Abstract
Traditionally, the footwear industry is labor intensive, and cost control is key to ensuring shoe companies can be competitive. The development of Industry 4.0 concepts, used in high-tech industries and blockchain production information systems, enables the creation of smart factories with online alarm management systems, to improve manufacturing efficiency and reduce human resource requirements. In this paper, the performances of the causal association assessment model and the technique for order preference by similarity to the ideal solution (TOPSIS) model in evaluating large data blockchain technologies and quality online real-time early warning systems for production and raw material supplier management are compared, to increase the intelligence of production and to manage product traceability.
Highlights
With improvements in global living standards, the traditional shoe industry has gradually changed; co-creation design practices have been adopted, allowing the client to dictate the product, while functional sports shoes, casual shoes, and other types of footwear have become essential items with a large consumer base
Data related to consumer interaction that is added through the Internet of Market share (MS) [39]
We developed a cause–effect grey relational analysis (CEGRA) model for evaluation of intelligent system suppliers being considered for future collaboration, for further improvement of the company’s operational efficiency
Summary
With improvements in global living standards, the traditional shoe industry has gradually changed; co-creation design practices have been adopted, allowing the client to dictate the product, while functional sports shoes, casual shoes, and other types of footwear have become essential items with a large consumer base In this industry, upstream raw materials include textiles, rubber, and plastics, which account for about 60% of the overall cost of the shoe. An IAMS provides an expert decision-making system for rapid management of large amounts of data and transformation of data into information, reducing human resource requirements and preventing misjudgment and negligence These systems have been applied to distributed control systems, validated in online tests, and accepted by plant operators. The performances of the cause-effect grey relational analysis (CEGRA) [11] and the technique for order preference by similarity to the ideal solution (TOPSIS) [12] models in evaluating the collaborative technology created by IAMS software contractors are compared, to reduce system faults that prevent these products from meeting end users’ requirements
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