Risk identification and grey whitening weight cluster evaluation for supply chain of medical consumables under SPD mode
10.3969/j.issn.1672-8270.2025.08.027
- VernacularTitle:供给-加工-配送模式下医用耗材供应链的风险识别与灰色白化权聚类评价研究
- Author:
Lu WANG
1
;
Tu TU
;
Lin YAN
;
Ming LYU
Author Information
1. 国家儿童医学中心 首都医科大学附属北京儿童医院采购中心 北京 100045
- Publication Type:Journal Article
- Keywords:
Medical consumables;
Supply-processing-distribution(SPD);
Risk identification;
Grey cluster;
Inventory quantity;
Information security
- From:
China Medical Equipment
2025;22(8):148-153,159
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct an intelligent evaluation model based on the grey whitening weight cluster algorithm for risk in supply chain of medical consumables,and explore its application value in the management for medical consumables.Methods:The risk source of supply chain of medical consumables under the supply-processing-distribution(SPD)mode was analyzed,and the problems in the management for supply chain were evaluated by using grey whitening weight cluster,and an intelligent evaluation model for risk in supply chain of medical consumables was constructed to conduct control and management for risk in supply chain of medical consumables.A total of 10.61 million pieces of 400 types of medical consumables that were purchased and used by the National Center for Children's Health,China,Beijing Children's Hospital,Capital Medical University from 2022 to 2023 were selected.In them,the 5.15 million pieces of 200 types of medical consumables that were purchased and used during January and December 2022 were controlled and managed for risk through the management mode of assessment and prediction with experts.The 5.46 million pieces of 200 types of medical consumables that were purchased and used during January and December 2023 were controlled and managed for risk through used the intelligent evaluation model based on the gray whitening weight cluster algorithm under the SPD mode in supply chain of medical consumables for risk(prediction management mode with evaluation model).The incidences of risk,and the accuracy of data in supply chain between the two management modes were compared.A self-made satisfaction questionnaire was used to investigate the satisfaction rates of medical staffs,medical technicians,managers of department,and keepers of warehouse regarding to the supply of medical consumables.Results:The incidence rates of risks in suppliers,inventory,finance,technical support and information security of supply chain were respectively 1.8%,2.2%,0.4%,1.5%and 3.1%by adopting prediction management mode with evaluation model in 100,000 randomly inspected cases of medical consumables,all of which were lower than those of the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=9.239,23.013,11.706,21.141,42.331,P<0.05).The accuracy rates of supply data of spot check for the consumables of auxiliary examination,the nursing consumables in ward,the consumables of surgical treatment,the consumables of oral treatment and other disposable consumables of prediction management mode with evaluation model were all higher than those of the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=11.628,15.842,7.790,7.289,7.448,P<0.05).The satisfaction rates of medical staffs,medical technicians,managers of department and keepers of warehouse for clinical supply of medical consumables in prediction management mode with evaluation model were all higher than those in the management mode of assessment and prediction with experts,and the differences were statistically significant(x2=4.824,5.703,5.529,5.143,P<0.05).Conclusion:The intelligent evaluation model based on the grey whitening weight cluster algorithm under the SPD mode for risk in supply chain of medical consumables can reduce the incidence rate of risk in the supply chain of medical consumables,and improve the accuracy of supply data of medical consumables,and enhance the satisfaction of staffs in hospital.
