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WHAT IS LATENT DEMAND AND THE P.I.E.?
The notion of latent demand is quite subtle. The word latent typically identifies something is dormant, not observable, you aren't yet realized. Demand could be the notion of an 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 the market when that market becomes accessible and attractive to offer by competing firms. It is really a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if India is served in an efficient manner. It is typically expressed because the total revenues potentially extracted by firms. The "market" is defined with a given level inside the value chain. There might be latent demand at the retail level, in the wholesale level, the manufacturing level, and also the raw materials level (the P.I.E. of upper levels of the value chain being always smaller than the P.I.E. of levels at lower levels with the same value chain, assuming all levels maintain minimum profitability).
The latent interest in hospital operating room cabinets, cases, tables, and also other furniture in India just isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be either lower or more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from your variety of factors, like the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior about the portion of firms. In general, however, latent demand is usually bigger 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 isn't 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 a substantial way compared to recent experience, actually sales may also exceed latent demand (not adjusted for inflation). On one other hand, latent demand could be typically more than actual sales as there are often distribution inefficiencies that reduce actual sales below the a higher level latent demand.
As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective from the players or products involved. In fact, all the current services or products on the market can cease to exist of their present form (i.e., at the brand-, R&D specification, or corporate-image level) and many types of the players might be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will probably still be latent demand for hospital operating room cabinets, cases, tables, as well as other furniture on 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 interest in hospital operating room cabinets, cases, tables, along with other furniture across the states or union territories and cites of India, I used a multi-stage approach. Before using the approach, one needs a basic theory from where such estimates are created. In this case, I heavily rely about the usage of certain basic economic assumptions. In particular, there's an assumption governing the shape and form of aggregate latent demand functions. Latent demand functions relate the income of an state or union territory, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level with the value chain, occurs if an equilibrium is realized. For firms for everyone a market, they must perceive a latent demand and be capable to serve that demand at a minimal return. The single most critical variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels in the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), or modifications in utility for that product in question.
Ignoring, for the moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption continues to 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 average propensity to consume would fall. The common propensity to take will be the amount of consumption divided with the level of income, or even the slope in the line from your origin to the consumption function. He estimated this relationship empirically and located it being true within the short-run (mostly depending on cross-sectional data). The higher the income, the lower the typical propensity to consume. This kind of consumption function is labeled "A" inside the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated the 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 typical propensity to use is constant.
Is it declining or perhaps is it constant? A amount 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 the time horizon, the greater consumption can depend upon wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to use is much more constant. Similarly, in the long run, households without any income eventually have zero consumption (wealth is depleted). As the debate surrounding beliefs about how precisely income and consumption are related is interesting, with this study a very particular school of thought is adopted. In particular, we have been considering the latent need for hospital operating room cabinets, cases, tables, along with other furniture through the states or union territories and cities of India. 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 these states or union territories having wealth; current income dominates the latent interest in hospital operating room cabinets, cases, tables, and other furniture. So, latent demand within the long-run has a zero intercept. However, I allow different propensities to take (including standing on consumption functions with differing slopes, which could take into account variations in industrial organization, and end-user preferences).
Given this overriding philosophy, Let me now describe the methodology utilized to make the latent demand estimates for hospital operating room cabinets, cases, tables, and other furniture in India. Since ICON Group has asked me to make use of this methodology to your large amount of categories, the rather academic discussion below is general and could be applied to a wide variety of categories and geographic locations, not just hospital operating room cabinets, cases, tables, and also other furniture in India.
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, We have found the optimal approach would be to assume that certain key indicators are more likely to reflect efficiency than others. These indicators receive greater weight than others inside estimation of latent demand compared to others for which no known data are available. Of the many alternatives, We have found the assumption that 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 can not assumed to be efficient). Aggregate income may be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average... --This text refers towards the Digital edition.
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