12%
cost to serve reduction
SHAiPE: the LTPlabs framework
Set the Decision
Structure workforce into key activities, roles, and workload drivers
frame critical decisions on staffing, scheduling, and allocation
Define service priorities and cost objectives
constraints, efficiency goals, SLA targets
Highlight what matters
Analyze current workforce, workload, and activity drivers
key activities, roles, workload dynamics
Identify key value drivers and metrics to guide decisions
service levels, workforce utilization, labor cost
Assess current performance and quantify gaps
SLA attainment, occupancy, cost per tasks, productivity per employee
Integrate data into a consistent and actionable baseline
workforce, operational, and demand data
Augment with AI
Breakdown expected demand into required tasks, segregating relevant skills and job locations
Simulate workforce scenarios to evaluate service, cost, and utilization trade-offs, aligning required with expected demand
- DataHistorical demandHistorical workloadWorkforce availabilityOperational constraintsAI ModelResultsExpected service levelWorkforce occupancyStaffing requirements
Prototype your solution
Run what-if scenarios with demand changes and different workforce constraints
Build a scenario generation interface allowing operational and human resources leaders to align needs, test and validate trade-offs, robustness under variability and uncertainty
Expand to scale
Deploy planning tools and enable continuous re-planning based on updated demand and conditions
Integrate into planning cycles and operational decision processes
Monitor KPIs and continuously optimize workforce allocation over time
Nutshell
What this means for your business
Optimized labor cost and improved workforce utilization through data-driven staffing and scheduling decisions
Improved service levels and SLA adherence by aligning workforce capacity with demand patterns
Challenge tasks preparation and anticipation to balance workforce needs, allowing for additional savings
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