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The purpose of this paper is to propose a method for performance assessment of organizations based on integrated approach of knowledge management and safety management using data envelopment analysis, and the proposed model is then applied in the car industry in Isfahan province to be checked. Therefore, deficiencies can be highlighted and possible strategies can be evolved to improve the performance.

As data envelopment analysis is a robust mathematical tool, it has been used to evaluate organizational performance. For discovering the organizational performance of knowledge management and safety management by data envelopment analysis (DEA), the first step is to specify proper criteria. To this end, in this method, the indices in both approaches of knowledge management and safety management were identified. Then, inputs and outputs were specified. Knowledge management and safety management were determined as input indices, and customer satisfaction and accident indicators were the output indices. It is noteworthy that each output index was used one time. In the next stage, performance of organizations was assessed based on both determined approaches and via data envelopment analysis. Finally, the organizations were ranked.

The suggested method was implemented in the car industry in the Isfahan province. The obtained results disclosed that among 12 decision-making units, 4 units are efficient when customer satisfaction is the output and 5 units are efficient when accidents indices are the output. In ranking with customer satisfaction as the output, Sepahan Atlas Pump Company was ranked first via super efficiency method, data envelopment analysis and similarity to ideal solution. In ranking with accidents as the output, Sepahan Atlas Pump Company ranked first via strong efficiency method and Sanatgar Company ranked first via data envelopment analysis and similarity to ideal solution.

Knowledge has been recognized as one of the valuable resources, and knowledge management would greatly effect improvement of job quality. Knowledge level increase is led by better performance and less errors. Consequently, it can enhance the organizational health and safety. There are some studies which have been conducted on safety management or knowledge management performance analysis. The organizational performance evaluation based on integrated approach of knowledge management and safety management is an important issue which is less considered in theory and practice. Thus, the authors have proposed a method which is able to evaluate the organization based on this integrated approach with functional indices, which resulted in accurate results, and finally, ranking can show the organization status to determine proper strategies.

The main purpose of this paper is to propose a new approach to determine the project activities weight factors using data envelopment analysis. Afterward, the model is applied in Mobarkeh Steel Company as a case study. Accordingly, the project schedule and plans can be written on the basis of the gained weight factors.

This study proposed an approach to determine the weights of activities using Data Envelopment Analysis. This approach consists of four phases. In the first phase, project activities are extracted based on the work breakdown structure. In the second phase, the parameters affecting the importance of activities are determined through a review of the related literature and based on the experts' opinions. In the third phase, the proper data envelopment analysis model is chosen and the inputs and outputs are signified. Then, the activities' weights are determined based on the efficiency numbers. Finally, the model is solved for the case of Isfahan Mobarakeh Steel Company.

The proposed method aimed to calculate the project activities weight factor. Thus, influential parameters on project activities importance include activity duration, activity cost, activity importance which includes successors and predecessors, activity difficulty which includes skill related (education and experience), safety, communication rate, intellectual effort, physical effort, unfavorable work conditions and work related hazards, have been recognized. Then, Projects' data were extracted from the organizational expert's opinions and recorded data in documents. Thereupon, applying DEA, the activities weight factor were calculated based on the efficiency numbers. The results show that the model is applicable and has promising benefits in real-world problems.

Planning is one the most fundamental steps of project management. The ever-growing business environment demands for more complex projects with larger number of activities wants more efficient project managers. Organizational resources are limited; therefore, activities planning is a critical from the perspectives of both managers and researchers. Knowing the importance of the activities can help to manage activities more efficient and to allocate time, budget, cost and other resources more accurate. Different elements such as cost, time, complexity, and difficulty can affect the activity weight factor. In this study, the proposed approach aims to determine the weights of activities using Data Envelopment Analysis.

The purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.

This study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.

Cluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.

The main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.

In this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.

This paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

The purpose of this study is to propose a model to evaluate the performance of organizational units considering intellectual capital (IC) and employee loyalty approach applying principal component analysis and data envelopment analysis (PCA-DEA) method.

Organization units are considered as decision-making units, IC components including human capital (HC), structural capital (SC) and customer capital are inputs and employee loyalty is output. The principal component analysis was used to converts inputs and outputs into the independent variables. As a return to scale is variable, a modified envelopment input-oriented BCC model applied to obtain the efficiency of organization units. Also, all units of organization are ranked. Eventually, sensitivity analysis performed to show how input variables influence on output variable.

Operation, design and construction, production planning, internal affairs, quality control and security were recognized as efficient units. Also, units of operation, internal affairs and quality control ranked first to third, and the human resource unit earned the last rank. In addition, results of sensitivity analysis on input variables showed that the order of impact intensity is: customer capital, HC and SC, respectively.

Existence a framework for the development of human resource strategies and prioritization in the allocation of organizational resources to improve the performance of the organization considering human resources is vital. Most of the previous studies, just have examined the impact of IC on different dimensions of organizational performance. Meanwhile, evaluating the performance of IC with employee loyalty approach, using PCA-DEA simultaneously can evaluate and measure the impact of IC on the performance of the organization and its units regarding employee loyalty, which has a significant impact on improving the organization’s level of IC and human resource management.

The purpose of this paper is to present a fuzzy Quality Function Deployment (QFD)-based approach for identifying and prioritizing organizational agility (OA) capabilities and enablers based on its drivers.

