scholarly journals Estimating Most Productive Scale Size in Data Envelopment Analysis with Integer Value Data

2018 ◽  
Vol 300 ◽  
pp. 012014
Author(s):  
Yunita Dwi Sari ◽  
Layla Angria S ◽  
Syahril Efendi ◽  
Muhammad Zarlis
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Scale Size ◽  
Most Productive Scale Size

scholarly journals Productivity of Timber Processing in Ondo State, Nigeria

2012 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Adegboyega Eyitayo Oguntade ◽  
Temitope Enitan Fatunmbi ◽  
Joshua Adio Folayan
Keyword(s):  
Data Envelopment Analysis ◽  
Sampling Technique ◽  
Fixed Cost ◽  
Data Envelopment ◽  
Forest Reserve ◽  
Scale Size ◽  
Multi Stage ◽  
Ondo State ◽  
Using Data ◽  
Most Productive Scale Size

<p>This study is aimed at evaluating the efficiency of timber processors in Ondo State, Nigeria, using Data Envelopment Analysis. Multi stage sampling technique was used to select two Local Government Areas with the highest number of sawmills, from each of which twenty saw millers were randomly selected, given a total of forty saw millers. Based on Constant Return to Scale Technical Efficiency, 35% of the saw millers were technically efficient while on the basis of Variable Return to Scale TE, 60% of the saw millers were technically efficient. About 35% of the saw millers were scale efficient. The Data Envelopment Analysis output revealed that 35% of the sampled saw millers were both technically and scale efficient and were hence operating at the most productive scale size. About 65% of the saw millers were operating at sub-optimal condition. Excesses in input utilization were observed in respect of total fixed cost, costs of electricity, servicing of mill, timber from forest reserve and operation of truck; and remuneration of labour. The inefficient firms should be encouraged to emulate the operating practices of the most productive firms so as to improve their performance.</p>

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

ESTIMATING THE EFFICIENCY OF PAHANG RUBBER SMALLHOLDERS USING DATA ENVELOPMENT ANALYSIS APPROACH

2016 ◽  
Vol 78 (12-3) ◽  
Author(s):  
Na’imah Ali ◽  
Noor Asiah Ramli ◽  
Faridah Zulkipli
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Input Output ◽  
Monthly Income ◽  
Constant Return ◽  
Optimal Input ◽  
Scale Size ◽  
Using Data

RISDA has targeted for the income of each smallholder to be at least RM2500 per month by the end of 2015. However, approximately almost 90% of the smallholders’ monthly income is still below the target. Hence, in order to observe if this target is achievable, a study was conducted to evaluate the efficiency level of producing rubber among 95 rubber smallholders in Pahang. In addition, the study also investigated if there was any opportunity for increment of production among the rubber smallholders. Therefore, the Data Envelopment Analysis (DEA) model, under the assumption of Variable Return to Scale (VRS) and Constant Return to Scale (CRS), was used to analyse the scale and the technical efficiency of the smallholders. Scale Efficiency was measured in order to estimate the return to scale of the smallholders. As a result, the study found that the average Overall Technical Efficiency (OTE) and Pure Technical Efficiency (PTE) scores of the smallholders were 43.47% and 43.78%, respectively. Thus, the majority of the smallholders were not technically efficient in producing rubber. Furthermore, based on the return to scale estimated, 41% of the smallholders were operating under the Increase Return to Scale (IRS), which implied that the smallholders had a sub-optimal scale size. The results obtained had been useful as the optimal input-output for the efficient rubber yield can be determined and may help RISDA, as well as agricultural planners, to devise a strategy in order to increase the productivity of rubber smallholders in Malaysia.   

Sign in / Sign up

Export Citation Format

Share Document