1. The Indian Pharmaceutical Industry – An Overview on Cost Efficiency using DEA.
Haritha Saranga1 & B.V.Phani2
Abstract
The Trade Related Intellectual Property Rights System (TRIPS) agreement is part of an effort of the international
community to move towards a global economy. India’s assent to comply with this is a part of its effort towards
increased globalization of the domestic economy. The Indian Pharmaceutical Industry (IPI) is one of the few
industries which will be affected in a major way due to this as the existing “Process Patent” regime would give way
to the “Product Patent” regime from the year 2005. This combined with the changes in the industry due to India’s
efforts over the past one decade to move towards a market economy created a dynamic environment for the firms in
the industry. As a result, IPI, comprising of more than 20,000 players, is slowly consolidating with mergers,
acquisitions and alliances; and getting ready to adapt to this new environment. In such a dynamic environment it
would be interesting to examine whether there are any common firm level factors which aid in the survival and
growth of a firm. This assumes importance due to the fact that with so many players it is almost impossible for any
single firm to control the factors which affect the industry as a whole. This is particularly true when the changes are
driven due to the process of globalization and not due to any policy changes of individual governments. With this
objective, we have used Data Envelopment Analysis (DEA) on a sample of 44 listed companies that have survived
the past one-decade, to determine the best practices if any in the Indian Pharmaceutical Industry. The results of DEA
have been analyzed along with their Compounded Annual Growth Rate (CAGR) to see if internal efficiencies and
growth rate are related in the Indian Pharmaceutical Industry. We have also used regression analysis to see the
correlations between various inputs/outputs and the growth rates. Various models of DEA like Constant Returns to
Scale (CCR), Variable Returns to Scale (BCC) and Assurance Region (AR) are used to substantiate the results
obtained.
Keywords: Globalization, Data Envelopment Analysis (DEA), Finance, strategy, efficiency, performance
1
Haritha Saranga is formerly Assistant Professor at Indian Institute of Management Calcutta
2
B.V.Phani is an Assistant Professor at the Indian Institute of Technology Kanpur.
1

2. Introduction
The pharmaceutical industry in India is going through a major shift in its business model in the
last few years in order to get ready for a product patent regime from 2005 onwards.
This shift in the model has become necessary due to the earlier process patent regime put in
place since 1972 by the Government of India. This was done deliberately to promote and
encourage the domestic health care industry in producing cheap and affordable drugs. As prior to
this the Indian pharmaceutical sector was completely dominated by multinational companies
(MNCs). These firms imported most of the bulk drugs (the active pharmaceutical ingredients)
from their parent companies abroad and sold the formulations (the end products in the form of
tablets and capsules, syrups etc.) at prices unaffordable for a majority of the Indian population.
This led to a revision of Government of India’s (GOI) policy towards this industry in 1972
allowing Indian firms to reverse engineer the patented drugs and produce them using a different
process that was not under patent. The entry of MNC’s was also discouraged by restricting
foreign equity to 40%. The licensing policy was also biased towards indigenous firms and firms
with lesser foreign equity1. All these measures by GOI laid foundations to a strong
manufacturing base for bulk drugs and formulations and accelerated the growth in the Indian
Pharmaceutical Industry (IPI), which today consists of more than 20,000 players1. As a result the
Indian pharmaceutical industry today not only meets the domestic requirement but has started
exporting bulk drugs as well as formulations to the international market.
Currently the main activities of Indian pharmaceutical industry are broadly restricted to producing
(i) bulk drugs and (ii) formulations with very few companies risking investing in primary research
aimed at developing and patenting new drugs. The bulk drug business is essentially a commodity
business, where as the formulation business is primarily a market driven and brand oriented
business. Multinational companies which have entered the Indian market have mostly restricted
2

3. themselves to formulation segment till date. The domestic pharmaceutical industry (MNC’s and
Domestic) meets about 90% of the country’s bulk drug requirement and almost the entire demand
for formulations2. The economics of bulk drug business and that of formulation business are quite
different. Since a majority of the Indian companies are producing both bulk as well as
formulations, these are considered together for the purpose of the present study.
The Changing Environment
During the early 1990s, markets were opened by removing restrictions on imports and in 1994
licensing was abolished for producing bulk drugs and formulations. Other than this FDI restrictions
into this sector have been modified to allow 74% foreign equity through the automatic route. More
favorable conditions are to follow in future particularly for MNCs as soon as ‘Product Patents’ and
‘Exclusive Marketing Rights’ (EMRs) are permitted.
In a situation like this, there is a lot of speculation that the indigenous companies that have been the
mainstay of the Indian pharmaceutical industry2 over the past couple of decades finally becoming a
formidable part of Indian economy and a major source of foreign income might be facing uncertain
market conditions in the future. It may also come down to a state where most of the small scale
companies have to close down, with the multinational companies dominating and monopolizing3
the industry once again.
