Abstract:
Increasingly complex global economies are making the process of choosing factory locations
much more difficult for policymakers and investors. In their traditional form, assessments of
factory site locations no longer represent current factors affecting factory placements such as
new and improved transportation infrastructure and the growth of Industry 5.0, and as a result
do not give a long-term perspective on future industrial site placements. Therefore, the
study presents a data-driven approach, combining industrial location theory and modern predictive
analytics, to identify potential future factory locations. The approach recommended
in this study will require that these factors be analyzed on a national level using predictive
analysis techniques to determine the likelihood of future location suitability. Site selection
factors will include analyzing six primary determinants (electric service reliability, transport/logistics
performance, gross domestic product, inflation, trade openness, and political stability)
influencing industrial location decisions in 151 countries from 2000-2024. Various forecasting
techniques, such as vector auto-regression, random forest, XGBoost, linear regression, LSTM,
and a VAR-XGBoost hybrid, determined each factor’s projected 2024 value. MSE and R2
metrics indicated the model’s accuracy. The random forest combination achieved the highest
accuracy. The unique combination random forest achieved the highest level of accuracy. By
also aggregating predicted values into a weighted Composite Factory Suitability Index allows
for the establishment of a predictor of industrial location potential as well future location of
factories. This research offers an adaptive, predictive approach to evaluating factory site suitability,
enabling strategic decision-making for policymakers, investors, and industries globally
in a rapidly changing business environment.
