The 2011-2016 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 pretty subtle. The phrase latent typically refers to something that is dormant, not observable, or otherwise yet realized. Demand may be the notion of your economic quantity a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is usually based on economists because the industry earnings of an market when that market becomes accessible and attractive for everyone by competing firms. It is 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 scheduled in a given level inside value chain. There could be latent demand on the retail level, at the wholesale level, the manufacturing level, along with the raw materials level (the P.I.E. better levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability).

The latent need for hospital operating room cabinets, cases, tables, and also other furniture in Greater China is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand may be either lower or maybe more than actual sales if a marketplace is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise coming from a amount of factors, like the insufficient international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is normally greater 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 with this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends). If inflation rates vary in the substantial way in comparison to recent experience, actually sales can also exceed latent demand (not adjusted for inflation). On another hand, latent demand may be typically higher than actual sales since there tend to be distribution inefficiencies that reduce actual sales below the amount of latent demand.

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

THE METHODOLOGY

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

Ignoring, for your 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 aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the common propensity to eat would fall. The average propensity to eat may be the degree of consumption divided from the amount of income, or even the slope from the line through the origin towards the consumption function. He estimated this relationship empirically and located it to become true inside short-run (mostly based on cross-sectional data). The bigger the income, the reduced the typical propensity to consume. This sort of consumption function is labeled "A" within the figure below (note the rather flat slope from the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that this marginal propensity to consume was rather constant (using time series data). This sort of consumption function is shown as "B" inside the figure below (note the higher slope and zero-zero intercept). The normal propensity to use is constant.





Is it declining or is it constant? A quantity of other economists, notably Franco Modigliani and Milton Friedman, inside the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter time horizon, the harder consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to use is more constant. Similarly, inside long run, households without income eventually have no consumption (wealth is depleted). Whilst the debate surrounding beliefs about how income and consumption are related is interesting, with this study a very particular school of thought is adopted. In particular, were taking into account the latent interest in hospital operating room cabinets, cases, tables, and also other furniture through the regions and cities of Greater China. The smallest cities have few inhabitants. I assume that all of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these states having wealth; current income dominates the latent demand for hospital operating room cabinets, cases, tables, and also other furniture. So, latent demand inside long-run includes a zero intercept. However, I allow different propensities to take (including located on consumption functions with differing slopes, which could be the reason of variations in industrial organization, and end-user preferences).

Given this overriding philosophy, I'll now describe the methodology used 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 make use of this methodology with a large quantity of categories, the rather academic discussion below is general and can be applied with a wide selection of categories and geographic locations, not just hospital operating room cabinets, cases, tables, along with 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've found that the optimal approach is always to think that certain key indicators are more probable to reflect efficiency than others. These indicators are given greater weight than the others in the estimation of latent demand in comparison to others which is why no known data are available. Of the many 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 is not sufficient (i.e. some cities have high aggregate income, but low income per capita and may not assumed to get efficient). Aggregate income may be operationalized inside a variety of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of... --This text refers for the Digital edition.