Efficiency Analysis and Flexibility: A Case Study of Cement Firms in India

Abstract

The present paper aims to study the efficiency and flexibility of cement firms in India. Different data envelopment analysis (DEA) models have been employed to assess the efficiency of cement industry in India. Major findings of the DEA analysis suggest that 43 % firms are found to be technically efficient. Overall, the industry shows good performance with a mean technical efficiency level of 0.885 (variable returns to scale model) and 0.859 (constant returns to scale model). The results relating to returns to scale indicate that 14 firms are experiencing increasing returns to scale, 12 firms are operating at decreasing returns to scale, and the remaining 21 firms are exhibiting constant returns to scale. Results show that foreign firms are technically more efficient than the domestic firms, and owing to the benefits of economies of scale, large-scale firms are more scale efficient than the small- and medium scale firms. The study highlights the importance of flexibility in the production processes of inefficient firms in order to bring their efficiency at par with efficient firms.

Introduction

Cement industry is one of the most important industries, which plays a vital role in the development of an economy. Currently, India is the world’s second largest producer of cement, and it produces about 7 % of the world’s cement production (Government of India 2013). Appendix Table 8 and Fig. 1 show the production of cement and growth rate of cement production in India, respectively.

Fig. 1

Growth rate of cement production in India Source: Government of India 2013

It can be seen from Appendix Table 8 that the cement production has increased manifold over the period of time. However, recently it has shown a declining trend due to weak demand from infrastructure and real estate sector, but it is expected to increase in the near future owing to the increased investment by government in infrastructure projects.

In India, cement industry is mainly dominated by just five firms that capture more than 50 % of the market share. Appendix Table 9 shows the market share of the top five cement firms from 2007–08 to 2012–13 based on the data from Prowess, Centre for Monitoring Indian Economy (CMIE).

Ultratech Cements Limited has emerged as a leader, with a market share of 21.06 %, whereas ACC Limited and Ambuja Cements Limited are the second and third largest players, with the market share of 11.56 and 10.14 % respectively. Century Textiles Limited and Shree Cement Limited are the fourth and fifth largest players in the market.

Cement production is an extremely energy intensive process. Expenditure on power and fuel consumption of Indian cement industry rose from Rs. 15,679.9 million in 1991–92 to Rs. 228,205 million in 2012–13, resulting in higher carbon footprint (see Fig. 2). Effective and efficient management of energy resources and innovative technology for production has become very crucial to meet the growing energy demand (Aye and Fujiwara 2014). Efficiency augmentation of cement industry will result in lesser pressure on energy inputs. Cement firms have started investing in research and development, which is largely targeted toward the development of energy– efficient technologies.

Fig. 2

Power and fuel expenses of cement industry in India from 1990–91 to 2012–13 Source: Prowess Data base 2014

Research and development expenditure in Indian cement industry has increased from Rs. 95.63 million in 1990–91 to Rs. 636.1 million in 2012–13 (see Fig. 3). To remain competitive in the international market, Indian cement firms will have to invest more in research and development.

Fig. 3

Research and development expenditure of cement industry in India from 1991–92 to 2012–13 Source: Prowess Data base 2014

With the implementation of environmental regulations and growing competition among the cement firms in India, firms need to find out ways to thrive in an extremely competitive environment. Two such ways are improvement in efficiency and flexibility of firms. Efficiency analysis of firms provides useful insights to policy makers and firms to formulate an appropriate strategy to improve their resource use. Further, productivity growth in the energy-intensive industries moderates the growth of energy demand (Mongia and Sathaye 1998). Boyd and Pang (2000) have found strong correlation between productivity and energy intensity. Increasing efficiency or productivity will result in low energy use and hence lower carbon footprint. However, optimum level of efficiency cannot be attained until and unless firms show flexibility in their production practices.

