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  • 1. The Indian Pharmaceutical Industry – An Overview on Cost Efficiency using DEA. Haritha Saranga1 & B.V.Phani2 AbstractThe Trade Related Intellectual Property Rights System (TRIPS) agreement is part of an effort of the internationalcommunity to move towards a global economy. India’s assent to comply with this is a part of its effort towardsincreased globalization of the domestic economy. The Indian Pharmaceutical Industry (IPI) is one of the fewindustries which will be affected in a major way due to this as the existing “Process Patent” regime would give wayto the “Product Patent” regime from the year 2005. This combined with the changes in the industry due to India’sefforts over the past one decade to move towards a market economy created a dynamic environment for the firms inthe 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 itwould be interesting to examine whether there are any common firm level factors which aid in the survival andgrowth of a firm. This assumes importance due to the fact that with so many players it is almost impossible for anysingle firm to control the factors which affect the industry as a whole. This is particularly true when the changes aredriven due to the process of globalization and not due to any policy changes of individual governments. With thisobjective, we have used Data Envelopment Analysis (DEA) on a sample of 44 listed companies that have survivedthe past one-decade, to determine the best practices if any in the Indian Pharmaceutical Industry. The results of DEAhave been analyzed along with their Compounded Annual Growth Rate (CAGR) to see if internal efficiencies andgrowth rate are related in the Indian Pharmaceutical Industry. We have also used regression analysis to see thecorrelations between various inputs/outputs and the growth rates. Various models of DEA like Constant Returns toScale (CCR), Variable Returns to Scale (BCC) and Assurance Region (AR) are used to substantiate the resultsobtained.Keywords: Globalization, Data Envelopment Analysis (DEA), Finance, strategy, efficiency, performance1 Haritha Saranga is formerly Assistant Professor at Indian Institute of Management Calcutta2 B.V.Phani is an Assistant Professor at the Indian Institute of Technology Kanpur. 1
  • 2. IntroductionThe pharmaceutical industry in India is going through a major shift in its business model in thelast 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 inplace since 1972 by the Government of India. This was done deliberately to promote andencourage the domestic health care industry in producing cheap and affordable drugs. As prior tothis 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 oftablets 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 1972allowing Indian firms to reverse engineer the patented drugs and produce them using a differentprocess that was not under patent. The entry of MNC’s was also discouraged by restrictingforeign equity to 40%. The licensing policy was also biased towards indigenous firms and firmswith lesser foreign equity1. All these measures by GOI laid foundations to a strongmanufacturing base for bulk drugs and formulations and accelerated the growth in the IndianPharmaceutical Industry (IPI), which today consists of more than 20,000 players1. As a result theIndian pharmaceutical industry today not only meets the domestic requirement but has startedexporting 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 researchaimed at developing and patenting new drugs. The bulk drug business is essentially a commoditybusiness, where as the formulation business is primarily a market driven and brand orientedbusiness. 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 andDomestic) meets about 90% of the country’s bulk drug requirement and almost the entire demandfor formulations2. The economics of bulk drug business and that of formulation business are quitedifferent. Since a majority of the Indian companies are producing both bulk as well asformulations, these are considered together for the purpose of the present study.The Changing EnvironmentDuring the early 1990s, markets were opened by removing restrictions on imports and in 1994licensing was abolished for producing bulk drugs and formulations. Other than this FDI restrictionsinto this sector have been modified to allow 74% foreign equity through the automatic route. Morefavorable 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 themainstay of the Indian pharmaceutical industry2 over the past couple of decades finally becoming aformidable part of Indian economy and a major source of foreign income might be facing uncertainmarket conditions in the future. It may also come down to a state where most of the small scalecompanies have to close down, with the multinational companies dominating and monopolizing3the industry once again.There is a justified reason for this, and that is, so far Indian companies have made use of the cheaplabor and the reverse engineering skills under the favorable conditions of process patent regimeand developed generic replicas to