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How Car Makers Use Analytics for Success

In the competitive auto industry, consumer demands are exceptionally high and the costs and burdens of regulatory oversight and manufacturing demands are extreme. As in few other industries, business intelligence (BI) is a mandatory enterprise function and must work to provide real-world efficiencies and clarity of vision across the entire corporate structure.


Business Intelligence

Providing focus and relevance to the massive flow of big data circulating through the levels of pre-production, design, manufacture, delivery, marketing and more, the business intelligence that the auto industry relies on must be thorough, deep, and comprehensive to allow optimal decisions to flow from it.

With the volume of informational output at the scale typical of an enterprise in the automobile industry, the value is derived in the processes of sorting, focusing, and intelligently analyzing the stream of data for presentation to decision makers and company leaders at all levels.

Use of big data can be based on an in-memory architecture able to handle vast quantities with rapid response rates to allow for diverse access and document chaining, or may come from data warehoused in IT infrastructure. A hybrid approach is functional, providing the ability to explore tons of data, both warehoused and in-memory data, giving the enterprise full visibility to the intelligence that supports decision-making.

Processing Big Data

The strength of the enterprise often rises on the value of its chain partners — providers of components and materials at one end and distributors and retail vendors, opposite. With the efficient capability to collaborate up and down chain, the company can decrease the timeframe within which market needs may be met to reduce cost and decrease risk during cycles of product development and rollout.

With visibility in real-time over the range of an extended value chain, manufacturing and operational efficiencies can be maximized with BI acuity aimed at friction spots on the product cycle that can be reduced for smooth flow.

While automotive industry enterprises function in a very strictly regulated market, and development of products come through drawn out and expensive development processes, the strength of BI flexibility and practicality can ameliorate the costly impacts of compliance and support.

Benefits of Analytics

With supply chain complexity well known in the industry, the network among industrial partners can be convoluted and difficult to perceive in complete perspective, without a robust operational intelligence platform that allows a company to:

  • Maximize procurement and sourcing abilities
  • Reduce waste and overages while increasing quality assurance
  • Use real-time insight into production process to coordinate operational value
  • Consider value-added products that contribute to future revenue potential
  • Strengthen all elements of regulatory compliance and risk-management oversight
  • Decrease immediate burdens on IT operations with available analytics

Business intelligence for marketing can help with customer segmentation. “Allocating limited resources to the right, and most profitable, customers and prospects is key to producing the greatest return on marketing investment,” according to Radnor, PA-based QlikView.

Combined with downstream insight into sales figures, marketing methodologies, and consumer attitudes, automotive enterprises that generate operational efficiencies through elimination of barriers to accessing real-time intelligence across all levels of the corporate structure are those that will rise above the field with superior sales and profits.

Using business intelligence for auto manufacturers and support industries is key to understanding the complexity of the market and becoming the company that will stand out from the competition.

Written By

Marvin Dumont writes about trends, lifestyle and business. His work has appeared on Forbes, HuffPost, TheStreet, Yahoo!, Fox News and other outlets. Marvin holds MPA, BBA and BA degrees from the University of Texas at Austin.



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