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Workforce optimisation for a global accountancy firm

We built a workforce optimisation system for a global accountancy firm's UK audit function, increasing profitability, billable capacity and reducing travel.

Challenge

Our client is one of the largest accountancy firms in the world. Their people are their most important source of value creation, as well as their largest cost base. Naturally, the effective utilisation, and continued well being of their people is a key strategic priority, and one in which we were tasked with solving.

  • 20 data sources to integrate.
  • 60+ business rules to account for.
  • 4,200 employees scheduled.
  • 8,600 clients engagements.

Outcomes

4%

improvement in workforce productivity

14%

reduction in employee travel time

4 weeks

additional billable capacity per person, per year

5%

reduction in staff handover

12 hours

to generate all solutions, compared to 6,400 hours

Outcomes

4%

improvement in workforce productivity

14%

reduction in employee travel time

4 weeks

additional billable capacity per person, per year

5%

reduction in staff handover

12 hours

to generate all solutions, compared to 6,400 hours

Solution

Using expert modelling and advanced optimisation algorithms, we developed a market leading workforce scheduling system, optimising the allocation of auditors to clients. It accounted for over 60 constraints across the business, including client demand, travel, regulations, profitability, employee skill sets, preferences and diversity.  During a short discovery period, we worked with a wide range of stakeholders to mathematically model their exact problem. This included capturing their objectives, their constraints and  relative weightings; adhering to regulations was a hard constraint and could not be broken, maintaining team continuity was a soft constraint, and could be broken if absolutely necessary.

Having understood and modelled 100% of their problem, we then fine-tuned our core components to suit their exact requirements. This included minor adjustments to our Solve Engine, which houses our algorithms, and the rapid development of their unique constraints - made faster by using our pre-existing libraries.

We realised early that trying to shoe-horn a generic product into an operation of this scale and complexity would not work. Instead, we focused on adapting our core technologies to their needs, resulting in the rapid deployment of a solution that had the performance of a custom solution, without the delay.