drugs that were under patent in developed countries, which thenwere sold in the domestic markets and exported to other unregulated markets elsewhere in theworld. This generic business enabled them to compete with multinational companies in India andabroad and resulted in good revenues. However, once the product patent regime gets implementedfrom 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, whichhave an impressive new product portfolio will get exclusive marketing rights to sell their productsat 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 whosepatent has lapsed and export the same to regulated markets4. This is possible only if these firms areable to formulate these products at much lower prices allowing then to face competition fromestablished players in the international markets. Other than this, avenues like contract research andmanufacturing for multinational companies have become popular business models for many smallscale and medium scale firms. Given this situation it is highly likely that individual firms adoptdifferent strategies for growth. These strategies are dependent more on the management’sperception of the individual firm’s strength in terms of finance, manpower and material in relationwith the other firms within the industry for a given environmental context. Some of these strategiesmay end in failure due to unexpected changes in the environment or bad judgment on the part ofthe management. The main question for which we try to provide an answer is ‘Do internalefficiencies have any role to play in the growth of a firm irrespective of the individual growthstrategies adopted in a dynamic environmental context’.The above question becomes very important for firms which operate in a transition economy. Thisis particularly true if the transition is aimed towards being a part of the global economy. Thiswould create an environment where firms are faced with a completely new opportunity set in termsof investment and growth. These opportunities encourage firms to adopt high growth strategies atthe cost of immediate returns. The success or failure of any such strategies is dependent on thenature of competition faced by these firms over time. Therefore it would be very reasonable toassume that a firm’s internal efficiencies may become the crucial deciding factor in dictating thesurvival and growth of these firms in various segments of pharmaceutical industry. We concentrate 4
  • 5. on the role of internal efficiencies in the growth of these firms independent of the individualmarketing strategies and long term visions adopted at the firm level.The following paragraphs try to analyze the role of internal efficiencies in fostering growth usingDEA. Three models of DEA have been used namely the CCR, BCC and AR models not only toascertain the relevance of the parameters used for fostering growth but also to throw light on theefficiency of these models in isolating the better firms irrespective of the individual growthstrategies used.Cost Structure/Performance indicators of Indian pharmaceutical industryThe pharmaceutical industry is characterized by low fixed asset intensity and high working capitalintensity (ICRA 2002). The Material cost, Marketing and selling cost and Manpower Costconstitute the three major cost elements for the Indian pharmaceutical industry, accounting forclose to 70% of the operating income. In the past 6-7 years, material costs, which account foralmost 50% of the operating cost have declined owing to the decrease in prices of bulk drugs andintermediates, increase in exports which enabled procurement of raw materials in large quantitiesand 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 fewyears owing to the increase in emphasis on sales of formulations. This increased focus onmarketing partly lead to the increase in the manpower costs of pharmaceutical companies duringthe last decade. The other factor for the increase in the manpower costs, at least in case of a fewcompanies might be due to an increase in R&D efforts, which requires quality research personnel.Data Envelopment Analysis as a measure of efficiencyEfficiency of a firm can be defined as the maximization of a set of outputs (Output-oriented) givena set of inputs or minimization of a set of inputs (Input-oriented) for a given output. Most DEA 5
  • 6. applications in the literature are Input-oriented and this is attributed to a general lack of suitablemultiple-output datasets. Traditional industry reports (e.g., ICRA2) on the trends in costs, marginsand returns generated by IPI analyze the industry with the help of various performance indicatorslike operating profit margins, net profit margins, fixed asset turnover, working capital intensity andinventory holding period etc. However, parameters like margins, returns and debt ratios can onlydescribe various performance characteristics in isolation as only one input and one output can betaken at a time. Comparison of these parameters in isolation across firms for a given industry mightprovide a biased picture of a firm’s efficiency vis-à-vis other firms in the same industry. Thisproblem