A STUDY ON RECEIVABLES MANAGEMENT OF INDIAN PHARMACEUTICAL INDUSTRY AND ITS IMPACT ON PROFITABILITY

A STUDY ON RECEIVABLES MANAGEMENT OF INDIAN PHARMACEUTICAL INDUSTRY AND ITS IMPACT ON PROFITABILITY Sunil Kumar 24 Ritesh Srivastava 25 Dr. Praveen Srivastava 26 ABSTRACT The creation of firms value is been positively expected by a well-designed and implemented receivables management. The purpose of this paper is to examine the trends in receivables management and its impact on firms performance and to examine significant difference between the profitability of firms and industries. Here we consider the return on total assets as dependent variable, which is used as a measure of profitability and relation between receivables management and their profitability. For this purpose here, we have taken a sample of ten Indian Pharmaceuticals firms (NSE listed) and the period of 2000-2014. In this study, we used panel data regression for the investigation of the results. The panel data regression shows that the highly involvement in receivables are associated with highly profitability. In previous empirical work, a strong significant relationship between receivables management and profitability has been found. In this way the final analysis of the operational efficiency, liquidity and profitability of the Indian Pharmaceuticals firms have shown a significant changes and it also shows how best practices are prevailing in the Cipla Ltd. and others, which are contributing to performance. KEYWORDS Receivables, Profitability, Liquidity & Operational Efficiency etc. INTRODUCTION Theoretical Understanding of Inventory Firms usually sell their products on credit, rather than requiring immediate payment. Such a transaction generates a commercial credit (usually short term) for the seller and a commercial debt (usually short term) for the client. The general name given to commercial credit is trade credit. Likewise, the commercial credit provided is often referred to as trade receivables, whereas the commercial credit received is often referred to as trade payables. Trade receivables represent a large portion of firm assets worldwide. As an example, victimisation 1986 Compustat using, Mian and Smith (1992) report that trade receivables account for 21st assets. a lot of recently, Molina and Preve (2009) use a sample from Compustat that covers the 1978 2000 period and realize that, on average, the ratio of trade receivables to assets is 18, that corresponds to fifty five days of sales finance. Note that these studies specialise in massive firms. Petersen and Rajan (1997), in contrast, use a dataset from the 1987 National Survey of tiny Business Finance and report that whereas massive companies show a trade receivables to sales ratio of regarding 18.5%, the same figure for tiny companies is lower, at 7.3%. Thus, according to Petersen and Rajan, tiny companies offer less credit to their customers than do massive companies, however even tiny companies offer their customers some credit. Such a large quantity of cash amount in providing client financing presents a remarkable puzzle. Why would a firm that is not within the business of disposal cash have an interest in extending finance to different firms? Furthermore, why would shoppers be willing to induce financing from these nonfinancial institutions, significantly, if banks are far-famed to have clear scale and information benefits in lending money? This puzzle has triggered a remarkable body of research that seeks to explain the existence and main patterns of commercial, or trade, credit. In early work on the subject, Meltzer (1960) finds that firms with better access to financial credit redistribute it to less