Wipro’s robust analytics solution shortened the planning and forecasting cycle by over 90% for an American chain of department stores
Client Background
The client is an American chain of off-price departmental stores, with over 1,200 stores spread across the country.
Industry Landscape
The planning and forecasting process for retail domain is undergoing a major shift. While the group or corporate level top-down approach to budgeting has been adopted by many customers already, the challenges associated with running a profitable retail chain like managing overheads, working with wafer-thin margins has led to the need for detailed store planning. Major retail chains are moving beyond sales and P&L planning to operational areas including merchandise, store operations, labor & manpower and integrating them with corporate financial planning.
Opportunity
The client had no unified system for budgeting, planning & forecasting and no centralized location for corporate plans. Their existing processes were manual, cumbersome and not user-friendly – resulting in an inability to handle multiple versions, iterations, data integrity, robust variance analysis, simulations and alternative scenarios evaluations. They had no system of record for official version of plans or audit-trail for adjustments. Data upload extended to about three days, which led to longer planning cycles.
The client needed to implement a unified and centralized budgeting and planning system that could perform on-the-fly evaluation of alternative scenarios, thus reducing the overall time taken for planning and forecasting.
The Soluition
Wipro enabled the client to implement an Integrated Budgeting Solution using the Hyperion EPM stack on Exalytics appliance. The solution enabled the client to replace existing desktop-based Cognos TM1 solution used for store expenses budgeting and extend the planning process.
Business Impact
Reduced data load time in budget cycles from 2-3 days to 4 hours
Enabled planning for a 5 year time horizon
Reduced IT manual intervention, enhanced accuracy of planning
Including multiple versions
Analyzed stores across 26 different attributes
Key highlights of the engagement are given below:
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