To Heritage the Business Intelligence of Traditional Industry by Using Decision Support System

2016 ◽  
Author(s):  
Shu-Ching Wang ◽  
Kuo-Qin Yan ◽  
Hsueh-Hsun Huang ◽  
Yan-Zhen Li
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Business Intelligence ◽  
Traditional Industry

scholarly journals A Framework for Enhancing Competitive Intelligence Capabilities using Decision Support System based on Web Mining Techniques

2009 ◽  
Vol 4 (4) ◽  
pp. 326 ◽  
Author(s):  
Ionuț Anica-Popa ◽  
Gabriel Cucui
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Strategic Management ◽  
Competitive Advantage ◽  
Support System ◽  
Business Intelligence ◽  
Web Mining ◽  
Business Strategies ◽  
Competitive Intelligence ◽  
Tools And Techniques

Nowadays Competitive Intelligence (CI) represents one of the most important pieces in strategic management of organizations in order to sustain and enhance competitive advantage over competitors. There are some studies that claim that a successful strategic management is influenced by the accuracy of external environment’s evaluation and, in the same time, in order to have correct and complete business strategies it is necessary to be sustained by competitive advantage. But till at the beginning of ’80 the things were totally different. This paper will present the evolution and the objectives of CI, the results of using CI in organizations and how can be improved the CI process using tools and techniques provided by business intelligence (BI). The study will propose a framework of a decision support system based on web mining techniques in order to enhance capabilities of organization’s competitive intelligence.

Implementation of a Decision Support System and Business Intelligence Algorithms for the Automated Management of Insurance Agents Activities

2021 ◽  
Vol 12 (03) ◽  
pp. 01-13
Author(s):  
Alessandro Massaro ◽  
Antonio Panarese ◽  
Michele Gargaro ◽  
Costantino Vitale ◽  
Angelo Maurizio Galiano
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Business Intelligence ◽  
Individual Characteristics ◽  
Training Dataset ◽  
Artificial Neural ◽  
Automated Management

Data processing is crucial in the insurance industry, due to the important information that is contained in the data. Business Intelligence (BI) allows to better manage the various activities as for companies working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes it possible to improve the efficiency of decisions and processes, by improving them to the individual characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help insurance companies to understand the current market and to anticipate future trends. The purpose of the present paper is to discuss a case study, which was developed within the research project "DSS / BI HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering, and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by changing the number of records in the dataset. More precisely, as the number of records increases, the accuracy increases up to a value equal to 0.9987.

Research on Application Strategy for ERP System in Enterprise Accounting Informationization

2011 ◽  
Vol 403-408 ◽  
pp. 426-431
Author(s):  
Chang Shan Li
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Business Intelligence ◽  
Accounting Information ◽  
Small Enterprises ◽  
Erp Implementation ◽  
Erp System ◽  
Intelligence System ◽  
Medium And Small Enterprises

With the rapid economic operation, intensified market competition, the enterprise’s requirement on the quality of accounting information is getting higher and higher. It is also a subject faced by enterprise that how to construct the accounting informationization scientifically, reasonably and efficiently to ensure that accounting information system is operated safely, effectively and in real-time. ERP represents a higher level among current accounting informationization. The implementation of ERP in medium and small enterprises has more advantages than that in large enterprises, if strategy properly, it will greatly improve the success rate for ERP implementation. Enterprises carried out ERP successfully should promote the establishment of decision support system and business intelligence system at proper time. Although the medium and small enterprises have a advantage ERP implementation, to implement ERP successfully, they need to have correct cognition on it, well solve the relationship between “top leader” and team building, make correct choice, construct ERP system in step with certain focal points according to their plan as well as persist in the training of entire staff and intensifying the job of enterprise data management. Enterprises successfully implemented ERP should take decision support system and business intelligence system as their next target in accounting informationization.

scholarly journals Business intelligence as a decision support system tool

2021 ◽  
Vol 16 (91) ◽  
pp. 52-58
Author(s):  
Oleg P. Kultygin ◽  
◽  
Irina Lokhtina ◽  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Business Intelligence ◽  
Support Systems ◽  
Design Stage ◽  
Management Decision ◽  
Business Analyst ◽  
Management Decision Support

The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

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