Platt Perspective on Business and Technology

Open markets, captive markets and the assumptions of supply and demand dynamics 4

Posted in macroeconomics by Timothy Platt on September 17, 2015

This is the fourth installment to a brief series on underlying assumptions as they arise and play out in economic systems, and in production and marketplace systems (see Macroeconomics and Business 2, postings 230 and loosely following for Parts 1-3.)

I began this series by at least briefly discussing supply side and demand side economics, and particularly the supply side approach to economic and marketplace modeling (see Part 1 and Part 2.)

And as a continuation of that discussion, in Part 3, I offered some thoughts on what would go into a more effective economic theory in general, based on the certainty of both congruence of, and conflict between goals and priorities held by different stakeholders, and on the inevitable emergence of friction in any real world economic systems. And I concluded that installment by stating that I would continue its discussion by turning to the issues of “time frames, and of how any relevance and value that might be obtained through operationally implementing an economic theory, depends on which time frames are being considered and by whom.”

I begin address that set of issues here, by at least briefly considering some of the timeframes that are more commonly observed and acted upon. And as a perhaps obvious starting point there, I note that these timing frameworks can be divided into recurringly cyclical, and non-cyclical patterns. Let’s begin considering these basic business and economic parameters with the more cyclical timing patterns that businesses and marketplaces, and that overall economies are shaped and constrained by, and with at least a few of what are perhaps the most fundamental, consistently followed patterns that most businesses function in terms of. And I begin that by noting:

1. The standard fiscal year. This benchmark timing interval can be set to correspond to the calendar year but for most businesses, as headquartered in most nations, their fiscal years are timed to begin and end in accordance with tax law in place and with annual income tax reporting requirements. The fiscal year is a fundamental required timeframe for reporting overall business performance and profitability.
2. And the basic fiscal year, with its between-business and business sector-wide synchrony, and certainly within legal and taxation system jurisdictions, is essentially always divided into business quarters. The ending dates of these quarters are also routinely identified as key expected and even required reporting periods. And like the end of year reports of the larger fiscal year cycle, these reporting results are used both for internal business purposes and for external reporting and accountability purposes. As an example of external use there, I cite stock market analysts and how they routinely evaluate the investment value of publically traded businesses, from these quarterly as well as from these annual reports.
3. And with this stated, I make note of some key external stakeholders whose findings and public statements significantly influence the public perception of a business’ performance and valuation, and certainly for publically traded companies: stock market analysts and business reporters. Their reports and pronouncements shape public understanding and perception, and of both individual businesses and entire industries, and of the market and the economy as a whole. Perception, and certainly widely held public perception can and does significantly shape reality as it shapes the due diligence analyses and findings that are carried out by prospective clients and customers, supply chain and other partner businesses, employees and other internal stakeholders, and outside investors.
4. And with that stated, I turn to consider billing cycles, and the number of days receivable that a business pays its bills according to, and that its customers pay it according to as well. Picking up on a detail from the bullet points 2 and 3 above, negative reports about a business and its next quarter performance potential can create pressures on the part of its suppliers to tighten their terms of sale to it, limiting for example the number of days receivable offered to that business before full payment is due from it for goods and services received. But this does not necessarily shorten the number of days receivable that this business would have to offer to its own customers in order to remain a preferred source of goods and services for them.

And now let me offer two other, quite different cyclical timeframes that can in specific contexts and under select circumstances become at least as significant and certainly for long-term financial and economic considerations:

5. My first here, is the average interval, generally measured in months and years, between recurring next technology generations and particularly when historical patterns of innovation emergence have led to basic and even highly predictable timing expectations.

A perhaps archetypally familiar example of this can be found in the integrated circuit industry, with the ongoing reliability of Moore’s Law in predicting the pace and timing of next generation technology breakthroughs there. In this context, I note that Moore’s Law as a source of marketplace expectation both drives innovative next technology generation expectations, and shapes release date timing for public roll-outs of new products as well.

A great many businesses and industries operate at least in significant part according to the dictates of counterparts to this, as for example when pharmaceutical manufacturers are driven to keep developing next generation product innovations, as their current ones lose their patent protected exclusivity, and become available for generic-brand alternative manufacturing. Patent lives are set and limited by law, so these timeframes can be just as reliable and more so, than any Moore’s Law expectations. And the costs of developing new next generation patent protected pharmaceuticals, coupled with the timeframe limitations of gaining significant returns on these investments fundamentally shape these businesses and even this entire industry and at all levels.

