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

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

The idea of latent demand is quite subtle. The word latent typically describes a thing that is dormant, not observable, or otherwise yet realized. Demand will be the notion of the economic quantity that the target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is often defined by economists since 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 India is served in an efficient manner. It is usually expressed since the total revenues potentially extracted by firms. The "market" is defined in a given level inside the value chain. There can be latent demand at the retail level, with 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 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, along with other furniture in India just isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand may be either lower or higher than actual sales if a marketplace is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise coming from a amount of factors, such as 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 larger 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 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 in comparison to recent experience, actually sales may also exceed latent demand (not adjusted for inflation). On the opposite hand, latent demand could be typically greater than actual sales as there tend to be distribution inefficiencies that reduce actual sales below the a higher level latent demand.

As mentioned within 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 on the market can cease to exist inside their present form (i.e., in a brand-, R&D specification, or corporate-image level) and all sorts of the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there'll be latent interest in hospital operating room cabinets, cases, tables, along with other furniture at the aggregate level. Product and service offerings, along with the actual identity in 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 through the states or union territories and cites of India, I used a multi-stage approach. Before using the approach, one needs a basic theory that such estimates are created. In this case, I heavily rely on the usage 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 your state or union territory, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an marketplace is efficient), at any level with the value chain, happens if an equilibrium is realized. For firms for everyone a market, they have to perceive a latent demand and become capable of serve that demand at the minimal return. The one most critical variable determining consumption, assuming latent demand exists, is income (or other savings 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), or modifications in utility for the product in question.

Ignoring, to 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 facet of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the typical propensity to eat would fall. The common propensity to take may be the amount of consumption divided through the a higher level income, or the slope of the line through the origin for the consumption function. He estimated this relationship empirically and found it to be true inside short-run (mostly based on cross-sectional data). The bigger the income, the reduced the common propensity to consume. This type of consumption function is labeled "A" within 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 that the 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 typical propensity to consume is constant.





Is it declining or perhaps is it constant? A amount of other economists, notably Franco Modigliani and Milton Friedman, within 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 enough time horizon, the harder consumption can depend upon wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households without any income eventually have zero consumption (wealth is depleted). Whilst the debate surrounding beliefs about how precisely income and consumption are related is interesting, with this study an extremely particular school of thought is adopted. In particular, we are thinking about the latent interest in hospital operating room cabinets, cases, tables, and also other furniture across the states or union territories and cities of India. The smallest cities have few inhabitants. I assume that most of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of those states or union territories having wealth; current income dominates the latent need for hospital operating room cabinets, cases, tables, along with other furniture. So, latent demand in the long-run includes a zero intercept. However, I allow different propensities to eat (including located on consumption functions with differing slopes, which may account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I am going to now describe the methodology accustomed to make the latent demand estimates for hospital operating room cabinets, cases, tables, and also other furniture in India. Since ICON Group has asked me to apply this methodology to a large quantity of categories, the rather academic discussion below is general and may be applied to some wide number of categories and geographic locations, not just hospital operating room cabinets, cases, tables, along with 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, I have found that the optimal approach is always to think that certain key indicators are much more likely to reflect efficiency than others. These indicators get greater weight than the others inside estimation of latent demand compared to others which is why no known data are available. Of the countless alternatives, We'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 is not sufficient (i.e. some cities have high aggregate income, but low income per capita and can not assumed to get efficient). Aggregate income could be operationalized in a very quantity 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... --This text refers on the Digital edition.