There is a justified reason for this, and that is, so far Indian companies have made use of the cheap
labor and the reverse engineering skills under the favorable conditions of process patent regime
and developed generic replicas to drugs that were under patent in developed countries, which then
were sold in the domestic markets and exported to other unregulated markets elsewhere in the
world. This generic business enabled them to compete with multinational companies in India and
abroad and resulted in good revenues. However, once the product patent regime gets implemented
from the year 2005, one is not allowed to reverse engineer drugs that are patented after 1995, and
3

4. the revenues from this business will suffer. Whereas, the multinational companies in India, which
have an impressive new product portfolio will get exclusive marketing rights to sell their products
at higher prices and will be in a position to dictate the terms.
Given the above, survival of Indian companies depends on producing generics of drugs whose
patent has lapsed and export the same to regulated markets4. This is possible only if these firms are
able to formulate these products at much lower prices allowing then to face competition from
established players in the international markets. Other than this, avenues like contract research and
manufacturing for multinational companies have become popular business models for many small
scale and medium scale firms. Given this situation it is highly likely that individual firms adopt
different strategies for growth. These strategies are dependent more on the management’s
perception of the individual firm’s strength in terms of finance, manpower and material in relation
with the other firms within the industry for a given environmental context. Some of these strategies
may end in failure due to unexpected changes in the environment or bad judgment on the part of
the management. The main question for which we try to provide an answer is ‘Do internal
efficiencies have any role to play in the growth of a firm irrespective of the individual growth
strategies adopted in a dynamic environmental context’.
The above question becomes very important for firms which operate in a transition economy. This
is particularly true if the transition is aimed towards being a part of the global economy. This
would create an environment where firms are faced with a completely new opportunity set in terms
of investment and growth. These opportunities encourage firms to adopt high growth strategies at
the cost of immediate returns. The success or failure of any such strategies is dependent on the
nature of competition faced by these firms over time. Therefore it would be very reasonable to
assume that a firm’s internal efficiencies may become the crucial deciding factor in dictating the
survival and growth of these firms in various segments of pharmaceutical industry. We concentrate
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5. on the role of internal efficiencies in the growth of these firms independent of the individual
marketing strategies and long term visions adopted at the firm level.
The following paragraphs try to analyze the role of internal efficiencies in fostering growth using
DEA. Three models of DEA have been used namely the CCR, BCC and AR models not only to
ascertain the relevance of the parameters used for fostering growth but also to throw light on the
efficiency of these models in isolating the better firms irrespective of the individual growth
strategies used.
Cost Structure/Performance indicators of Indian pharmaceutical industry
The pharmaceutical industry is characterized by low fixed asset intensity and high working capital
intensity (ICRA 2002). The Material cost, Marketing and selling cost and Manpower Cost
constitute the three major cost elements for the Indian pharmaceutical industry, accounting for
close to 70% of the operating income. In the past 6-7 years, material costs, which account for
almost 50% of the operating cost have declined owing to the decrease in prices of bulk drugs and
intermediates, increase in exports which enabled procurement of raw materials in large quantities
and hence at low prices and finally due to increase in production efficiencies. On the other hand,
the marketing and selling expenses, comprising of promotional expenses, trade discounts,
advertising and distributing costs; and freight and forwarding costs have increased in the past few
years owing to the increase in emphasis on sales of formulations. This increased focus on
marketing partly lead to the increase in the manpower costs of pharmaceutical companies during
the last decade. The other factor for the increase in the manpower costs, at least in case of a few
companies might be due to an increase in R&D efforts, which requires quality research personnel.
Data Envelopment Analysis as a measure of efficiency
Efficiency of a firm can be defined as the maximization of a set of outputs (Output-oriented) given
a set of inputs or minimization of a set of inputs (Input-oriented) for a given output. Most DEA
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6. applications in the literature are Input-oriented and this is attributed to a general lack of suitable
multiple-output datasets. Traditional industry reports (e.g., ICRA2) on the trends in costs, margins
and returns generated by IPI analyze the industry with the help of various performance indicators
like operating profit margins, net profit margins, fixed asset turnover, working capital intensity and
inventory holding period etc. However, parameters like margins, returns and debt ratios can only
describe various performance characteristics in isolation as only one input and one output can be
taken at a time. Comparison of these parameters in isolation across firms for a given industry might
provide a biased picture of a firm’s efficiency vis-à-vis other firms in the same industry. This
problem with this kind of analysis can be overcome by defining or developing a performance
indicator using the various parameters with suitable weights to come up with a composite index
comparable across firms. This strategy would also limit the interpretation of the results due to the
static nature of the weights so assigned.
Data Envelopment Analysis (DEA), one of the more recent and a highly popular tool among
researchers overcomes this problem by simultaneously analyzing multiple inputs and outputs to
come up with a single scalar value as a measure of efficiency. DEA has been used to successfully
measure relative efficiencies of DMUs in various public and private sector industries like banks,
computer industry, health care sector, pharmacies, car manufacturing industry, fisheries and search
engines on the internet etc since its development in 1978 (Charnes et al. 1978). In the Indian
context Saha et al3 used DEA to measure the relative efficiencies of Indian banks, in a changing
environment of financial sector reform initiatives by the Indian government since the early 1990s.