The notion of flexibility has received a considerable attention from managers, researchers and policy makers. Many researchers (Zelanovic 1982; Gerwin 1987; Bonetto 1988; Gupta and Goyal 1989; Mandelbaum and Buzacott 1990; Upton 1997; Jagannathan 2000; Byrd and Turner 2000; Kalleberg 2001; Sushil 1997; Wadhwa and Rao 2004; Sánchez and Pérez 2005; Pathak 2005; Nadkarni and Narayanan 2007; Merschmann and Thonemann 2011; Roberts and Stockport 2014) have attempted to define various types of flexibility. Different terms refer to the same type of flexibility, and definitions for these terms, at times, are not in conformity with one another (Sethi and Sethi 1990). Flexibility is multidimensional in character and may carry different connotations in different contexts (Sushil 2012). Broadly, it can be defined as the ability of a system to respond effectively to changing circumstances (Piore 1989). It generates managerial effectiveness, business excellence, and corporate success (Sushil 2001). Flexibility is regarded as an essential condition for firms wishing to survive (Sanchez 1995). An extensive review of studies regarding flexibility has been carried out by Sethi and Sethi (1990), Upton (1994), Sharma et al. (2010b) and Mishra et al. (2014). Though flexibility has received wide attention from researchers, very few studies (Bolwijn and Kumpe 1990; Adler et al. 1999; Ebben and Johnson 2005; Tan and Wang 2010; Eisenhardt et al. 2010) have been conducted to study the relationship between efficiency and flexibility of a firm. The present study adds to the scant literature on the efficiency and flexibility of a firm.

With the above background in our mind, our study aims to address following five questions:

• 1) How do cement firms in India are performing in terms of technical efficiency?;

• 2) Does ownership affects firm’s efficiency?;

• 3) Does size affects firm’s efficiency?;

• 4) How can the technical efficiency of inefficient cement firms be enhanced? and

• 5) What is the significance of flexibility of a firm to attain the optimum level of efficiency?

Review of Literature

Select studies on productivity growth of Indian cement sector

Many researchers (Pradhan and Barik 1998; Schumacher and Sathaye 1999; Mongia et al. 2001; Sharma 2007; Mandal and Madheswaran 2012) have tried to analyze the productivity of cement industry in India. Table 1 provides a brief outline of the select productivity studies conducted in the cement sector of India.

Table 1 Select studies on productivity growth of Indian cement sector

Gaps and Objectives

Gaps in the literature: Reviewing the existing literature on the productivity and efficiency analysis of cement industry in India, we can conclude that:

• 1) Most of existing studies captured data of relatively too older time period. With the implementation of new policy reforms and environmental regulations, there is a need to conduct the efficiency analysis of cement firms in India using the most recent data.

• 2) There seems to be no study that has analyzed the possible determinants of efficiency of cement firms.

• 3) None of the earlier studies seems to have analyzed the possible impact of presence of outliers in the sample data.

• 4) No study seems to have examined the relationship between efficiency and flexibility of cement firms in India.

Objectives of the study: The present study aims to fill the above gaps in the literature with the following objectives:

• 1) to analyze the relative technical, scale and super efficiency performance of cement firms in India for the year 2012-13 using DEA;

• 2) to study the impact of size and ownership on the efficiency of a firm;

• 3) to identify and remove the outliers in the sample data;

• 4) to obtain target values of inputs and output for inefficient firms; and

• 5) to study the relationship between efficiency and flexibility of a firm.

Methodology

There are two main techniques for studying efficiency of firms; namely, Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). SFA is a parametric method which imposes a priori the functional form for the frontier. Assuming an inappropriate functional form for the frontier can lead to inaccurate results. DEA does not impose any assumption about the functional form of production technology. Further, DEA allows considering multiple inputs and outputs for calculating efficiency, while SFA cannot consider multiple outputs. Also, super efficiency analysis of firms is not possible using SFA. Therefore, present study uses DEA technique for efficiency analysis of cement firms in India. DEA model can be either an input reduction or an output augmentation. The former improves the efficiency of a decision making unit (DMU) through proportional reduction of inputs, thereby producing the same level of output, while the latter improves the efficiency through proportional augmentation of output of a DMU using the same levels of inputs. In this study, an input reduction model has been followed. For each firm, DEA also determines the increasing returns to scale (IRS), decreasing returns to scale (DRS), or constant returns to scale (CRS). A firm operating at CRS has the optimum scale size and a percentage increase in inputs leads to same percentage increase in output. A firm operates at DRS when a proportionate increase in output is less than the proportionate increase in inputs. Further, when the percentage increase in output is more than the percentage increase in inputs, a DMU exhibits IRS. DEA assumes that all the firms use the same level of technology and makes the following assumptions about the production technology: (i) All actually observed input–output combinations are viable, (ii) production possibility set is convex, and (iii) inputs and outputs are freely disposable. Table 2 shows the select studies on efficiency analysis using DEA.

