A Supplier Selection Model Based on Classifying Its Strategic Impact for a Company's Business Results

Gonçalo, Thomas Edson Espíndola; Alencar, Luciana Hazin

doi:10.1590/0101-7438.2014.034.02.0347

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…Among the above methods, the K-nearest neighbors method deserves attention, which is used to classify data and to construct regression dependences [10]. Its practical implementation is reflected in instrument engineering as well [11].…”

Section: Research Of Existing Solutions To the Problemmentioning

confidence: 99%

Errors classification method for electric motor torque measurement

Кulyk

Kvasnikov

Kvashuk

et al. 2021

TAPR

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The use of high-precision measuring instruments for determining the torque of electric motors in such areas as medicine, motor transport, shipping, aviation requires the improvement of the metrological characteristics of measuring instruments. This, in turn, requires an accurate assessment of their error. Of particular importance is the measurement of power at high-speed installations, where in some cases conventional measurement systems are either unsuitable or have low accuracy. Thus, the use of high-speed turbomachines in aviation, transport, and rocketry creates an urgent need for the development of high-quality measuring instruments for conducting precise research. In turn, in the absence of means for accurately determining the error, attempts are made to predict them. This makes it possible to timely identify the influence of many factors on the accuracy of measuring instruments. The increase in the error arises, as a rule, through abrupt changes in the measurement conditions. Such errors are unpredictable, and their significance is difficult to predict. In the course of the study, the K-nearest neighbors method was used, to establish criteria for which a gross error may occur. The results obtained make it possible to establish threshold values at which the maximum deviation can be established under various conditions of the experiment. In a computational experiment using the K-nearest neighbors method, the following factors were investigated: vibration; temperature rise of measuring sensors; instabilities in the supply voltage of the electric motor, which affect the accuracy of the strain gauge and frequency converter. As a result, the maximum errors were obtained depending on the indicated influence factors. It has been experimentally confirmed that the K-nearest neighbors method can be used to classify deviations of the nominal value of the error of measuring instruments under various measurement conditions. A metrological stand has been developed for the experiment. It includes a strain gauge sensor for measuring torque and a photosensitive sensor for measuring the speed of the electric motor. Signal conversion from these sensors is implemented on the basis of the ESP8266 microcontroller

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Section: Research Of Existing Solutions To the Problemmentioning

confidence: 99%

Errors classification method for electric motor torque measurement

Кulyk

Kvasnikov

Kvashuk

et al. 2021

TAPR

View full text Add to dashboard Cite

show abstract

“…A noncompensatory rationality was considered by Gonçalo and Alencar [24] who proposed a multicriteria decision support model for supplier selection which had two phases: the analysis of suppliers' products/services that needed to be evaluated using the PROMSORT method and the analysis of suppliers whose products/services are considered crucial using PROMETHEE II. Awasthi et al [25] presented a multicriteria decision-making approach using the TOPSIS method with partial information.…”

Section: Literature Related To Supplier Selection Problemsmentioning

confidence: 99%

A Multicriteria Decision Model for Supplier Selection in a Food Industry Based on FITradeoff Method

Frej

Roselli

Almeida

et al. 2017

Mathematical Problems in Engineering

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This article puts forward a decision model for solving a supplier selection problem in a food industry by considering multiple objectives that influence the decision-making process. In times of increasing competitiveness, companies strive hard to improve their profitability, and selection of supply sources may help if an appropriate decision is made through a well-structured decisionmaking process. Preference modeling is conducted in a flexible and interactive elicitation manner with the decision-maker (DM), aided by FITradeoff method. Partial information is gathered about the DM's preferences in such a way that less effort is spent on finding a final solution for the problem.

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“…The narrowing and maintenance of the relationship between buyer companies and their suppliers have already been flagged as essential factors for the competitiveness of companies (Cambra-Fierro & Polo-Redondo, 2008;Nagati & Rebolledo, 2013;Yawar & Seuring, 2018), being considered one of the most important aspects for the success of the companies (Gonçalo & Alencar, 2014). This perception has led an increasing number of companies to act, directly or indirectly, in the development of their suppliers (Botelho & Bourguignon, 2011;Nagati & Rebolledo, 2013) as a means of ensuring that these companies meet the contractor's supply requirements (Freitas, 2009).…”

Section: Introductionmentioning

confidence: 99%

Satisfaction of professionals of participating companies with the performance of supplier development programs

2021

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With the aim of demonstrating the elements that lead to the satisfaction of the professionals of participating companies with the performance of the Supplier Development Programs - SDPs, something that can improve the performance of this type of program, this research aimed to identify if the trust, in its affective, behavioral and cognitive components, influences the commitment and the perception of less risks, leading, consequently, to the satisfaction of the professionals of participating companies with the performance of the SDPs to which they are linked. Based on studies involving all the analyzed constructs, relationships between them were suggested and a structural model was proposed associating them. A quantitative, descriptive and cross-sectional study was carried out involving professionals from buyers and suppliers that participate in SDPs from all over Brazil, obtaining a sample, characterized as non-probabilistic and by accessibility, of 609 respondents from the application of a self-administered electronic questionnaire. For the analysis of the data, the modeling of structural equations was used, which indicated a positive relation for almost all the hypotheses, with only one being rejected. The findings suggest that reinforcing trust, mainly behavioral and cognitive, can increase the commitment of companies to the actions developed by the SDPs to which they are linked, as well as generate the perception that there are fewer risks when transacting with companies also linked to these programs, resulting in the satisfaction of buyers and suppliers. This may result in better program efficiency and effectiveness, and this is a contribution of this study, by identifying what leads participants to SDPs to be satisfied.

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A Supplier Selection Model Based on Classifying Its Strategic Impact for a Company's Business Results

Cited by 13 publications

References 31 publications

Errors classification method for electric motor torque measurement

Errors classification method for electric motor torque measurement

A Multicriteria Decision Model for Supplier Selection in a Food Industry Based on FITradeoff Method

Satisfaction of professionals of participating companies with the performance of supplier development programs

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