One of the instances where DEA was used in the financial analysis of pharmaceutical companies
was by Smith4, who used financial statements of 47 firms producing pharmaceutical products to
show the advantages of DEA to the traditional ratio analysis in describing the multivariate nature
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7. of firms5. The objective of DEA application in the current study is to see if there are any best
practices developed in IPI that are not influenced by the external environment.
The Methodology of Data Envelopment Analysis
Data envelopment analysis offers several characteristics that are quite unique and useful in
comparison to traditional financial analysis methods like ratio analysis or regression analysis.
Although all these techniques have their own advantages and disadvantages, one of the most
important feature of DEA is the ability to compare many parameters simultaneously and come up
with a scalar measure of overall performance. DEA provides the relative efficiency of each of the
firms (which usually are called Decision Making Units (DMUs)) in a given set of firms. These
DMUs are assumed to be in the business of producing various outputs by consuming a set of
inputs. In general several inputs are required to produce one or more outputs for a DMU.
However, in DEA only a few inputs and outputs are chosen depending on how critical their
contribution is to the effective performance of the DMU, in order not to dilute the efficiency
analysis with too many parameters. The selection of inputs and outputs is of paramount
importance in any DEA calculations as the results of the study can vary with different sets of
inputs and outputs. These vary from industry to industry, and even within an industry depending
on the objective of the efficiency analysis being carried out. It always helps to begin with 2-3
inputs (outputs) and slowly build up the number noting down the effect of each additional input
(output) on the efficiency scores.
Another unique feature of DEA is that the type of units used for all the inputs and outputs does
not have to be the same, as long as same set of inputs and outputs are used for all DMUs, and the
measure of efficiency becomes “units invariant”5. This gives a tremendous flexibility in choosing
the inputs and outputs, and a convenient way to compare relative efficiencies of DMUs.
7

8. Data Envelopment analysis, first proposed by Charnes, Cooper and Rhodes in 1978, is a non-
parametric method which assumes the production function is unknown. DEA involves solving a
linear programming (LP) problem where the solution provides a numerical description of a
piecewise linear production frontier.
Since the formal introduction of DEA, the basic concepts and principles have developed into
four types of DEA models6. Those are the CCR ratio model, BCC returns to scale model,
additive model and multiplicative model. In a comparative study Ahn et al7 proved theoretically
that the results in the form of efficiency or inefficiency are robust, even though different models
are applied.
Here we give a brief description of one of the most basic DEA models, the CCR model,
proposed by Charnes, Cooper and Rhodes in 1978. We use the following notation:
xi, j → ith input of DMU j where i = 1,…,m and j = 1,…,n.
yi, j → ith output of DMU j where i = 1,…,s and j = 1,…,n.
ui → ith weight corresponding to output yi, o where i = 1,…,s and o = 1…n is the DMU that is
being evaluated.
vi → ith weight corresponding to input xi, o where i = 1,…,m and o = 1…n is the DMU that is
being evaluated.
In the above notation, we are assuming n DMUs, with m inputs and s outputs. The CCR model of
DEA can be expressed in terms of the following linear programming model5.
Max θ = u1 y1o + L + u s y so (1)
Subject to v1 x1o + L + vm xmo = 1 (2)
u1 y1 j + L + u s y sj ≤ v1 x1 j + L + v m x mj j = 1,..., n (3)
v1 , v2 ,L vm ≥ 0 (4)
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9. u1 , u 2 ,Lu s ≥ 0 (5)
θ gives the efficiency of the DMU O. Since there are n companies, we will have n optimisations
to measure the efficiency of each DMU. DMU O is CCR-efficient if θ * = 1 and there exists at
least one optimal solution (v * , u * ) with v * > 0 and u * > 0 , where ( θ * , v * , u * ) is the optimal
solution to the LP (1) – (5). Otherwise, DMU O is CCR-inefficient. In case of inefficient DMU,
DEA also gives the degree of inefficiency and benchmarks a corresponding reference set of
efficient DMUs, also called peer group. The peer DMUs are the efficient units closest to it and
are observed to produce the same or higher level of outputs with the same or less inputs in
relation to the inefficient DMU being compared. This enables the inefficient DMUs to know if
there is excessive wastage of inputs and/or if there is any scope for improvement in outputs.