So far, cyclical timeframe factors 1, 2 and 3 are more firmly set by and established according to the calendar, and factor 4 is less so. But it is still highly predictable as to timing, and certainly as long as a cyclical pattern of this generic type persists (e.g. where Moore’s Law will eventually run its course and probably be replaced by a next predictive new-development timing model for that industry, or if the timing constraints of drug patents were to be legally redefined.) I turn next to consider a consumer-side cycle, and one that like factor 4, above, is not pegged to the calendar as are numbers 1 through 3, but that can be equally predictable and certainly statistically:

6. Consumer purchasing cycles, as for example are observed for car sales and for the sale and purchase of big ticket item appliances, but also for more commonly recurring product purchases too.

Individual consumers can and do follow their own distinctive individual purchasing patterns, even if they usually do make purchases, and of essentially all types according to what are relatively consistent patterns for them. Just considering the big ticket item purchase of a new or “new to the buyer” used car:

• Some people who buy and own cars, do so as if on a relatively set schedule (e.g. every three or four years) and with that timing set by their individual preference determinations.
• And some people more consistently own and drive a car until its repair and maintenance costs reach a tipping point where it would now be more cost effective to replace it with a new(er) model. And there, the predictive timeframe is the predictive lifespan of the vehicle as it remains cost-effective to maintain.
• But statistically and across purchasing demographics, the expected timing between new or used car purchases can become quite predictable, and certainly where the numbers of buyers and potential buyers under consideration become large enough to qualify as a statistical universe (i.e. a large enough overall population that would be sampled from, to be able to support valid use of predictive statistical tests.)

Consumer cycles of this type significantly serve to determine production level requirements and available inventory level requirements, and both for individual businesses and across entire industries. And they determine the effective market size that would be available to manufacturers and to retailers, as pools of actively buying consumers at any given time. Then with this serving as an averaged baseline for benchmarking comparison, consumers impose seasonal and other buyer activity cycles (e.g. during year-end annual bonus receipt periods for potentially buying consumers, and during manufacturer and retail initiated peak sales opportunity periods as for example when new next year model release dates come around for automotive sales and older models have to be moved off of dealership lots.)

Businesses and marketplaces, and even overall economies are driven by all of these and similar cyclical patterns. But they are also driven by non-cyclical timing considerations too. And as a perhaps obvious example there, I would cite the business and industry reshaping potential for disruptive change that can and does emerge with the release of disruptive new technologies and of initial products based upon them. Sometimes innovation and even disruptive innovation can in effect become forced into a predictably cyclical pattern, as exemplified by the seemingly ongoing reliability of Moore’s Law. But it is of the nature of disruptive innovation that it not be readily predictable for its occurrence and for its emergence into the marketplace. So I have been writing about cyclical patterns here, but I have of necessity also included acknowledgment of wild card deviations from their patterns as well, and with innovation and with disruptive innovation serving to set next development-phase cyclical patterns as their potential is developed and diffused out.

• As a final point for this posting, I note that while this portion of this series is largely about innovation and its diffusion into marketplace acceptance, and the economics thereof, and certainly for any industries that are driven by innovation,
• I have been writing here about monetizable value and how it is perceived and evaluated and by whom as a key to understanding innovation in a business and marketplace context. Ultimately, making innovation work in a business and economic context is and has to be grounded in economic realities and in how they are defined by marketplace participants.
• And with that in mind I continue with this series’ more general discussion of economic models per se.

With this line of discussion in mind, I am going to turn in my next series installment to more fully consider the nature and role of friction in businesses and in marketplaces, and in economic systems as a whole. And as a part of that discussion, I will consider how timing cycles can be viewed very differently by differing stakeholders. And I will take a game theoretic approach to business strategy in that, where friction and its attending uncertainties can be and often are the driving factors determining which strategies (e.g. win-win or zero-sum oriented), differing stakeholders would seek to employ in pursuing their own goals. Meanwhile, you can find this and related postings at Macroeconomics and Business and its Page 2 continuation.

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