The 2009-2014 Outlook for Hospital Operating Room Cabinets, Cases, Tables, and Other Furniture in Greater China [Paperback]

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WHAT IS LATENT DEMAND AND THE P.I.E.?

The idea of latent demand is rather subtle. The term latent typically refers to something which is dormant, not observable, or otherwise yet realized. Demand will be the notion associated with an economic quantity which a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists because the industry earnings of an market when that market becomes accessible and attractive to serve by competing firms. It is often a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if Greater China is served within an efficient manner. It is normally expressed since the total revenues potentially extracted by firms. The "market" is determined with a given level in the value chain. There may be latent demand on the retail level, at the wholesale level, the manufacturing level, as well as the raw materials level (the P.I.E. of higher levels from the value chain being always smaller than the P.I.E. of levels at lower levels from the same value chain, assuming all levels maintain minimum profitability).

The latent demand for hospital operating room cabinets, cases, tables, and also other furniture in Greater China isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand might be either lower or more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from the amount of factors, including the insufficient international openness, cultural barriers to consumption, regulations, and cartel-like behavior around the a part of firms. In general, however, latent demand is usually bigger than actual sales in a market.

For reasons discussed later, this report does not consider the notion of "unit quantities", only total latent revenues (i.e., a calculation of price times quantity is rarely made, though one is implied). The units used within this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends). If inflation rates vary inside a substantial way when compared with recent experience, actually sales may also exceed latent demand (not adjusted for inflation). On another hand, latent demand may be typically more than actual sales as there are often distribution inefficiencies that reduce actual sales below the amount of latent demand.

As mentioned inside introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective from the players or products involved. In fact, all of the current products or services on the market can cease to exist in their present form (i.e., with a brand-, R&D specification, or corporate-image level) and many types of the gamers could be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will probably nevertheless be latent demand for hospital operating room cabinets, cases, tables, and also other furniture on the aggregate level. Product and repair offerings, as well as the actual identity from the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand.

THE METHODOLOGY

In order to estimate the latent demand for hospital operating room cabinets, cases, tables, as well as other furniture over the regions and cites of Greater China, I made use of a multi-stage approach. Before applying the approach, one needs a basic theory that such estimates are created. In this case, I heavily rely for the usage of certain basic economic assumptions. In particular, there is certainly an assumption governing the shape and form of aggregate latent demand functions. Latent demand functions relate the income of your region, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an marketplace is efficient), at any level from the value chain, occurs if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and stay capable to serve that demand at the minimal return. The only most important variable determining consumption, assuming latent demand exists, is income (or other money at higher levels of the value chain). Other factors that may pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), or changes in utility to the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption may be a central theme in economics. The figure below concisely summarizes one part of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the typical propensity to use would fall. The typical propensity to consume could be the level of consumption divided from the level of income, or perhaps the slope in the line in the origin towards the consumption function. He estimated this relationship empirically determined it being true within the short-run (mostly according to cross-sectional data). The larger the income, the reduced the average propensity to consume. This kind of consumption function is labeled "A" inside the figure below (note the rather flat slope in the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that this marginal propensity to use was rather constant (using time series data). This sort of consumption function is shown as "B" in the figure below (note the higher slope and zero-zero intercept). The common propensity to use is constant.





Is it declining or possibly it constant? A amount of other economists, notably Franco Modigliani and Milton Friedman, inside the 1950s (and Irving Fisher earlier), explained why both functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter some time horizon, the greater consumption can rely on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to take is much more constant. Similarly, in the long run, households without having income eventually have zero consumption (wealth is depleted). Even though the debate surrounding beliefs about how precisely income and consumption are related is interesting, within this study an extremely particular school of thought is adopted. In particular, we are thinking about the latent demand for hospital operating room cabinets, cases, tables, along with other furniture across the regions and cities of Greater China. The smallest cities have few inhabitants. I assume that all of the cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of the states having wealth; current income dominates the latent need for hospital operating room cabinets, cases, tables, and other furniture. So, latent demand within the long-run features a zero intercept. However, I allow different propensities to use (including standing on consumption functions with differing slopes, which can take into account variations in industrial organization, and end-user preferences).

Given this overriding philosophy, I'll now describe the methodology utilized to produce the latent demand estimates for hospital operating room cabinets, cases, tables, as well as other furniture in Greater China. Since ICON Group has asked me to use this methodology to some large number of categories, the rather academic discussion below is general and may be applied to a wide variety of categories and geographic locations, not just hospital operating room cabinets, cases, tables, and also other furniture in Greater China.

Step 1. Product Definition and Data Collection

Any study of latent demand requires that some standard be established to define "efficiently served". Having implemented various alternatives and matched these with market outcomes, I have found that this optimal approach is always to believe that certain key indicators are more likely to reflect efficiency than others. These indicators get greater weight than these in the estimation of latent demand compared to others that no known data are available. Of the numerous alternatives, I have found the assumption how the highest aggregate income and highest income-per-capita markets reflect the best standards for "efficiency". High aggregate income alone just isn't sufficient (i.e. some cities have high aggregate income, but low income per capita and will not assumed to become efficient). Aggregate income may be operationalized in the amount of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or amount of households times average household... --This text refers to the Digital edition.