Min θ (6)
subject to the constraints,
n
∑λ x
j=1
j ij ≤ θxio , i = 1, 2 , ..., m (7)
m
∑λ
j =1
j y rj ≥ y ro , r = 1, 2, ..., s (8)
λ j ≥ 0 , j = 1, 2 , ..., n (9)
The above-mentioned Constant Returns to Scale (CRS) DEA model implies that the size of a
DMU should not matter for the efficiency. To facilitate ease of calculations, the dual of the LP-
model (1)-(5) was developed, where a virtual DMU, which is the linear combination of all the
DMUs of the sample, is compared with each DMU under evaluation, to calculate the efficiencies
as follows:
Where λ j are the multipliers corresponding to each of the DMUs in the linear combination of
the virtual DMU, and therefore the weights of inputs and outputs of the virtual DMU. Each
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10. DMU is compared with the virtual DMU to see if it can produce equal or more output than the
virtual DMU with the same or lesser input. If it can, then that particular DMU is efficient and
forms a part of efficient frontier with θ = 1, λo = 1 and λ j = 0, ∀j ≠ 0 . If not, it is inefficient and
the degree of inefficiency depends on the efficient companies on the frontier.
Banker, Charnes and Cooper8 (BCC) developed a DEA-model that calculates “pure” technical
efficiency, which is consistent with a maintained hypothesis of Varying Returns to Scale (VRS).
The BCC model is given by the dual of CCR model (6)-(9), with an extra constraint on λ j , given
below by equation (10), which restricts the feasible region to a convex hull and at the same time
ensuring the varying returns to scale.
λ1 + λ2 + L + λn = 1 (10)
In fact, an efficiency score obtained using the CCR-model is called Technical Efficiency, which
comprises of both Scale Efficiency and “pure” Technical Efficiency. In a case where a DMU is
found to be inefficient, one can decompose this total inefficiency to see in what degree this is due
to scale inefficiency or technical inefficiency.
At this point, one should note that the resulting weights assigned by the DEA, in CCR and BCC
models are not necessarily the correct weights as management or the analyst might assign since
the weights are designed to place the organization under evaluation in the best light possible.
DEA provides a conservative performance evaluation and gives the DMU the best weighting
possible whether or not the weightings represent the balance of outputs and inputs desired by
management or an analyst. For example, a DMU producing a high level of operating income and
little operating cash flow may not be considered by an analyst to be as healthy as a DMU with a
more balanced production of financial outputs. However, it is possible for this less-healthy DMU
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11. to receive a higher DEA score. To avoid a situation, where unfair amounts of weights are being
assigned to any input and/or output, Assurance Region model was developed9. In this model,
weights of any two inputs/outputs may be controlled with the help of upper/lower limits.
Application of DEA to Indian Pharmaceutical Industry
In the present study we have considered a sample of 44 pharmaceutical companies, whose data is
available throughout the period 1992 - 2002. The main reason for choosing this sample is the fact
that we have continuous availability of data for a common sample, which enables measurement
of various performance characteristics of those pharmaceutical companies that have survived at
least 11 years or more. A point to note here is that the selection of such a sample in itself gives a
set of companies that have successfully survived at least the last 11 years, and includes most of
the market leaders on the top and the companies that are struggling to make ends meet in the
bottom. Thus we hope that the sample is representative enough to include all kinds of firms with
a history of 11 years or more, except the ones, which have started after 1992, and the ones that
have closed down or got merged before 2002.
Table 1. Composition of the sample
Category Number of % of each
Companies category in the
Sample
Indigenous Companies 29 65.91%
Multi National Companies 15 34.09%
Bulk & Formulations 21 47.73%
Only Formulations 22 50%
Big (Turnover ≥ 300 Crores) 15 34.09%
Small(Turnover < 300 Crores) 29 65.91%
The composition of the sample is given in Table 1, which is differentiated under 3 different
criterion, first in terms of origin: indigenous versus multi nationals, secondly in terms of
business: Bulk & Formulations versus only Formulations and finally, size wise: big versus small.
11

12. Our aim is to see how the companies in different categories will fare in terms of efficiency
ratings.
The DEA analysis on this sample would give relative efficiencies of these 44 firms with respect
to each other and not with respect to all the 20,000 companies of Indian pharmaceutical industry.
This means, there might be other efficient/inefficient companies, with better/worse practices in
the larger population, that are not included in this sample, and whose inclusion might
reduce/increase the respective efficiencies of the firms in the present sample. However, for now,
we restrict ourselves to the present sample and focus on their best practices and try to analyse the
emerging trends in Indian pharmaceutical industry.
Inputs and outputs for the Data Envelopment Analysis
The choice of the inputs and outputs is very crucial for the relative efficiencies to be useful in
arriving at meaningful conclusions. For any given firm in an industry, performance or efficiency
is purely relative. There can be no predefined efficiency indicators given the general constraint
that the sum total of output should always be greater than the sum total of input. Given this
relative efficiency depends on the firm’s capability or to be precise the management’s capability
in utilizing the given resources better than the competition. This will provide these firms with
surplus output or slack, which can be used to face market uncertainty and take advantage of any
new opportunities thus enhancing the growth of the firm. This is also true in case of Indian
pharmaceutical industry, which is faced with a major period of uncertainty and an unprecedented
opportunity for growth. Most of the parameters fostering growth are external in nature like
demand in external markets etc. The one factor which is internal and under the direct control of
the management are the costs expended for a given output. The major cost elements, which
contribute towards 70% of the operating income2 of a pharmaceutical firm in India are chosen as
inputs for the application of DEA in the current paper, as follows: (i) Cost of Production and
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13. selling (ii) Cost of Material and (iii) Cost of Manpower. The outputs are (i) Profit margin (ii) Net
Sales and (iii) Exports.