with this kind of analysis can be overcome by defining or developing a performanceindicator using the various parameters with suitable weights to come up with a composite indexcomparable across firms. This strategy would also limit the interpretation of the results due to thestatic nature of the weights so assigned.Data Envelopment Analysis (DEA), one of the more recent and a highly popular tool amongresearchers overcomes this problem by simultaneously analyzing multiple inputs and outputs tocome up with a single scalar value as a measure of efficiency. DEA has been used to successfullymeasure relative efficiencies of DMUs in various public and private sector industries like banks,computer industry, health care sector, pharmacies, car manufacturing industry, fisheries and searchengines on the internet etc since its development in 1978 (Charnes et al. 1978). In the Indiancontext Saha et al3 used DEA to measure the relative efficiencies of Indian banks, in a changingenvironment 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 companieswas by Smith4, who used financial statements of 47 firms producing pharmaceutical products toshow the advantages of DEA to the traditional ratio analysis in describing the multivariate nature 6
  • 7. of firms5. The objective of DEA application in the current study is to see if there are any bestpractices developed in IPI that are not influenced by the external environment.The Methodology of Data Envelopment AnalysisData envelopment analysis offers several characteristics that are quite unique and useful incomparison to traditional financial analysis methods like ratio analysis or regression analysis.Although all these techniques have their own advantages and disadvantages, one of the mostimportant feature of DEA is the ability to compare many parameters simultaneously and come upwith a scalar measure of overall performance. DEA provides the relative efficiency of each of thefirms (which usually are called Decision Making Units (DMUs)) in a given set of firms. TheseDMUs are assumed to be in the business of producing various outputs by consuming a set ofinputs. 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 theircontribution is to the effective performance of the DMU, in order not to dilute the efficiencyanalysis with too many parameters. The selection of inputs and outputs is of paramountimportance in any DEA calculations as the results of the study can vary with different sets ofinputs and outputs. These vary from industry to industry, and even within an industry dependingon the objective of the efficiency analysis being carried out. It always helps to begin with 2-3inputs (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 doesnot have to be the same, as long as same set of inputs and outputs are used for all DMUs, and themeasure of efficiency becomes “units invariant”5. This gives a tremendous flexibility in choosingthe 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 alinear programming (LP) problem where the solution provides a numerical description of apiecewise linear production frontier.Since the formal introduction of DEA, the basic concepts and principles have developed intofour 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 theoreticallythat the results in the form of efficiency or inefficiency are robust, even though different modelsare 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 isbeing evaluated.vi → ith weight corresponding to input xi, o where i = 1,…,m and o = 1…n is the DMU that isbeing evaluated.In the above notation, we are assuming n DMUs, with m inputs and s outputs. The CCR model ofDEA 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) 8
  • 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 optimisationsto measure the efficiency of each DMU. DMU O is CCR-efficient if θ * = 1 and there exists atleast one optimal solution (v * , u * ) with v * > 0 and u * > 0 , where ( θ * , v * , u * ) is the optimalsolution 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 ofefficient DMUs, also called peer group. The peer DMUs are the efficient units closest to it andare observed to produce the same or higher level of outputs with the same or less inputs inrelation to the inefficient DMU being compared. This enables the inefficient DMUs to know ifthere 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 aDMU 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 theDMUs of the sample, is compared with each DMU under evaluation, to calculate the efficienciesas follows:Where λ j are the multipliers corresponding to each of the DMUs in the linear combination ofthe virtual DMU, and therefore the weights of inputs and outputs of the virtual DMU. Each 9
