Assessing location attractiveness for manufacturing automobiles

Journal of Industrial Engineering and Management JIEM, 2017 – 10(5): 817-852 – Online ISSN: 2013-0953 – Print ISSN: 2013-8423 https://doi.org/10.3926/jiem.2321 Assessing Location Attractiveness for Manufacturing Automobiles Edward Hanawalt , William Rouse General Motors, Stevens Institute of Technology (United States) , Received: April 2017 Accepted: July 2017 Abstract: 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 show 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. -817- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.2321 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. Keywords: automobile, manufacturing, attractiveness, decision making, footprint, optimization, site selection 1. Introduction The question of where to manufacture and sell goods is paramount for any durable goods manufacturer. Expanding sales into global markets or stretching a supply chain to provide goods from factories thousands of miles away impacts not only the bottom line of an enterprise, but also increases financial and supply chain risks and a companies’ brand image. In “Car Wars”, we discussed the factors driving product success and failure in the automotive market from a product perspective (Hanawalt & Rouse, 2010). “Car Wars” evaluated success or failure in the United States only. However, today’s decisions for durable goods manufacturers are predominately global in nature. Once a product has been planned for development, often the next step is to determine where to sell it and where to build it. While integrated product development processes do most of this work in parallel today, extension of sales into additional markets to further spread out fixed costs or the creation of additional manufacturing footprints is still a very common exercise. The leanest supply chain is ideally as close to the customer as possible. By building the product close to the customer, the time for the product to get to market and inventory to support the supply chain is minimized. Ideally the “build where you sell” principle increases responsiveness to customer preferences and reduces cost. However, few multi-national companies have enough capital or resources to build production facilities in every market a product is sold in. Given the constraints, companies work to optimize profit by delivering products to market at the lowest cost possible. This paper presents a two-step multi-attribute decision framework for assessing the attractiveness of a country as a location for manufacturing automobiles. -818- Journal of Industrial Engineering and Management – https://doi.org/10.3926/jiem.2321 2. Traditional Manufacturing Allocations The traditional mindset of manufacturers has always been to seek the lowest labor costs in manufacturing location selection. However, the lowest labor cost does not always prove to the best choice for the company in terms of financial returns or risks to a firm’s reputation. The decision to manufacture in a country is a long-term commitment by a company, often 20 years without incurring any financial penalties in terms of write downs of assets or the refunding of government incentives. A wrong bet can transform what should be a competitive advantage into a mess of underutilized or high-cost assets (Lamarre, Pergler & Vainberg, 2009). Much has been written about the outsourcing and insourcing of manufacturing jobs over the last two decades. BCG recently issued a report citing the shifting economics of global manufacturing which identified a trend away from pure low cost manufacturing locations (The Boston Consulting Group, 2014). Manufacturing location selection is much more complicated than purely seeking a country with the lowest labor costs. The location decision needs to be evaluated over various performance measures to ensure a robust decision. Often the high growth potential of an emerging market prompts businesses to invest in order to capitalize on the growth. But too often, as has been the case in India, the results are only news of scams, cases of graft, endemic corruption, enforcement and whistle-blowers (EY, 2014). The stability of the local governments impact manufacturers when issues such as foreign exchange controls can grind production to a halt when dollars are not available to import raw materials, as has been the case in Egypt (Voice of America, 2016). 3. Literature Review The ideal location of facilities is a very broad area of research particularly due to its importance to multi-national corporations operating in the global economy. Most models employ an empirical approach to finding the lowest cost or minimizing shipping distances. Badri el al developed models that supplement or complement traditional approaches to industrial location analysis (Badri, Davis & Davis, 1995). Hoffman and Schniederjans present a two‐stage model that combines the concepts of strategic management, the management science technique of goal programming, and micro computer technology to provide managers with a more effective and efficient method for evaluating global facility sites and making selection decisions (Hoffman & Schniederjans, 1996). Bartelsman et al. evaluated the cross-country variation in the within-industry covariance between size and productivity through an empirical model (Bartelsman, Haltiwanger & Scarpetta, 2013). Bozarth and colleagues model supply chain complexity and empirically test i (...truncated)