Social Science Journal for Advanced Research

1. Introduction

The sugar industry is one of the most important agro-based industries in India, contributing significantly to employment generation, rural development, and national economic output. Among the Indian states, Maharashtra holds a leading position in both sugarcane cultivation and sugar production. The cooperative sugar industry, in particular, has been the backbone of rural development in the state, playing a crucial role in ensuring socio-economic welfare through community ownership and local empowerment. A noteworthy development has been the emphasis on fuel ethanol production from sugarcane molasses. With the Government of India promoting ethanol blending in petrol as a national policy, sugar mills have been encouraged to diversify into ethanol production. Maharashtra, as a leading sugar and ethanol producing state, has been central to this transition. Cooperative sugar mills, many of which have established distilleries, are key players in this shift. However, challenges related to capacity utilisation, procurement prices, and infrastructural gaps remain. In this context, the present study applies Exploratory Factor Analysis to identify the underlying dimensions that influence the performance of cooperative sugar-ethanol mills in Maharashtra.

2. Literature Review

Globally, sugarcane-based ethanol has emerged as a key renewable energy source, particularly in countries like Brazil and the United States. In Brazil, sugarcane is considered a highly efficient bioenergy crop due to its high energy output-to-input ratio and the potential for co-products such as bagasse and bioelectricity. Macedo et al. (2008) emphasised the role of modern technology and integrated mill-distillery models in achieving cost-effective and sustainable ethanol production. Similarly, Goldemberg and Guardabassi (2009) underlined how policy support and technological efficiency have made Brazil a benchmark for sugar-ethanol integration.

In the Indian context, ethanol is primarily produced from molasses, a by-product of sugar production. Soam et al. (2015) noted that India's emphasis on molasses-based ethanol is driven by the need to balance biofuel development with food security.

The 71 mills selected for study are located in the main sugar producing regions of Maharashtra. This includes 19 mills from Kolhapur region, 26 mills from Pune region, 11 from Ahmednagar region, 7 from Aurangabad and 8 mills from Nanded region. Thus 71 mills have been studied over a period of ten years from 2006-07 to 2015-16 and the data set presented for analysis is a panel data or longitudinal data that captures both spatial and temporal dimensions.

For identifying the major components responsible for variations in total cost of production, Factor Analysis using Principal Component Analysis (PCA) was applied. PCA is an extraction method that reduces the dimensionality of data consisting of a large number of correlated variables, while retaining the maximum variance. This is achieved by transforming the data into a new set of uncorrelated variables, or principal components, ordered by the amount of variance they capture.

4. Results

1. Profile of Mills
The 71 mills taken for study have been categorized based on certain criteria as is given below.

• Mills producing only sugar- These include mills which produce sugar as the main product. Other by products like bagasse, molasses, press mud etc does not contribute significantly towards the revenue generation of the mills. For this reason, these mills have been termed as ‘sugar only mills’.
• Mills with sugar and distillery- These include mills which produce sugar and rectified spirit as the major products and all other byproducts contribute insignificantly towards the revenue of these mills.
• Mills with sugar, distillery and fuel ethanol- This category of mills produce sugar, rectified spirit and fuel ethanol as the major products and all other byproducts contribute insignificantly towards the revenue generation.

Table 1: Composition of sugar mills in Maharashtra

Source: VSI publications, 2006-07 to 2015-16^*

In the case of mills with ‘sugar and distillery’, there were only 10 mills in 2006-07 which had distillery in operation and it increased to 11 the next year. In the succeeding years it is seen that one of the mills stopped operating distillery and this has continued till the last year where only 10 mills had distillery in operation. In the case of mills with ‘sugar, distillery and fuel ethanol’ it was found that there were only 6 to 8 mills with fuel ethanol facility in operation from 2006-07 to 2009-10. But in the year 2010-11 it can be seen that the number increased to 21 which is a very large increase. In the last year there were 27 mills with fuel ethanol facility in operation and 3 were not in operation in terms of fuel ethanol production. The large increase in the number of sugar mills producing fuel ethanol in the year 2010-11 is attributed to the implementation of the National Biofuel Policy of India in 2009 which aimed at increasing the ethanol blend in petrol in the coming years.

Since initial factor extraction did not yield interpretable factors, rotation of factors was done so as to obtain factors that could be grouped together, named and interpreted. This step made the larger loadings larger and smaller ones smaller than before so that each variable was associated with minimum number of factors. Thus the variables which loaded strongly together on a particular factor gave a clear picture of the influence of principal components affecting the cost. The descriptive statistics regarding the variables affecting the total cost of production is given in table 3.

Table 3: Descriptive Statistics of Variables Affecting Total Cost of Production

The test for sample adequacy was done by the KMO- Bartlett’s test. The KMO statistic varies between 0 and 1. A value of 0 indicates that the sum of partial correlations is large relative to the sum of correlations, indicating diffusion in the pattern of correlations which may cause factor analysis to be inappropriate. A value close to 1 indicates that pattern of correlation is relatively compact and so factor analysis should yield distinct and reliable factors. For the present data set, the value is 0.757, so we can be confident that factor analysis is appropriate for this data.

Table 4: Test for sampling adequacy for PCA

Bartlett’s measure tests the null hypothesis that the original correlation matrix is an identity matrix.

The first factor accounts for 19.19 % of the variance, the second accounts for 14.99%, the third accounts for 12.07% and the fourth accounts for 11.07%. Together, the first four factors account for 57.32% of the variability of the original 13 variables.

Table 7: Rotated component matrix

From the rotation matrix it was identified that the first factor influencing the total cost of production was basic input cost, reflected by the variables, cane price, salary and wages, power, overheads and interest on working capital.

The fourth component represents long-term recurring costs, including interest on term loans, depreciation, and interest on deposits. These reflect capital-related and financing burdens that extend beyond daily operations.

Together, these components explain 57.33% of the total variance in production costs, underscoring the multi-dimensional nature of cost management in the cooperative sugar-ethanol sector. The findings align well with the article’s title, which emphasises the need to identify and understand multi-dimensional performance factors.

Understanding these grouped cost structures enables targeted interventions. For instance, reducing interest burdens and improving energy efficiency could ease long-term and core input costs. Similarly, technological upgrades may address subsidiary and procurement inefficiencies. This analysis provides a strategic lens for policymakers and mill administrators to design co

6. Conclusion

This study has identified key cost-based dimensions influencing the performance of cooperative sugar-ethanol mills in Maharashtra using Principal Component Analysis. By extracting four principal components—basic input costs, subsidiary costs, procurement costs, and long-term recurring costs—the analysis reveals the structural complexity behind total cost variations. These findings provide strong empirical support for the notion that mill performance is shaped by multi-dimensional and interrelated cost categories, rather than singular operational metrics.

The 57.33% cumulative variance explained underscores the analytical strength of factor-based decomposition in understanding production economics in the cooperative sugar sector. These dimensions offer mill administrators a more strategic lens for prioritising interventions, improving cost efficiency, and adapting to policy-driven diversification into ethanol production.

This cost-centred factor analytical approach contributes both to academic research and to practical policymaking. Future studies could build on these insights by incorporating governance variables, production scale, or external price shocks, and validating the extracted dimensions through Confirmatory Factor Analysis across different regions or time periods.