As the objective of our study is to look at the internal efficiencies of pharmaceutical companies,
the natural choices for outputs are net sales and profit margin, which explicitly state the
performance of the firm. Even though exports are part of net sales, it is taken separately as the
third output, as a representative of a firm’s export business, which is going to play a very crucial
role in a firm’s ability to survive and grow in a post product patent regime.
Results of the DEA Analysis
We used both CCR and BCC models in order to find scale efficiency and pure technical
efficiencies of the 44 companies in our sample. We also used Assurance Region model with
restrictions on weights of the inputs according to the ratio 7:5:1 respectively, which are derived
from the past trend in the cost structure of these inputs in the Indian pharmaceutical industry, as
discussed in the previous section. We have divided the results of the sample into three groups,
the group-I consisting of top efficiency ranking firms, group-II consisting of medium efficiency
rankings and finally group-III consisting of the least efficient companies. Table 2 gives the top 8
companies in terms of CCR efficiency ratings from the sample. Columns 2 and 3 and 4 give the
number of times each company in column 1 has come as efficient, using CCR, BCC and
Assurance Region (AR) models during the Financial Years (FY) 1992-2002. Finally column 5
gives the Compounded Annual Growth Rate of these companies during the period 1992-2002
and the last two columns give the net sales of these companies in the years 1992 and 2002
respectively.
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14. Most Efficient Companies – Group I
Table 2. The Efficient Companies -Scores of CCR, BCC & Assurance Region
Company CCR BCC AR-Total CAGR 1992-Net sales 2002-Net sales
Morepen Laboratories Ltd. 10 11 0 39.93227 12.47 502.3
Aarti Drugs Ltd. 10 11 0 26.52159 12.05 160.25
Bharti Healthcare Ltd. 9 10 6 19.01344 3.49 23.68
Neuland Laboratories Ltd. 9 9 6 30.04413 5.28 94.98
Organon (India) Ltd. 9 9 1 12.09056 47.33 166.11
Dr. Reddy'S Laboratories Ltd. 8 10 8 28.85608 100.13 1628.24
Gujarat Themis Biosyn Ltd. 8 9 0 34.46274 0.95 24.68
Ranbaxy Laboratories Ltd. 5 11 3 17.84943 372.42 2267.96
Group-I companies, as is evident from Table 2 is an interesting mix of 5 small and 3 big
companies. However, out of 8 companies, there are 7 Indian companies and 1 MNC; and 6
companies are in the business of Bulk & Formulations. Another interesting observation is that
the Compounded Annual Growth Rate (CAGR) of these companies is quite high with Morepen
Laboratories Ltd (MLL) which is BCC-efficient throughout the 11-year period and CCR efficient
in 10 out of 11 years, topping the CAGR score. The average CAGR of Group-I companies is
26.1%, which is much higher than the industry average.
Looking at the scores of the above 8 companies a pattern can be observed. Out of these 8
companied 4 companies MRR, Aarti, Organon and Gujarat Themis are both CCR and BCC
efficient but fail to score in the AR-model. In the remaining four, 3 companies Bharti, Neuland
and Dr. Reddy’s are found to be efficient in all the three models. Ranbaxy was found to be
highly BCC efficient but failed to score in both CCR and AR models.
It is interesting to note that this discrepancy in terms of model efficiencies seems to be dependent
on the growth strategy adopted by these firms. These growth strategies also define the nature and
relevance of the various internal factors used in the analysis. It is evident that these firms have
very high growth rates. The four firms which have not been found to be AR efficient have one
thing in common in spite of vast differences in the size of the firms. All the four firms are bulk
drug manufacturers. Bulk drug business is characterized by relatively low risk and is more cost
14

15. driven but requires very low marketing and selling expenses. The low marketing and selling
expenses of these firms have precluded them from scoring in the AR-model. Since the AR-model
predefines the limits of the parameters used in the model.
In case of three companies which have been found efficient in all the three models their products
require more marketing and selling costs. Whereas in case of Ranbaxy which was found to be
only BCC efficient it is interesting to note that inspite of the high growth figures the growth is
driven by low margins. This is possible as Ranbaxy is focusing on increasing its market presence
globally using pricing as its main strategy which is reflected in its reducing margins.
