Application of Data Envelopment Analysis Approach to Improve Economical Productivity of Apple Fridges
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1 International Research Journal of Applied and Basic Sciences 2013 Available online at ISSN X / Vol, 4 (6): Science Explorer Publications Application of Data Envelopment Analysis Approach to Improve Economical Productivity of Apple Fridges Hadi Fazli Ahmadabad *1, Seyed Saeid Mohtasebi 2, Seyed Hashem Mousavi-Avval 2 Mohamad Reza Marjani 3 1. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Saveh, Iran 2. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran 3. Department of Industrial Engineering, Qom University of Technology, Qom, Iran * Corresponding author hadi_fazliahmadabad@yahoo.com ABSTRACT: This study utilizes non-parametric technique of data envelopment analysis to examine the efficiencies of apple fridges using production and cost data for individual units. Data used in this study were obtained from 18 apple fridges in Tehran province, Iran. The results revealed that, total cost of production was averagely as 59.6 $ per 1000 kg of processed product. Also the results of efficiency analysis with data envelopment analysis (DEA) showed that, 5 units were recognized as efficient units on the basis of variable returns to scale assumptions. The technical, pure technical and scale efficiencies of units were found to be 0.76, 0.84 and 0.89, respectively. Moreover, total cost in target condition was calculated as MJ ha -1 ; accordingly, about 13% from total cost in present condition could be saved if the units follow the input package recommended by the study. From total cost saving, the highest share was obtained by labor (68%), followed by building rent (20%) and depreciation of equipments (8%), respectively. Applying a better machinery management technique to reduce human labor requirement may help to save the input costs for apple fridges and to improve the economical productivity of units. Key words: Input cost; Technical efficiency; DEA; Apple fridges; Tehran INTRODUCTION The economics, energy, and environment are mutually dependent (Tabatabaeefar et al. 2009). The economical and energy analyses as well as environmental analysis are important necessities in agriculture and food industries (Mobtaker et al. 2010). At the present time, the productivity and profitability of production and processing units depend upon input consumption. Production management systems need to be designed to help units maintain economic profitability while conserving external energy resources and operating in an environmentally responsible manner (Gundogmus 2006). Economic decision-making tools may help to alleviate the risk associated with converting to new production practices. Effective use of inputs in agriculture and food industries is one of the conditions for sustainable production, since it provides financial savings, fossil fuels preservation and air pollution reduction (Rafiee et al. 2010). The development of agricultural and food industrial systems with low input compared to the current level of output would result in improving the production efficiency and reducing the environmental impacts (Dalgaard et al. 2001). Due to the high advantages of DEA, there are several applications of this technique to determine the efficiency in agricultural and food industrial enterprises (Fraser and Cordina 1999; Fraser and Hone 2001; Chambers et al. 2011). Moreover, Dhungana et al. (2004) applied the DEA technique to analyze the economic inefficiency of Nepalese rice farms. They reported that the significant variations in the level of inefficiency across sample farms were attributed to the variations in the use of resources such as seed, labor, fertilizers and mechanical power. Finally, Mousavi-Avval et al. (2011) employed this technique to analyze the technical efficiency of apple producers with respect to the energy and cost data for apple production in Tehran province of Iran. With considering no study on economical efficiency in apple fridges systems, the present study employs DEA technique to analyze the efficiencies of units using production and cost data. This study also aimed to discriminate efficient units from inefficient ones and to identify the wastage of different inputs in the production period.
2 MATERIALS AND METHODS Data collection Data used in this study were obtained from total of 18 apple fridges in Tehran province of Iran. A survey approach was used to collect quantitative information on all direct and indirect inputs used in the process and revenue as well as socio-economic structures of apple fridges. The collected information belonged to the 2010/2011 production period. This region had a relatively homogenous condition. There were temperate climatic conditions. The costs of inputs used in apple fridges were specified in order to calculate the total production costs in the study area. The inputs were in the forms of fuels and energy, depreciation of equipments, human labor and land and building rent. The output was revenue from processed apple. For calculating the total costs, the inputs were calculated per 1000 kg of apple processed. DEA approach For DEA application in apple fridges enterprises, the costs of different inputs including depreciation of equipments, human labor, land and building rent and fuels and energy were defined as input variables, and the revenue of apple fridges was considered as output variable. Also, each apple fridge called a DMU. DEA models are broadly divided into two categories on the basis of orientation: input-oriented and outputoriented. Input-oriented models have the objective of minimizing inputs while maintaining the same level of outputs, whereas output-oriented models focus on increasing outputs with the same level of inputs (Malana and Malano 2006). The agriculture and food industries rely on finite and scarce resources; therefore the use of input-oriented DEA models is more appropriate to reduce inputs consumed in the production process (Chauhan et al. 2006; Malana and Malano 2006). For evaluating the economical