Search results

The purpose of this paper is to present an integrated framework for performance evaluation and analysis of human resource (HR) with respect to the factors of health, safety, environment and ergonomics (HSEE) management system, and also the criteria of European federation for quality management (EFQM) as one of the well-known business excellence models.

In this study, an intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy data envelopment analysis (FDEA) are developed and employed to assess the performance of the company. Furthermore, the impact of the factors on the company’s performance as well as their strengths and weaknesses are identified by conducting a sensitivity analysis on the results. Similarly, a design of experiment is performed to prioritize the factors in the order of importance.

The results show that EFQM model has a far greater impact upon the company’s performance than HSEE management system. According to the obtained results, it can be argued that integration of HSEE and EFQM leads to the performance improvement in the company.

In current study, the required data for executing the proposed framework are collected via valid questionnaires which are filled in by the staff of an aviation industry located in Tehran, Iran.

Managing HR performance results in improving usability, maintainability and reliability and finally in a significant reduction in the commercial aviation accident rate. Also, study of factors affecting HR performance authorities participate in developing systems in order to help operators better manage human error. This paper for the first time presents an intelligent framework based on ANFIS, FDEA and statistical tests for HR performance assessment and analysis with the ability of handling uncertainty and vagueness existing in real world environment.

Reverse logistics refer to processes related to the reuse of products. The role of suppliers’ performance is crucial in achieving quality, cost, service and delivery aims of a supply chain. The selection of suppliers is regarded as one of the critical issues encountered by purchasing and operations managers in a supply chain to enhance organization’s global marketplace competitiveness. Most of the supply chain models are rather complex problems. Consequently, it is impossible to propose systematic models to handle them. Therefore, in this paper a new integrated approach based on experimental design and computer simulation is proposed for supplier selection. The paper aims to discuss these issues.

In this study, a simulation approach is implemented to determine certain equivalent of parameters values in a CLSC network design which cannot be computed through mathematical model. Suppliers’ order quantities are investigated by Taguchi method for planning time horizon. Moreover, data envelopment analysis (DEA) is applied to assess suppliers based on quality, cost, delivery time, production capabilities, services and technology.

In the numerical example, there are three suppliers for different regions. Purchase value of each supplier is measured in three years successively. According to the proposed method, the authors find the minimum level of costs together with the maximum number of high-quality products.

The objective functions of model are minimizing the costs and maximizing number of high-quality products. The integrated approach introduced in this paper enables managers to select their suppliers effectively in their real system.

Most supply chain models are complex and the identification of proper and optimal solutions in complex real-world systems often requires the solution of multi-objective problems involving multiple stochastic variables. Therefore, the paper introduces a new integrated approach for supplier selection in closed loop supply chain. To the best of the authors knowledge, this is the first study that integrates DEA, computer simulation and Taguchi method for supplier selection in closed loop supply chains.

The purpose of this study is to investigate the relationship between pH of water or other daily fluid intake and risk of kidney stones in adult men.

This case–control study included 120 adult men with kidney stones and 240 healthy controls matched by age. Participants were selected from the Urmia Imam Khomeini Educational Hospital in the northwest of Iran by the available sampling method between 2017 and 2018. The mean total daily fluid intake of the subjects over the past one year period was collected using a validated 139-item Food Frequency Questionnaire, and the pH of drinking water samples consumed over the past six months was determined using an electrochemical pH meter. Information on physical activity level and socio-demographic characteristics of all participants was collected.

The mean pH of daily water consumption was 7.1 ± 250.3 and 7.4%± 250.3 in case and control groups, respectively. In multivariate logistic regression analysis after adjusting for the covariates 25 a significant relationship was detected between the pH of drinking water (not for other fluid intake26) and kidney stone disease (OR = 0.15, CI 0.06–0.4, P > 1. Moreover, no association was found between the volume of total fluid intake and risk of kidney stones.

These findings support potential protective effects of drinking water intake with higher pH level on preventing kidney stones.

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.