Best Practices of Efficient Companies in Group I
As discussed earlier, the DEA methodology tries to show every DMU in its best possible light,
by giving more weighting to those inputs that are lowest and those outputs that are at the highest
for the DMU under evaluation. Thus, an in-depth analysis of the weights can reveal those
resources that were more efficiently utilized by an efficient DMU, and hence resulted in a full
efficiency score. A close look at the weights of CCR-scores, for Group-I companies shows that
all the 7 indigenous companies have got maximum weighting to Cost of Manpower, consistently
for all the years (total of 74 instances), except in 3 instances. Ranbaxy got more weighting to
Cost of Material in the year 1999, whereas, DRL got more weighting to Cost of Material in the
year 2000, and to Cost of Production and Selling in the year 2002. However, the only MNC,
Organon got more weighting to Cost of Material throughout the sample period (9 out of 11
instances), except for 1995 & 2002, where Cost of Manpower got more weighting. Thus, it is
clear that the best practice for the indigenous companies is the efficient management of their low
cost Manpower, whereas those MNC’s, which are managing the Raw Material well can fare well
in the efficiency ratings. Perhaps, Organon being in the business of both Bulk & Formulations, is
15

16. in a position to utilize its Raw Material better, and since most of the MNCs are only in the
business of Formulations, could not make it to the group of most Efficient companies.
Medium Efficient Companies – Group II
Table 3. Medium Efficient Companies – Group II
Company CCR BCC AR CAGR 1992-Sales 2002-Sales
Cipla Ltd. 4 7 1 22.366406 139.51 1284.96
Torrent Pharmaceuticals Ltd. 4 9 2 17.263818 65.21 375.94
Pfizer Ltd. 4 6 2 10.175804 116.9 339.44
Duphar-Interfran Ltd. 4 5 3 -8.263622 44.6 17.27
Nicholas Piramal India Ltd. 2 5 1 25.870393 64.24 807.17
Wockhardt Ltd. 2 4 2 23.780281 58.4 610.35
Armour Polymers Ltd. 2 4 1 10.717512 4.31 11.93
Zandu Pharmaceutical Works Ltd. 2 2 1 10.523856 33.93 102
Burroughs Wellcome (India) Ltd. 2 2 2 3.6205268 100.38 148.44
Elder Health Care Ltd. 1 2 0 23.892135 1.26 13.3
J B Chemicals & Pharmaceuticals Ltd. 1 2 0 14.284366 60.73 263.79
Dental Products Of India Ltd. 1 10 0 3.3994571 3.42 4.94
Lupin Ltd. 1 5 0 11.95 250.53 866.87
Out of the 12 companies in Table 3 that are on the CCR-efficient frontier at least in one year, 2
are MNCs and the rest are indigenous companies. Exactly 50% of companies in Group II are in
the business of only formulations, and the other 50%, in the business of Bulk drug and
formulations, with both MNCs dealing with only formulations. There are 5 companies that have
been BCC-efficient for 5 or more years, and 9 companies that have come up as efficient with the
Assurance Region (AR) model, at least in one year. A point to note here is that the average
CAGR of Group-II companies is 13.04% which is much lower than that of Group-I companies.
However, the negative CAGR rate of Duphar-Interfran has contributed to this lower rate to some
extent, which otherwise is 14.82% (excluding Duphar-Interfran).
As one can see from Graph I, the top three companies of Group II have achieved a CCR-
efficiency score of 1 throughout the period 1998 – 2000. In fact Cipla started off its efficiency
journey, a year early, from 1997 till 2000, and although dipped a bit in 2001-2002, only
marginally to .95 and .96 respectively. One can see from the graph that the initial period from
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17. 1992-1994 was not very good for Cipla, and the CCR-efficiency ratings increased steadily from
1995 onwards. This consistency is reflected in the BCC-efficiency ratings of 0.96 in year 1995
and a 1 throughout the 7-year period 1996-2002.
Graph I. CCR-Score comparisions for the top 4 companies of Group II
1.2
1
Efficiency
0.8
0.6
0.4
0.2
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Period --- 1992 - 2002
Cipla Torrent Pfizer Duphar-interfran
On the other hand, both Torrent and Pfizer started doing well from 1997 on wards, with Pfizer,
an MNC achieving full efficiency during 1998-2001 and dipping to lower rate of 0. 94 in 2002.
Pfizer has a volatile performance during 1992-1995, after which it has a steady growth in its
efficiency scores. Pfizer has been an outsourcing hub to its global major Pfizer Inc. of the US
and also conducts clinical development of new molecules with an R&D base, and has not
launched many new drugs due to its parent’s policy on patented drug introduction in the Indian
market. Torrent Pharmaceutical Limited (TPL), an indigenous company, has been BCC- efficient
during 1992-1995 and 1998-2000, dipping only slightly in between; and scale efficient in 1992
and during 1998 – 2000, which shows its consistency in being efficient in general, which stayed
between 0.86 and 1. TPL’s CAGR at 17.26 during 1992-2002 can be attributed to its presence in
high growth therapeutic segments and introduction of new products in high growth segments like
central nervous system, gastro intestinal and new molecules in antibiotics. Its alliance with
Novo-Nordisk (India) Limited, to which TPL supplies insulin formulations, ensures a steady
market for its products. TPL has been focusing on R&D of NCEs and NDDS in recent times,
17

18. with 6 NCEs in its pipeline and is geared for a post product patent regime, and is also planning to
offer contract research facilities to international as well as domestic players. Duphar-Interfran on
the other hand is an interesting example for a small company, which has stayed efficient even
with a negative growth that has resulted due to down sizing.