favored firms via commercial credit. Work that is more recent finds evidence consistent with the redistribution explanation of trade credit. However, trade credit may also have other explanations. For instance, the use of trade credit can help firms fight for market share - a firm that seeks to grow at the expense of another firm s business may seek to increase its sales by increasing the financing it offers clients. Similarly, firms facing profitability problems may seek to increase sales or market share by increasing the provision of commercial credit to clients (Petersen and Rajan, 1997; Molina and Preve, 2009). Molina and Preve (2009) find evidence that this trend reverses, however, when firms enter financial distress (i.e., face cash flow problems), and that a decrease in client financing causes a significant drop in performance for distressed firms. This result is consistent with firms being able to invest in commercial credit only if they are financially unconstrained (again, somehow consistent with the redistribution explanation of trade credit). 24 Assistant Professor, School of Business, ITM University, Gwalior, India, skvermamc@gmail.com 25 Assistant Professor & HoD, Sherwood College of Engineering Research & Technology, Barabanki, India, sri.rit@gmail.com 26 Assistant Professor, School of Business, ITM University, Gwalior, India, praveen.srivastava.mba@itmuniversity.ac.in 2068 P a g e

Several empirical papers also address trade receivables. Among the first and more cited of these papers is Petersen and Rajan (1997), who give a comprehensive examination of the determinants of trade credit. Using data from the 1987 National Survey of tiny Business Finance, they analyze each trade receivables and trade payables, and look at the theories described antecedently. According to the information advantage explanation of trade credit, they realize proof that better and faster access to information makes companies additional competitive lenders than financial institutions, particularly once their clients are credit constrained. Mian and Smith (1992) seek to supply proof on however companies will manage the trade credit process. They divide the commercial lending process into five functions, namely, credit risk assessment, credit granting, account receivables financing, credit collection, and credit risk bearing. They show that companies will manage these numerous functions of trade receivables by (1) establishing a captive finance subsidiary, (2) issue account receivables secured debt, (3) using factoring, (4) using a credit employing firm, (5) retentive a credit collection agency, and (6) purchasing credit insurance, either internally managing or outsourcing every of those activities. In a recent study, Molina and Preve (2009) examine the impact of financial distress on the investment in trade receivables. Their paper s main finding is that companies tend to extend their investment in trade receivables after they begin having profitableness problems; however, as soon as they enter financial distress (and start having cash flow problems), they show a decrease in client funding. If we assume that companies that aren't facing financial issues have a best investment policy, then we will infer that companies in financial distress have a suboptimal policy of under investing in financing clients. Such suboptimal investment policy contains a cost that is among the various costs of financial distress. OBJECTIVES & RESEARCH HYPOTHESES The objective of this research is to distinguish the relationship between methods of receivables management and its impact on profitability of Indian Pharmaceutical companies and to examine the causes for any significant