  • 10. DMU is compared with the virtual DMU to see if it can produce equal or more output than thevirtual DMU with the same or lesser input. If it can, then that particular DMU is efficient andforms a part of efficient frontier with θ = 1, λo = 1 and λ j = 0, ∀j ≠ 0 . If not, it is inefficient andthe degree of inefficiency depends on the efficient companies on the frontier.Banker, Charnes and Cooper8 (BCC) developed a DEA-model that calculates “pure” technicalefficiency, 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 , givenbelow by equation (10), which restricts the feasible region to a convex hull and at the same timeensuring 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, whichcomprises of both Scale Efficiency and “pure” Technical Efficiency. In a case where a DMU isfound to be inefficient, one can decompose this total inefficiency to see in what degree this is dueto scale inefficiency or technical inefficiency.At this point, one should note that the resulting weights assigned by the DEA, in CCR and BCCmodels are not necessarily the correct weights as management or the analyst might assign sincethe 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 weightingpossible whether or not the weightings represent the balance of outputs and inputs desired bymanagement or an analyst. For example, a DMU producing a high level of operating income andlittle operating cash flow may not be considered by an analyst to be as healthy as a DMU with amore balanced production of financial outputs. However, it is possible for this less-healthy DMU 10
  • 11. to receive a higher DEA score. To avoid a situation, where unfair amounts of weights are beingassigned 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 IndustryIn the present study we have considered a sample of 44 pharmaceutical companies, whose data isavailable throughout the period 1992 - 2002. The main reason for choosing this sample is the factthat we have continuous availability of data for a common sample, which enables measurementof various performance characteristics of those pharmaceutical companies that have survived atleast 11 years or more. A point to note here is that the selection of such a sample in itself gives aset of companies that have successfully survived at least the last 11 years, and includes most ofthe market leaders on the top and the companies that are struggling to make ends meet in thebottom. Thus we hope that the sample is representative enough to include all kinds of firms witha history of 11 years or more, except the ones, which have started after 1992, and the ones thathave 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 differentcriterion, first in terms of origin: indigenous versus multi nationals, secondly in terms ofbusiness: 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 efficiencyratings.The DEA analysis on this sample would give relative efficiencies of these 44 firms with respectto 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 inthe larger population, that are not included in this sample, and whose inclusion mightreduce/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 theemerging trends in Indian pharmaceutical industry.Inputs and outputs for the Data Envelopment AnalysisThe choice of the inputs and outputs is very crucial for the relative efficiencies to be useful inarriving at meaningful conclusions. For any given firm in an industry, performance or efficiencyis purely relative. There can be no predefined efficiency indicators given the general constraintthat the sum total of output should always be greater than the sum total of input. Given thisrelative efficiency depends on the firm’s capability or to be precise the management’s capabilityin utilizing the given resources better than the competition. This will provide these firms withsurplus output or slack, which can be used to face market uncertainty and take advantage of anynew opportunities thus enhancing the growth of the firm. This is also true in case of Indianpharmaceutical industry, which is faced with a major period of uncertainty and an unprecedentedopportunity for growth. Most of the parameters fostering growth are external in nature likedemand in external markets etc. The one factor which is internal and under the direct control ofthe management are the costs expended for a given output. The major cost elements, whichcontribute towards 70% of the operating income2 of a pharmaceutical firm in India are chosen asinputs for the application of DEA in the current paper, as follows: (i) Cost of Production and 12
  • 13. selling (ii) Cost of Material and (iii) Cost of Manpower. The outputs are (i) Profit margin (ii) NetSales 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 theperformance of the firm. Even though exports are part of net sales, it is taken separately as thethird output, as a representative of a firm’s export business, which is going to play a very crucialrole in a firm’s ability to survive and grow in a post product patent regime.Results of the DEA AnalysisWe used both CCR and BCC models in order to find scale efficiency and pure technicalefficiencies of the 44 companies in our sample. We also used Assurance Region model withrestrictions on weights of the inputs according to the ratio 7:5:1 respectively, which are derivedfrom the past trend in the cost structure of these inputs in the Indian pharmaceutical industry, asdiscussed 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 efficiencyrankings and finally group-III consisting of the least efficient companies. Table 2 gives the top 8companies in terms of CCR efficiency ratings from the sample. Columns 2 and 3 and 4 give thenumber of times each company in column 1 has come as efficient, using CCR, BCC andAssurance Region (AR) models during the Financial Years (FY) 1992-2002. Finally column 5gives the Compounded Annual Growth Rate of these companies during the period 1992-2002and the last two columns give the net sales of these companies in the years 1992 and 2002respectively. 13