First, several models for agility drivers (ADs), agility capabilities (ACs) and agility enablers (AEs) are reviewed and ranked, and the best for each one is selected. Second, ADs’ indexes are weighted by using experts’ comments and fuzzy numbers. Finally, by using a proposed fuzzy QFD approach, ACs and AEs are prioritized. In addition, the proposed approach has been examined within a real case study, Golnoor Company in Esfahan, Iran.

Results reveal that among ADs’ criteria, “Changes in competition criteria” have the highest weights for the case study. In addition, “Leadership in the use of current technology” and “Knowledge management” have been ranked as the first place among ACs and AEs, respectively.

After conducting a comprehensive literature review, the authors did not find any particular framework, which consider AEs and ACs based on ADs simultaneously. Accordingly, the authors’ main novelty is proposing a fuzzy QFD to prioritize the OA capabilities and enablers based on its drivers.

This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

This paper aims to propose a new approach for determining a decoupling point in leagile chain, based on Lean and agile criteria regarding market and customer demands and internal capabilities of the chain with the ultimate goal of fulfilling customer needs and increasing chain profit.

In the new approach, Lean and agile criteria have been defined for assessing the effectiveness and efficiency of supply chain. The efficiency and effectiveness ratios have been calculated for Lean and agile processes using input- and output-oriented Banker, Charnes and Cooper (BCC) methods, respectively. Based on the results, inefficient and ineffective units have been addressed and the decoupling point has been determined.

Findings indicate that the decoupling point can be regarded as a borderline between two strategies of Lean and agile production, and fuzzy decoupling point and lean–agile distance can provide the basis for distinguishing the two strategies.

Determining the decoupling point has an important role in dynamic performance of a supply chain. By the proposed approach, managers can estimate the most probable area for the decoupling point. Moreover, by appropriate determination of decoupling point, an organization can increase its public responsibility by appropriate usage of its resources and responding faster to customers’ requirements.

In this study, in addition to determining a decoupling point in a supply chain with the aim of increasing productivity, the subject of leagile strategy of supply chains has been developed.

The purpose of this study is to present a hybrid analytic network process (ANP) and data envelopment analysis (DEA) method for ranking organizational units as well as prioritizing organization’s human capital management criteria.

In the proposed DEA model, human capital management criteria are considered as the inputs and organizational commitment considered as the output of DEA model. Afterward, the organizational unit’s efficiency and the weight of human capital management criteria are evaluated through DEA and the data are considered as the basis of ANP model.

The results of prioritizing the drivers of human capital management were showed that leadership practices, human capital management and learning capacity are the most important ones. Also, the findings proved that the proposed integrated DEA/ANP can be helpful in managerial issues.

Filling the super-matrix and designing the questionnaire of ANP have always been the challenge of scholars because of the large number of data it requires. One of the main advantages of the proposed hybrid method in this research is that it resolves the above-mentioned problem of ANP.

The results of the proposed method may provide managers this opportunity to better analyze the condition of organizational units from human capital management perspective and focus on the most important activities in this context. Moreover, the proposed hybrid method can help scholars to better use both DEA and ANP techniques and obtain more reliable findings.

In this study, DEA efficiency measures of units, and the weights of inputs and outputs of DEA model are used to fill the super-matrix of ANP. In addition to having a logical approach, this method provides more reliable results and enjoys the advantages of both ANP and DEA techniques.

The purpose of this paper is to prioritize service quality dimensions (SQDs) using importance-performance analysis (IPA) and to compare the results for the customer groups segmented by data envelopment analysis (DEA).

Customers of three four-star hotels located in Isfahan have been asked to report on their expectation of service prior to receiving it as well as their perception of the service after receiving it. Afterwards, DEA has been used to assign the customers into two groups of fastidious (with relatively high expectations but low perceptions) and ordinary (with relatively low expectations but high perceptions). Next, IPA has been exploited for prioritizing SQDs in each group. Eventually, the prioritized SQDs in each group have been compared and insightful information has been extracted for further research and practical purposes.

Findings imply that despite occasional similarities, there is a significant difference in the way the two groups value SQDs. Also, the segmented groups have different priorities of SQDs.

Findings revealed that in order to decrease the ambiguity of customer voice in prioritizing SQDs, it might be helpful to first segregate the participants based on their attitudes towards the quality of service to have a more congruent sample and then, to analyze the data.

In this study, a smarter approach for prioritizing SQDs based on the data of two major groups of customers has been proposed, which can help hoteliers in devoting their resources more efficiently.

The combination of DEA and IPA in homogenizing the sample and prioritizing SQDs for each homogenized group within the sample seems relatively novel.

The purpose of this paper is to propose a model for analysing the influence of costs of quality.

A model is designed by using a causal loop diagram and is analysed through a system dynamics approach. The model simulation is prepared by Vensim software.

Prevention and appraisal costs are the two effective cost factors. The model represented in this paper reveals that prevention costs have the most effect on total cost of quality and especially external failure costs. Hence, in order to achieve the customer expected quality level, prevention and appraisal costs should be considered.

Calculating and measuring non‐conformance costs is very difficult in organizations and some errors and mistakes may happen.

A system dynamics approach can analyse and measure the amount of prevention cost effects on cost of quality in different organizations.

The proposed methodology demonstrates the use of an innovative approach in developing a cost of quality concept and constructing a practical framework for system dynamics in a real case.