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Content archived on 2024-04-30

High performance parallel computing for medium sized retailers

Objective



Food and non-food retailing are extremely competitive markets. Large retail groups concentrate their efforts on the exploitation of new consumer groups. They have developed information systems which deliver the necessary marketing information and also shorten the time delay between consumer demand and stock replenishment. High performance parallel computing is ultimately required for data warehouses. Present installations at the large retailing chains are based on massively parallel IT systems from US companies. Medium sized retailing companies are concerned that - although high performance computing is considered extremely beneficial for efficient consumer response tracking - these large systems are prohibitively expensive and not scalable to fit the needs of a medium sized company.

By clustering several symmetric multiprocessor nodes, a high performance data warehouse computer can be built at a reasonable cost. However, there is a lack of availability of decision support tools for consumer response tracking. It is not possible to develop these tools with the resources of a single medium sized retailer. The project will (i) satisfy the decision support needs of many medium sized retailers through a scalable solution, (ii) add user value by tailoring the functionality of the tools for retailers, and (iii) build a high performance parallel data loader for collecting sales info from many stores.

The result will be a scalable architecture and a set of pre-configured decision support tools that will help to improve the average trade margins not only overall but also for individual sales outlets, allow benchmarking between different outlets and help to optimize stock replenishment.

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Call for proposal

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Coordinator

Bull Ag
EU contribution
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Address
Theodor-Heuss-Str. 60-66
51149 Koeln
Germany

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Total cost
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Participants (8)