efficiencies of units, three types of efficiencies were estimated as follow: Technical efficiency (TE) Technical efficiency is basically a measure by which DMUs are evaluated for their performances relative to other DMUs in the sample. Technical efficiency score ( c ) in the presence of multiple-input and output factors can be calculated by the ratio of sum of weighted outputs to the sum of weighted inputs or in a mathematical expression as follows (Cooper et al. 2004): s r u y + u y u y u yrj (1) c x 1 1 j 2 2 j s sj = 1 r = = m v1x1 j + v2 x2 j vm xmj v i= 1 i ij Pure technical efficiency (PTE) The TE, comprehend both the technical and scale efficiencies. So, Banker et al. (1984) developed a model in DEA, which was called BCC model to calculate the PTE scores ( v ). This model assums variable returns to scale (VRS), indicating that a change in inputs is expected to result in a disproportionate change in outputs. Because the VRS analysis is more flexible and envelops the data in a tighter way than the CRS analysis, the PTE score ( v ) is generally equal to or greater than TE score ( c ). Scale efficiency (SE) Scale efficiency relates to the most efficient scale of operations in the sense of maximizing the average productivity. Using the relationship between PTE and TE computed above, the scale-efficiency (SE) measure is computed as followes (Dhungana et al. 2004): c s = (2) v where s denote the scale efficiency score. SE gives quantitative information of scale characteristics. It is the potential productivity gained from achieving optimum size of a DMU. For the analysis, the Microsoft Excel spreadsheet and the DEA software Efficiency Measurement Systems (EMS), Version 1.3 (Scheel 2000) were employed to analyze the data. 1604
3 RESULTS AND DISCUSSION In this study, 18 apple fridges from surveyed region were selected to benchmark the productive efficiency of this food processing industry. For investigating the correlation between different variables a principal factor analysis was applied. The correlation coefficients of input variables with output revealed that the output variable was highly correlated with all of the input variables. Moreover, there was no autocorrelation between the input variables. In Table 1 the economic analysis of apple fridges are presented. As it is seen, the human labor costs in the surveyed fridges was 21 ($/1000kg); also, it varied from 5.6 to 82.5 ($/1000kg). Moreover, depreciation cost of equipment was calculated as 17.1 ($/1000kg) with the standard deviation of 6.1. Moreover, total costs was calculated as 59.6 ($/1000kg) with the standard deviation of On the other hand, total revenue of apple fridges was found to be 79.6 ($/1000kg), with the standard deviation of The variation in both the levels of input costs and output is noticeable; such variations were mainly due to the mismanagement of resource usage between the units, indicating that there is a great scope for improving the economical productivity of apple fridges in the region. For investigating the efficiency scores of units, both the constant and variable returns to scale (CRS and VRS) DEA models were applied to the specified input and output variables. The summarized information for the distribution of efficiency scores of units are presented in Table 2. The results revealed that, from the total of 18 units considered for the analysis, 3 and 5 units were identified as efficient units on the basis of constant and variable returns to scale assumptions, respectively. Moreover, from these efficient units 3 units were fully efficient in both the technical and pure technical efficiency scores, indicating that they were globally efficient and operating at the most productive scale size of production; while, the remainder of 2 efficient units were only locally not globally efficient; implying that, they have not used the correct level of inputs in the period; however, they moved toward the BCC efficient frontier when the effect of scale size was omited. The results of returns to scale indicated that 10 units were operating at CRS, showing the optimum scale of their practices; whereas, 7 units were found to be operating at DRS and the remainder of 1 unit was operating at IRS. This indicates that the majority of apple fridges in the region were operating at their optimal scale; and some of them were operating upon their optimal scale; therefore a proportionate decrease in all inputs leads to more than the proportionate decrease in outputs. Fraser and Cordina (1999) applied DEA to investigate the technical efficiency of dairy farms in Australia. They reported that a significant number of units were operating at IRS. Also, Jaforullah and Whiteman (1999) examined the scale efficiency of New Zealand dairy farms; they reported that 19% of farms were operating at optimal scale size, 28% at above optimal scale and 53% at bellow optibmal scale. Table 1. Economic analysis of apple fridges in Tehran, Iran. DMU Depreciation of equipment Labor cost Energy and fuel cost Building rent Total costs Total revenue Benefit to cost ratio ($/1000kg) ($/1000kg) ($/1000kg) ($/1000kg) ($/1000kg) ($/1000kg) Ave SD Min Max Table 2. Three types of efficiencies of individual apple fridges in Tehran, Iran. DMU Technical efficiency Pure technical efficiency Scale efficiency Return to scale Constant Constant Decreasing Decreasing Constant Increasing Decreasing Decreasing Constant Constant Decreasing Constant Decreasing Constant Constant Constant Constant Decreasing 1605