Table 2 Select studies on efficiency analysis using DEA

Technical Efficiency Model

The relevant DEA model to measure technical efficiency is as follows:

$$ \theta^{*} = { \hbox{min} }\;\theta $$

Subject to the following constraints:

$$ \mathop \sum \limits_{j = 1}^{n} x_{ij} \lambda_{j} \le \theta x_{im} \left( {{\text{i}} = {\text{ capital}},{\text{ labour}},{\text{ energy}},{\text{ material}}} \right); \, $$

(1)

$$ \mathop \sum \limits_{j = 1}^{n} y_{j} \lambda_{j} \ge y_{m } \left( {\text{output}} \right); \, $$

(2)

$$ \mathop \sum \limits_{j = 1}^{n} \lambda_{j} = 1; \, $$

(3)

$$ \lambda_{j} \ge 0, \quad j = 1, 2, \ldots n, \, $$

(4)

Note that Inequality (i) suggests that weighted combination of inputs of all firms must be less than or equal to the inputs employed by the firm m. Inequality (ii) ensures that the weighted combination of output of all the firms should be at least equal to the output of the firm m. Equation (3) indicates the variable returns to scale. In case of constant returns to scale, condition (iii) is relaxed. θ* is the efficiency score of firm m.

Super-Efficiency Model

The present study applies the Andersen and Petersen’s (1993) super-efficiency model to rank the cement firms in India. In the technical efficiency model, all the efficient firms have efficiency value equal to 1, making it impossible to differentiate their performance. The super-efficiency DEA model allows the efficient firms to take any value higher than or equal to 1, thereby making it plausible to rank all the efficient firms. The central idea behind this model is to exclude the test firm out of the reference set. If the firm is efficient, the frontier will change and the firm can take a value greater than 1. On the other hand, if the test firm is inefficient, then its efficiency score remains unaffected with its omission from the reference set.

The relevant DEA super-efficiency model can be explained as follows:

$$ \beta^{*} = { \hbox{min} }\;\beta $$

Subject to the following constraints:

$$ \mathop \sum \limits_{j = 1,j \ne m}^{n} x_{ij } \lambda_{j} \le \beta x_{im} \left( {{\text{i }} = {\text{ capital}},{\text{ labour}},{\text{ energy}},{\text{ material}}} \right); \, $$

(5)

$$ \mathop \sum \limits_{j = 1,j \ne m}^{n} y_{j} \lambda_{j} \ge y_{m } \left( {\text{output}} \right); \, $$

(6)

$$ \mathop \sum \limits_{j = 1, j \ne m}^{n} \lambda_{j} = 1; \, $$

(7)

$$ \lambda_{j \ne m} \ge 0, \quad j = 1, 2, \ldots {\text{n}}, \, $$

(8)

To assess the super-efficiency score of firm m, the firm m is excluded from the reference set.

Database

For measuring technical and super-efficiency, the sample involved the firms with data for 2012–13 from PROWESS, a statistical database that is produced and maintained by the CMIE, India. These 47 firms captured around 99 % share of the market. The data on value of output and four input variables for the year viz., raw material expenses, salaries and wages, power and fuel expenses, and capital for the year 2012–13 have been extracted from PROWESS. The results have been obtained using Deap 2.1 and EMS software. Table 3 shows the descriptive statistics of variables used in the study.

Table 3 Descriptive statistics of select variables

No earlier study on cement sector of India seems to have detected the possible effect of presence of outliers in the sample data. Outliers can significantly affect the results of empirical analysis, therefore detection and deletion of outliers is always advisable. According to Avkiran (2001) and Kumar (2011), any firm with a super-efficiency value higher than 2 confirms the presence of outlier in the sample data. Therefore, we first calculated Andersen-Petersen’s DEA super-efficiency scores to detect the outliers in the sample data. Table 4 provides the results of Andersen-Petersen’s super-efficiency scores for cement firms in India.

Table 4 Super efficiency and ranks of cement firms in India

As can be seen from Table 4, none of the firms have a super-efficiency score >2, indicating absence of outliers in the sample data. Therefore, we proceed further to analyze the technical efficiency scores of all the 47 firms.