differences between the Indian pharmaceutical companies. H 0 : A Significant relationship exists between the receivables conversion period in days and return on total. RESEARCH METHODOLOGY The primary aim of this study is to investigate the impact of receivables management on corporate profitability of Indian Pharmaceutical Industry. This is achieved by developing a similar empirical framework first used by Shin and Soenen (1998) and the subsequent work of Deloof (2003). We extend our study to examine the possible causes for any significant differences between the Companies. Our study focuses exclusively on those Indian Pharmaceutical companies are listed in NSE Pharma Indices. Thus, the empirical study is based on a sample of 10 Indian Pharmaceutical Companies. The data has been collected from Prowess database and companies annual reports. The sample was drawn from the list of NSE Pharma indices. All companies data was available for a 14 years period, covering the accounting period 1999-2000 to 2012-2013. This has given a balanced panel data set of 140 firm-year observations for a sample of 10 firms. Explanatory Variables The efficiency ratios, namely accounts receivable have been computed, using the formulas as follows: Control Variables Accounts receivable Conversion period (in days) = (Accounts Receivable * 365)/Sales Control variables In order to account for firm s size and the other variables that may influence profits so we use sales a proxy for size (the natural logarithm of sales), the gearing ratio (financial debt/total assets), the gross working capital turnover ratio (sales/current assets) and the ratio of current assets to total assets are included as control variables in the regressions. ANALYSIS OF RECEIVABLES In the study, the total Receivables of Indian pharmaceutical companies are shown in the table 1. Receivables depend upon credit policy of the company. The average Receivables of Indian pharmaceutical industry is Rs 6012.69 million. In receivable table depicts that Divi'S Laboratories Ltd. holds minimum Receivables Rs. 233.9 millions in 2000 and on an average Glaxosmithkline Pharmaceuticals Ltd. holds minimum Receivables of Rs 904.6 million, whereas the maximum Dr. Reddy s Laboratories Ltd. of Rs 13227.68 million is holding Receivables. The nature of Receivables is much fluctuating in each pharmaceutical company. The largest fluctuation can be seen in Dr. Reddy'S Laboratories Ltd. from the year 2009 to 2013. In Cipla Ltd. Receivables has increased from Rs 1497.9 million in 2000 to Rs 17858.9 million in 2013. During 2000 to 2008, Receivables increased 2069 P a g e

continuously and after 2000 with fluctuation, it has decreased. Maximum Receivables held by Cipla Ltd. is Rs 18822.1 million in 2008 and minimum Rs 1497.9 million in 2000. Table 2 shows average percentage of receivables to currents assets of pharmaceutical companies, which shows the size of the receivables in current assets. The average percentage of receivables to currents assets of pharmaceutical industry is 42.68%. The highest Receivables to current assets ratio is 78.21% of Glenmark Pharmaceuticals Ltd. in the year 2013 and the annual average is also highest at 63.36%, from 2005 Glenmark Pharmaceuticals Ltd. held receivables more than 50% that is the main reason why its annual average is highest during the study period. On the other hand, Cipla Ltd. holds fifth largest receivables on an average 43.87%, it is fluctuating between 34.34% to 54.87% throughout the study period. Table-1: Receivables Management (Rs in millions) Aurobindo Pharma Ltd. 2063.8 3432.3 4137.1 4667.8 4536.6 5916.2 6551.2 8307.5 Cadila Healthcare Ltd. 540 693 1391 1668 1097 1872 2419 