4 Table 3. Average efficiencies of apple fridges in Tehran, Iran. Particular Average SD Min Max Technical efficiency Pure technical efficiency Scale efficiency The average efficiency scores of units are presented in Table 3. The technical, pure technical and scale efficiency scores were calculated as 0.76, 0.84 and 0.89, respectively. Also, technical efficiency varied from 0.42 to 1; indicating that all of the units were not fully aware of the right production techniques or did not apply them at the proper time in the optimum quantity. In another study the technical and pure technical efficiencies of energy consumption for apple producers were calculated as 0.79 and 0.90, respectively (Mousavi-Avval et al. 2011). The results of optimization of input costs for apple fridges are shown in Table 4. As it is seen, the total cost of production in target condition was calculated as 52 ($/1000kg); from which the major contribution was required for Depreciation of equipment (16.4 $/1000kg), followed by human labor cost (15.8 $/1000kg) and building rent (13.7 $/1000kg), respectively. Moreover, the required expenditures for energy and fuel was found to be 6.4 ($/1000kg). The total cost saving for apple fridges was found to be 7.6 ($/1000kg); which consisted of as 12.72% from total cost in present condition (Table 4). Furthermore, the results revealed that, the costs of depreciation of equipment, labor, energy and fuel and building rent could be saved by 3.65%, 24.60%, 5% and 9.77%, respectively. Mousavi-Avval et al. (2011) investigated the optimization of input costs for apple production in Iran. They reported that, about 8% from total cost of production in present condition could be saved if the units follow the input package recommended by their study. Also, the costs of chemicals, chemical fertilizers and machinery inputs reduced by 20.98%, 15.97% and 15.73%, respectively. Costs Table 4. Present, target and savings of input costs for apple fridges in Tehran, Iran Costs in present Costs in target Saving value conditions ($/1000kg) conditions ($/1000kg) ($/1000kg) Saving (%) Depreciation of equipment Labor cost Energy and fuel cost Building rent Total costs To sum it up, the results of DEA application revealed that there was a great potential for improving economical efficiencies of apple fridges in Tehran, Iran. There was a common belief between the units that increased input use, especially equipment, labor and energy will increase the revenue. Effectiveness of use of all these inputs can be achieved by informed and efficient processing systems. Extension programs toward the development of such systems should be put into effect for establishing the economically efficient and environmentally sensitive apple fridge systems in the region. CONCLUSIONS In this study the input-oriented CCR and BCC DEA models have been applied to analyze the performance of 18 apple fridges with respect to input costs and revenue of apple fridges in Tehran province of Iran. The methodology presented in this paper demonstrates how units may benefit from applying operational management tools to assess their performance. The results revealed that only 5 units were operated efficiently and inefficient units generally used higher inputs resulting in excess costs and risk on environment and human health. The analysis also reveals that inefficient units may save considerable resources by adopting the best practices of highperforming ones. Based on the results, applying a better machinery management technique to increase machinery employment and to reduce human labor requirement a may help to save the input costs for apple fridges and to improve the economical efficiencies of units. 1606
5 However, it should be noted that low productive units may not become efficient by simply reducing the level of inputs. Instead the detailed analyses are required to determine other underlying causes of inefficiencies, including energy, environmental factors and agricultural practices. ACKNOWLEDGEMENT The financial support provided by the Science and Research Branch of Islamic Azad University, Saveh, Iran, is gratefully acknowledged. REFERENCES Adler N, Friedman L, Sinuany-Stern Z Review of ranking methods in the data envelopment analysis context, European Journal of Operational Research 140, Banker R, Charnes A, Cooper W Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30, Chambers RG, Hailu A, Quiggin J Event-specific data envelopment models and efficiency analysis, The Australian Journal of Agricultural and Resource Economics 55, Chauhan NS, Mohapatra PKJ, Pandey KP Improving energy productivity in paddy production through benchmarking--an application of data envelopment analysis, Energy Conversion and Management 47, Cooper W, Seiford LM, Tone K Data Envelopment Analysis, A comprehensive text with models, applications, references and DEA-solver software. Massachusetts, USA: Kluwer Academic Publishers. Dalgaard T, Halberg N, Porter JR A model for fossil energy use in Danish agriculture used to compare organic and conventional farming, Agriculture,Ecosystems & Environment 87, Dhungana BR, Nuthall PL, Nartea GV Measuring the economic inefficiency of Nepalese rice farms using data envelopment analysis, Australian Journal of Agricultural and Resource Economics 48, Fraser I, Cordina D An application of data envelopment analysis to irrigated dairy farms in Northern Victoria, Australia, Agricultural Systems 59, Fraser I, Hone P Farm-level efficiency and productivity measurement using panel data: wool production in south-west Victoria, Australian Journal of Agricultural and Resource Economics 45, Jaforullah M, Whiteman J Scale efficiency in the New Zealand dairy industry: a non-parametric approach, Australian Journal of Agricultural and Resource Economics 43, Lee WS Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes. Energy 35, Malana NM, Malano HM Benchmarking productive efficiency of selected wheat areas in Pakistan and India using data envelopment analysis, Irrigation and Drainage 55, Mobtaker HG, Akram A, Keyhani A Economic modeling and sensitivity analysis of the costs of inputs for alfalfa production In Iran: A case study from Hamedan province, Ozean Journal of Applied Sciences 3, Mousavi-Avval SH, Rafiee S, Mohammadi A Optimization of energy consumption and input costs for apple production in Iran using data envelopment analysis, Energy 36, Rafiee S, Mousavi Avval SH, Mohammadi A Modeling and sensitivity analysis of energy inputs for apple production in Iran, Energy 35, Tabatabaeefar A, Emamzadeh H, Varnamkhasti MG, Rahimizadeh R, Karimi M Comparison of energy of tillage systems in wheat production, Energy 34,
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