There will be a special automotive track at the Teradata Enterprise Intelligence Summit in Berlin: Representatives from Daimler and component supplier Continental AG will explain to what 
already using data warehousing for quality analysis and quality assurance. Considering that quality problems have been pestering various manufacturers recently, I think that they will be addressing a very relevant field.
It seems that while some industries, such as retail, banking and telecommunications, are quite mature in the sense that data warehousing is widely seen as key to market success, other industries like the utilities and oil production are still very much at an early stage. The automotive sector, and manufacturing in general, seems to be somewhere in between. There is a vast potential of data warehousing…
There will be a special automotive track at the Teradata Enterprise Intelligence Summit in Berlin: Representatives from Daimler and component supplier Continental AG will explain to what
already using data warehousing for quality analysis and quality assurance. Considering that quality problems have been pestering various manufacturers recently, I think that they will be addressing a very relevant field.
It seems that while some industries, such as retail, banking and telecommunications, are quite mature in the sense that data warehousing is widely seen as key to market success, other industries like the utilities and oil production are still very much at an early stage. The automotive sector, and manufacturing in general, seems to be somewhere in between. There is a vast potential of data warehousing in automotive. My colleague Martin Willcox has pointed out some of it in another post, including early warning systems that identify component failures, making it possible to replace them proactively (and thus in a customer-friendly way). The most obvious application is, of course, supply chain management (SCM).
Now let’s look at the German automotive industry, for example. A recent study by European Business School, a university near Frankfurt, suggests that the potentials are far from fully exploited here: four out of five decision-makers feel inadequately supported by their intelligence systems. There seems to be a patchwork of analytical systems that allow for a limited perspective rather than a holistic view. Analysts seem to spend a great deal of time preparing data when they should be analyzing them. Unsurprisingly, only a minority has capabilities like early warnings (35 percent) or event-based analytics (33 percent). Which basically sounds exactly like what we heard from other industries a couple of years ago.
Building an all-encompassing, reliable data base in the automotive industry is difficult simply because there is a lot of complexity in its supply and production chains: global sourcing networks, myriads of components etc. (On the other hand, this is precisely why the benefits would be so high.) It requires strong determination from top management as well as a great deal of cooperation between suppliers and producers. In some cases, this process could be nudged along by an unexpected driver: IT efficiency. With Teradata manufacturers will be able to consolidate various systems in a centralized data warehouse to save operating costs – and integrate their data sources at the same time. The main driver, of course, will be competition and the need for more efficient processes, just like in retail, telecommunications and other industries in recent years.