  • 14. Most Efficient Companies – Group ITable 2. The Efficient Companies -Scores of CCR, BCC & Assurance RegionCompany CCR BCC AR-Total CAGR 1992-Net sales 2002-Net salesMorepen Laboratories Ltd. 10 11 0 39.93227 12.47 502.3Aarti Drugs Ltd. 10 11 0 26.52159 12.05 160.25Bharti Healthcare Ltd. 9 10 6 19.01344 3.49 23.68Neuland Laboratories Ltd. 9 9 6 30.04413 5.28 94.98Organon (India) Ltd. 9 9 1 12.09056 47.33 166.11Dr. ReddyS Laboratories Ltd. 8 10 8 28.85608 100.13 1628.24Gujarat Themis Biosyn Ltd. 8 9 0 34.46274 0.95 24.68Ranbaxy Laboratories Ltd. 5 11 3 17.84943 372.42 2267.96Group-I companies, as is evident from Table 2 is an interesting mix of 5 small and 3 bigcompanies. However, out of 8 companies, there are 7 Indian companies and 1 MNC; and 6companies are in the business of Bulk & Formulations. Another interesting observation is thatthe Compounded Annual Growth Rate (CAGR) of these companies is quite high with MorepenLaboratories Ltd (MLL) which is BCC-efficient throughout the 11-year period and CCR efficientin 10 out of 11 years, topping the CAGR score. The average CAGR of Group-I companies is26.1%, 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 8companied 4 companies MRR, Aarti, Organon and Gujarat Themis are both CCR and BCCefficient but fail to score in the AR-model. In the remaining four, 3 companies Bharti, Neulandand Dr. Reddy’s are found to be efficient in all the three models. Ranbaxy was found to behighly 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 dependenton the growth strategy adopted by these firms. These growth strategies also define the nature andrelevance of the various internal factors used in the analysis. It is evident that these firms havevery high growth rates. The four firms which have not been found to be AR efficient have onething in common in spite of vast differences in the size of the firms. All the four firms are bulkdrug 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 sellingexpenses of these firms have precluded them from scoring in the AR-model. Since the AR-modelpredefines 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 productsrequire more marketing and selling costs. Whereas in case of Ranbaxy which was found to beonly BCC efficient it is interesting to note that inspite of the high growth figures the growth isdriven by low margins. This is possible as Ranbaxy is focusing on increasing its market presenceglobally using pricing as its main strategy which is reflected in its reducing margins.Best Practices of Efficient Companies in Group IAs 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 highestfor the DMU under evaluation. Thus, an in-depth analysis of the weights can reveal thoseresources that were more efficiently utilized by an efficient DMU, and hence resulted in a fullefficiency score. A close look at the weights of CCR-scores, for Group-I companies shows thatall the 7 indigenous companies have got maximum weighting to Cost of Manpower, consistentlyfor all the years (total of 74 instances), except in 3 instances. Ranbaxy got more weighting toCost of Material in the year 1999, whereas, DRL got more weighting to Cost of Material in theyear 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 11instances), except for 1995 & 2002, where Cost of Manpower got more weighting. Thus, it isclear that the best practice for the indigenous companies is the efficient management of their lowcost Manpower, whereas those MNC’s, which are managing the Raw Material well can fare wellin 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 thebusiness of Formulations, could not make it to the group of most Efficient companies.Medium Efficient Companies – Group II Table 3. Medium Efficient Companies – Group IICompany CCR BCC AR CAGR 1992-Sales 2002-SalesCipla Ltd. 4 7 1 22.366406 139.51 1284.96Torrent Pharmaceuticals Ltd. 4 9 2 17.263818 65.21 375.94Pfizer Ltd. 4 6 2 10.175804 116.9 339.44Duphar-Interfran Ltd. 4 5 3 -8.263622 44.6 17.27Nicholas Piramal India Ltd. 2 5 1 25.870393 64.24 807.17Wockhardt Ltd. 2 4 2 23.780281 58.4 610.35Armour Polymers Ltd. 2 4 1 10.717512 4.31 11.93Zandu Pharmaceutical Works Ltd. 2 2 1 10.523856 33.93 102Burroughs Wellcome (India) Ltd. 2 2 2 3.6205268 100.38 148.44Elder Health Care Ltd. 1 2 0 23.892135 1.26 13.3J B Chemicals & Pharmaceuticals Ltd. 1 2 0 14.284366 60.73 263.79Dental Products Of India Ltd. 1 10 0 3.3994571 3.42 4.94Lupin Ltd. 1 5 0 11.95 250.53 866.87Out of the 12 companies in Table 3 that are on the CCR-efficient frontier at least in one year, 2are MNCs and the rest are indigenous companies. Exactly 50% of companies in Group II are inthe business of only formulations, and the other 50%, in the business of Bulk drug andformulations, with both MNCs dealing with only formulations. There are 5 companies that havebeen BCC-efficient for 5 or more years, and 9 companies that have come up as efficient with theAssurance Region (AR) model, at least in one year. A point to note here is that the averageCAGR 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 someextent, 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 efficiencyjourney, a year early, from 1997 till 2000, and although dipped a bit in 2001-2002, onlymarginally to .95 and .96 respectively. One can see from the graph that the initial period from 16
