Platt Perspective on Business and Technology

Quantifying business intelligence valuation in terms of systems-indeterminacy 11: information determinacy and indeterminacy and the prediction of value 3

Posted in macroeconomics by Timothy Platt on February 4, 2014

This is my eleventh installment to a series in which I discuss and analyze the valuation of information in a business context and from a due diligence and risk remediation perspective, and in terms of what in a physical systems context would be called quantum indeterminacy (see Macroeconomics and Business, postings 137 and following for Parts 1-10.)

I wrote Part 10 in large part as a specific example of how predictive business intelligence is assembled and used, and of the uncertainties that it contains. And I begin this posting with a couple of follow-up notes for that working example that I offer here as points of clarification.

• I cited and at least briefly discussed a military systems example in Part 10 and not a traditional business, and the predictive business intelligence that I focused upon there involved knowing and predicting troop strengths and by location and general areas of specialization. In this, essentially any organization that produces, obtains and relies on information for its functioning and success can be considered a business for purposes of this general discussion and all such information can be considered business intelligence, and certainly as its access and use bring value to that organization.
• Towards the end of Part 10, I wrote the following: “When you acquire this type of intelligence, you are buying the type of black box-wrapped information product that I first began writing about in Part 2. Before you complete your purchase you do not know precisely what you will be getting, and certainly for accuracy – unless that is you can also acquire as bundled with that …, supporting information that would validate its level of accuracy, and as a result indicate where it might have been strategically altered which in and of itself might be useful information.”
• If it sounds strange to represent a nation’s military as a business, it probably sounds at least as strange to represent essentially all intelligence gathering as fitting a purchase acquisition model. True, some military intelligence information, to follow through on this example, is in fact acquired in exchange for cash payment. But I would argue here that any such acquisition process carries costs, some of which come more directly as monetary expenses that have to be met and some of which come as risk which if realized translate directly into monetizable cost too. So pursuing an information gathering effort always costs, and in terms that can be monetized even if these exercises also often carry cost elements that are more difficult to put a precise monetary value on, as well. One way or other this type of acquisition is always a purchasing process too.

With that in place, I come to the end of Part 10 where I noted that I would “delve into my second, pharmaceutical industry example as noted above” next (e.g. the highly competitive manufacture and sale of over-the-counter, non-prescription drugs.)

I begin this from the sales side. There is a wide range of over-the-counter, or non-prescription medications and preparations that sell at relatively stable levels regardless of season, but there are also a great many products that show seasonal sales peaks and drop-offs. Allergy medications, for example, are more sought after in warm weather months when allergen levels such as pollen counts are high. Cold and flu season with increased demand for over-the-counter symptom relief from those ailments, peaks in colder weather months. True, some people take allergy medications in the middle of winter, and people do get at least some colds in the middle of summer too but overall, sales levels for these products follow predictable seasonal trends.

And as a crucial additional point, all of these products have shelf lives and in many venues they come with product expiration dates printed on them. Pharmacies and other businesses that sell seasonal over-the-counter products do not want to keep large amounts of their working assets tied up in out of season inventory, and when consumers can see those printed expiration dates they tend to really look at them and to select packages that show as being fresher. Items that show as being too close to their expiration date can become essentially unsellable, except perhaps at a discount and as a means of limiting loss. So information that would help in better determining what precise volumes of which items to have in stock to meet consumer needs but without creating overstock problems, becomes very valuable here.

Consider cold weather illness symptom relief products here, and for this more specific example consider in particular products that offer severe cold and flu relief. The critical business intelligence that would go into planning out and executing effective inventory levels for them, begins with gaining reliably predictive information as to the start and the anticipatable severity of the upcoming flu season, which might begin to really take hold anywhere from mid to late fall and even into the winter months. So predictive business intelligence can make the difference as to how these businesses succeed or fail to position themselves for upcoming highly competitive opportunity.

A pharmacy that can offer and sell desired flu relief medication when it is needed, is probably going to also capture collateral customer business through sales of Kleenex of similar products that help their customers deal with watery eyes, sneezing and coughing. There is a wide range of products that a consumer already making at least one purchase here might also make when they are dealing with this illness that would make them more comfortable. In fact on average, a customer buying a flu or severe cold relief product can be expected to buy several other items too – increasing the business value of offering these right products in the right numbers at the right times.

From a manufacturer’s perspective, product needs-level information has to be even more long-term predictive as lead times to manufacture and distribute to retail businesses is longer than the lead time to sell once these products have arrived in a store. If a manufacturer pursues anything like a just in time production strategy, the timing and accuracy of this information is crucial, as customers who switch product brands from lack of availability of your product and who find relief from it are likely to stay with that new brand alternative.

I am going to conclude this posting here and continue its discussion in a next series installment where I will look into the types of business intelligence that are available for addressing this business need. And in anticipation of that, I add that I will also discuss how different sources of business intelligence of potential use here, influence each other’s perceived marketplace value and shape the competitive marketplace that business intelligence providers face as they seek to market and sell in this arena. Meanwhile, you can find this and related postings at Macroeconomics and Business.

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