This paper measures the relative efficiencies of individual sugar mills of Uttar Pradesh, India, and sets targets for relatively inefficient mills to improve their performance. The study is based on the cross-sectional data collected for the year 2002-03 from a sample of 36 sugar mills. The Data Envelopment Analysis (DEA) is applied for assessing efficiencies of individual sugar mills. Tobit regression analysis is conducted to examine the impact of various background variables on the efficiencies. The paper finds that about 14% of sugar mills operate at the maximum degree of efficiency under Constant Returns to Scale (CRS) technology assumption. It also evinces that an average sugar mill has the scope of producing the same level of output with the inputs that are 9% lesser than the existing level. The study shows that several sugar mills have been able to make efficient use of their inputs but they suffer from disadvantageous plant sizes. The regression analysis reveals that the net sugar recovery and plant size have a significant positive impact on the overall technical efficiency and scale efficiency. Labor input is found to be highly underutilized in almost all inefficient mills. This calls for an amendment in the labor laws to deal with the issue of overstaffing and to provide scope for the organization to employ young, motivated and talented workforce. The paper suggests that efficiency in the sugar industry may be increased by expanding its capacity as sugar mills in the state are mostly found to operate at increasing returns to scale.
Performance
of a firm largely depends on how efficiently inputs
are used in the production, marketing and distribution
processes. As resources at its disposal are limited
and have competitive use, they are to be optimally
applied to enhance productivity and efficiency.
In order to identify up to what extent a firm produces
output efficiently and cost-effectively, its economic
efficiency is estimated. Economic efficiency is
the product of technical efficiency and allocative
efficiency. Classical production theory assumes
that given the level of technology, a firm produces
output with 100% technical efficiency. This implies
that if a firm wants to improve its economic efficiency,
it should focus only enhancing its allocative efficiency.
However, in reality, a firm's realized output may
be below the potential output and, as a result,
it may have technical inefficiency. Therefore, an
assessment of the 'individual firm's technical efficiency'
becomes crucial to know the extent of deviation
of the firm's actual output from its potential output.
There are two most popular approaches to estimate
technical efficiencyData Envelopment Analysis (DEA)
and Stochastic Production Frontier Analysis (SPFS).
In this paper, we have applied the DEA approach
to measure technical efficiencies of sugar mills
in Uttar Pradesh. DEA is a non-parametric mathematical
programming approach, developed by Charnes, Cooper
and Rhodes in 1978 (CCR model) and further extended
by Banker, Charnes, and Cooper in 1984 (BCC model). |