  • 17. 1992-1994 was not very good for Cipla, and the CCR-efficiency ratings increased steadily from1995 onwards. This consistency is reflected in the BCC-efficiency ratings of 0.96 in year 1995and 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-interfranOn 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 itsefficiency scores. Pfizer has been an outsourcing hub to its global major Pfizer Inc. of the USand also conducts clinical development of new molecules with an R&D base, and has notlaunched many new drugs due to its parent’s policy on patented drug introduction in the Indianmarket. Torrent Pharmaceutical Limited (TPL), an indigenous company, has been BCC- efficientduring 1992-1995 and 1998-2000, dipping only slightly in between; and scale efficient in 1992and during 1998 – 2000, which shows its consistency in being efficient in general, which stayedbetween 0.86 and 1. TPL’s CAGR at 17.26 during 1992-2002 can be attributed to its presence inhigh growth therapeutic segments and introduction of new products in high growth segments likecentral nervous system, gastro intestinal and new molecules in antibiotics. Its alliance withNovo-Nordisk (India) Limited, to which TPL supplies insulin formulations, ensures a steadymarket 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 tooffer contract research facilities to international as well as domestic players. Duphar-Interfran onthe other hand is an interesting example for a small company, which has stayed efficient evenwith a negative growth that has resulted due to down sizing.Nicholas Piramal India Ltd. and Wockhardt, which rank 5th and 7th in turnover according toFY2002’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 23.78respectively, which questions the relationship between growth and efficiency scores. NicholasPiramal India Limited (NPIL), which is in the business of formulations, has been busy expandingand 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 thebusinesses 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 notonly 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 realizedfor many years to come.Least Efficient Companies – Group – IIIThere 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-efficiencyscores are shown in the second and third columns of Table 4 respectively. The average CAGR ofGroup-III companies is 9.84%, which re-instates the lower efficiency scores. There were in total15 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 inboth Bulk & Formulation business in general produce at least some of the raw materials requiredfor formulations, and therefore can be more efficient. This may also be one of the reason for thehigh percentage of MNCs in the least efficient group, as shown in Table 4, as they are mostlyinvolved in only the Formulation business.As one can see from Table 4, there are Indian branches of some of the global majors like GlaxoSmithkline Pharmaceuticals Ltd, Aventis Pharma Ltd, Novartis India Ltd and Abbot India Ltdpresent in the least efficient group. Most of these companies have reduced introduction of newproducts in Indian market, as within a short period after introduction of new products, indigenouscompanies come up with reverse-engineered products at much lower prices. After spendingmillions of dollars on R&D of these products, the MNCs can not realize the costs by competingwith the indigenous companies at such low prices. Thus MNCs usually introduce new productsin Indian market, if there are no substitutes, and/or there is sufficient market and there is noimmediate 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 99.63 268.16Efficiency 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 MNCThe 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 multinationalcounterparts in all the three groups. 20
