This client is a provider of innovative technology solutions. They not only dominate the PC market, but venture into high-end computing technology, offering solutions in cloud computing, data storage, and virtual reality.
Our Client
Industry
Technology
Company Size
130,000+
Revenue
$90B+
Location
United States
Objective
To gain the most valuable insights for strategic planning, they wanted to conduct a comparative analysis of workforce productivity across different geographies. Through this research, they hoped to standardize processes with the goal of improving processing times and First Pass Rates (FPRs) . Lastly, they were looking for unbiased automation insights in core process applications to build a well-informed automation strategy.
$13M
Cost Savings
in OPEX savings by building a flatter, faster, more flexible customer service function.
45%
Processing Time
improvement in processing time
Solution
Skan AI began by mapping core processes end-to-end, considering real human work actions and unit costing. This provided a clear understanding of how resources were utilized and enabled real-time measurement of workforce capacity utilization and productivity.
By analyzing the core process variance patterns, Skan AI shared insights on areas for optimization and standardization, providing valuable recommendations for improvement. New data highlighted rework loops and identified the root cause of costly inefficiencies. From these insights, Skan AI generated recommendations for resolution and automation opportunities that could improve future performance.
Outcome
By implementing the insights gleaned through Process Intelligence, the client was able to achieve staggering OPEX savings totaling $13M.
Standardizing activities reduced errors and redundant work, dramatically improving processing time by an impressive 45%. Secondly, the client could see what applications were damaging work efficiency. By removing these tools from employee workflows, they also boosted team productivity by 10-15%.
Lastly, the 34% reduction in average case time was observed by the automation of legacy processes and they also reduced average time per case by 20% by increasing the use of connected systems (with installed customer base).
They also improved customer effort score by 19% (from 1.6 to 1.3).