Search results

Concern for health, safety and environment (HSE) is increasing in many developing countries, especially in energy industries. Improving power plants efficiencies in terms of HSE issues requires considering these issues in performance assessment of power generation units. This study aims to discuss the use of data envelopment analysis methodology for the performance assessment of electrical power plants in Iran by considering HSE and conventional indicators.

Installed capacity, fuel consumption, labor cost, internal power, forced outage hours, operating hours and total power generation along with HSE indices are taken into consideration for determining the efficiency of 20 electric power plants or decision-making units (DMUs). Moreover, DMUs are ranked based on their relative efficiency scores.

Results show that HSE factors are significant in performance assessment of the power plants studied in this research, and among HSE factors, health has the most powerful impact on the efficiency of the power plants.

The approach of this study could be used for continuous improvement of combined HSE and conventional factors. It would also help managers to have better comprehension of key shaping factors in terms of HSE.

This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange, to help managers generate what-if scenarios? For performance improvement and benchmarking.

The study empirically tested the three-stage DEA-ANN framework. First, DEA was used as a preprocessor of the banks’ efficiency scores. Second, a back-propagation neural network as a multi-layer perceptron-ANN’s model was designed using expected data sets from DEA to learn optimal performance patterns. Third, the superior performance of banks was forecasted.

The results indicated that banks are not operating under their most productive operations, and there is room for potential improvements to reach outperformance. Moreover, the neural networks’ empirical test results showed high correlations between the actual and expected values, with low prediction errors in both the test and prediction phases.

Based on best performance prediction, banks can generate alternative scenarios for future performance improvement and enabling managers to develop effective strategies for performance control under uncertainty and limited data. Besides, supporting the decision-making process and proactive management of performance.

Despite the growing research stream supporting DEA-ANN integration applications, these are still limited and scarce, especially in the Middle East and North Africa region. Therefore, the study trying to fill this gap to help bank managers predict the best financial performance.

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

Project management generally covers many important areas such as cost, quality and time in different industrial settings, but it is deficient in relation to integration of health, safety and environmental risks. Poor knowledge of project managers about HSE management necessitates the studying on the mutual effects of HSE and project management. Hence, investigating the impact of project management on health monitoring programs, safety prevention monitoring, environmental monitoring plans and finally the effectiveness of professional health monitoring programs and determining their importance are main objectives of this research. The paper aims to discuss these issues.

A model based on structural equations was designed and developed. The constructs of this model are project management, health monitoring and safety prevention monitoring program. Based on the conceptual model, some questionnaires were prepared and distributed among the experts of strategic project management.

The results of applied structural modeling suggest that project management focuses on each aspect of HSE management, including health monitoring programs, safety prevention monitoring programs, environmental monitoring plans and effectiveness of professional health monitoring programs. HSE management can also be strengthened by empowering project management. Checking fire protection systems, using appropriate techniques to identify contamination and disposal of waste and incorporating techniques for brainstorming or other ideas creation in the group are the most important tasks in HSE-enabled project management frameworks.

Since there is still no strategic alignment model that includes components of project management and HSE management, a model for achieving this goal is vital. This paper elaborates this alignment based on literature and using a field study.

Considering the direct impact of retailers' performance on the economy, this paper aimed to propose a comprehensive framework to evaluate the performance of different branches of a retailer.

Through a case study, the weights of indicators were calculated by the best-worst method (BWM) and the branches' performance was appraised using data envelopment analysis (DEA).

The branches were ranked in terms of performance, and sensitivity analysis and statistical tests were conducted to realize the weaknesses and strengths of the branches. Then, some strategies were proposed using strengths, weaknesses, opportunities and threats (SWOT) analysis to improve the performance of the weak branches.

This paper contributes to previous studies on the evaluation of retailers' performance by proposing a triple framework based on resilience, sustainability and sales-marketing indicators. This paper focused on branches' operations and branches' optimization by improving performance in terms of these three indicators. This paper also offers a qualitative and quantitative analysis of retailers' performance, which has received less attention in previous studies.

