Industrial Management Journal
Year:2020 | Volume:12 | Issue:3|Papers Count:6

Objective: Measuring the efficiency of innovation to manage innovation investment in the era of "knowledge economy" is being considered by more researchers every day. The evaluation of innovation efficiency helps identify the best innovators for benchmarking and identifies ways to improve efficiency by identifying the weaknesses. In this paper, a new formulation approach for dynamic network data envelopment analysis is presented to evaluate the efficiency of multi-period and multi-division systems (MPMDS) while controlling the weights. Methods: To prevent facing the black-box of innovation, at the first, a conceptual dynamic network structure of the universities’ innovation was developed, and then, the proposed dynamic network DEA approach is used to overcome the fundamental shortcomings to control the weights of factors in line with enabling the desired management weights. Results: The findings depicted that, among 13 universities surveyed, one university (about 7%), was recognized as efficient in the total process of innovation, and the average efficiency was equal to 0. 79 for universities. In both sub-processes of R&D and application, one university (7%) was considered efficient and their average efficiency was 0. 82 and 0. 47, respectively, which indicates the poor performance of universities in implementation and ideas commercialization. Also, the changes in the average efficiency of the sub-process of applying the results are quite the opposite of the research and development sub-process. Conclusion: The results reflect that the model presented in this study, by solving the conventional DNDEA model problems in weight control, improves the discriminating power of efficient and inefficient units.

Objective: According to many experts, methodological weakness is one of the weaknesses of master and doctoral dissertations in Iran. A meta-methodological study is a kind of comprehensive study that examines and compares the methodological aspects of previous researches. Accordingly, the purpose of this study is to identify and evaluate the status of the methodological components of master and doctoral dissertations in the field of industrial management at Shiraz University from 1991 to 2019. Methods: This research is a descriptive and analytical type that used Meta-method to review the dissertations in the field of industrial management. VOS viewer software is used to analyze data from Excel software and keyword co-event. Results: The results of the data analysis showed that 89% of the dissertations were applied research t and the most used tool of the researchers was questionnaire (31%). In addition, the results indicated the dominance of quantitative methods (79%), positivism paradigm (80%) and survey strategy (57%) and Statistical test methods (30%) in the analysis of findings in industrial management researches. Finally, by extracting 713 unique keywords, 56 words were repeated at least 5 times, including applied research, descriptive, quantitative, positivism, library and field research, survey, documentation and questionnaire. Conclusion: The results of this study can lead to understanding the past situation and by presenting a picture of trends in the selection of methods used in dissertations, identify the existing gaps and be used for future educational and research planning.

Objective: Data Envelopment Analysis (DEA) is an effective method for evaluating the relative efficiency of decision-making units (DMUs). The classical approach considers each organizational unit as a black box and limits evaluation to primary inputs and final outputs and neglects internal processes. This problem with the introduction and use of DEA in network structures for more accurate performance analysis, taking into account its internal processes, has been resolved. In most of the proposed models, the inputs and outputs of DMUs are definite, but in many cases, those data cannot be measured in a precise way. Therefore, this paper seeks to introduce a new model of Network Data Envelopment Analysis with a parallel structure by considering inputs and outputs as uncertain variables. The approach used is to develop the mathematical model from a theoretical point of view, to prove the theoretical properties of the model, the mathematical validity and its application. Methods: In this paper, the assumptions of uncertainty theory and models of Network Data Envelopment Analysis to evaluate DMUs with parallel structure and imprecise inputs and outputs. Results: According to the results of the implementation of the proposed model in the Social Security Organization, the efficiency of all DMUs and its sub-system has been evaluated between zero and one. Conclusion: Due to the multiplicity of the sub-system, none of the 12-provincial social security managing directorates as DMUs were efficient (one efficiency score), but among 313 branches, three branches were efficient. The final results of the implementation of the uncertain model proved the assumptions of the definitive model.

Objective: Data envelopment analysis is a well-known method based on mathematical programming to measure the efficiency of decision-making units. This approach identifies some units as efficient units set. According to these units, it constitutes an efficient frontier. In this case, discernment between efficient decision-making units are is impossible because several decision-makers have the same efficiency score. Methods: This study presents a new method for ranking efficient units in fuzzy data envelopment analysis. In this study, using a fuzzy inference system for ranking efficient units is proposed as a new method. In the proposed method, the efficient and inefficient units are first separated from each other using data envelopment analysis. Then, the concepts of fuzzy inference system are used to rank efficient units. Results: The information of inefficient units is in a way which the fuzzy data envelopment analysis fails to assign an equivalent value of one to these unit’ s efficiency. According to this concept, in the proposed method, each of these inefficient units is considered as a rule, and the amount of these rules are fired by the efficient units, has used as an indicator for their ranking. Conclusion: Finally, a numerical example is performed to check the accuracy of the model's performance. In this example, the data used in one of the basic articles in this field were used and it was found that the results obtained from the proposed method are quite similar to the results of the mentioned research.

Objective: Humanitarian operations begin quickly with disasters to save lives, reduce the suffering of the injured and meet their needs. Appropriate strategies and solutions should be used to help the injured. In this regard, this paper aims to provide a hybrid approach to select effective strategies in the humanitarian supply chain. Methods: In this study, firstly the needs of the injured were identified and categorized, and then the weight of each one was obtained using Fuzzy SWARA technique. Then, a combination of QFD approach and multi-objective modeling was used to select strategies to meet the needs of the injured. The epsilon-constraint method and GAMS software were used to solve the multi-objective model. Results: The needs of earthquake victims in the country were identified and classified into five categories: food needs, hygienic, mental health, housing and living facilities. The results showed that the need for nutrition and food, evacuation of the affected groups from the accident site and access to drinking water are the most important needs of the victims during the earthquake. To help victims, 14 strategies were divided into three categories: strategies related to construction, infrastructure improvement, and strengthening relief processes. Conclusion: In order to reduce the damage during an earthquake, light materials should be used in construction, the culture of lightening should be strengthened, and at the same time, the awareness of people in using durable materials should be promoted. Also, wornout water, electricity and gas systems should be developed and integration should be between them. In addition, the improved communication system, and inventory management and evacuation location should be strengthened during relief efforts.

Objective: In Workplaces, organizations need to balance warehouse inventory and maintenance costs. Therefore, inventory control is one of the major concerns of organizations. In many organizations, they use the ABC classification method for controlling a large volume of inventory. in the traditional ABC classification, items are categorized only by a single criterion; but considering other criteria is also essential, so in such cases, the multi-criteria inventory classification is used. The purpose of this study is to present a new model in multi-criteria inventory classification. Methods: In this study, the data envelopment analysis method has been used to present an appropriate model for inventory classification. In the literature of Radial and nonradial models of data envelopment analysis have been used in inventory classification and the comparison of the results of the models indicates that non-radial models provide a more rational classification. Hence, other types of non-radial models have been proposed in this paper to improve inventory classification. Results: the proposed method does not have the weakness of the radial models and improves the multi-criteria inventory classification using non-radial models. Conclusion: 47 items of inventory related to a common numerical example in the literature as well as 80 items of inventory items of Pars Khazar industrial company have been used in order To implement the new models and compare them with existing models. The results of comparing the proposed model with the existing ones in the literature show the superiority of the proposed model.