2860 Cipla Ltd. 1497.9 2549.7 3553.9 4984 5875 8823.9 10290.9 14047.7 Divi'S Laboratories Ltd. 233.9 524.2 568.5 868.1 1023.8 1074.8 1644.6 2148.7 Dr. Reddy'S Laboratories Ltd. 2867.1 4545.8 4507.6 4636.3 4332.9 5981.5 10768.3 9190 Glaxosmithkline Pharmaceuticals Ltd. 1341.7 1263.1 1007.9 880.8 994.2 798 762.7 488.1 Glenmark Pharmaceuticals Ltd. 471.8 763.6 1263.9 1343.8 1874.6 2901.5 4308.6 6080.2 Lupin Ltd. 2996.8 3257.4 4022.2 2158.3 2369.4 3500.9 4810 6352.6 Piramal Enterprises Ltd. 929.7 1254.2 1785.1 1904.7 1645.2 2053.7 2690 3481.2 Sun Pharmaceutical Inds. Ltd. 939.9 1077.6 1973.1 1286.1 2371.1 2599.6 5713.3 10645.5 Industry Average 1388.26 1936.09 2421.03 2439.79 2611.98 3552.21 4995.86 6360.15 Aurobindo Pharma Ltd. 11373.1 11752.7 15044 14598.5 17643.1 29849.1 9990.929 Cadila Healthcare Ltd. 3594 4126 4779 6171 6844 7229 3234.5 Cipla Ltd. 18822.1 16363.2 15812 16579.7 17206.6 17858.9 11018.96 Divi'S Laboratories Ltd. 2836.8 2346 3952.2 5366.6 5595 7918.5 2578.693 Dr. Reddy'S Laboratories Ltd. 14381 10834 17844 19637 29829 45833 13227.68 Glaxosmithkline Pharmaceuticals Ltd. 693.6 612.4 546 939.8 1265.1 1066.2 904.2571 Glenmark Pharmaceuticals Ltd. 4256.8 3477 2084.5 3778.5 5758.4 11563.6 3566.2 Lupin Ltd. 7144.1 9235.9 12412.8 14980 18847.4 28966.7 8646.75 Piramal Enterprises Ltd. 4144.6 3256.5 2354.3 2708.2 2589.5 2694 2392.207 Sun Pharmaceutical Inds. Ltd. 6863.9 5610.8 5502.4 7177.2 7507 4666.6 4566.721 Industry Average 7411 6761.45 8033.12 9193.65 11308.51 15764.56 6012.69 Table 3 shows the relationship between Receivables and current liability. This Ratio shows liquidity position of the company. Receivables to current liability Ratio shows liquidity position of the company. Overall industry average is 140.92%, mainly Aurobindo Pharma Ltd. whose average of receivable to current liability ratio is 214.97%, Glenmark Pharmaceuticals Ltd. 185.27%, Dr. Reddy'S Laboratories Ltd. 169.32%, Lupin Ltd. 163.19%, and Sun Pharmaceutical Inds. Ltd. 154.6% and Divi'S Laboratories Ltd. 151.69 are the main responsible companies for this higher ratio. This figure exhibits that company has more than one and half of receivable than current liability. Two more company Cipla Ltd. and Cadila Healthcare Ltd. have cash bala nce more than the current liability, 146.01% and 101.74% respectively. In the year 2003 the receivables to current liability ratio of Glenmark Pharmaceuticals Ltd. is 300.97%, this is the highest ratio. In the contrast 15.77% receivables to current liability ratio is of Glaxosmithkline Pharmaceuticals Ltd. that is the lowest figure of this table. In case of Cipla Ltd. receivables to current liability ratio is moving between 89.18% in 2000 to 194.86% of 2006. Table-4 shows the receivables turnover ratio of Indian Pharmaceutical companies. The receivables turnover ratio is helpful to analyse the velocity of receivables in the company. It is a relationship between net sales and average receivables, the higher the ratio shows the higher the efficiency of receivables management. The receivables turnover ratio is helpful to analyse the velocity of receivables in the company. It is a relationship between net sales and average receivables, the higher the ratio shows the higher the efficiency of receivables management. The industry average of Indian pharmaceutical companies is 6.10 times. The highest receivables turnover ratio of Glaxosmithkline Pharmaceuticals Ltd. at 40.47 times in 2010 and on an annual average of 21.88 times and lowest is of Glenmark Pharmaceuticals Ltd. with 1.95 time in 2006 and lowest annual average of Aurobindo Pharma Ltd. is 3.09 times. The receivables turnover ratio of Cipla Ltd. is moving between 2.82 times to 7.10 times. The table 2 shows that the receivables turnover ratio of Cipla Ltd. is good. 2070 P a g e

