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WHAT IS LATENT DEMAND AND THE P.I.E.?
The idea of latent demand is pretty subtle. The term latent typically refers to something that is dormant, not observable, you aren't yet realized. Demand is 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 commonly defined by economists as the industry earnings of an market when that market becomes accessible and attractive to offer 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 an efficient manner. It is typically expressed since the total revenues potentially extracted by firms. The "market" is determined in a given level within the value chain. There might be latent demand in the retail level, in the wholesale level, the manufacturing level, along with the raw materials level (the P.I.E. better levels in the value chain being always smaller compared to the P.I.E. of levels at lower levels in the same value chain, assuming all levels maintain minimum profitability).
The latent interest in hospital operating room cabinets, cases, tables, and other furniture in Japan isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand might be either lower or maybe more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise coming from a variety of factors, such as insufficient international openness, cultural barriers to consumption, regulations, and cartel-like behavior about the part of firms. In general, however, latent demand is normally greater than actual sales inside 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 isn't 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 a substantial way when compared with recent experience, actually sales can also exceed latent demand (not adjusted for inflation). On one other hand, latent demand might be typically higher than actual sales as there tend to be distribution inefficiencies that reduce actual sales below the amount of latent demand.
As mentioned inside the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective with the players or products involved. In fact, all the current services or products about the market can cease to exist of their present form (i.e., in a brand-, R&D specification, or corporate-image level) and the gamers might be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will probably still be latent need for hospital operating room cabinets, cases, tables, as well as other furniture with the aggregate level. Product and service offerings, and also the actual identity from the players involved, while very important to 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, along with other furniture across the prefectures and cites of Japan, I oftentimes tried a multi-stage approach. Before using the approach, one needs a basic theory where such estimates are created. In this case, I heavily rely around the utilization of certain basic economic assumptions. In particular, there's an assumption governing the shape and type 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 industry is efficient), at any level of the value chain, occurs if an equilibrium is realized. For firms for everyone a market, they have to perceive a latent demand and become in a posture to 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 of 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 the 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 average propensity to eat would fall. The common propensity to eat could be the level of consumption divided from the amount of income, or the slope with the line from the origin for the consumption function. He estimated this relationship empirically determined it to become true in the short-run (mostly depending on cross-sectional data). The larger the income, the lower the typical propensity to consume. This type of consumption function is labeled "A" inside the figure below (note the rather flat slope with 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" within 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 amount of other economists, notably Franco Modigliani and Milton Friedman, inside 1950s (and Irving Fisher earlier), explained why the 2 functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to eat is much more constant. Similarly, inside the long run, households with no income eventually don't have any consumption (wealth is depleted). Whilst the debate surrounding beliefs about how precisely income and consumption are related is interesting, on this study an extremely particular school of thought is adopted. In particular, we have been thinking about the latent need for hospital operating room cabinets, cases, tables, as well as other furniture through the prefectures and cities of Japan. The smallest cities have few inhabitants. I assume that most of those cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of the prefectures having wealth; current income dominates the latent need for hospital operating room cabinets, cases, tables, and other furniture. So, latent demand inside long-run includes a zero intercept. However, I allow different propensities to take (including being on consumption functions with differing slopes, which could be the cause of differences in industrial organization, and end-user preferences).
Given this overriding philosophy, I will now describe the methodology accustomed to make the latent demand estimates for hospital operating room cabinets, cases, tables, and also other furniture in Japan. Since ICON Group has asked me to use this methodology to your large quantity of categories, the rather academic discussion below is general and may be applied to a wide selection 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 have found that the optimal approach is always to assume that certain key indicators are more likely to reflect efficiency than others. These indicators get greater weight than these within the estimation of latent demand when compared with others that no known data are available. Of the countless alternatives, I've found the assumption that 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 can not assumed to be efficient). Aggregate income might be operationalized in the number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or quantity of households times average household income).
Latent demand... --This text refers for the Digital edition.
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