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 refers to something is dormant, not observable, or not yet realized. Demand may be the notion of your economic quantity which a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is often based on economists because the industry earnings of your market when that market becomes accessible and attractive to serve by competing firms. It is 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 normally expressed because the total revenues potentially extracted by firms. The "market" is defined at the given level inside value chain. There may be latent demand in the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of upper levels from the value chain being always smaller than 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, along with other furniture in India isn't 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 marketplace is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a variety 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 bigger 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 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 in comparison to recent experience, actually sales may also exceed latent demand (not adjusted for inflation). On the opposite hand, latent demand might be typically higher than actual sales since there in many cases are 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 from the players or products involved. In fact, all the current products or services on the market can cease to exist inside their present form (i.e., in a brand-, R&D specification, or corporate-image level) and the players could be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be latent interest in hospital operating room cabinets, cases, tables, as well as other furniture at the aggregate level. Product and repair offerings, and the actual identity in 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 other furniture across the states or union territories and cites of India, I used a multi-stage approach. Before applying the approach, one needs a basic theory that such estimates are created. In this case, I heavily rely around the utilization of certain basic economic assumptions. In particular, there is certainly 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 from the value chain, takes place if an equilibrium is realized. For firms for everyone a market, they need to perceive a latent demand and stay in a situation to serve that demand with a minimal return. The only most significant variable determining consumption, assuming latent demand exists, is income (or other money at higher levels of 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, to 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 use would fall. The typical propensity to use will be the amount of consumption divided from the level of income, or slope with the line from your origin to the consumption function. He estimated this relationship empirically determined it to get true within the short-run (mostly based on cross-sectional data). The bigger the income, the lower the average propensity to consume. This sort of consumption function is labeled "A" in 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 the marginal propensity to eat 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 average propensity to use is constant.





Is it declining or perhaps is it constant? A quantity 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 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 a whole lot more constant. Similarly, inside the long run, households without any income eventually have zero consumption (wealth is depleted). While the debate surrounding beliefs about how exactly income and consumption are related is interesting, within this study a very particular school of thought is adopted. In particular, we have been taking into consideration the latent demand for hospital operating room cabinets, cases, tables, and other furniture through the states or union territories and cities of India. The smallest cities have few inhabitants. I assume that most of the 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 need for hospital operating room cabinets, cases, tables, along with other furniture. So, latent demand within the long-run features a zero intercept. However, I allow different propensities to eat (including located on consumption functions with differing slopes, which can are the main cause of variations in industrial organization, and end-user preferences).

Given this overriding philosophy, I am going to now describe the methodology utilized to produce the latent demand estimates for hospital operating room cabinets, cases, tables, along with other furniture in India. Since ICON Group has asked me to utilize this methodology to your large quantity of categories, the rather academic discussion below is general and could be applied with 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, I've found that this optimal approach would be to feel that certain key indicators are much more likely to reflect efficiency than others. These indicators get greater weight than the others inside the estimation of latent demand in comparison to others for which no known data are available. Of the countless alternatives, We have found the assumption that this 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 being efficient). Aggregate income might be operationalized in the amount of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or variety of households... --This text refers towards the Digital edition.