Analysis Decision Support Systems in Assisting the Marketing Manager at PT. Borsumij Wehry Indonesia Through a Program Microsoft Exel

Company Borsumij Wehry Indonesia is a multi division and geographically dispersed, and vertically integrated as the distribution of canned food. This company is typical of today's companies, namely, that the mobilization of resources as an organization is directed to the various markets for the benefit of shareholders groups. But such cases are not common characteristics of many who owned a large company, which is able to profitably use the information system that processes transactions from 20 different organizations with a system together and then present it to the management company quickly. Will obtain the appropriate level of benefits, in addition to control information to determine the basis for organizational components and compared with the plan. Top management received a brief report that shows the benefits of each organization, based on division and overall by a period and the current year, which compared with the plan. Part responsible for the marketing of income data with data submitted contributions are less direct cost of the standard. As far as the involvement of the marketing department, direct costs of the standard is fixed. Therefore, this part of the answer can not be held accountable in maximizing the contribution by varying the items that were under the control of volume, price, customer and product ingredients. The need for hardware and software continue to increase along with the rapid development of computers today. A sophisticated hardware that will not be useful without supported by software that helps in preparing the report as information for each management through computer programs. On this occasion, the author tries to analyze and design a marketing system on PT. Borsumij Wehry Indonesia. Analysis and design of the system is given a lesson in data processing curriculum

Web-Based Decision Support Systems Application of Stock Recommendation Using Bayesian Methods

We propose an application that can support traders by providing recommendation about the right stock transaction. The expected impact from this application is to reduce the risk of loss, even achieve the maximum profit for traders who use this application. Recommendation that resulted by application is based on Bayesian methods calculation and four technical analysis indicators that most commonly used by stock experts, i.e. Bollinger Bands, Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Stochastic Oscillator. Methodology used in this paper consists of data collection, data analysisa, application design, implementation, and testing. From the results of application testing, the accuracy of the application is 87,37%

Decision support systems

Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly-designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions. DSS belong to an environment with multidisciplinary foundations, including database reasearch, artificial intelligence, human computer interaction, simulation methods, software engineering and telecomunicationdecision support system, decision makers, computer-based

Decision support systems

Метою навчальної дисципліни: вивчення студентами математичних моделей систем підтримки прийняття рішень та алгоритмів побудови різних систем підтримки прийняття рішень.Целью учебной дисциплины: изучение студентами математических моделей систем поддержки принятия решений и алгоритмов построения различных систем поддержки принятия решений.The purpose of discipline: study of mathematical models of decision support systems and algorithms for various decision support systems

Emergent Frameworks for Decision Support Systems

Knowledge is generated and accessed from heterogeneous spaces. The recent advances in in-formation technologies provide enhanced tools for improving the efficiency of knowledge-based decision support systems. The purpose of this paper is to present the frameworks for developing the optimal blend of technologies required in order to better the knowledge acquisition and reuse in large scale decision making environments. The authors present a case study in the field of clinical decision support systems based on emerging technologies. They consider the changes generated by the upraising social technologies and the challenges brought by the interactive knowledge building within vast online communities.Knowledge Acquisition, CDDSS, 2D Barcodes, Mobile Interface

Decision Support Systems

The current decision-making problems is more complex than it was in the past, prompting the need for decision support. Most real-world decision-making situations are subject to bounded rationality; whereby the technical and economic evaluation of all solution alternatives (branches) is bounded by the consideration of dominant subjective constraints. The early definition of DSS introduced it as a system that intended to support decision makers in semi-structured problems that could not be completely supported by algorithms. DSSs were planned to be an accessory for managers to expand their capabilities but not to replace them. Decision support systems could provide the means to complement decision makers by quantitatively supporting managerial decisions that could otherwise be based on personal intuition and experience. In addition to the traditional DSS characteristics (i.e., data and model orientation, interactivity), the inclusion of an intelligent knowledge base would be required to quantify the impacts of both technical (hard) and subjective (soft) constraints

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference