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WHAT IS LATENT DEMAND AND THE P.I.E.?
The notion of latent demand is pretty subtle. The phrase latent typically refers to something which is dormant, not observable, or not yet realized. Demand may be the notion of the economic quantity which 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 since the industry earnings of your 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 Japan is served in a efficient manner. It is usually expressed as the total revenues potentially extracted by firms. The "market" is defined at the given level inside the value chain. There might be latent demand on the retail level, on the wholesale level, the manufacturing level, as well as the raw materials level (the P.I.E. of upper levels in the value chain being always smaller compared to 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, along with other furniture in Japan is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand could be either lower or maybe more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from the variety of factors, such as the not enough 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 very 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 never made, though one is implied). The units used on 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 compared to recent experience, actually sales also can exceed latent demand (not adjusted for inflation). On the other hand, latent demand might be typically greater than actual sales since there are often 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 of the current services or products about the market can cease to exist in their present form (i.e., at the brand-, R&D specification, or corporate-image level) and all sorts of the players could be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there'll nevertheless be latent need for hospital operating room cabinets, cases, tables, and other furniture in the aggregate level. Product and service offerings, along with 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 need for hospital operating room cabinets, cases, tables, and also other furniture through the prefectures and cites of Japan, I oftentimes tried a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the usage 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 an prefecture, 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, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and become capable to serve that demand at the minimal return. The single most critical variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels with the value chain). Other factors that will pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), as well as modifications in utility to the product in question.
Ignoring, for your moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one facet of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the common propensity to use would fall. The average propensity to take will be the a higher level consumption divided by the level of income, or perhaps the slope from the line in the origin towards the consumption function. He estimated this relationship empirically determined it to become true in the short-run (mostly based on cross-sectional data). The higher the income, the low the common propensity to consume. This type of consumption function is labeled "A" in 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 eat was rather constant (using time series data). This type of consumption function is shown as "B" inside the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.
Is it declining or possibly it constant? A variety of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the 2 functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter enough time horizon, the greater consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is a whole lot more constant. Similarly, inside long run, households without income eventually don't have any consumption (wealth is depleted). Whilst the debate surrounding beliefs about how exactly income and consumption are related is interesting, on this study a really particular school of thought is adopted. In particular, we have been thinking about the latent demand for hospital operating room cabinets, cases, tables, along with other furniture throughout the prefectures and cities of Japan. The smallest cities have few inhabitants. I assume that all of those cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of those prefectures having wealth; current income dominates the latent need for hospital operating room cabinets, cases, tables, and also other furniture. So, latent demand within the long-run has a zero intercept. However, I allow different propensities to use (including located on consumption functions with differing slopes, which could take into account variations in industrial organization, and end-user preferences).
Given this overriding philosophy, I'll now describe the methodology used to create the latent demand estimates for hospital operating room cabinets, cases, tables, and other furniture in Japan. Since ICON Group has asked me to utilize this methodology with a large quantity of categories, the rather academic discussion below is general and can be applied to some wide variety of categories and geographic locations, not just hospital operating room cabinets, cases, tables, and also other furniture in Japan.
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 the optimal approach would be to assume that certain key indicators are much more likely to reflect efficiency than others. These indicators receive greater weight than the others in the estimation of latent demand in comparison to others that no known data are available. Of the numerous alternatives, We've found the assumption how the highest aggregate income and highest income-per-capita markets reflect the best standards for "efficiency". High aggregate income alone isn't sufficient (i.e. some cities have high aggregate income, but low income per capita and will not assumed to be efficient). Aggregate income might be operationalized in a number 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 households times average household... --This text refers for the Digital edition.
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