Manufacturing & Engineering · HPC
Snowflake · Python · Power BI
Two six-month windows, job-level data
Delivered · back-tested against a live year

CSCapacity decision support

How many cores do we actually need?

A global engineering group had bought more cores the year before, and it had worked: queue times fell by three quarters while the workload grew. The open question was how long that would hold. We built a simulation on their own job logs, proved it by having it predict a year it had never seen, and turned the next hardware decision into a single number the business owns.

Discrete-event simulation
Medallion pipeline
Back-tested to two decimal places
Runs inside the client's estate
Outcome
delivered
−75%
95th-percentile wait, year on year
2.56 h
predicted and observed, on a year the model had never seen
+9%
more cores answers a 25% workload increase
~1,500
cores beyond which more hardware stops buying time

The client

A global engineering group running large-scale simulation workloads - structural and crash analysis, fluid dynamics, and the overnight batch. Identity withheld at the client's request.

The challenge

Engineering was asking for more cores. Average utilisation read 54%, which made the case look weak. Nobody could price the next purchase.

The build

A discrete-event simulation that replays the real job trace under a chosen growth rate and core count, on a medallion pipeline inside the client's own warehouse.

The delivery

A Power BI report the committee drives itself, a companion screen that plays a scenario out visually, and a published record of where the model has and has not been tested.