Table-2: Receivable / Current Assets (in Percentage) Aurobindo Pharma Ltd. 51.07 67.94 62.56 60.43 56.88 50.38 37.40 45.99 Cadila Healthcare Ltd. 29.98 38.25 42.95 45.76 33.13 46.43 41.46 44.90 Cipla Ltd. 34.34 37.86 36.96 46.09 43.24 46.09 46.76 52.08 Divi'S Laboratories Ltd. 29.87 51.46 44.20 43.04 41.61 35.63 41.96 42.66 Dr. Reddy'S Laboratories Ltd. 61.82 40.12 32.68 40.90 26.58 35.06 35.64 43.77 Glaxosmithkline Pharmaceuticals Ltd. 41.42 34.52 22.80 24.48 24.64 22.17 20.27 11.23 Glenmark Pharmaceuticals Ltd. 52.87 67.49 72.27 60.17 45.97 55.99 65.24 68.10 Lupin Ltd. 64.96 66.31 71.97 48.37 47.12 31.37 36.23 43.03 Piramal Enterprises Ltd. 40.46 42.18 48.67 47.33 36.74 45.82 51.41 54.18 Sun Pharmaceutical Inds. Ltd. 37.22 39.54 45.71 35.15 17.99 14.57 26.52 43.17 Industry Average 44.40 48.57 48.08 45.17 37.39 38.35 40.29 44.91 Aurobindo Pharma Ltd. 56.00 52.94 50.52 51.07 50.05 60.45 53.83 Cadila Healthcare Ltd. 46.96 50.10 46.85 48.74 48.91 47.94 43.74 Cipla Ltd. 54.87 48.13 42.49 45.06 40.30 39.84 43.87 Divi'S Laboratories Ltd. 40.95 32.26 41.43 44.19 39.77 45.85 41.06 Dr. Reddy'S Laboratories Ltd. 56.22 45.51 57.77 46.68 53.46 65.15 45.81 Glaxosmithkline Pharmaceuticals Ltd. 5.57 3.04 2.34 3.80 5.02 4.21 16.11 Glenmark Pharmaceuticals Ltd. 74.99 69.10 52.59 62.84 61.27 78.21 63.36 Lupin Ltd. 49.53 53.02 55.48 51.39 51.76 60.34 52.21 Piramal Enterprises Ltd. 56.96 51.36 2.37 5.69 6.55 17.43 36.23 Sun Pharmaceutical Inds. Ltd. 27.57 45.26 22.36 26.17 35.45 11.62 30.59 Industry Average 46.96 45.07 37.42 38.56 39.25 43.11 42.68 Table 5 shows the collection period of receivables, it is shown from the table that the industry average of receivables collection period is 88 days (approx). Glenmark Pharmaceuticals Ltd. is taking most time of 188 days (approx) in 2006 and on an average is 131 days (approx) in collection of receivables, whereas Glaxosmithkline Pharmaceuticals Ltd. has shown the best performance as it takes just 9 days (approx) in 2010 and on an average is 21 days (approx) in collection of receivables. Cipla Ltd. stands at 4 th position in collection period of receivables. It takes 90 days (approx) in receivables collection, Cipla Ltd. has performed well in last four years from 2010 to 2013, as it is moving between 76 days to 90 days. Table-3: Receivable / Current Liabilities (in percentage) Aurobindo Pharma Ltd. 161.34 240.95 200.35 300.97 232.47 208.29 203.65 201.24 Cadila Healthcare Ltd. 74.03 81.53 80.73 91.70 59.49 93.65 64.27 103.03 Cipla Ltd. 89.81 103.23 99.00 132.78 118.82 138.76 194.86 161.29 Divi'S Laboratories Ltd. 51.01 149.94 109.03 163.12 153.19 93.33 132.70 136.14 Dr. Reddy'S Laboratories Ltd. 270.25 277.25 215.07 159.11 119.28 108.11 170.00 134.97 Glaxosmithkline Pharmaceuticals Ltd. 116.77 83.26 49.45 42.74 45.30 30.98 30.79 19.94 Glenmark Pharmaceuticals Ltd. 188.64 181.68 237.93 216.39 271.48 262.96 231.07 222.91 Lupin Ltd. 204.84 211.01 219.73 109.71 98.87 116.88 135.24 144.91 Piramal Enterprises Ltd. 78.09 76.38 113.73 101.78 74.39 100.20 114.05 124.02 Sun Pharmaceutical Inds. Ltd. 157.04 180.02 234.53 113.24 173.06 156.43 117.48 146.57 Industry Average 139.18 158.52 155.96 143.15 134.64 130.96 139.41 139.50 Aurobindo Pharma Ltd. 230.08 197.47 194.21 228.72 183.35 226.43 214.97 Cadila Healthcare Ltd. 122.41 106.31 122.95 127.66 159.01 137.62 101.74 Cipla Ltd. 185.83 170.24 169.67 179.63 161.39 138.79 146.01 Divi'S Laboratories Ltd. 175.54 143.71 170.81 181.57 194.81 268.71 151.69 Dr. Reddy'S Laboratories Ltd. 136.94 74.85 155.06 114.03 191.11 244.48 169.32 Glaxosmithkline Pharmaceuticals Ltd. 25.54 19.34 15.77 34.63 38.56 28.92 41.57 Glenmark Pharmaceuticals Ltd. 186.86 177.05 99.92 94.45 84.43 137.96 185.27 Lupin Ltd. 92.52 151.86 177.86 170.24 191.08 259.84 163.19 Piramal Enterprises Ltd. 126.68 90.13 28.98 34.02 34.33 34.93 80.84 2071 P a g e