Nicholas Piramal India Ltd. and Wockhardt, which rank 5th and 7th in turnover according to
FY2002’s figures in the IPI, two of the top league indigenous companies, have a volatile CCR-
efficiency and a steady BCC-efficiency during the period 1992-2002, as is evident from graph II.
However, their performance has been pretty impressive with a CAGR of 25.87 and 23.78
respectively, which questions the relationship between growth and efficiency scores. Nicholas
Piramal India Limited (NPIL), which is in the business of formulations, has been busy expanding
and forming alliances with international players like F. Hoffmann La Roche and Boots plc.,
which provide NPIL access to their products in niche areas and over the counter (OTC) segment.
Thus, even though NPIL has managed to increase its turnover with acquisition of brands and the
businesses of other pharmaceutical companies, the very investments required for expansion have
Graph II. CCR vs BCC for Nicholas Piramal & Wockhardt
1.2
1
Efficiency
0.8
0.6
0.4
0.2
0
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
period - 1992 -2002
Nicholas Piramal (CCR) Wockhardt (CCR) Nicholas Piramal (BCC) Wockhardt (BCC)
reduced its internal efficiency scores. Similar arguments hold good for Wockhardt, which has not
only opened subsidiaries in UK, Europe and China, but also invested heavily in the R&D of
18

19. Biotechnology, which has resulted in successful new products, whose revenues, will be realized
for many years to come.
Least Efficient Companies – Group – III
There are 12 each of indigenous and MNC firms in the least efficient companies, i.e., Group III,
as listed in Table 4. These are the companies which never got a full CCR-efficiency score of 1,
throughout the period 1992-2002. The minimum and maximum values of their CCR-efficiency
scores are shown in the second and third columns of Table 4 respectively. The average CAGR of
Group-III companies is 9.84%, which re-instates the lower efficiency scores. There were in total
15 companies in the Formulations business and 9 companies in Bulk & Formulations business,
highlighting the scale inefficiencies involved in the Formulations business as against Bulk &
Formulation business. One can attribute this result to the possibility that companies involved in
both Bulk & Formulation business in general produce at least some of the raw materials required
for formulations, and therefore can be more efficient. This may also be one of the reason for the
high percentage of MNCs in the least efficient group, as shown in Table 4, as they are mostly
involved in only the Formulation business.
As one can see from Table 4, there are Indian branches of some of the global majors like Glaxo
Smithkline Pharmaceuticals Ltd, Aventis Pharma Ltd, Novartis India Ltd and Abbot India Ltd
present in the least efficient group. Most of these companies have reduced introduction of new
products in Indian market, as within a short period after introduction of new products, indigenous
companies come up with reverse-engineered products at much lower prices. After spending
millions of dollars on R&D of these products, the MNCs can not realize the costs by competing
with the indigenous companies at such low prices. Thus MNCs usually introduce new products
in Indian market, if there are no substitutes, and/or there is sufficient market and there is no
immediate competition and so on.
19

20. Table 4. Least Efficient Companies
Company LLimit ULimit BCC CAGR 1992-Sales 2002-Sales
Abbott India Ltd. 0.6791 0.95 0 12.31 102.06 365.89
Albert David Ltd. 0.6007 0.89 0 9.98 33.74 96.03
Alpha Drug India Ltd. 0.5443 0.93 0 2.84 12.83 17.46
Amrutanjan Ltd. 0.6652 0.99 0 10.72 18.84 57.77
Anglo-French Drugs & Inds. Ltd. 0.7001 0.92 0 15.01 12.23 56.96
Astrazeneca Pharma India Ltd. 0.6287 0.98 1 10.86 26.29 81.73
Aventis Pharma Ltd. 0.6806 0.92 2 7.32 258.86 562.81
East India Pharmaceutical Works Ltd. 0.6378 0.89 0 4.88 39.56 66.79
F D C Ltd. 0.6678 0.98 0 11.94 51.86 179.3
Fulford (India) Ltd. 0.6925 0.85 0 8.83 48.43 122.84
Geoffrey Manners & Co. Ltd. [Merged] 0.7303 0.92 1 5.54 82.66 149.56
German Remedies Ltd. 0.7045 0.99 0 10.86 62.56 194.53
Glaxosmithkline Pharmaceuticals Ltd. 0.6806 0.86 5 8.71 432.58 1084.44
Ipca Laboratories Ltd. 0.6997 0.99 3 14.15 96.34 412.99
Makers Laboratories Ltd. 0.619 0.85 0 16.16 5.94 30.85
Merck Ltd. 0.7053 0.9 0 12.20 95.31 338.18
Novartis India Ltd. 0.7092 0.87 2 3.10 326.85 457.21
Parke-Davis (India) Ltd. [Merged] 0.7447 0.98 6 7.24 93.66 202.01
Pharmacia Healthcare Ltd. 0.6765 0.85 0 8.54 33.66 82.93
Span Diagnostics Ltd. 0.7667 0.96 0 14.39 5.97 26.19
T T K Healthcare Ltd. 0.5592 0.86 0 9.01 46.26 119.46
Unichem Laboratories Ltd. 0.6351 0.9 0 12.30 75.2 269.49
Wyeth Ltd. 0.7119 0.98 1 9.42 99.63 268.16
Efficiency ratings of different categories of the sample
Graph 3. % of Indigenous companies versus MNCs in Groups - I, II &
III
Efficient companies in %
30
25
20
15
10
5
0
Most Efficient Medium Efficient Least Efficient
Indigenous MNC
The graphs describe how the pharmaceutical companies of the sample, under different categories
(refer to Table 1 for composition of the sample) have fared with respect to the efficiency scores.
