Analysis of Manufacturing Costs in Pharmaceutical Companies

J Pharm Innov (2008) 3:30?40 DOI 10.1007/s12247-008-9024-4

PERSPECTIVE

Analysis of Manufacturing Costs in Pharmaceutical Companies

Prabir Basu & Girish Joglekar & Saket Rai & Pradeep Suresh & John Vernon

Published online: 4 March 2008 # International Society for Pharmaceutical Engineering 2008

Abstract In the pharmaceutical industry, costs attributed to manufacturing are a major part of a company's total expenses. In this paper, trends in various expense and income categories of pharmaceutical companies have been analyzed with particular emphasis on manufacturing costs to gain an insight into their relationships and how they may differ among types of pharmaceutical companies such as brand name, generics, and biotechs. The study includes data published in the annual reports of leading pharmaceutical companies from 1994 to 2005. Twenty-two pharmaceutical companies were selected based on the annual revenues. The set was further divided into three groups: brand names, generics, and biotechs. The analysis shows that, between 1994 and 2005, manufacturing costs (as a percentage of total sales) are different for the three groups of companies listed above. Additionally, each group of companies differs in how savings are leveraged strategically. The data on brand-name pharmaceutical companies also indicate that there is a strong correlation between the reduction of the cost of goods sold (COGS) and the increase in R&D expenditure. This suggests the validity of Vernon's theory that for brand-name companies, a reduction in COGS will

P. Basu (*) : G. Joglekar : S. Rai

Pharmaceutical Technology and Education Center, Purdue University, W. Lafayette, IN 47907, USA e-mail: prabir1960@purdue.edu

P. Suresh School of Chemical Engineering, Purdue University, W. Lafayette, IN 47907, USA

J. Vernon Department of Finance, University of Connecticut, Storrs, CT, USA

likely have a positive impact on investments in R&D, presumably resulting in much needed innovations and future health benefits for the society.

Keywords COGS . Pharmaceutical manufacturing

Background

Prescription drugs often provide effective alternatives to expensive medical procedures and hospital stays. Consequently, spending on prescription drugs as a percentage of the total national health care spending is increasing. In 1999, prescription drugs accounted for 8.2% of the total national health spending; that share was 11% in 2003 and is expected to reach 14% by 2010 [1]. Although this is still a relatively small proportion as a percentage of the total national healthcare spending, it is one of the fastest growing components of healthcare spending, increasing at double digit rates from 1995 to 2003 [2].

The cost of bringing a new drug to the market place has also been steadily increasing, with recent estimates projecting a required investment of over $2 billion to progress from a laboratory idea to successful commercialization [3]. Pharmaceutical companies are spending more money on R&D [4], while the productivity of their R&D investment, computed as the number of drugs introduced to the market place per year, is declining. Manufacturing costs are a substantial part of their total cost structure [5]. According to some estimates, these costs can be as high as 27?30% of sales for manufacturers of brand-name pharmaceuticals [1, 3], more than double the share of costs for research and development [1]. No such estimates are currently available in the literature for generic pharmaceuticals or drugs manufactured by biotech companies.

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The manufacturing cost of pharmaceuticals, commonly known as cost of goods sold (COGS), was approximately $90 billion in 2001 for the top 16 drug companies [1]. In 2008, it is estimated that (in absolute terms) the total COGS for all pharmaceutical products could be as much as $200 billion (estimated using 27% of estimated global pharmaceutical sales of $735 billion in 2008 [6]). In comparison, the total spending in absolute terms on R&D by the Pharmaceutical Research and Manufacturers of America companies was $55 billion in 2007 [4].

Factors Affecting COGS of Pharmaceutical Companies

The pharmaceutical industry is strictly regulated due to its direct impact on consumer health and well being. To ensure that pharmaceutical products are safe and efficacious, the Food and Drug Administration (FDA) periodically inspects the facilities and procedures of all manufacturing operations in the USA and those overseas operations that sell their products in the USA. Consequently, these facilities and procedures must be registered with the FDA and must comply with the "(current) good manufacturing practices" (cGMP) established by the FDA. The high COGS of pharmaceutical products is the consequence, in part, of the methods by which excellence in delivered product is achieved. Other factors contributing to the high COGS and the potential for savings in COGS have been summarized in a previous report [3].

