The 2016-2021 World Outlook for 3D Medical Imaging

1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 14
1.3.6 Step 6. Aggregation and Benchmarking 14
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 14
1.4 Frequently Asked Questions (FAQ) 15
1.4.1 Category Definition 15
1.4.2 Units 15
1.4.3 Methodology 16
2.1 The Worldwide Market Potential 18
3.1 Executive Summary 20
3.2 Algeria 22
3.3 Angola 24
3.4 Benin 24
3.5 Botswana 25
3.6 Burkina Faso 26
3.7 Burundi 27
3.8 Cameroon 28
3.9 Cape Verde 29
3.10 Central African Republic 29
3.11 Chad 30
3.12 Comoros 31
3.13 Congo (formerly Zaire) 32
3.14 Cote d'Ivoire 33
3.15 Djibouti 33
3.16 Egypt 34
3.17 Equatorial Guinea 35
3.18 Eritrea 36
3.19 Ethiopia 37
3.20 Gabon 38
3.21 Ghana 38
3.22 Guinea 40
3.23 Guinea-Bissau 40
3.24 Kenya 41
3.25 Lesotho 43
3.26 Liberia 44
3.27 Libya 44
3.28 Madagascar 45
3.29 Malawi 46
3.30 Mali 46
3.31 Mauritania 47
3.32 Mauritius 48
3.33 Morocco 48
3.34 Mozambique 49
3.35 Namibia 50
3.36 Niger 51
3.37 Nigeria 52
3.38 North Sudan 54
3.39 Republic of Congo 55
3.40 Rwanda 56
3.41 Sao Tome E Principe 56
3.42 Senegal 57
3.43 Sierra Leone 58
3.44 Somalia 58
3.45 South Africa 59
3.46 South Sudan 60
3.47 Swaziland 61
3.48 Tanzania 61
3.49 The Gambia 62
3.50 Togo 63
3.51 Tunisia 64
3.52 Uganda 65
3.53 Western Sahara 66
3.54 Zambia 66
3.55 Zimbabwe 67
4.1 Executive Summary 69
4.2 American Samoa 71
4.3 Australia 72
4.4 Bangladesh 73
4.5 Bhutan 75
4.6 Brunei 75
4.7 Burma 76
4.8 Cambodia 77
4.9 China 77
4.10 Christmas Island 79
4.11 Cook Islands 80
4.12 Fiji 80
4.13 French Polynesia 81
4.14 Guam 82
4.15 Hong Kong 82
4.16 India 83
4.17 Indonesia 85
4.18 Japan 87
4.19 Kiribati 90
4.20 Laos 91
4.21 Macau 92
4.22 Malaysia 92
4.23 Maldives 93
4.24 Marshall Islands 94
4.25 Micronesia Federation 94
4.26 Mongolia 95
4.27 Nauru 96
4.28 Nepal 96
4.29 New Caledonia 97
4.30 New Zealand 98
4.31 Niue 99
4.32 Norfolk Island 99
4.33 North Korea 100
4.34 Palau 101
4.35 Papua New Guinea 101
4.36 Philippines 102
4.37 Seychelles 106
4.38 Singapore 107
4.39 Solomon Islands 107
4.40 South Korea 108
4.41 Sri Lanka 110
4.42 Taiwan 110
4.43 Thailand 111
4.44 The Northern Mariana Island 112
4.45 Timor - Leste, Democratic Republic of 113
4.46 Tonga 113
4.47 Tuvalu 114
4.48 Vanuatu 114
4.49 Vietnam 115
4.50 Wallis and Futuna 116
4.51 Western Samoa 116
5 EUROPE 117
5.1 Executive Summary 117
5.2 Albania 119
5.3 Andorra 120
5.4 Austria 120
5.5 Belarus 121
5.6 Belgium 122
5.7 Bosnia and Herzegovina 123
5.8 Bulgaria 124
5.9 Croatia 125
5.10 Cyprus 125
5.11 Czech Republic 126
5.12 Denmark 127
5.13 Estonia 128
5.14 Finland 129
5.15 France 130
5.16 Georgia 132
5.17 Germany 132
5.18 Greece 133
5.19 Hungary 135
5.20 Iceland 136
5.21 Ireland 137
5.22 Italy 137
5.23 Kazakhstan 139
5.24 Kosovo 141
5.25 Latvia 141
5.26 Liechtenstein 142
5.27 Lithuania 143
5.28 Luxembourg 143
5.29 Macedonia 144
5.30 Malta 145
5.31 Moldova 145
5.32 Monaco 146
5.33 Montenegro 147
5.34 Norway 147
5.35 Poland 148
5.36 Portugal 150
5.37 Romania 151
5.38 Russia 152
5.39 San Marino 155
5.40 Serbia 155
5.41 Slovakia 156
5.42 Slovenia 157
5.43 Spain 158
5.44 Sweden 160
5.45 Switzerland 161
5.46 The Netherlands 162
5.47 The United Kingdom 164
5.48 Ukraine 166
6.1 Executive Summary 169
6.2 Argentina 171
6.3 Belize 174
6.4 Bolivia 175
6.5 Brazil 176
6.6 Chile 179
6.7 Colombia 180
6.8 Costa Rica 182
6.9 Ecuador 183
6.10 El Salvador 184
6.11 Guatemala 185
6.12 Guyana 186
6.13 Honduras 186
6.14 Mexico 187
6.15 Nicaragua 189
6.16 Panama 190
6.17 Paraguay 190
6.18 Peru 191
6.19 Suriname 192
6.20 The Falkland Islands 193
6.21 Uruguay 194
6.22 Venezuela 195
7.1 Executive Summary 197
7.2 Antigua and Barbuda 198
7.3 Aruba 199
7.4 Barbados 199
7.5 Bermuda 200
7.6 Canada 201
7.7 Cuba 203
7.8 Dominica 204
7.9 Dominican Republic 205
7.10 Greenland 206
7.11 Grenada 207
7.12 Haiti 207
7.13 Jamaica 208
7.14 Puerto Rico 209
7.15 St. Kitts and Nevis 210
7.16 St. Lucia 210
7.17 St. Vincent and the Grenadines 211
7.18 The Bahamas 212
7.19 The British Virgin Islands 212
7.20 The Cayman Islands 213
7.21 The U.S. Virgin Islands 214
7.22 The United States 214
7.23 Trinidad and Tobago 216
8.1 Executive Summary 217
8.2 Afghanistan 219
8.3 Armenia 220
8.4 Azerbaijan 221
8.5 Bahrain 222
8.6 Iran 222
8.7 Iraq 225
8.8 Israel 226
8.9 Jordan 227
8.10 Kuwait 227
8.11 Kyrgyzstan 228
8.12 Lebanon 229
8.13 Oman 230
8.14 Pakistan 230
8.15 Palestine 232
8.16 Qatar 233
8.17 Saudi Arabia 234
8.18 Syrian Arab Republic 235
8.19 Tajikistan 237
8.20 The United Arab Emirates 237
8.21 Turkey 238
8.22 Turkmenistan 241
8.23 Uzbekistan 242
8.24 Yemen 243
9.1 Disclaimers & Safe Harbor 244
9.2 ICON Group International, Inc. User Agreement Provisions 245


