Globalization of business means more complexity and retail is a perfect example of this. Given the old adage that retailing is all about having the right product, in the right place at the right time then the science of optimising product ranges at store level and maintaining on-shelf availability is critical and these disciplines were some of the first to be improved with the advent of data warehousing.
Delivering insight for a single country is demanding enough but it is taken to the extreme when the queries required for a huge number of countries all run on the same engine. Look at Metro for instance. Metro operates in 32 countries in Europe, Northern Africa and Eastern Asia, spanning seven time zones. Every single one of its thousands of stores has a unique customer base, from hypermarkets in China to small markets in Paris, and each one needs a tailored assortment to best meet its own customers’ needs.
About three quarters of the goods that Metro sells are supplied by regional producers, which makes sense economically (and ecologically), but which also makes demand chain management more complex. And, just to add another challenge to the list, demand forecasting…
Globalization of business means more complexity and retail is a perfect example of this. Given the old adage that retailing is all about having the right product, in the right place at the right time then the science of optimising product ranges at store level and maintaining on-shelf availability is critical and these disciplines were some of the first to be improved with the advent of data warehousing.
Delivering insight for a single country is demanding enough but it is taken to the extreme when the queries required for a huge number of countries all run on the same engine. Look at Metro for instance. Metro operates in 32 countries in Europe, Northern Africa and Eastern Asia, spanning seven time zones. Every single one of its thousands of stores has a unique customer base, from hypermarkets in China to small markets in Paris, and each one needs a tailored assortment to best meet its own customers’ needs.
About three quarters of the goods that Metro sells are supplied by regional producers, which makes sense economically (and ecologically), but which also makes demand chain management more complex. And, just to add another challenge to the list, demand forecasting and trend analysis is a critical differentiating capability in the competitive landscape of the retail industry. Let me remind you of the point articulated so well by Magnus Lindkvist that trend analysis is always “a race against time”.
All of this describes the business side of the challenge. But how does Metro ensure that its decision support keeps pace with the growing complexity? First of all, it is a matter of performance which obviously depends on the scalability of the data warehouse. (It’s not coincidence that the bedrock of Teradata’s customer base includes many retailers.)
Then there is availability – downtime, whether planned or otherwise is never a good thing, but when you cater for seven time zones, it’s simply not an option and Metro’s “Dual” system effectively ensures this. At the same time, this also means that workload requirements vary during the day. CRM users in East Asia will be working while Europe is asleep, so tactical queries from China that support simple screen refreshes need to be given priority to batch processes in Germany. Therefore, Metro has refined its workload management to ensure that every user group will be serviced at the required level.
Metro runs a powerful and sophisticated data warehousing environment and in his keynote speech at the Teradata Enterprise Intelligence Summit in Berlin, Dr. Gerd Wolfram of Metro will tell us more about how Metro uses its data to respond to changing information needs in a more and more complex business.