Sun Pharmaceutical Inds. Ltd. 119.77 213.10 175.27 154.43 147.58 75.87 154.60 Industry Average 140.22 134.40 131.05 131.94 138.57 155.35 140.92 Table-4: Receivable Turnover Ratio (in times) Aurobindo Pharma Ltd. 4.85 3.03 2.88 2.87 2.56 2.49 3.04 2.90 Cadila Healthcare Ltd. 9.00 8.00 7.09 6.82 10.50 7.15 6.36 6.15 Cipla Ltd. 7.10 5.49 4.43 4.12 4.09 3.52 3.55 3.06 Divi'S Laboratories Ltd. 8.41 4.19 4.58 3.69 3.60 3.67 4.50 4.88 Dr. Reddy'S Laboratories Ltd. 3.48 3.54 3.57 3.79 3.78 3.60 3.90 3.93 Glaxosmithkline Pharmaceuticals Ltd. 7.10 8.87 11.59 13.73 15.00 19.97 22.43 36.09 Glenmark Pharmaceuticals Ltd. 4.09 3.42 2.64 2.84 2.87 2.14 1.95 2.32 Lupin Ltd. 2.71 2.70 2.51 5.55 5.14 4.91 4.27 4.19 Piramal Enterprises Ltd. 6.18 7.66 6.46 7.58 7.96 7.35 6.35 5.75 Sun Pharmaceutical Inds. Ltd. 5.95 6.45 4.00 6.94 4.40 5.20 3.01 2.28 Industry Average 5.89 5.34 4.97 5.79 5.99 6.00 5.94 7.15 Aurobindo Pharma Ltd. 2.54 2.82 2.81 3.00 3.16 2.44 2.96 Cadila Healthcare Ltd. 4.96 4.76 4.94 4.45 4.52 5.11 6.41 Cipla Ltd. 2.82 3.47 4.05 4.27 4.82 5.31 4.29 Divi'S Laboratories Ltd. 4.24 4.03 3.36 3.47 3.83 3.20 4.26 Dr. Reddy'S Laboratories Ltd. 3.15 4.51 3.24 3.45 2.81 2.14 3.49 Glaxosmithkline Pharmaceuticals Ltd. 25.90 32.17 40.47 26.27 21.68 25.00 21.88 Glenmark Pharmaceuticals Ltd. 2.05 2.98 5.86 4.32 3.56 2.11 3.08 Lupin Ltd. 4.19 4.04 3.63 3.61 3.79 3.10 3.88 Piramal Enterprises Ltd. 5.76 8.33 3.51 4.30 5.15 5.73 6.29 Sun Pharmaceutical Inds. Ltd. 4.13 3.33 3.64 3.47 3.27 6.42 4.47 Industry Average 5.97 7.04 7.55 6.06 5.66 6.05 6.10 Table-5: Average Collection Period (in days) Aurobindo Pharma Ltd. 75.28 120.64 126.60 127.02 142.52 146.33 120.10 125.86 Cadila Healthcare Ltd. 40.57 45.62 51.49 53.55 34.76 51.03 57.39 59.36 Cipla Ltd. 51.40 66.44 82.47 88.50 89.31 103.77 102.69 119.37 Divi'S Laboratories Ltd. 43.41 87.05 79.73 98.92 101.48 99.52 81.07 74.87 Dr. Reddy'S Laboratories Ltd. 104.93 102.97 102.38 96.42 96.55 101.35 93.66 92.78 Glaxosmithkline Pharmaceuticals Ltd. 51.44 41.16 31.50 26.58 24.34 18.27 16.27 10.11 Glenmark Pharmaceuticals Ltd. 89.29 106.64 138.27 128.59 127.15 170.59 187.50 157.54 Lupin Ltd. 134.90 135.38 145.57 65.80 70.97 74.40 85.57 87.12 Piramal Enterprises Ltd. 59.05 47.65 56.47 48.13 45.85 49.69 57.46 63.49 Sun Pharmaceutical Inds. Ltd. 61.32 56.55 91.18 52.57 82.87 70.13 121.09 160.08 Industry Average 71.16 81.01 90.57 78.61 81.58 88.51 92.28 95.06 Aurobindo Pharma Ltd. 143.88 129.22 129.81 121.69 115.62 149.87 126.75 Cadila Healthcare Ltd. 73.63 76.66 73.92 81.98 80.73 71.48 60.87 Cipla Ltd. 129.40 105.18 90.17 85.50 75.69 68.75 89.90 Divi'S Laboratories Ltd. 86.01 90.66 108.54 105.16 95.23 114.11 90.41 Dr. Reddy'S Laboratories Ltd. 115.84 80.99 112.72 105.71 129.70 170.81 107.63 Glaxosmithkline Pharmaceuticals Ltd. 14.09 11.34 9.02 13.90 16.84 14.60 21.39 Glenmark Pharmaceuticals Ltd. 177.82 122.67 62.28 84.42 102.63 173.07 130.60 Lupin Ltd. 87.10 90.42 100.64 101.06 96.20 117.77 99.49 Piramal Enterprises Ltd. 63.37 43.83 103.90 84.97 70.89 63.73 61.32 Sun Pharmaceutical Inds. Ltd. 88.41 109.59 100.19 105.28 111.74 56.86 90.56 Industry Average 97.95 86.06 89.12 88.97 89.53 100.10 87.89 2072 P a g e

