Application of Data Mining in Effect Evaluation of Lean Management

Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.

Evaluation System of Smart Logistics Comprehensive Management Based on Hospital Data Fusion Technology

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.

Quality Practitioners’ Differing Perspectives on Future Software Quality Assurance Practices

Constant changes in a business context and software development make it important to understand how software quality assurance (SQA) should respond. Examining SQA from supplier and client perspectives, this study explores how different groups of SQA practitioners perceive future needs. A survey (n = 93) conducted in fall 2017 explored the views of SQA organizations on future trends. The results indicate that SQA organizations differ slightly in their attitudes to quality categories, as do different groups of SQA practitioners. It is argued that these differences should be taken into account when developing and implementing future SQA strategy. It is further argued that the found basic enables SQA management, evaluation of new practices, and allocation of resources to ensure that all quality categories remain balanced in the future.

CASE STUDY OF A BAKER COMPANY: WHEAT STOCK MANAGEMENT EVALUATION

The goal problem of this work is to propose improvement of wheat inventory management of a bakery, applying the inventory management method “P” and opportunity cost analysis. The study makes historical analysis of the company and proposes the adequacy of the model in order to reduce waste and production inefficiency. The adopted methodology is the case study, because the author does not have direct action during the elaboration of the work. The study shows through the basic calculations of the management model literature “P”, in order to propose the adequacy. The improvement proposal involves new quantities and periodicities of purchase, showing the feasibility of the opportunity cost study, seeking to insert the company's management model to the proposed one.

Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations

This article introduces an improved sales percentage method to quantitatively calculate the evaluation process of the corporate sales cash flow percentage method in order to obtain more evidence-based financial data and increase the accuracy of the evaluation results. At the same time, the paper uses SPSS to perform regression analysis on related financial indicators and sales revenue and obtains quadratic regression equations and linear regression equations. The thesis predicts other financial index data based on the predicted future sales revenue, uses the revised linear regression equation to obtain the company's future net cash flow and calculates the company value.

Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization

It was to explore the application of nursing defect management evaluation and deep learning in nursing process reengineering optimization. This study first selects the root cause analysis method to analyse the nursing defect management, then realizes the classification of data features according to the convolution neural network (CNN) in deep learning (DL) and uses the constructed training set and verification set to obtain the required plates and feature extraction. Based on statistical analysis and data mining, this study makes statistical analysis of nursing data from a macroperspective, improves Apriori algorithm through simulation, and analyses nursing data mining from a microperspective. The constructed deep learning model is used, CNN network training is conducted on the selected SVHN dataset, the required data types are classified, the data are analysed by using the improved Apriori algorithm, and nurses’ knowledge of nursing process rules is investigated and analysed. The cognition of nursing staff on process optimization and their participation in training were analyzed, the defects in the nursing process were summarized, and the nursing process reengineering was analyzed. The results show that compared with Apriori algorithm, the running time difference of the improved Apriori algorithm is relatively small. With the increase of data recording times, the line trend of the improved algorithm gradually eases, the advantages gradually appear, and the efficiency of data processing is more obvious. The results showed that after the optimization of nursing process, the effect of long-term specialized nursing was significantly higher than that of long-term nursing. Health education was improved by 7.57%, clinical nursing was improved by 6.55%, ward management was improved by 9.85%, and service humanization was improved by 8.97%. In summary, the reoptimization of nursing process is conducive to reduce the defects in nursing. In the data analysis and rule generation based on deep learning network, the reoptimization of nursing process can provide reference for decision-making departments to improve long-term nursing, improve the quality and work efficiency of clinical nurses, and is worthy of clinical promotion.