Several factors guide the selection of which countries a business locates and operates within. The Global Competitiveness Report (GCR) is a yearly report published by the World Economic Forum which ranks countries based on the Global Competitiveness Index (Wikipedia).
Labor factors are often a driving force in country selection. The International Labor Comparisons Program (ILC) of the U.S. Bureau of Labor Statistics (BLS) adjusts economic statistics (with an emphasis on labor statistics) to a common conceptual framework in order to make data comparable across countries. Its data can be used to evaluate the economic performance of one country relative to that of other countries and to assess international competitiveness (Wikipedia).
Location intelligence tools tools leverage a variety of data sources including aerial maps, geographic information systems (GIS), consumer demographics as well as a user’s own customer records (Wikipedia). Exchange rate risks are inherent in these decisions (Wikipedia). The total cost of operations must include all business costs for operations in a country (tangible and intangible). Political risk, values and culture are common intangible factors. The level of corruption is also a factor. Transparency International (TI) publishes the Corruption Perceptions Index (CPI) annually ranking countries "by their perceived levels of corruption, as determined by expert assessments and opinion surveys." The CPI generally defines corruption as "the misuse of public power for private benefit." As of 2010, the CPI ranks 178 countries "on a scale from 10 (very clean) to 0 (highly corrupt)." (Wikipedia)
Once country location has been identified, the specific region for operations is selected.
The weighted central of gravity method is a logistics decision modeling technique that attempts to identify the “best” location for a single warehouse, store or plant, given multiple demand points that differ in location and importance.
Weighted X coord = X* = Sum (Wi * Xi) / Sum (Wi)
Weighted Y coord = Y* = Sum (Wi * Yi) / Sum (Yi)
|Store Location||X Coordinate||Y Coordinate||Number Shipped|
X opt = [(30*2000) + (90*1000) + (130*1000) + (60*2000)] / [2000 + 1000 + 1000 + 2000] = 66.7
Y opt = [(120*2000) + (110*1000) + (130*1000) + (40*2000)] / [2000 + 1000 + 1000 + 2000] = 93.3