| Helping a retail company synchronize its supply chain data warehouse initiative |
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| The customer |
| Our customer is one of the largest sports good retailer and manufacturer in the world selling athletic footwear, apparel and equipment. |
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| The challenge |
| The client wanted to synchronize its "Sell", "Plan-Buy", "Make" and "Deliver" processes and systems with its factories, suppliers, service providers and retailers. For the success of this initiative, the client decided to implement a supply chain data warehousing solution. The data warehouse was built to address four functional groups - planning, sales, logistics and finance. As several source systems were involved, the client faced challenges to extract, transform and load the source data. It also needed IT consulting for analysis and modeling of data warehouse. |
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| The solution |
Team Wipro overcame the following initial challenges
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Providing flexible data warehouse infrastructure to accommodate DSS specific requirements.
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Microstrategy tool's alignment towards snow flaked schemas |
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Database views, materialized views to address complex Brio reports |
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Non-complex joins for MS Access users |
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Data mart implementation against highly dynamic source system changes |
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Building a staging area to address requirements of downstream systems other than the data mart |
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Understanding and modeling data relationships from complex systems such as SAP, I2 demand planning/supply chain planning, Siebel, etc... |
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Accommodating for the different kinds of seasons as well as differing history requirements on product, season, etc. |
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Huge volumes of data and a small load window to load |
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Working around tool challenges |
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Modeling an hybrid Product dimension accommodating for the SCD type I, II requirements |
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Modeling data for the SAP centric view and providing flexibility for legacy system data view |
In order to avoid long running maps, all the fact loads were split into small and simple mappings that loaded temporary tables concurrently, which were eventually used to load the facts. Oracle's processing power was harnessed by maximizing the use of the source qualifier queries and was used over Informatica's processing capabilities, which led to faster load times. Job control tables and good design of staging tables ensured good restart and reprocessing capabilities, which has always been tools' Achilles Heel. Temporary tables and efficient maps ensured that the true increment was captured without compromising on the load time, hence ensuring good data quality without hitting the performance. The following technologies were used
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Hardware: HP-UNIX N9000 running HP-Unix Ver 11 |
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Data Warehouse and Staging Database: Oracle 8.1.7.0.0 |
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Data ETL: Informatica Power Center 5.0 |
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Modeling Tool - Erwin 3.5, ModelMart 3 |
The solution has helped client to reduce Inventories without sacrificing on stock-outs, plan the product distribution, marketing efforts and promotions and track the sales team's performance. |
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