To offset the effects of rising costs of commercialization, shorter effective exclusivity periods, and diminishing returns on R&D investment, manufacturing costs may be a source of savings for the pharmaceutical industry [3]. Reducing manufacturing costs without sacrificing quality could be a way to effect social good in an environment where more and more investment is required to find new therapies for unmet medical needs along with a need to control or slow down the rate of price increases of prescription drugs. In fact, there are important linkages between the efficiency of pharmaceutical manufacturing, drug prices, and public health in the USA [7]. It can be predicted that reductions in manufacturing costs will lead to gains in consumer surplus (the difference between consumers' willingness to pay and what consumers actually pay; the standard economic measure of social welfare) worth trillions of dollars.

Objectives

The main objective of this study was to assess and analyze manufacturing costs of pharmaceutical products across important industry categories. The companies comprising this sector were divided into three groups: brand name,

generic, and biotech. The financial information for top companies in each group, as disclosed in the annual reports published by publicly traded companies, was used in the analysis. The main objective can be divided into the following sub-objectives:

? Gain a better understanding of the trends in various expense and income categories within companies of each group.

? Study the trends of the overall expenses and revenues. Identify the differences in the trends, if any, of various expense and income categories for each group of companies. If there are differences, identify the causes.

? Explore possible correlations between various expense and income categories and propose the cause of the relationships.

Methodology

The data used in this work were extracted from the annual reports of the pharmaceutical companies studied. The selected companies, based on annual revenues for the year 2005, are shown in Tables 1, 2, and 3. Brand-name pharmaceutical companies are the original developers of the drugs and have annual revenues of at least $10 billion. Generic pharmaceutical companies manufacture off-patent drugs, with typical annual revenues of less than $5 billion. In 2008, more than two thirds of all prescriptions written in the USA are expected to be for generics, and the generics sales is expected to grow to more than $70 billion [6]. Biotech pharmaceutical companies are the original developers of drugs made through biosynthetic processes. Brandname companies account for the greatest share of the market, though the weighted coverage of all companies is well over 50% of the total market.

Table 1 Sample US brand-name pharmaceutical data set

Company

% Total market share

Pfizer Inc.

8.5

Johnson & Johnson Inc.

8.4

GlaxoSmithKline PLC

6.3

Sanofi-Aventis

5.4

Novartis

5.4

Astra Zeneca PLC

4.0

Abbott Laboratories

3.7

Merck & Co. Inc.

3.7

Bristol-Myers Squibb

3.2

Wyeth

3.1

Eli Lilly & Co.

2.4

Schering-Plough Corp.

1.6

55.6

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J Pharm Innov (2008) 3:30?40

Table 2 Sample US generic pharmaceutical data set

Company

% Generics market share

Teva

9.5

Ivax* (2004)

3.3

Watson

3.0

Mylan

2.3

Barr

1.9

Alpharma

1.0

Par

0.8

21.9

Financial Information

The financial information about the companies was extracted from the Wharton Research Data Services (WRDS) Compustat [8] database. The data for the following revenue and expense categories were extracted:

(a) Sales (SALES)--aggregate sales of a company's complete product offering.

(b) Cost of goods sold (COGS)--also referred to as materials and production cost. Cost of goods sold is an aggregate figure that includes all costs incurred in producing the goods including write-offs from plant, property and equipment, raw materials, inventory, etc.

(c) Research and development expense (R&D)--research and development expenses that are separate from the cost of goods sold.

(d) Selling, general, and administrative expense (XSGA)-- these expenses provide for sales, marketing, as well as general expenses incurred by the product pipeline. XSGA provided by the WRDS database includes salaries, rent, and research and development (R&D) cost. In this study, R&D and general expense were treated as separate categories.

(e) General expense = XSGA - R&D. (f) Taxes--taxes paid by the company. (g) Depreciation--the steady loss in the value of capital

goods over a specified time period. (h) Operating income (after depreciation and taxes)--

profits after depreciation and taxes; these are company earnings from core operations after deducting the cost of goods sold, and selling and general operating expenses.

