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MANUFACTURINGUNDER NDA2023-present

Optimizing production and planning with data

Data-driven optimization of selected production and planning processes in a large automotive operation.

PRODUCTION LINEOEE92 %STATION1STATION2STATION3
Client
Automotive manufacturer (Europe)
Engagement
Development and analysis
Timeline
Ongoing
Stack
Python · data
The challenge

Decisions balanced on spreadsheets and experience.

Part of planning rested on spreadsheets and individual experience. They were looking for a way to back the same decisions with data and compute them repeatably.

Our approach

Build a calculation that can be repeated.

We focused on specific processes, framed them as an optimization problem, and built a calculation in Python that produces consistent, repeatable outputs.

Processes framed as an optimization problem
Calculation in Python
Repeatable, consistent outputs
Inputs for decision-making
OPTIMIZATION MODELOPTIMUMBEFOREAFTERCAPACITYTIMECOST
The outcome

Decisions backed by a calculation.

Data-driven
INSTEAD OF GUESSWORK
Repeatable
CONSISTENT OUTPUTS
Python
OPTIMIZATION

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