scholarly journals Application of Data Mining in the Evaluation of Enterprise Lean Management Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jinpeng Yang ◽  
Ying Liu
Keyword(s):  
Data Mining ◽  
Evaluation System ◽  
Enterprise Management ◽  
Data Mining Algorithm ◽  
Lean Management ◽  
Modern Enterprise ◽  
Interaction Interface ◽  
Function Module ◽  
Intelligent Data Processing ◽  
Management Effect

In order to improve the effect of enterprise lean management, this study proposes a lean data mining algorithm based on the characteristics of lean data in enterprise management. This study connects data mining and lean production to study the data of enterprise management operation, proposes an intelligent data processing model suitable for modern enterprise management, and constructs the model function module in combination with the enterprise operation management process. Moreover, this study constructs an evaluation system for the effect of enterprise lean management based on data mining. The system provides a human-computer interaction interface, and operators can use various functions and services provided by the system through a visual interface. Through experimental research, it can be known that the enterprise lean effect evaluation system based on data mining proposed in this study can play an important role in enterprise lean management.

scholarly journals Application of Data Mining in Effect Evaluation of Lean Management

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Song Ding ◽  
Jun Li ◽  
Jiye Li
Keyword(s):  
Data Mining ◽  
Evaluation Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Enterprise Management ◽  
Data Mining Algorithm ◽  
Lean Management ◽  
Effect Evaluation ◽  
Management Evaluation ◽  
Management Effect

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.

scholarly journals Performance Evaluation System for Decision Tree Algorithms

2013 ◽  
Vol 11 (8) ◽  
pp. 2879-2886
Author(s):  
Deepali Saini ◽  
Prof. Anand Rajavat
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Supervise Learning Algorithm ◽  
Build Time ◽  
Study Implementation ◽  
Tree Algorithms

In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.  Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.

MONITORING OF OPERATIONAL LOGISTIC PROCESSES IN GENERAL CARGO WAREHOUSES USING PREDICTIVE ANALYSIS

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert
Keyword(s):  
Data Mining ◽  
Human Error ◽  
Predictive Analysis ◽  
Data Mining Algorithm ◽  
It Infrastructure ◽  
Monitoring Procedure ◽  
Manual Recording ◽  
Mining Tool ◽  
General Cargo ◽  
Different Characteristics

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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