The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that 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 a 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 a market is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of 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 demand for 3D medical imaging is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a country 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 in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends) and not adjusted for future dynamics in exchange rates. If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level 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. If fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be an international latent demand for 3D medical imaging at the aggregate level. Product and service offering details, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand.


In order to estimate the latent demand for 3D medical imaging on a worldwide basis, I used 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 use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a country, city, state, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources 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), and or changes in utility for the product in question.

Ignoring, for the moment, exogenous shocks and variations in utility across countries, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in 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 consume was rather constant (using time series data across countries). This type of consumption function is show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.

Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in 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 the time horizon, the more consumption can depend on 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, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for 3D medical imaging across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for 3D medical imaging. So, latent demand in the long-run has a zero intercept. However, I allow firms to have different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for 3D medical imaging. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories, not just 3D medical imaging.

Step 1. Product Definition and Data Collection

Any study of latent demand across countries 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 to assume that certain key countries are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries for which no known data are available. Of the many alternatives, I have found the assumption that the world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can 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 income per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).

The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany.

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “3D medical imaging” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within 3D medical imaging falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all countries and the world at large (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, in this report we define the sales of 3D medical imaging as including all commonly understood products falling within this broad category, irrespective of product packaging, formulation, size, or form. Companies participating in this industry include Barco NV, GE Healthcare, Medison America, Philips Medical Systems, and Siemens Healthcare. In addition to the sources indicated below, additional information available to the public via news and/or press releases published by players in the industry (including reports from AMR Research, Global Industry Analysts, Forrester Research, Frost & Sullivan, Gartner, IDC, and was considered in defining and calibrating this category. All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2015).

Step 2. Filtering and Smoothing

Based on the aggregate view of 3D medical imaging as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.

Step 3. Filling in Missing Values

In some cases, data are available for countries on a sporadic basis. In other cases, data from a country may be available for only one year. From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national income. Based on the overriding philosophy of a long-run consumption function (defined earlier), countries which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country.

Step 4. Varying Parameter, Non-linear Estimation

Given the data available from the first three steps, the latent demand in additional countries is estimated using a “varying-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand is assumed to be constant across countries unless there is empirical evidence to suggest that this effect varies (i.e., the slope of the income effect is not necessarily same for all countries). This assumption applies across countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for 3D medical imaging is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries).

This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these countries is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).

Step 5. Fixed-Parameter Linear Estimation

Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for 3D medical imaging is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand 3D medical imaging). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country closest to it on the aggregate consumption function.

Step 6. Aggregation and Benchmarking

Based on the models described above, latent demand figures are estimated for all countries of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.

Step 7. Latent Demand Density: Allocating Across Cities

With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another.

Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the world's cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others.
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Genre World Outlook Report
Pages 245
  • The 2016-2021 World Outlook for 3D Medical Imaging
  • ISBN: 000890353G
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