The Case of the Unidentified Industries
The Case of the Unidentified Industries
“The Case of the Unidentified Industries” challenges the reader to match 14 firms operating in 14 different industries with 14 sets of financial data from the year ending in 2005. This section aims to enlighten the reader about the methodology used to derive the responses shown in the subsequent section.
First, the industries are placed in one of the following groups: service industry, manufacturing, and retail. Several sub-groups are also created to better compartmentalize the problem (i.e. online retailer or food service).
Second, some basic financial information is deduced for each group (and/or sub-group). For example, we expect most of the service businesses to have zero inventories (excluding the food service industry). We expect online retailers, restaurants and grocery stores to have high inventory turnovers. We expect the accounts receivable collection period to be longer for wholesalers than for retailers. We expect the majority of retailers to have high sales volumes with small margins. We should also be able to predict, based on the state of the economy in 2005, which businesses should have been more profitable. All these financial trends are used to establish relationships between the financial data and the industry groups established earlier.
Third, the unique characteristics specific to each business are used to differentiate the businesses with similar balance sheets (i.e. low inventory turnover in a book store, high other assets for a pharmaceutical manufacturer, high profitability with low inventory for a software developer, long accounts receivable period for an H.M.O or advertising agency, etc…). This information should be sufficient for a preliminary matching of each business with a unique set of financial data.
Finally, wherever possible, a 2005 corporate financial statement was obtained and cross-referenced with the benchmark values presented in the case study to verify the selection.
SELECTIONS AND JUSTIFICATION:
A) Online bookseller (Amazon.com Inc.)
The key identifiers that A) was an online retailer were the relatively low inventory with a high turnover, low P&E, short AR collection period and high asset turnover with low profit margins. What was unique to this account was the large amount of cash and LT debt as well as the low amount of equity. This was explained in the MD&A and ‘Notes to Consolidated Financial Statements’ of Amazon.com Inc.’s 2005 annual report (which it turns out was the company used in this exercise). The large amount of cash is explained by the company’s focus on growth in free cash flow through increasing operating income while limiting capital expenditures. The large debt and low equity exist because the company issued close to $2 billion (between 1999 and 2000) in notes and securities (due between 2009 and 2010) that can be converted at the holder’s option to common stock.
B) Bookstore chain (Barnes & Noble Inc.)
The key indicators that B) was a retailer were the high inventory, large P&E, short receivable collection period as well as high asset turnover with low profit margins. The key to identifying this account as a bookstore chain was the low inventory turnover typical of a large retailer incorporating hundreds of stores with miles of shelves stocked full of books. In this case, the company was Barnes & Noble Inc.
C) Online direct factory to customer personal computer vendor (Dell Inc.) It was surmised that C) was an online retailer because of the low inventory, low P&E, and the high asset turnover with low profit margins. The low P&E and remarkably low inventory fit perfectly with an online distributor that ships directly from a factory. The high accounts payable is explained through the outsourcing of manufacturing. Furthermore, the long AR collection period is due to the large number of sales to corporate accounts. The high ROA is also typical of the boom/bust nature of the computer industry. The financial data presented in Exhibit 1 is from Dell’s fiscal year ending February 3rd, 2006.
D) Pharmaceutical Manufacturer (Pfeizer Inc. and Subsidiary Companies) The best indicators that D) was a pharmaceutical manufacturer were the large amount of other assets (bought patents and goodwill), the low inventory turnover (indicative of a large batch process typical in the pharmaceutical industry), and a long AR collection period (typical of wholesale to retailers, governments, and H.M.O.’s). The financial data is confirmed as belonging to Pfeizer Inc. for the fiscal year ending in 2005.
E) Advertising Agency (Omnicom Group Inc. and Subsidiaries) It was concluded that E) was in the service industry because there was zero inventory. The large amount of AR and other assets (i.e. acquired goodwill), as well as the very long AR collection period were highly indicative of a large advertising agency. The long AR collection period is probably best explained by the billing process, where expenses are invoiced throughout a prolonged advertisement campaign. The financial data in Exhibit 1 belongs to the Omnicom Group Inc.’s 2005 financial statements.
