Banking workforce optimisation

optimising workforce allocation across a National banking network

When a regional banking group sought to determine optimal staffing levels across their national branch network, Trusted Data were engaged to develop a workforce optimisation solution that would schedule the right staffing allocation, based on localised customer demand attributes, at each branch

How we captured $22 mn annualised savings through forecasting and workforce scheduling 

Identifying optimal staffing levels across a national network of branches is particularly complex when there is no uniform behaviour between all branches.

With customer demand varying between locations, our Client, a leading banking group with a large network of remittance branches, needed to determine how best to staff each branch based upon localised demand, wihtout compromising customer service levels.

This needed to be undertaken in 2 phases, firstly to assess current staff utilisation rates by branch and shift, before moving on to develop a dynamic scheduling that would provide Branch managers with staffing requirements by shift.

Predicting branch-level customer demand to optimise Staffing levels

Challenge

177 branches and each branch had its’ own pattern of customer footfall.

The Client had limited knowledge on pereceived overstaffing levels and this needed to be clearly defined.

Scheduling was more complex as branches operated 3 daily working shifts and the shift times were not uniform across the network.

The final scheduling solution therefore needed to predict staff requirements by shift at each branch.

Customer footfall data was not immediately captured at entry and exit from the branch and so Trusted Data utilised individual customer service rep transactions (start and end time) to map customers by rep, shift and branch.

 

Solution

As there was no standardised workforce pattern that would fit all branches, dynamic models needed to be built for each branch, considering for customer footfall, shift times and staff headcount. These models would accurately predict staff requirements by:

  • Time of day
  • Day of week
  • Week of month
  • Day of month

The initial insights work uncovered that average staff (UR) utilisation rates (i.e. the shift time that staff were were actually dealing with customers) was only 47% and that nine-tenths of the workforce had less than a 60% utilisation rate.

Trusted Data then modelled a solution that could dynamically re-adjust the staff headcount by branch / shift, by forecasting customer demand for the forward 7 days, based on the historic productivity trend. Forecasts were delayered by morning/afternoon/evening shift to allocate appropriate resources at each branch during their opening hours.

 

 

Annualised Savings in year one

$22 mn

average Staff utilisation rate (ur) increase

48%

WORKFORCE PLANNING HOURS SAVED PER ANNUM

9,200

REDUCTION IN NO. of BRANCHES BELOW TARGET Utilisation

73%