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Article
Publication date: 8 April 2014

Fahimeh Ramezani and Jie Lu

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects…

Abstract

Purpose

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

Design/methodology/approach

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

Findings

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Research limitations/implications

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

Originality/value

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.

Details

Journal of Enterprise Information Management, vol. 27 no. 3
Type: Research Article
ISSN: 1741-0398

Keywords

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Article
Publication date: 1 November 2011

Abouzar Zangoueinezhad and Asghar Moshabaki

The application of fuzzy multiple attribute decision making (FMADM) approach in evaluation of organizations has grown recently, and it is combined with knowledge‐based…

Abstract

Purpose

The application of fuzzy multiple attribute decision making (FMADM) approach in evaluation of organizations has grown recently, and it is combined with knowledge‐based university evaluation parameters in this study. The paper seeks to propose a FMADM approach for measuring university performance on the four knowledge‐based perspectives of a balanced scorecard.

Design/methodology/approach

The approach first summarizes the evaluation indexes extracted from the university performance literature. Then, the relative weights of the chosen evaluation indexes are calculated using the fuzzy analytic hierarchy process (FAHP). The fuzzy sets theory was adapted to university performance analysis.

Findings

The results reveal the critical aspects of the evaluation criteria as well as the gaps to improve university performance in order to achieve the aspired/desired level.

Research limitations/implications

The paper reveals the key issues in the existing performance evaluation method, especially in the university context.

Practical implications

This research analyses the performance of a university based on the knowledge‐based indexes in the four BSC perspectives, using a FME‐MADM approach. It considers specific knowledge‐based metrics for each perspective.

Originality/value

Although implementation of the performance measures in universities are now widespread, there is no considerable literature that sufficiently addresses the various issues faced by organizations during university implementation. The paper proposes application of the balanced knowledge‐based scorecard to universities aiming at evaluating performance annually.

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Article
Publication date: 8 June 2012

T.R. Manoharan, C. Muralidharan and S.G. Deshmukh

The purpose of this paper is to develop an innovative method of performance appraisal that will be useful for designing a structured training programme.

Abstract

Purpose

The purpose of this paper is to develop an innovative method of performance appraisal that will be useful for designing a structured training programme.

Design/methodology/approach

Employees' performance appraisals are conducted using new approaches, namely data envelopment analysis and an integrated fuzzy model. Interpretive structural modelling is used to design a training programme for employees.

Findings

Performance appraisals using data envelopment analysis focus on output enhancement, while an integrated fuzzy model using quality function deployment (QFD) and multi‐attribute decision‐making focuses on input enhancement. For overall and continuous improvement of employees' knowledge, skills and attributes, this composite model provides an in‐depth analysis and also offers a means for designing a structured and effective training programme through interpretive structural modelling.

Research limitations/implications

In data envelopment analysis, the number of employees for performance appraisal should be equal to or greater than three times the selected number of input and output factors. In the integrated fuzzy model, the number of main factors should not exceed seven for pairwise comparison. The size of the QFD matrix should not be more than 30.

Practical implications

The factors selected for appraisal and the method of appraisal should be known by the employees concerned. Consensus among all those concerned is necessary for effective application and utilization of the model.

Social implications

This model provides a means to increase the knowledge, skills and attributes of employees by adopting a structured approach to designing a training programme for employees of various categories. The approaches used are well‐established and can be applied in many other fields.

Originality/value

In this paper, approaches used for appraisals and designing training programmes are new to this field of study, although they have been successfully proven in many other fields. The results obtained using these methods are useful for helping management to make decisions on training needs, bonuses, incentives and promotions. For the employees, a structured training programme design improves their KSA, quality and standards.

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Article
Publication date: 15 January 2018

Nitin Kumar Sahu, Atul Kumar Sahu and Anoop Kumar Sahu

Robot appraisement under various dimensions and directions is a crucial issue in real-time manufacturing scenario. Logistic robots are programable-independent movable…

Abstract

Purpose

Robot appraisement under various dimensions and directions is a crucial issue in real-time manufacturing scenario. Logistic robots are programable-independent movable devices capable of transporting stuffs in a logistic cycle. The purpose of this paper is to opt for the most economical robot under chains of criteria, which is always considered as a sizzling issue in an industrial domain.

Design/methodology/approach

The authors proposed a cluster approach, i.e. ratio analysis, reference point analysis and full mutification form, embedded type-2 fuzzy sets with weighted geometric aggregation operator (WGAO) to tackle the elected problem of industrial robot. The motive to use WGAO coupled with type-2 fuzzy sets is to effectively undertake the uncertainty associated with comprehensive information of professionals against defined dimensions. Furthermore, the cluster approach is used to carry out the comparative analysis for evaluating robust scores against candidate robot’s manufacturing firms, considering 59 crucial beneficial and non-beneficial dimensions. A case research study is carried out to demonstrate the validity of the proposed approach.