Operating income ? Sales ? COGS ? XSGA

? Depreciation ? Taxes

For the categories given above, data were extracted for a span of 12 years, from 1994 to 2005.

Data Collection and Analysis

To compare data in various categories across the three company groups, the extracted data were normalized with respect to the annual sales for the corresponding year (represented as COGS%, general expense, etc.). The normalized data were used for the detailed analysis.

Assumptions and Limitations

The authors have assumed that the data reported by the companies in the financial statements are based on the same interpretation of various categories. For example, COGS should be truly inclusive of all costs pertaining to drug manufacturing only. Although all companies follow generally accepted accounting practices, it is difficult to enforce and monitor uniformity in their interpretations. One of the limitations of the information obtained from financial statements pertains to the lack of itemized information by product, that is, we cannot associate a certain cost to a certain product or process. From financial statements alone, we cannot determine a firm's unique or competitive advantage in manufacturing capabilities. Also, some companies are, in reality, large conglomerates reporting all business units, including non-pharmaceutical operations, under one single filing.

For our analysis, we have chosen a sample space of companies. Although these companies account for a major share of the market, a significant number of companies have not been included in the study.

The following procedures were applied to study the aggregate data for each group of companies, namely, brand names, generics, and biotechs:

? Polynomial or linear trend lines were fitted to the data set and general trends were observed.

? The significance of change in each category was established by using a t test, which assesses whether the means of two groups are statistically different from each other. Relationships were proposed among the

Table 3 Sample US biotech pharmaceutical data set

Company

% Total share

Amgen Inc.

24

Genentech Inc.

13

Genzyme Corp.

5

Biogen Idec Inc.

5

46

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COGS%

Brand Name (with merck) Generics

Biotech

Brandname Avg. (26%)

Generics Avg. (52%)

70%

Biotech Avg. (14%)

60%

Reinhardts Avg. (27%)

50%

COGS %

40%

30%

20%

10%

0% 1994

1996

1998

2000

2002

2004

2006

Year

Fig. 1 COGS% for different types of companies

factors that showed significant changes and trends. The significance level was chosen to be 0.05 (alpha value). The period under consideration was divided into two sub-periods of 6 years each. The arithmetic averages of various expenses and incomes were calculated for each sub-period. A t test was then done to find the significance of the change from one sub-period to another. ? The relationships were quantified by computing the correlations among different factors. ? For the factors that showed significant changes, the corresponding averages were compared with Reinhardt's average from financial data collected for eight brandname companies in 1998 [1]. To provide an economic perspective on the pharmaceutical industry, Reinhardt cites a Deutsche Banc Alex Brown research report and a Banc of America Securities LLC report that shows breakdowns of the disposition of the sales revenue earned by the largest research-based pharmaceutical manufacturers (defined as brand-name companies in this paper) in 1988. Reinhardt also urges caution in interpreting the data and provides some of the reasons for that. Similar caution must be exercised in interpreting the financial data reported in this study.

Key Observations

Trends Analysis COGS%, R&D%, Operating Income, and General

COGS

The trend of COGS, R&D, operating income, and general expense as a percentage of total sales for the different types of pharmaceutical companies over a 12-year time period

was analyzed. The variations over the years for different groups of companies were plotted, and the corresponding averages were compared with Reinhardt's average. Figure 1 shows the trends in the COGS% data for brand name, generics, and biotechs, along with their corresponding arithmetic averages. The average COGS% for brand-name companies is nearly equal to the average estimated by Reinhardt. However, the average COGS as a percentage of sales, is almost half the arithmetic average for COGS as a percentage of sales for generics and roughly double that of biotechs. The higher value of COGS as a percentage of sales for generics is possibly a reflection of lower expenditure on R&D and sales, and marketing for the generic industry. However, it is also quite revealing to realize that the COGS% is significantly lower for biotechs.

The COGS as a percentage of total sales (COGS%) for brand names appears to have declined during the years 2000 to 2005. On the other hand, the COGS% for generics increased until the year 1996, and since then, there appears to be a gradual decline. The COGS% values of generics show a gradual reduction over the last 8 years. Biotechs show more fluctuations than either generics or brand-name companies.