Results and Discussion

Findings of the empirical analysis conducted on cement firms in India for the year 2012–13 using DEA are reported in Appendix Table 11.

Appendix Table 11 shows that out of 47 firms, 20 firms (43 %) are relatively efficient. These 20 efficient firms are A C C Ltd., Binani Cement Ltd., Bokaro Jaypee Cement Ltd., Burnpur Cement Ltd., Cement Corpn. of India Ltd.,Cement International Ltd., Dalmia Cement (Bharat) Ltd., Deccan Cements Ltd., Dirk Pozzocrete (M P) Pvt. Ltd., Gangotri Cement Ltd., Gujarat Sidhee Cement Ltd., Hemadri Cements Ltd., India Cements Ltd., J K Lakshmi Cement Ltd., Prism Cement Ltd., Sanghi Industries Ltd., Saurashtra Cement Ltd., Shree Cement Ltd., Shri Keshav Cements & Infra Ltd. and Ultratech Cement Ltd. While remaining 27 firms (57 %) are relatively inefficient. Table 5 presents the frequency distribution of VRSTE, CRSTE and SE scores and their descriptive statistics.

Table 5 Distribution of cement firms for VRSTE, CRSTE and SE

From the Table 5, we can observe that 43 % firms have VRSTE score equal to 1, whereas 55 % firms have a VRSTE score between 0.60 and 0.99. The average VRSTE score of 0.885 (with a standard deviation equal to 0.94) implies that it would be possible for the Indian cement industry to produce the same output with 88.5 % of the inputs. The mean CRSTE score is 0.859 (with a standard deviation equal to 0.887), and the CRSTE scores range from a lowest of 0.592 to a highest of 1. The average scale efficiency for Indian cement industry is quite high 0.972 (with a standard deviation equal to 0.998). Around 55 % firms are operating at either DRS or IRS. Remaining 45 % operating at a most optimum scale size and these firms have a scale efficiency score equal to 1.

Overall, the industry shows good performance with a mean technical efficiency level of 0.885 (varying returns to scale model) and 0.859 (constant returns to scale model) respectively. Still there is a significant scope for the industry to further improve its efficiency level by contracting its inputs. The mean technical efficiency of 0.885 (VRS) implies that cement firms can produce the same output with 11.5 % lesser inputs. Keerthi Industries Ltd. is the worst performer that can produce the same output using just 53.9 % of the inputs. Inefficient cement firms need to incorporate input flexibility to attain the optimum level of efficiency. Input flexibility signifies the ability of a firm to produce the same output using lesser inputs. It can be observed from Appendix Tables 10 and 12 that there is a significant difference between the observed and target values of inputs, which indicates the inefficient utilization of inputs by these firms. Firms confront difficulties in adjusting inputs, especially fixed factors like capital with change in market conditions. Different factors like administrative rules and regulation, adjustment costs etc. slow down the ability of a firm to adjust the inputs. Input flexibility will provide cement firms the desired momentum to contract their inputs to the minimum possible level to reach the efficiency frontier. Appendix Table 12 presents the target values of inputs for inefficient cement firms to reach the maximum possible efficiency level.

Table 6 shows the distribution of cement firms for scale economies. Scale efficiency is calculated by dividing CRSTE by VRSTE. If SE = 1, then a firm is scale-efficient and is operating at the most optimum scale size and if SE < 1, then the firm is not scale-efficient and is operating at either IRS or DRS.

Table 6 Distribution of cement firms for scale economies

Scale flexibility is regarded as the potent weapon for firms to reach the optimum scale size, wherein the average cost of production is minimum. It indicates the ease with which a firm can alter its size of operation with the change in external conditions. It can be seen from Table 6 that firms exhibiting CRS have higher scale efficiency than those exhibiting DRS or IRS. Inefficient firms should have enough scale flexibility to adjust their scale of operations to produce at lower costs. It can be observed that 14 firms are showing IRS, suggesting the underutilization of plant capacities (see Table 6). These firms can gain from the economies of scale by expanding their operations up to the optimum-scale size. Twelve firms exhibit DRS, indicating over utilization of their plants (see Table 6). These firms need to cut down their size of operation to the scale-efficient size. Twenty one firms are showing CRS implying they are operating at the most optimum scale size. Flexibility in scale size will allow the firms experiencing IRS or DRS to quickly shift to optimum scale size.

