Research On Supply Chain Risk Control Of Garment Industry Based On Association Rules

In the era of vigorous development of big data and the acceleration of global economic integration,the competition among enterprises in the clothing industry is becoming increasingly fierce,from products to services to supply chains.Because the production of business risks of enterprises is sudden and uncertain,and at the same time,the strong domestic and foreign market competition leads to the increasingly complex internal structure of enterprises.At the same time,enterprises are also faced with various business risks in their operations.It can be seen that the prevention and management of supply chain business risks will become increasingly critical.The progress of the clothing industry in the world today is rapid,but the existence of its supply chain risk has become a key factor restricting its long-term development and steady growth.Compared with developed countries,China’s research on industrial supply chain and its business risk is still in its infancy,which puts China’s traditional enterprises in a disadvantageous position in the process of integrating into economic globalization.Therefore,in order to obtain long-term and sustainable development momentum,China’s traditional enterprises should constantly consolidate and enhance their core competitiveness,and must increase research on industrial supply chain and its risk prediction management.So this paper,based on a large number of enterprise risk research methods,uses data mining technology and Apriori and FP-Tree association rule algorithm,through investigating nearly 100 node companies in the clothing brand supply chain,finds out the relationship change rules in information system risk,business risk,collaboration risk,science and technology risk,financial risk,environmental protection risk and so on in the clothing brand enterprises,Combined with the quantitative analysis and Research on the business situation and risk behavior of enterprises in the clothing industry,the data mining algorithm is used to mine the irregular association between the risks in the supply chain of the clothing industry,so as to obtain the changing rules of the relationships among various risk factors,which can help clothing brand enterprises predict the possibility of the recurrence of the next risk factor after the occurrence of a certain risk factor.At the same time,this paper quotes the research results of enterprise risk management,subdivides the risk factors of clothing supply chain according to the types and causes of risk,and obtains the data set needed for mining through questionnaire survey and event adjustment.Using PYTHON software and association rule algorithm,combined with the professional knowledge of the clothing industry,the association relationship of risk factors in the supply chain of clothing enterprises,such as information system risk,management risk,collaboration risk,market risk,financial risk,environmental risk,etc.,is mined to obtain the association rules among various risk factors,It proves that the data mining technology is effective in the supply chain risk prevention of the relatively traditional clothing industry.At the same time,this paper puts forward a number of reasonable suggestions and measures around the internal and external environment of the organization based on the mining results in line with the basic understanding of the industry,which brings beneficial inspiration to the risk prevention and control and risk transfer of the clothing industry supply chain,and also provides relevant reference for the clothing enterprises in the practice of supply chain risk control.