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1 – 10 of 14In 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…
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.
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Gia Sirbiladze, Harish Garg, Irina Khutsishvili, Bezhan Ghvaberidze and Bidzina Midodashvili
The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of…
Abstract
Purpose
The attributes that influence the selection of applicants and the relevant crediting decisions are naturally distinguished by interactions and interdependencies. A new method of possibilistic discrimination analysis (MPDA) was developed for the second stage to address this phenomenon. The method generates positive and negative discrimination measures for each alternative applicant in relation to a particular attribute. The obtained discrimination pair reflects the interaction of attributes and represents intuitionistic fuzzy numbers (IFNs). For the aggregation of applicant's discrimination intuitionistic fuzzy assessments (with respect to attributes), new intuitionistic aggregation operators, such as AsP-IFOWA and AsP-IFOWG, are defined and studied. The new operators are certain extensions of the well-known Choquet integral and Yager OWA operators. The extensions, in contrast to the Choquet aggregation, take into account all possible interactions of the attributes by introducing associated probabilities of a fuzzy measure.
Design/methodology/approach
For optimal planning of investments distribution and decreasing of credit risks, it is crucial to have selected projects ranked within deeply detailed investment model. To achieve this, a new approach developed in this article involves three stages. The first stage is to reduce a possibly large number of applicants for credit, and here, the method of expertons is used. At the second stage, a model of improved decisions is built, which reduces the risks of decision making. In this model, as it is in multi-attribute decision-making (MADM) + multi-objective decision-making (MODM), expert evaluations are presented in terms of utility, gain, and more. At the third stage, the authors construct the bi-criteria discrete intuitionistic fuzzy optimization problem for making the most profitable investment portfolio with new criterion: 1) Maximization of total ranking index of selected applicants' group and classical criterion and 2) Maximization of total profit of selected applicants' group.
Findings
The example gives the Pareto fronts obtained by both new operators, the Choquet integral and Yager OWA operators also well-known TOPSIS approach, for selecting applicants and awarding credits. For a fuzzy measure, the possibility measure defined on the expert evaluations of attributes is taken.
Originality/value
The comparative analysis identifies the applicants who will receive the funding sequentially based on crediting resources and their requirements. It has become apparent that the use of the new criterion has given more credibility to applicants in making optimal credit decisions in the environment of extended new operators, where the phenomenon of interaction of all attributes was also taken into account.
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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 university…
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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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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Key performance indicators (KPIs) play a pivotal role in evaluating the level of success of an organization in achieving its business objectives. The objective of the current…
Abstract
Purpose
Key performance indicators (KPIs) play a pivotal role in evaluating the level of success of an organization in achieving its business objectives. The objective of the current research is to identify and prioritize effective KPIs in branding products and construction projects, which contribute to the success of construction companies in a competitive environment.
Design/methodology/approach
The present research is of an inferential, descriptive and survey nature. In this study, we identified the influential key performance indicators of construction companies in branding products and construction projects for success in a competitive environment through a literature review and expert opinions. The data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy multiple attribute decision-making (FMADM) method was employed for analysis.
Findings
The results indicate that the most influential key performance indicators for construction companies in branding products and construction projects for success in a competitive environment are, in order of significance, the following indices: “Marketing and Advertising,” “Financial,” “Creativity,” “Technical and Operational” and “Social and Political.”
Originality/value
The present research examines the importance of branding construction products and projects for the success of construction companies by improving their business objectives and utilizing key performance indicators throughout the product lifecycle (production and construction). This study provides solutions on how construction companies can increase their competitive advantage through branding and achieve long-term success in the global construction industry.
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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 devices…
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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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 management. The…
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.
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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 only bring…
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.
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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 lean…
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.
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Zahir Irani, Muhammad Kamal, Cengiz Kahraman, Basar Oztaysi and Ozgur Kabak and Irem Ucal Sari