Resources Case Study
Delivered actionable intelligence for replenishment and procurement planning by processing data of over a million SKUs across 250 stores country-wide.
Landmark group is a multinational conglomerate based in Dubai, UAE. The group is in retail business of apparel, footwear, consumer electronics, cosmetics & beauty products, home improvement and baby products and many more and operates over 2,300 outlets, encompassing over 30 million square feet across 22 countries. With over 50 world class brands, Landmark offers customers a diverse portfolio of own and franchise brands that have grown into category leaders.
The finance team of Max fashions had to go to the IT team for each query, sometimes slow system processing and other teams’ reports piled up with the IT team made everything time consuming and because of that delayed decisions making became a challenge for the finance team.
For querying and reporting, SQL and Microsoft Excel were used respectively, but the limitation of SQL and Excel created a challenge for the planning team because the process of commanding for various queries on SQL is time consuming. After fetching the data from SQL from the IT team, the reports to be made on Excel was done manually by the finance team. And above that, to provide analytical reports on Excel required highly skilled manpower which again created a challenge of being costly and time consuming.
The goal was to reduce time by automating the entire system to simplify the process, reduce the manpower, implement analytics team to provide faster and better analytical reports to the finance team.
Max fashions had the license of SAS but was not clear about how to move ahead and implement it and, hence, GainInsights came as an expertise in SAS to implement it. It also had Oracle DB where they stored the data and data was extracted from the same database.
Initially, queries were processed through SQL which was time consuming. When SAS Enterprise Guide, the backend tool of SAS was implemented, it took down to just 4 days from 15 days to convert the entire 500 GB data of 2 years. But, to further reduce the 4 days lag, Informatica, a backend tool was recommended and implemented which could extract, transform and load the data in one day. This helped the planning team to process the data quicker and solve any of their queries in just a day or two.
Earlier, when the data was converted, reports were created using Microsoft Excel but because of its limitation, i.e. manual and highly skilled manpower to provide reports, the finance team could not get analytical reports in real-time to improve on their planning and decision making for various divisions such as product analysis, size analysis, trend analysis, time hierarchy, location and seasons. Hence, SAS Visual Analysis was implemented to overcome this challenge which was an automated tool and required very less manpower where different dashboards were created which would provide information to the finance team on trend analysis, size analysis, price band analysis, sales mix, selling price VS cost price and promotional performance. This enabled the finance team to visually explore the data on interactive dashboards based on a variety of measures and at a faster speed.