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The Big Data Application Strategy for Cost Reduction in Automotive Industry

Journal Article
2014-01-2410
ISSN: 1946-391X, e-ISSN: 1946-3928
Published September 30, 2014 by SAE International in United States
The Big Data Application Strategy for Cost Reduction in Automotive Industry
Citation: Ge, X. and Jackson, J., "The Big Data Application Strategy for Cost Reduction in Automotive Industry," SAE Int. J. Commer. Veh. 7(2):588-598, 2014, https://doi.org/10.4271/2014-01-2410.
Language: English

Abstract:

Cost reduction in the automotive industry becomes a widely-adopted operational strategy not only for Original Equipment Manufacturers (OEMs) that take cost leader generic corporation strategy, but also for many OEMs that take differentiation generic corporation strategy. Since differentiation generic strategy requires an organization to provide a product or service above the industry average level, a premium is typically included in the tag price for those products or services. Cost reduction measures could increase risks for the organizations that pursue differentiation strategy.
Although manufacturers in the automotive industry dramatically improved production efficiency in past ten years, they are still facing the pressure of cost control. The big challenge in cost control for automakers and suppliers is increasing prices of raw materials, energy and labor costs. These costs create constraints for the traditional economic expansion model. Lean manufacturing and other traditional 6 Sigma processes have been widely utilized to reduce waste and improve efficiency in the automotive industry. However, these processes and measures are still a reactive strategy and will not provide break-through impacts on automotive OEMs.
It is very challenging for an organization that pursues differentiation generic strategy to drive down cost without affecting its premium pricing strategy. Big Data technologies, which have evolved rapidly in past ten years in the Information Technology (IT) industry, show promise in linking aggregated real-time customers' application pattern to the product design and manufacturing phase. The Big Data technologies enable automotive OEMs to pursue innovative measures to drive down costs.
When Big Data technologies are used according to typical reactive strategies in cost reduction, they do not bring revolutionary improvement for cost control as well. The ultimate power of Big Data technologies relies on the implementation of new strategies. Real-time data analytics for the adaptive calibration and circular-economy development are given in this paper as Big Data application strategy in the automotive industry.