other stakeholders, and the smart localization process guidance. Secondly, the following expectations were detected in the Application domains area: the smart replacement function for local material code, the Nest local material & components information to the expert system, localization validation tools, and smart coding systems for material and component. Thirdly Data and information areas, are covered by the local material information acquisition, data analysis, and local data sharing. The expectations distribution in PLM stages covered mainly in embodiment design and detail design phases in design-development, as well in the procurement and supply phases in the manufacturing stage, and the client service phase in the service stage was observed with expectations.