An overview of driving operational efficiency via people, process, tools, outliers, and automation
Fin Analytics gives customers the ability to do time-and-motion studies at scale. The full-data-set of how each team member is completing specific each task serves as the baseline for a handful of specific strategies that help drive operational efficiency. While there are nuances to how each organization uses fin to optimize, there are 5 key patterns for driving efficiency using the Fin Analytics data set:
(1) Improving People with Better Coaching and Training
With Fin Analytics you can easily identify the top performing team-members by workflow (p80) and compare that to the average (p50). Size the opportunity for improvement and focus coaching on moving average performance ‘up’.
(2) Improving Process
Fin Analytics enables you compare your actual team process (and best demonstrated process) to documented SOP, to find opportunities to improve. We find the maximum leverage for teams that focus process improvement work on the ‘average’ 30th to 70th percentile of cases by workflow type & work on improving process on ‘average’ cases.
(3) Improving Tools
Fin Analytics lets you identify the specific tools and workflow components most heavily used in each given workflow. Focus on improving the speed and efficiency of the most heavily used tools and services first for for maximum impact.
(4) Drive Down Outlier Impact
Using Fin Analytics many teams find that the the slowest 5% of cases take 25%+ of total active operations team time. Focusing on bringing the time spent on those cases down by zeroing in on specific workflows and team members that drive outliers has an outsized impact on efficiency.
(5) Targeted Automation Efforts
Rather than letting averages guide automation, Fin Analytics helps teams look across workflows and target repeated-patterns for automation based on best demonstrated practice. Fin further enables teams to size the automation opportunity and complexity before execution.