Assessing location attractiveness for manufacturing automobiles

Edward Hanawalt, William Rouse


Purpose: Evaluating country manufacturing location attractiveness on various performance measures deepens the analysis and provides a more informed basis for manufacturing site selection versus reliance on labor rates alone. A short list of countries can be used to drive regional considerations for site-specific selection within a country.

Design/methodology/approach: The two-step multi attribute decision model contains an initial filter layer to require minimum values for low weighted attributes and provides a rank order utility score for twenty three countries studied. The model contains 11 key explanatory variables with Labor Rate, Material Cost, and Logistics making up the top 3 attributes and representing 54% percent of the model weights.

Findings: We propose a multi attribute decision framework for strategically assessing the attractiveness of a country as a location for manufacturing automobiles.

Research limitations/implications: Consideration of country level wage variation, specific tariffs, and other economic incentives provides a secondary analysis after the initial list of candidate countries is defined.

Practical implications: The results of our modeling shows China, India, and Mexico are currently the top ranked countries for manufacturing attractiveness. These three markets hold the highest utility scores throughout sensitivity analysis on the labor rate attribute weight rating, highlighting the strength and potential of manufacturing in China, India, and Mexico.

Originality/value: Combining MAUT with regression analysis to simplify model to core factors then using a “must have” layer to handle extreme impacts of low weight factors and allowing for ease of repeatability.


automobile, manufacturing, attractiveness, decision making, Footprint, optimization, site selection

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Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2017

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience