Wipro proposed an analytics based approach to precisely determine the payroll need (workforce allocation) for the store’s expected workload to increase the likelihood of meeting their corporate sales goal. The client used to have an ad-hoc method for payroll allocation/store where in a simple average of historical sales data was arrived at, by the corporate team, to allocate man hours required per store without considering the factors affecting individual stores.
While the client had followed a top-down approach based on sales forecast, Wipro recommended a bottoms-up framework, that factored in the needs of each store, work centers and workload drivers (like number of pull trips, number of returns, Number of Items in Planograms etc.). Instead of relying only on ‘sales’ as the single payroll driver for the stores, Wipro’s framework reviews other factors in store which impact the payroll.
Payroll Optimization Framework
- Identify potential workload drivers impacting payroll hours
- Perform Correlation model to finalise the workload drivers
- Develop forecasting model for the identified drivers
- Develop regression model to forecast the payroll hours based on workload driver forecasts
- Important to align corporate payroll budget with individual store needs
- Optimize payroll allocation based on store performance ranking among their peer group
Key highligts included:
- Alignment of individual store planning with corporate planning: The framework initiated the discovery of all the potential workload drivers and identification of key drivers amongst them. The historical data of 10-15 key drivers for last 12 months for a group of stores at weekly level was collected and analyzed to understand the yearly payroll consumption at weekly level.
- Accelerators and models employed: A forecasting model was developed to estimate the actual payroll requirement based on a Correlation Analysis between drivers and required workforce hours. Wipro employed certain accelerators like maturity framework models, forecasting and optimization models etc
- Store performance ranking: The payroll and labor hour allocation was further optimized based on store performance ranking among peer groups and identifying leader stores with potential to improve top line and laggard stores to enhance their productivity.
- Estimated savings of up to US$100 MILLION
- ~50% more stores were able to meet their sales plan
- 2-4% Y-o-Y improvement in employee productivity
- Enhanced Customer Experience
Wipro optimized payroll hours to the right store at the right time for the right task to meet the corporate productivity goals by gaining sales and improving customer experience. we helped the client in terms of:
Designing a new operating model:
To enable adoption of the current dynamics of the payroll allocation according to the current performance, corporate goals and sales
Redefined the business processes for the corporate level payroll in order to percolate at the store level and provide remove a comprehensive plan for remove store planning.
End- to-End Solution Architect:
Provisioned the solution from identification of the problem to implementation of the solution by creating forecasting and regression models.