Graph 3 above describes the % wise comparison of indigenous firms with their multinational
counterparts in all the three groups.
20

21. Graph 4 describes the % wise comparison of companies in Bulk and Formulation business with
the companies that are only in the Formulation business in all the three groups.
Graph 4. % of Bulk & Form ulation com panies Versus only Form ulation
Com panies in Groups I, II & III
Efficient companies
40
30
in %
20
10
0
Most Efficient Medium Least Efficient
Efficient
Bulk & Formulations Only Formulations
And finally, graph 5 describes the % of big versus small companies.
Graph 5. % of Big versus Sm all com panies in Groups I, II & III
Efficient companies in %
50
40
30
20
10
0
Most Efficient Medium Least Efficient
Efficient
Big (turnover >=300 Crores) Small (turnover < 300 Crores)
Table 5 gives composition of various categories in the three efficient groups in terms of figures
and percentages.
Table 5. %s of Indian vs MNC; Bulk&Formulations Vs Only Formulations; Big Vs Small Companies in each
efficiency Group
Group I % Group II % Group III % Total
Indian 7 15.9091 11 25 11 25 29
MNC 1 2.27273 2 4.54545 12 27.2727 15
Bulk & Formulations 6 13.6364 6 13.6364 9 20.4545 21*
Formulations 1 2.27273 7 15.9091 14 31.8182 22
Big** 3 6.81818 6 13.6364 6 13.6364 15
Small*** 5 11.3636 7 15.9091 17 38.6364 29
* One company does Business other than Bulk and Formulations
** Big is defined as companies with turnover > 300 Crores in the year 2002
*** Small is defined as companies with turnover < 300 Crores in the year 2002
21

22. Conclusions
The study of Indian Pharmaceutical Industry, using DEA, to ascertain the role of internal
efficiencies in the growth of an individual firm given the opportunities and threats of
globalization in case of a developing economy provided some very important insights. First and
foremost is the evidence that there appears to be a direct relationship between internal
efficiencies and higher growth rates except in the case of a few companies which being in the
mode of expansion have not been able to achieve full efficiencies (Cipla, Nicholas Piramal and
Wockhardt). This result is also found to be independent of the size of the firm in the sample. On
the whole, it can be concluded that irrespective of the growth strategies adopted by the individual
firms internal efficiencies did play an important role in the survival and growth of these firms
over the last one decade. This result is very important as management does tend to neglect or
reduce their focus on internal efficiencies in an environment which provides them with what they
perceive as a high growth, high return opportunity set. This reduction in focus on the internal
efficiencies of the firm in pursuit of new opportunities does work in the short run as the initial
period of any such change is characterized by high margins. As the industry tends to mature and
competition heightens, margins tend to decline. This combined with any unforeseen industry
shocks makes the survival of the individual firm very uncertain. We conclude and our results
also corroborate the view that given such circumstances, firms which tend to focus on internal
efficiencies will have a higher probability of survival and growth. This leads us to anticipate that
focus on these efficiencies would help firms in the IPI to overcome any new challenges arising
out of the change in the patent process from the year 2005.
22

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1
Indian pharmaceutical market was valued at around Rs. 231 billion in 2001. The domestic market was valued at Rs. 154 billion, representing
1.6% of the global market in the financial year 2001 –2002, and is growing at an annual rate of 8 to 9%.
2
Currently IPI consists of around 280 players (Sales > 10 Million) who constitute the organized sector with another 6,000 players present in the
small-scale sector. These indigenous manufacturers produce about 1300 bulk drugs and drug intermediates.
3
Currently MNC’s share is reduced to one-third of the market with only 17 out of the top 50 firms belonging to them as against the 80% market
share enjoyed by them in 1971 with 38 of the top 50 firms under their control.
4
This trend is clearly visible from the fact that during 1991-2001, the production of bulk drugs increased at a compounded annual growth rate
(CAGR) of 20%, and the formulations, at a CAGR of 17% (ICRA 2002).
5
The objective of his study was to see how efficiently a firm can make use of debt and equity to provide better earnings to the share holders. Thus, he
chose average debt and average equity as two inputs and earnings available to shareholders, interest payments and tax payments as three outputs for
the DEA efficiency calculations.
23