Table 7 provides the descriptive statistics of VRSTE, CRSTE, and SE scores by ownership and size groups. Mean VRSTE for foreign firms and Indian firms is 0.96 and 0.88, respectively. Mean VRSTE of foreign firms is greater than the domestic firms. This may be due to the reason that foreign-owned firms use modern and advanced technology resulting in more efficient use of inputs. As stated in Bhattacharya et al. (2012) “…. the opportunities are feared to be slipping into the hands of large size international companies having greater capability and expertise of delivery. Companies in India thus, face a situation where they have to win against competitors who are stronger in both size and expertise”.

Table 7 Descriptive statistics of efficiency measures by ownership and size groups

To study the impact of size on the efficiency of a firm, we have divided the firms into two parts based on their investment in plant and machinery, according to the Micro, Small and Medium Enterprises Development (MSMED) Act, 2006, that is (i) for small and medium firms—Investment in plant and machinery exceeds 25 lakhs but not more than 10 crores. (ii) for large firms, the investment in plant and machinery is more than 10 crores (Government of India 2006).

From the results presented in Table 7, it can be inferred that small- and medium-scale firms are technically more efficient than large-scale firms. The reason could be large-scale firms suffer from bureaucratic issues and they are difficult to monitor than small- and medium-scale firms. But large firms are more scale efficient than the small and medium scale firms. This is primarily because large firms reap the benefits of economies of scale, resulting in low-cost of production and increased efficiency.

Conclusion

The present paper tries to analyze the technical, super efficiency and flexibility of cement firms in India. DEA has been employed to evaluate the efficiency of cement firms. None of the earlier studies on the efficiency of cement firms in India have focussed on the flexibility and super efficiency analysis. Further, the present study seems to be the first to examine the impact of size and ownership on the efficiency of cement firms in India. In addition, target values of inputs and output of firms to reach the optimum level of efficiency have been provided. The comprehensive analysis of efficiency of cement firms in India will help the managers and policy makers to formulate appropriate strategy to enhance their efficiency. Results show that 20 firms are found to be technically efficient. Overall, the industry shows good performance with a mean technical efficiency level of 0.885 (VRS model) and 0.859 (CRS model). However, efficiency performance of cement industry can be further enhanced by incorporating input and scale flexibility in the production processes of inefficient firms. Our finding of high technical efficiency of cement firms in India is compatible with the results of earlier study by Sharma (2008). The results relating to returns to scale indicate that 14 firms are showing increasing returns to scale, indicating they operate below the optimum scale of operations; and 12 firms are showing decreasing returns to scale signifying they are operating above the optimum scale of operations. Results also show that small firms are technically more efficient than the large firms and foreign–owned firms are more efficient than the domestic firms. The low efficiency of few cement firms is mainly due to poor infrastructural facilities, inadequate supply of raw material, high taxes on cement compared to other building materials like steel and poor adoption and adaptation capabilities of the imported technology. Government should ensure adequate and regular supply of good quality of coal to cement firms and should bring down the taxes on cement at par with other building materials. Export incentives should be provided to cement firms to encourage the export of surplus cement.

Scope for Future Research

This study can be further extended to study efficiency over the period of time using the panel data. Using malmquist DEA index technique, total factor productivity of the firms can be studied. Undesirable output like carbon emissions can also be considered along with the desirable output to calculate the efficiency of firms. This study has primarily focused on input and scale flexibilities of firms. Other aspects of flexibility like financial flexibility, organizational flexibility etc. may also be incorporated in the future studies.

Key Questions

• 1. How do cement firms in India are performing in terms of efficiency?

• 2. Does ownership affects firm’s efficiency?

• 3. Does size affects firm’s efficiency?

• 4. What is the significance of flexibility of a firm to attain the optimum level of efficiency?

• 5. How can the efficiency of inefficient cement firms be enhanced?

Appendix

See Tables 8, 9, 10, 11, 12.

Table 8 Production of cement in India

Table 9 Market share of leading cement firms in India (in percent)

Table 10 Observed values of inputs and output (in Rs. million)

Table 11 Efficiency scores for cement firms in India: 2012–13

Table 12 Target values of inputs for inefficient firms (in Rs. million)