An Intelligent Knowledge-Based System for Product Cost Modelling

Shehab, Essam; Abdalla, Hassan

doi:10.1007/pl00003967

Cited by 93 publications

(33 citation statements)

References 14 publications

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“…Similar studies have been presented, including in [16][17][18]. There have also been efforts made in an attempt to use the fuzzy-logic approach for estimating the cost [19]. This approach to cost estimation is especially helpful in handling uncertainty.…”

Section: Cost Estimation Methods-a Reviewmentioning

confidence: 80%

Knowledge-based methods for cost estimation of metal casts

Macioł

2016

Int J Adv Manuf Technol

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The aim of the research presented in this paper was to verify the hypothesis that the problem of estimating the variable costs of metal casts can be solved by using knowledge-based systems. The estimation of variable costs determines the adequate pricing of products, which is a crucial marketing problem in enterprises. The problem of estimating the direct costs is especially important in companies whose participation in the market is defined as versatile manufacturing. This problem was the target of many studies presented in this paper. The assumptions that the direct costs of a metal cast are dependent on the physical features of the cast and the most effective method of cost estimation is an intuitive qualitative technique based on a rule-based system were considered. The nature of the relationships between the casts' features and the direct costs of production could be effectively formed by experts as reasoning rules. In this paper, the results of experiments conducted on the basis of the example of the production program of a foundry that manufactures water-system fittings is presented. Three methods of knowledge-definition and reasoning techniques were examined: the classical method of crisp reasoning, fuzzy reasoning with the Mamdani's method, and fuzzy reasoning with the Takagi-Sugeno model. Proper experiments were conducted. The results of the experiments confirmed the hypothesis, which allows the assessment of different reasoning methods.A. Maciol

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Section: Cost Estimation Methods-a Reviewmentioning

confidence: 80%

Knowledge-based methods for cost estimation of metal casts

Macioł

2016

Int J Adv Manuf Technol

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“…In the 'cost segment', examples of relent works are Siegel and Korvin (2001) in cost allocation, Nachtmann and Needy (2001,2003) in activity based costing, Shehab and Abdalla (2002), modeling a cost allocation method, and Smith (2003) in the development of an activity based system (ABC). More specifically in the 'target costing segment', Bayou and Reinstein (1998) examined target costing mechanisms in the automobile industry.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Modeling the Subjectivity in the Target Costing Process: An Experimental Approach Based on the Fuzzy Logic Concepts

Dal-Ri¹,

Alonso²,

Duarte³

2005

IJDAR

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Abstract. The target costing process involves an inherent subjectivity due to the fact that most of the utilized information is both imprecise and ambiguous. The objective of this paper is to propose a methodology based on the fuzzy logic concepts to take into consideration the uncertainty and subjectivity inherent in the Target Costing Process. An example utilizing the production of a tennis racquet has been developed in order to illustrate these fuzzy concepts. The software FuzzyTECH® has been utilized to model the fuzzy target costing process. This research shows evidences that fuzzy logic enables a decision-maker to gain additional insights in the relationship between costs components and products.

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“…Some of them are Shehab & Abdalla (2002) and Murcia, Borba, & Souto-Maior (2005) in a costing system; Sahin & Dogan (2003) in supplies and customers relationship; Jiang & Hsu (2003) in manufacturing and business cycle evaluations; Lin, Hwang, & Becker (2003) in accounting frauds detection; Beynon, Peel, & Tang (2004) in price composition of auditing services; Wang & Hsu (2008) in tourism demand; Keles, Kolcak, & Keles (2008) in domestic debt; Wang, Chang, & Tzeng (2011) and Wu (2010Wu ( , 2011 in automobile sales; and Cheng, Tsai, & Sudjono (2010) in project cash flow control.…”

Section: Introductionmentioning

confidence: 99%

S&P 500 Index Direction Forecasting from 1976 to 2010: A Fuzzy System Approach

Souto-Maior¹,

Borba²,

Costa³

2011

IJDAR

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Abstract. Investors are not always completely rational and they do not always work only with numbers. Sometimes, they use linguistic concepts to make their decisions. Fuzzy logic is helpful in handling such situations. This article differs from other studies in this area by applying fuzzy logic for forecasting the direction/sign of stock market indexes. The article also presents a model for forecasting the direction/sign of stock market indexes using a fuzzy system, and this model is applied in the prediction of the S&P 500 Index. The rules of the model were established between 1970 and 2009, and the test period was from 1976 to 2010. Despite the fact that the estimated model produced a linguistic output, it was possible to delineate a statistically significant investment strategy, which outperformed a buy-and-hold one. In addition, the success rate calculated was also statistically significant. Another difference from other studies is that the proposed model does not return an exact output, but a probabilistic one of linguistic variables. The proposed model, with its probabilistic output, can be used as a support for investment decisions. In other words, the linguistic output does not force the investor to blindly follow the proposed strategy.

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An Intelligent Knowledge-Based System for Product Cost Modelling

Cited by 93 publications

References 14 publications

Knowledge-based methods for cost estimation of metal casts

Knowledge-based methods for cost estimation of metal casts

Modeling the Subjectivity in the Target Costing Process: An Experimental Approach Based on the Fuzzy Logic Concepts

S&P 500 Index Direction Forecasting from 1976 to 2010: A Fuzzy System Approach

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