- OperationsIndustry
- Operations teamsClient
- Automation & OptimizationCategory
- Phased rolloutTime to impact
The challenge
- Assigning work by hand means juggling skills, current load, vacations and deadlines in someone’s head.
- The result is uneven, slow, and often the wrong person on the wrong task.
Assigns each task to the right person by skill, capacity and constraints, so work gets done faster and better.
An optimizer that looks at a department’s people, their skillsets and constraints like capacity and vacations, and decides who should take each task. It aims for the assignment most likely to deliver fast, high-quality work in the least time.
What we built
- 01Models each department’s people, skills and capacity.
- 02Factors in constraints like load, vacations and deadlines.
- 03Optimizes who should take each task.
- 04Aims for the fastest, highest-quality outcome.
How it works
What it can do
Skill matching
Routes tasks to the right skillset.
Capacity-aware
Accounts for load, vacations and availability.
Constraint optimization
Balances all the constraints at once.
Speed & quality
Optimizes for fast, high-quality delivery.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
An optimizer that looks at a department’s people
The team lead
Decideswhether to accept the assignment. The optimizer proposes, it does not assign
Seesthe proposal with the skills, load and constraints that produced it
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
Each task goes to the person most likely to do it fast and well, given who is actually available.
The right person on the right task.
Work balanced across the team.
Faster delivery at higher quality.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Model layer
- Optimization
- Constraint solver
- ML
Common questions
The same answers as above, written out.
An optimizer that looks at a department’s people, their skillsets and constraints like capacity and vacations, and decides who should take each task. It aims for the assignment most likely to deliver fast, high-quality work in the least time.
Assigning work by hand means juggling skills, current load, vacations and deadlines in someone’s head. The result is uneven, slow, and often the wrong person on the wrong task.
Feed in the team, skills and constraints. The optimizer scores possible assignments. It proposes who should take each task.
The team lead decides whether to accept the assignment. The optimizer proposes, it does not assign. They see the proposal with the skills, load and constraints that produced it.
Model layer: Optimization, Constraint solver, ML.
The right person on the right task. Work balanced across the team. Faster delivery at higher quality.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
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