F) Software Developer (Microsoft Corporation)
It was surmised that F) belonged to the software industry because of the large amount of other assets (acquired copyrighted software), low inventory, low P&E as well as the very high profit margins and ROA. This high profitability was typical of software developers circa 2006. The financial data in Exhibit 1 is from the Microsoft Corp. financial statements for the fiscal year ending June 30th, 2012.
G) H.M.O. (N/A)
It was known that G) belonged to the service industry because there was zero inventory. The only discernible difference between this set of financial data and that of E) Advertising Agency were the much higher asset turnover and lower profit margins for the former. This could be explained by the large amount of money involved in the medical industry coupled with the low profit margins typical of HMO’s. H) Family restaurant chain (Darden Restaurants Inc.)
The principal indicators that H) was in the food service industry were the low inventory with a high turnover rate, the high P&E, and the high asset turnover (indicative of large revenues). The data in Exhibit 1 is obtained from Darden Restaurants Inc. financial statements for the fiscal year ending May 28th, 2006. The low current and acid test ratios seem disturbing at first glance but the MD&A states that all revenues are generated as liquid cash and that short term financing is achieved through a commercial paper program supported by a Credit Agreement with a consortium of banks through which they can borrow up to $500 million.
I) Retail grocery chain (The Kroger Co.)
It was concluded that I) was a retailer because of the high inventory, large P&E, short AR collection period and the high asset turnover with small profit margins. What separated the retail grocery chain from the other retailers was the high inventory turnover (due to the perishable nature of the good sold). What separated the retail grocery chain from the food service industry were the grocery chain’s higher inventories with lower turnover and smaller P&E. The financial data in Exhibit 1 was matched to The Kroger Co.’s fiscal year ending January 28th, 2006.
J) Department store chain (Macy’s Inc.)
It was surmised that J) was a retailer because of its large inventory, high P&E, large AP and relatively low profit margins. What separated the department store chain was its long AR collection period due to its “own brand” card. In this case the “own brand” is Macy’s Inc. and the large other assets is explained by the merger in 2005 with The May Department Stores (approx. $10 billion of goodwill and intangibles were acquired in the merger).
K) Retail drug chain (Walgreen Co. and Subsidiaries)
The main reasons that K) was assumed to be a retailer were the very high inventory, high P&E, high AP, and the high asset turnover with low profit margins. What made the retail drug chain unique were the high AR and AP coupled with a long AR collection period. This makes sense considering that the pharmacies purchase medication directly from the manufacturers (AR collection period = 68 days for Pfeizer Inc.) and must deal with H.M.O.’s and Medicaid, resulting in longer AR collection periods for the retail drug chain. The data in Exhibit 1 is obtained from the Walgreen Co.’s annual report for the fiscal year ending August 31st, 2005.
L) Electric and gas utility (N/A)
It was concluded that L) was an electric and gas utility because of the low inventory with a high turnover (only 28% of revenue from natural gas), high P&E (power plants, transmission lines, etc…), and long AR collection period (monthly or bi-monthly billing cycle). Because the company upon which the data presented in Exhibit 1 was based could not be found, the lack of cash could not be accurately explained.
M) Airline (Southwest Airlines Co.)
It was known that M) belonged to the service industry because it contained no inventory. Furthermore, the high P&E, low ROA and even lower ROE were indicative of the struggling airline industry in 2005. The data in Exhibit 1 belonged to Southwest Airlines Co.’s fiscal year ending December 31st, 2005.
N) Commercial bank (N/A)
It was known that N) was part of the service industry because it contained no inventory. The massive amounts of AR and notes payable separated the bank from the other service industries. Also, the collection period of 4,071 days was typical of the long term loans commonly associated with banks.
Subject: Balance sheet,
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 22 November 2016
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