Taken together, such post-factory events can greatly reduce the dollar value of sales—and therefore the revenue recognized
for accounting purposes. As a result, companies use historical data relating to returns, discounts, and the like for a best
guess of the total downward adjustment of recognized revenue. As long as this estimate is accounted for as a liability on
the financial statement, the manufacturer is presumed SOX compliant. But if these best guesses are over-or underestimated
by a significant degree, a company opens itself to second-guessing by regulatory authorities.
Any suspicions that regulators have set their sights on the revenue-recognition practices of pharma are well-founded. Though
the SOX legislation doesn't target the industry emphatically, officials are keenly aware of past improprieties. In particular,
some companies have inaccurately reported higher sales of and earnings from their products than the market could reasonably
be expected to bear. Under the sell-in model, these shipments are counted as sales for revenue-recognition purposes.
The SOX regulations aim to eliminate or at least minimize such practices. Additional tests, including after-the-fact analyses,
require greater accuracy and reliability. Companies that fail to meet these new standards face consequences ranging from lower
stock valuations (as auditors assign lower ratings to the quality of the information reported) to the delisting of shares
from public stock exchanges (a powerful incentive indeed).
In the end, IMS's client came to the conclusion that it not only needed to restate prior earnings but that the degree of variability
in its entire forecast was unacceptable. Not content with a best guess, it opted for a closer approximation of what was actually
taking place in its sales channels.
THE SUPPLY CHAIN IN REAL TIME
In industries from high-tech to retailing, financial services to manufacturing, the mantra in business intelligence is "real
time." That's because the more a company knows about its actual stocking at the retail level, the better it can maximize inventories,
production runs, advertising, and additional business processes. It can also better answer a slew of formerly riddling questions
like: What is this retailer's inventory of my products right now? What sales patterns are developing that could affect demand
right now? How much should we ship to this customer right now?
For large retailers like Wal-Mart in the United States or Tesco in Britain, knowledge of retail inventories and sales in real
time (or near–real time) is readily achievable because it has to be—in order to avoid stockouts. The sophisticated mechanisms these chains
have devised for sharing data with manufacturers and distributors keep products on shelves, prices low, and customers happy.
That's the incentive for retailers—who basically own the supply chain and whose business models are based on competitive costs—to
collaborate with manufacturers. Accordingly, manufacturers "in the loop" have real-time information to exploit across a range
of critical business processes.
By contrast, the systems available to pharmaceutical companies for capturing retail and inventory data look, well, Stone Age.
And for good reasons. Most of all, until SOX, there was little incentive for tracking actual sales of drugs and other pharma
products because demand has tended to have little to do with price. For that matter, price has tended, at least until recently,
not to be an issue. (The growing competition from generics is, of course, increasing the influence of cost.) In addition,
product volumes tend to be lower. As a result, managing retail costs more actively never much mattered to the participants
of the supply chain.
Combined with a lack of incentive on pharma's part are a daunting number of disincentives, starting with the chasm between manufacturers and the point of sale. Because companies are prohibited from direct
interaction with their end-users—the patients—products are sold through intermediate distributors, then transferred to customer
groups ranging from pharmacies to physicians, hospitals, and clinics. This, in turn, makes for an enormously diverse range
of contract structures.
Such a highly regulated and segmented setup allows companies little visibility into the downstream demand chain. The many
rebates, discounts, charge-backs, product dating, and gross-to-net deductions only further complicate the accuracy of revenue