As a special case, the time series data for ScheringPlough are shown in Fig. 2. In the case of Schering-Plough, COGS% was significantly lower than the industry average until about 2001. However, it increased significantly over the years 2002?2004, growing more than the industry average and then appears to have dropped back to the level of the industry average. Correspondingly, the operating income, which was slightly above the overall industry average, dropped sharply over the years 2002?2004 and then showed an increasing trend in 2005. The possible explanation for this is that Schering-Plough entered a consent decree with the FDA in 2002, agreeing to

Schering-Plough

COGS%

Brand-Name

35%

OIADP&T%

30%

R&D%

25%

20%

15%

10%

5%

0% -5%1994

1996

1998

2000

2002

2004

2006

-10%

-15%

Year

Fig. 2 Schering Plough Corporation time series plot

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R&D%

Brand name Generics

Biotech (without year 2002)

Brand name Avg. (13%)

40%

Generics Avg. (8%)

35%

Biotech Avg. (26 %)

Reinhardt's Avg.( 13%)

30%

25%

R&D%

20%

15%

10%

5%

0% 1994

1996

1998

2000

2002

2004

2006

Year

Fig. 3 R&D for different groups of companies (without 2002 data for biotechs)

Operating Income

The trend in operating income as a percentage of sales is shown in Fig. 4. The average operating income for brandname companies is around 19%. There is a gradual increase in its value until the year 2003. After that, the trend indicates a gradual decrease. However, the slope is very small, and no definitive conclusions can be drawn. The yearly average for generics is around 12%, which is lower than that of brand-name and biotechs. The operating income data for generics show a significant dip in 1997, and after that, the trend indicates that it has been gradually increasing. However, there is more fluctuation in the data for generics as compared to that for brand-name companies. For biotechs, the total number of companies in the data set is small. However, there appears to be an increasing trend of operating income in recent years.

revalidate the manufacturing processes at several sites in the USA and Puerto Rico, resulting in significant increase in COGS% since 2002. In addition, it discontinued certain older profitable products and outsourced other products. The fact that, until 2002, the COGS% at Schering-Plough was significantly lower than the industry average for brandname companies could be reflecting a lower investment in plant, equipment, and cGMP systems, which could have resulted in the findings of cGMP deficiencies.

R&D

The trends in R&D expenditures as a percentage of sales (R&D%) are shown in Fig. 3. The data point for 2002 was removed for biotechs, which is considered as an outlier. The R&D% for brand-name companies appears to be gradually increasing over the past few years. The average value is very close to Reinhardt's average. The trend for the generics is substantially flat with peaks in 2000 and 2004. The average value is slightly greater than half of the average estimated by Reinhardt and the brand-name industry average. This is expected since most generics are not expected to devote resources to discovering new drugs. As a result, their R&D% expenditures are largely directed to developing marketable formulations of a large number of drugs that are either out of patent or may be soon. The trend for biotechs shows a record high R&D expenditure in the year 1996 when it reached nearly 32%. Since 1996, the R&D% has been fluctuating with an overall decreasing trend. For biotechs, the average R&D expenditure as a percentage of sales is twice Reinhardt's average for brandname pharmaceuticals. Biotechs heavily invest in R&D to discover new therapies; their R&D success rates are lower, and they have a smaller portfolio of marketed products.

General Expense

The trend in general expenses as a percentage of sales is shown in Fig. 5. The trend for brand-name companies is flat, and the average value is slightly less than the average reported by Reinhardt in 2001. The trend for generics shows more fluctuations compared to the brand-name companies, with the lowest value being 12% in the year 2004. The average value of general expenses for generics is nearly half of Reinhardt's average, which confirms that the generics spend less money in marketing and sales than brand names or biotechs. The average value of general expense for biotechs is slightly higher that that of generics but lower than that of brand-name companies. Biotech drugs are unique, have less competition from similar (metoo) drugs in the market, and treat specific unmet needs. Therefore, the biotechs may not require as much marketing and sales effort as do brand-name companies. The trend for

Brand name

Generics

0.4

Biotech

Brand name Avg. (19%)

Generics Avg. (12%)

0.3

Biotech Avg. (22%)

Operating Income/Sales

0.2

0.1

0 1994

1996

1998

2000

2002

2004

2006

Year

Fig. 4 Operating income for different types of companies

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