Findings

The most challenging task in real-time manufacturing scenario is robot selection for a particular industrial application. This problem has become more complex in recent years because of advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. In the past decade, robots have been selected in accordance with cost criteria excluding other beneficial criteria, which results in declined product quality, customer’s expectation, ill productivity, higher deliver time, etc. The proposed research incorporates the aforesaid issues and provides the various important attributes needed to be considered for the optimum evaluation and selection of industrial robots.

Research limitations/implications

The need for changes in the technological dimensions (speed, productivity, navigation, upgraded product demands, etc.) of robot was encountered as a hardship work for managers to take wise decision dealing with a wide range of availability of robot types and models with distinct features in the manufacturing firms. The presented work aids the managers in taking their decisions effectively while dealing with the aforesaid circumstances.

Originality/value

The proposed work suggests chains of dimensions (59 crucial beneficial and non-beneficial dimensions) that can be used by managers to measure the economic worth of robot to carry out logistic activities in updated manufacturing environment. The proposed work evolves as an effective cluster approach-embedded type-2 fuzzy sets with WGAO to assess manufacturing firms under availability of low information.

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Article
Publication date: 28 January 2014

Anoop Kumar Sahu, Saurav Datta and Siba Sankar Mahapatra

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain…

Abstract

Purpose

In today's competitive global marketplace, performance management has been identified as a key strategic consideration towards achieving an efficient supply chain management. The task of estimating supply chain performance extent is seemed a complex problem entitled with multiple subjective performance measures and metrics; subjected to decision-making environment which involves an inherent vagueness, inconsistency and incompleteness associated with decision-makers (DMs) (expert panel) commitment towards assessment of various subjective (quantitative) evaluation indices. Consequently, it becomes difficult towards making a comparative study on performances of alternative supply chains. It is, therefore, indeed essential to conceptualize and develop an efficient appraisement platform helpful for benchmarking of alternative supply chains based on their performance extent. The paper aims to discuss these issues.

Design/methodology/approach

The work explores the concept of grey numbers combined with multi-objective optimization by ratio analysis (MOORA) in perceptive to evaluate best alternative from among available alternative supply chains.

Findings

The method has been found fruitful to facilitate such a multi-criteria group decision-making (MCGDM) problem under uncertain environment and provides an appropriate compromise ranking order with respect to available possible alternatives.

Originality/value

Supply chain performance appraisement provides necessary means by which an organization can assess whether its supply chain is performing well, whether it has been improved or degraded as compared to the past record. The purpose of this research is to develop and to empirically test a multiple-indices hierarchical appraisement model for benchmarking of supply chain performance and its impact on competitiveness of manufacturing industries.

Details

Grey Systems: Theory and Application, vol. 4 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 8 October 2018

Anil Rana and Emosi V.M. Koroitamana

The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not…

Abstract

Purpose

The purpose of this paper is to provide a framework for measuring the imprecise and subjective “effectiveness” of a major maintenance activity. Such a measure will not only bring objectivity in gauging the effectiveness of maintenance task carried out by the workforce without any intervention from an expert but also help in measuring the slow degradation of the performance of the concerned major equipment/system.

Design/methodology/approach

The paper follows a three-step approach. First, identify a set of parameters considered important for estimating the maintenance activity effectiveness. Second, generate a set of data using expert opinions on a fuzzy performance measure of maintenance activity effectiveness (output). Also, find an aggregated estimate of the effectiveness by analysing the consensus among experts. This requires using a part of the “fuzzy multiple attribute decision making” process. Finally, train a neuro-fuzzy inference system based on input parameters and generated output data.

Findings

The paper analysed major maintenance activity carried out on diesel engines of a power plant company. Expert opinions were used in selection of key parameters and generation of output (effectiveness measure). The result of a trained adaptive neuro-fuzzy inference system (ANFIS) matched acceptably well with that aggregated through the expert opinions.

Research limitations/implications

In view of unavailability of data, the method relies on training a neuro-fuzzy system on data generated through expert opinion. The data as such are vague and imprecise leading to lack of consensus between experts. This can lead to some amount of error in the output generated through ANFIS.

Originality/value

The originality of the paper lies in presentation of a method to estimate the effectiveness of a maintenance activity.

Details

Journal of Quality in Maintenance Engineering, vol. 24 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 8 November 2019

Yau-Ren Shiau and Hui-Min Chang

The framework of Six Sigma Define-Measure-Analyze-Improve-Control was applied in this study, and various tools and techniques were used at different stages to implement…

Abstract

Purpose

The framework of Six Sigma Define-Measure-Analyze-Improve-Control was applied in this study, and various tools and techniques were used at different stages to implement lean measures to ensure quality. The purpose of this paper is to develop a decision-making framework that assesses key quality performance to ensure that practitioners improve quality and control by modeling and optimizing production processes.

