Data Envelopment Analyses
Elham Zaker Harofteh; Faranak Hosseinzadeh Saljooghi
Abstract
Purpose: In this paper, the combined model for determining capacity utilization by considering inputs and outputs based on efficiency is discussed and the loss due to the lack of suitable use of capacity utilization is also calculated.
Methodology: The current research is practical in terms of ...
Read More
Purpose: In this paper, the combined model for determining capacity utilization by considering inputs and outputs based on efficiency is discussed and the loss due to the lack of suitable use of capacity utilization is also calculated.
Methodology: The current research is practical in terms of the type of purpose and fundamental in terms of the type of study. The capacity utilization evaluation method is based on data envelopment analysis model which is suitable for evaluating efficiency and function and it also has the ability to designate the capacity utilization. In the present models, the capacity utilization evaluation method is stated by assuming the possibility of changing all inputs/outputs with a multiplicative constant (Radial model) or assuming a distinct change in all factors affecting production. But in reality, some inputs/outputs might change radially and some of them non-radially in organizations and companies. In this article, a new model is submitted to designate the capacity utilization. It measures the capacity utilization simultaneously in the presence of radial and non-radial factors; furthermore, it has the ability to detect losses caused by any of the items such as the price of outputs/inputs or amount of output deficit and input surplus and it is a suitable model for evaluating the capacity utilization in practical and real issues.
Findings: The proposed approach in this article combines the points of the CCR radial model and the SBM non-radial model with the aim of determining capacity utilization and not just measuring efficiency, and with its help, we can evaluate the capacity utilization with the presence of non-radial data in addition to the radial data. In a case study of twelve hospitals with a fixed input of a doctor, and a variable input of a nurse and two outputs of outpatients and inpatients, it was observed that by eliminating the variable inputs in the presence of radial and non-radial outputs, there is no improvement in efficiency. On the other hand, the results show that some hospitals should improve the use of their capacity, and in some hospitals, by increasing the number of nurses, the number of outpatients or inpatients can be increased and the performance of hospitals can be improved. Then, using efficiency analysis, the inefficiency factor and its amount were determined. The combined model shows a lower number of inefficient units than the output-oriented BCC model.
Originality/Value: In this article, the combined model of capacity utilization in the presence of radial and non-radial indicators is presented, which can be an introduction to the presentation of DEA models of capacity utilization under different input and output conditions.
supply chain management analyzing/modelling
Amir Rahimi; Faranak Hosseinzadeh Saljooghi
Abstract
In recent years, a new approach which has been dominant over operation management is the “supply chain management approach”. Supply chain management has attracted most researchers' attentions in recent years. This is the way to improve the economic, social and environmental performance. Therefore, ...
Read More
In recent years, a new approach which has been dominant over operation management is the “supply chain management approach”. Supply chain management has attracted most researchers' attentions in recent years. This is the way to improve the economic, social and environmental performance. Therefore, the evaluation of SSCM is an important task for all types of organizations. Among the methods of evaluation SSCM, it seems that data envelopment analysis (DEA) is an appropriate approach. Some of decision-making units composed of several sections or stages that make a network of sub-processes. In order to evaluate such units, data envelopment analysis (DEA) is applied. This paper presents two approaches to calculate supply chain management as a network process. In the first approach, we consider the generalization of weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as single-objective approach. In the second one, we introduce the weighted sum model to calculate efficiency and returns to scale (RTS) of supply chain with two-stage process as multi-minded approach, so we try to make it to be single-objective approach and then calculate the efficiency of its total production process, according to the decision-maker's ideas and interests. In the next section of this paper according to the two above approaches, we determine the percent of returns to scale (RTS) of supply chain. Suggested ideas are used to evaluate the efficiency and returns to scale (RTS) of supply chain in resin production companies.
Data Envelopment Analyses
Abbasali Noora; Faranak Hosseinzadeh Saljooghi; Maryam Khodadadi
Abstract
In the real world, there are decision-making units in which the production process can be considered as a two-stage or multi-stage process. In order to evaluate these types of units, the network data envelopment analysis method is used. In this paper, Two-stage units have been investigated, which in ...
Read More
In the real world, there are decision-making units in which the production process can be considered as a two-stage or multi-stage process. In order to evaluate these types of units, the network data envelopment analysis method is used. In this paper, Two-stage units have been investigated, which in the two-stage process are the outputs of the first stage of the second stage inputs, which are referred to as "middle sizes".The purpose of this research is to determine the most effective scale of the production unit scale using a two-step process based on the demand level.In this regard, while determining the units of MPSS with ordinary DEA methods, we will generalize it in two-stage models.Then, the maximum and minimum amount of production, the production units that are in the most efficient scale of the scale, are obtained at each of the stages separately and then generalized for the whole process.We consider supply and demand as two output indicators and we determine the demand level for each step separately and then the whole process so that we can obtain the maximum and minimum amount of demand.