Towards Developing a Model-Based Decision Support Method for Enterprise Restructuring

2013 ◽  
pp. 17-27 ◽  
Author(s):  
Eduard Babkin ◽  
Alexey Sergeev
Keyword(s):  
Decision Support ◽  
Enterprise Restructuring ◽  
Model Based ◽  
Decision Support Method

Decision Support System Based on Fuzzy Logic for Assessment of Expected Corporate Income Performance

2021 ◽  
pp. 2150009
Author(s):  
Arthur Yosef ◽  
Eli Shnaider ◽  
Rimona Palas ◽  
Amos Baranes
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Operating Income ◽  
Explanatory Variables ◽  
Section Model ◽  
Evaluation Mechanism ◽  
One Year ◽  
High Degree ◽  
Decision Support Method ◽  
Very High

This study presents a decision-support method to estimate the next year performance of corporate Operating Income Margin (OIM). It is based on a unique combination of cross-section model and the rules-based evaluation mechanism. The estimate is done in terms of broad categories, and not precise numerical values. The model is constructed as follows: its dependent variable (OIM) is one year ahead vs. the corresponding explanatory variables. This structure of the model allows us to view explanatory variables as reflecting financial potential of corporations. The evaluation component consists of a set of rules designed to identify the companies whose “potential” clearly points to an opportunity to invest. For the method presented here to succeed, it is necessary to utilize a highly reliable modeling method, even if it is “Fuzzy”. We apply Soft Regression (SR), which is a Soft Computing modeling tool based on Fuzzy Logic, and utilize all available proxy variables by creating intervals of values. Advantages of utilizing SR, and the intervals’-based modeling are extensively discussed. Modeling results for five consecutive years are consistent and stable, thus indicating high degree of reliability. Testing indicates very high success rate for the stock market related domain, the lowest being 87.9%.

Crop model based decision support for maize (Zea mays L.) silage production in Portugal

2008 ◽  
Vol 28 (3) ◽  
pp. 224-233 ◽  
Author(s):  
R.P. Braga ◽  
M.J. Cardoso ◽  
J.P. Coelho
Keyword(s):  
Zea Mays ◽  
Decision Support ◽  
Zea Mays L ◽  
Crop Model ◽  
Model Based ◽  
Silage Production

scholarly journals A decision support method for evaluating database designs

2012 ◽  
Vol 9 (1) ◽  
pp. 81-106 ◽  
Author(s):  
Erki Eessaar ◽  
Marek Soobik
Keyword(s):  
Decision Support ◽  
Data Model ◽  
Processing System ◽  
Transaction Processing ◽  
Database Management System ◽  
Pairwise Comparisons ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Decision Support Method

It is possible to produce different database designs based on the same set of requirements to a database. In this paper, we present a decision support method for comparing different database designs and for selecting one of them as the best design. Each data model is an abstract language that can be used to create many different databases. The proposed method is flexible in the sense that it can be used in case of different data models, criteria, and designs. The method is based on the Analytic Hierarchy Process and uses pairwise comparisons. We also present a case study about comparing four designs of SQL databases in case of PostgreSQL? database management system. The results depend on the context where the designs will be used. Hence, we evaluate the designs in case of two different contexts - management of measurements data and an online transaction processing system.

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