Design/methodology/approach

A model of a quality performance index system was established. The weights of factors and sub-factors, which were estimated using an FAHP, were used as a reference for the decision maker under fuzzy uncertainly to make a decision, and thus, results present the bottlenecks in processes. Furthermore, any other factors that may affect the key process bottlenecks must be considered. The critical to quality characteristics were determined, and factor levels were set. The interaction between the factors was analyzed, their significance was studied using the Design of experiments and the parameters were predicted. Finally, quality improvement decisions were made through failure mode and effects analysis.

Findings

The implementation results of this research prove that the proposed model could successfully determine the key processes and focus on the improvement of critical quality factors under limited resources.

Originality/value

This study establishes a set of performance appraisal methods for production systems, which can be used for improving productivity and quality.

Details

The TQM Journal, vol. 32 no. 1
Type: Research Article
ISSN: 1754-2731

Keywords

Content available
Article
Publication date: 8 April 2014

Zahir Irani, Muhammad Kamal, Cengiz Kahraman, Basar Oztaysi and Ozgur Kabak and Irem Ucal Sari

Abstract

Details

Journal of Enterprise Information Management, vol. 27 no. 3
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 8 June 2012

Xiaoxin Chen

The purpose of this paper is to attempt to propose a method based on evidential reasoning for hybrid grey attribute decision‐making problems where the attribute weights…

Abstract

Purpose

The purpose of this paper is to attempt to propose a method based on evidential reasoning for hybrid grey attribute decision‐making problems where the attribute weights are partially known, in which the attribute values are interval grey numbers and linguistic grades.

Design/methodology/approach

The method is that the decision‐maker gives whitenization of each interval grey number based on their preference and belief structure of the form of qualitative attribute values, whitenization of quantitative attributes can be equivalently expressed in the form of belief structure with the principle of utility value equivalence, and then the grade belief structure decision matrix can be determined. By using the analytical evidential reasoning algorithm, belief degrees of each alternative belonging to each linguistic grade are obtained. Two pairs of nonlinear optimization models which are solved by genetic algorithms (GA) are constructed to compute the maximum and the minimum expected utilities of each alternative, respectively.

Findings

The results show that decision‐maker based on his/her risk preference gives whitenization of each interval grey number and selects corresponding alternative policy.

Practical implications

The method exposed in the paper can be used to deal with problems of grey multiple attribute decision making and hybrid grey multiple attribute decision making.

Originality/value

The paper succeeds in constructing two pairs of nonlinear optimization models based on the analytical evidential reasoning algorithm which are solved by genetic algorithms and the hybrid grey multiple attribute decision‐making approach with partial weight information.

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Article
Publication date: 17 August 2012

Sanjeev Goyal and Sandeep Grover

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused…

Abstract

Purpose

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused approach to manufacturing effectiveness. Selecting a proper AMS is a complicated task for the managers as it involves large tangible and intangible selection attributes. Failure to take right decision in selecting proper AMS alternative may even lead industry to losses. The purpose of this paper, therefore, is to rank the AMS alternatives by using fuzzy grey relational analysis, which will help managers when choosing an appropriate AMS.

Design/methodology/approach

This research proposes a multi‐attribute decision‐making (MADM) method, fuzzy grey relational analysis (FGRA), for AMS selection. The methodology is explained as follows. AMS alternatives and selection attributes will be chosen. The qualitative attributes will be converted into quantitative using fuzzy conversion scale. Then these data will be pre‐processed to normalize every value. This step is done to convert all alternatives into a comparability sequence. According to these sequences a reference sequence (ideal target sequence) is defined. Then, the grey relational coefficient between all comparability sequences and the reference sequence is calculated. Finally, based on these grey relational coefficients, the grey relational grade between the reference sequence and every comparability sequences is calculated. If a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the best choice. Fuzzy logic is used here to convert linguistic data into crisp score.

Findings

The proposed method is validated and compared by taking two examples from literature. The traditional statistical techniques require large data sets for evaluating attributes while grey theory on the contrary solve the multi attribute decision making problems with small data sets. This methodology will significantly increase the efficiency of decision making and overall competitiveness for manufacturing industries. This approach will motivate more and more industries to invest in AMS.

Practical implications

This method will help managers to weigh the AMS alternatives before actually buying them, which will in turn save money and time. This will build confidence of the top management for investing in costly technology such as AMS.

Originality/value

From time to time, various researchers have proposed various techniques to select the AMS. However, a survey on current evaluation methods shows that they are all less objective, lack accurate data processing, involve large calculations because of their complexity. In this paper, the authors attempt to solve the problem of AMS selection with FGRA, which is more logical, axiomatic, generates results in fewer steps with less calculations and is easy to understand. This paper succeeds in getting AMS alternatives' ranking using fuzzy grey relational analysis.

Details

Grey Systems: Theory and Application, vol. 2 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

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