Regression Analysis To investigate the impact of inventory management on profitability, the model used for the regressions analysis is expressed within the general form as given in equation 3: ROTA = f (ln sales, gear, cata, clta, turnca, ardays) Equation ROTA = β 0 + β 1 lnsales it + β 2 gear it + β 3 cata it + β 4 clta it + β 5 turnca it + β 6 ardays it +ε it [model] Where i denoting companies (cross-section dimension) starting from one to ten and t denoting years (time-series dimension) starting from one to fourteen. The model specifies on top of is estimated using the regression-based framework (Fixed Effects and Pooled OLS) as used by Deloof (2003). Our model differs, 1 st by using ROTA as a comprehensive measure of profitability and also the model includes asset-management and financing policy as control variables. the info set used for this part is pooled across companies and years, given associate balanced panel data set of 140 firm-year observations. A classical test for panel data is one among fixed effects model (FEM) versus Random Effects Model (REM). In the REM, it's assumed that there's one common intercept term, however that the intercepts for individual corporations vary from this common intercept in a random manner. To work out that of those estimators are additional acceptable to use, each a set effects and a random effects estimator was used to estimate the coefficients in model one. The Hausman take a look at, that could be a test for the null hypothesis of no correlation, rejects this null hypothesis and so the choice is taken to use a fixed effects framework. Table-6: Regression Analysis Coefficient Std. Error t-ratio p-value const 0.416478 0.147968-2.8147 0.00568 *** GEAR 0.807582 0.269186-3.0001 0.00326 *** CATA 0.820799 0.125168 6.5576 <0.00001 *** CATURN 0.0920843 0.0192296 4.7887 <0.00001 *** CLTA 0.586492 0.256488-2.2866 0.02391 ** LNSales 0.0115042 0.0122271 0.9409 0.3486 ARDAYS 0.00179609 0.000521868 3.4417 0.00079 *** R-squared 0.392095 The Table 6 represents the results of regression three, applying a set effects methodology, wherever the intercept term is allowed to vary across companies. It is now obvious from the adjusted R-squared values that the use of a firm specific intercept improves the instructive power of those models. In Regression, the adjusted R-squared make a case for 39.20% of the variation in profitability In regression equation 3, a extremely significant relation is found between ROTA and number of days accounts receivable (pvalue = zero.0008), which suggests that an increase within the number of days accounts receivable by 1 day is related to a decrease in profit by 0.0018%. The coefficients of the other variables included within the model are significant, apart from LNSales and the coefficient of gear and CLTA variables are negative in this regression. The explanatory variables make a case for 39.2 % variation reciprocally on total assets. One percent modification in ROTA will increase CATA, CATURN and LNsales by 0.821 %, 0.0921 % and 0.0115 % respectively. one percent modification in ROTA decrease GEAR and CLTA by 0.807 % and 0.586 % respectively, which is statistically significant at 1% and 5 % level respectively. CONCLUSION The different analyses have identified critical management practices and are expected to assist managers in identifying areas where they might improve the financial performance of their operation. The results have provided to managers with information regarding the basic financial management practices used by their peers and their peer s attitudes toward these practices. This study has shown that the Cipla Ltd.has been able to achieve high scores on the receivable management and it has slightly positively impact on its profitability. On this premise this industry may be referred as the hidden champions and could thus be used as best practice among the Indian Pharmaceutical Industry. 2073 P a g e

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