The purpose of this paper is to review current literature analyzing human factors in maintenance, and areas in need of further research are suggested.

The review applies a novel framework for systematically categorizing human factors in maintenance into three major categories: human error/reliability calculation, workplace design/macro-ergonomics and human resource management. The framework further incorporates two well-known human factor frameworks, i.e., the Swiss Cheese model and the ergonomic domains framework.

Human factors in maintenance is a pressing problem. The framework yields important insights regarding the influence of human factors in maintenance decision making. By incorporating various approaches, a robust framework for analyzing human factors in maintenance is derived.

The framework assists decision makers and maintenance practitioners to evaluate the influence of human factors from different perspectives, e.g. human error, macro-ergonomics, work planning and human performance. Moreover, the review addresses an important subject in maintenance decision making more so in view of few human error reviews in maintenance literature.

The authors answer calls for research on the experiences of international professionals' career transitions by investigating how highly skilled immigrants make sense of their career changes in the host country's labor market.

The authors report on a qualitative, inductive and elaborative study, drawing on sensemaking theories and career transitions literature and nine semi-structured reflective interviews with highly skilled Canadian immigrants.

The authors identified four career change narratives: mourning the past, accepting the present, recreating the past and starting fresh. These narratives are made sense of in a transnational context: participants contended with tensions between past, present and future careers and between relevant home and host country factors affecting their career decisions. Participants who were mourning the past or recreating the past identified more strongly with their home country professions and struggled to find resources in Canada. In accepting the present and starting fresh, participants leveraged host country networks to find career opportunities and establish themselves and their families in the new environment.

A transnational ontology emphasizes that immigrants' lives are multifaceted and span multiple national contexts. The authors highlight how the tensions between the home and host country career contexts shape immigrants' sensemaking narratives of their international career change. The authors encourage scholars and practitioners to take a transnational contextual approach (spatial and temporal) to guide immigrants' career transitions and integration into the new social environment.

The purpose of this research is to identify the prevalent condition of productivity in the automotive manufacturing industry of Pakistan and to indicate the possible areas for enhancing productivity.

Secondary data for the last ten years were gathered. Total productivity and all partial productivities were computed using methodology proposed by Sumanth, and total factor productivity (TFP) was computed using Cobb‐Douglas production function. Regression analysis and Pearson correlations were run to determine labor elasticity and capital elasticity.

Results indicated very low levels of labor productivity and capital productivity, resulting in huge losses and stagnant growth of these firms. Increasing returns to scales (IRTS) with high values of labor elasticity and low and even negative value of capital elasticity were computed. Low values of TFP showed minimal utilization of technology in these firms.

One of the limitations of this research is that only two automotive manufacturing companies of Pakistan i.e. Honda Atlas and Indus Motors were targeted, which limits the generalizability of findings.

Findings of this research revealed that effective utilization of technology can enhance the productivity of Pakistani manufacturing firms significantly. IRTS with high values of labor elasticity and low value of capital elasticity depict the areas of productivity enhancement.

In Pakistan not enough effort has been put into measuring the productivity of manufacturing industry. The contribution of this paper is that it indicates the productivity blemishes in this industry and also the areas of focus for productivity enhancement.

This study presents a combined artificial neural network (ANN) and multivariate approach for performance evaluation and optimization of gas refineries. This study introduces standard financial and non-financial indicators for performance evaluation of the gas refineries. Data are collected from gas balance sheets and the detailed statistics of gas refineries. Two cases have been considered for performance evaluation. In the first case the financial indicators and in the second case the financial and non-financial indicators are used and tested over five years period. The refineries are evaluated by data envelopment analysis (DEA), principal component analysis (PCA), numerical taxonomy and artificial neural network (ANN). Finally, a complete sensitivity analysis is performed for each stated method. The results show that DEA is more resistant to noise than other methods. Also, there is slight difference between results of financial and combined financial and operational indicators. This suggests the use of combined financial and operational indicators for future practical studies in gas refineries. This is the first study that presents an integrated approach for combined performance of financial and operational indicators in gas refineries.