  • 21. Graph 4 describes the % wise comparison of companies in Bulk and Formulation business withthe 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 FormulationsAnd 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 figuresand 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. ConclusionsThe study of Indian Pharmaceutical Industry, using DEA, to ascertain the role of internalefficiencies in the growth of an individual firm given the opportunities and threats ofglobalization in case of a developing economy provided some very important insights. First andforemost is the evidence that there appears to be a direct relationship between internalefficiencies and higher growth rates except in the case of a few companies which being in themode of expansion have not been able to achieve full efficiencies (Cipla, Nicholas Piramal andWockhardt). This result is also found to be independent of the size of the firm in the sample. Onthe whole, it can be concluded that irrespective of the growth strategies adopted by the individualfirms internal efficiencies did play an important role in the survival and growth of these firmsover the last one decade. This result is very important as management does tend to neglect orreduce their focus on internal efficiencies in an environment which provides them with what theyperceive as a high growth, high return opportunity set. This reduction in focus on the internalefficiencies of the firm in pursuit of new opportunities does work in the short run as the initialperiod of any such change is characterized by high margins. As the industry tends to mature andcompetition heightens, margins tend to decline. This combined with any unforeseen industryshocks makes the survival of the individual firm very uncertain. We conclude and our resultsalso corroborate the view that given such circumstances, firms which tend to focus on internalefficiencies will have a higher probability of survival and growth. This leads us to anticipate thatfocus on these efficiencies would help firms in the IPI to overcome any new challenges arisingout of the change in the patent process from the year 2005. 22
  • 23. References1. Chaudhuri S (1999). Growth and Structural Changes in the Pharmaceutical Industry in India in Sen Anindya, Gokarn Subir and Vaidya Rajendra (eds), The Structure of Indian Industry, Oxford University Press, New Delhi.2. “The Indian Pharmaceutical Industry” ICRA Industry watch series, ICRA Limited, 2002.3. Saha A and Ravisankar TS (2000). Rating of Indian Commercial Banks: A DEA approach. Euro J of Op Res 124: 187-203.4. Smith P (1990). Data Envelopment Analysis Applied to Financial Statements. OMEGA Int. J. of Mgmt Sci 18:131-138.5. Cooper W.W. et al (2000). Data Envelopment Analysis. Kluwer Academic Publishers 21-39.6. Charnes A W et al (1994). Data envelopment analysis: Theory, methodology, and application. Dordrecht; Boston and London, Kluwer Academic.7. Ahn, T A et al (1988). Efficiency characterizations in different DEA models Socio-Economic Planning Sciences 22(6), 253-257.8. Banker R D et al (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Mgmt Sc. 30:1078- 1092.9. Thompson R G et al (1986) Comparative Site Evaluations for Locating a High-Energy Physics Lab in Texas. Interfaces 16: 35-49.10. Roland B E and Vassdal T. Estimation of Technical Efficiency by using DEA, with relevance to fisheries By Norwegian College of Fishery Science, University of Tromsø, N-9037 Tromso, Norway11. How WTO/TRIPS threatens the Indian pharmaceutical industry by Richard Gerster. a. http://www.twnside.org.sg/title/twr120h.htm (last accessed on 14th October 2003)12. Sectoral Reports Pharmaceutical Industry – Update by Ajit Ranade, Chief Economist, Sanchita Basu Das, Assistant Economist, India a. http://www.abnamroindia.com/Research/pdf/pharma-apr0103.pdf (last accessed on 14th October 2003)13. Pharma business – a changing scenario by Dr Cedric Nazareth http://members.tripod.com/pharmapage/nazareth30.htm(last accessed on 14th October 2003)14. Are midcap gains justified? By Equitymaster.com a. http://in.biz.yahoo.com/030911/21/27pnl.html (last accessed on 14th October 2003)15. India 2003-2004 Reliable Business Partner Attractive FDI Destination – Pharmaceuticals, Published by Investment & Technology Promotion Division, Ministry of External Affairs, Government of India. a. http://meaindia.nic.in/indiapublication/Pharmaceuticals.htm (last accessed on 14th October 2003)16. A few good men by Indian Express News Paper dated October 22, 1999. http://www.financialexpress.com/fe/daily/19991022/ffe19090.html(last accessed on 14th October 2003)17. CRAMS… The Untold Story by Abhimanu Verma, India Infoline.com http://www.indiainfoline.com/nevi/crea.html (last accessed on 14th October 2003)18. Pharma stocks: Exercise caution by Equitymaster.com http://in.biz.yahoo.com/030918/21/27uza.html (last accessed on 14th October 2003)19. William F. Bowlin, “An analysis of the Ænancial performance of defense business segments using data envelopment analysis” Journal of Accounting and Public Policy 18, pp. 287-310 (1999).20. Singh, G and Surendar T, “Small & Smart: Pharma SMEs’ plan for 2005 and beyond”, Business World, ABP Private Ltd., Vol 23, Issue21, October 2003.1 Indian pharmaceutical market was valued at around Rs. 231 billion in 2001. The domestic market was valued at Rs. 154 billion, representing1.6% 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 thesmall-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% marketshare 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, hechose average debt and average equity as two inputs and earnings available to shareholders, interest payments and tax payments as three outputs forthe DEA efficiency calculations. 23