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Back to Work/TextileMode Capacity Engine
Client workRunning in production[ev-tm-01]

TextileMode Capacity Engine

Context: Mid-market textile manufacturer operating 27 industrial looms with daily batch constraints.

The Operational Problem

“Daily scheduling and production routing across 27 industrial looms required 10 dispatch clerks running manual spreadsheets, managing PKR 3 crore ($107,000/mo) in monthly throughput with persistent delivery backlogs.”

Timeline8 weeks from diagnostic sprint to floor deployment, 18+ months live in continuous production
ClassificationClient work
StatusRunning in production
Truth Gate Evidence[ev-tm-01]
Operational Constraints

Real-world boundaries

▸Must coordinate 27 industrial looms simultaneously
▸Zero tolerance for inventory over-commitments
▸Yarn color batching & changeover constraints
Engineered Solution

What was deployed

Engineered an automated capacity-matching system calculating loom throughput, yarn lots, and lead times with strict constraint satisfaction and continuous production oversight.

How it is kept safe
Deterministic checksums on floor batch allocations
Human review operator approval gate before loom dispatch
Immediate alert if yarn lot numbers mismatch warehouse ledger
Measured Outcomes

Verified results

10 clerks → 1 operator
Method: Dispatch staffing compressed from manual spreadsheets to 1 review operator
Evaluation Period: 18+ months live in continuous production
PKR 3 crore ($107k/mo)
Method: Continuous monthly loom production managed across 27 looms
Evaluation Period: 18+ months live in continuous production

“We replaced a room of 10 dispatch clerks with 1 review operator. All 27 looms run on predictable batches with zero scheduling backlogs.”

— Operations Director, Textile Manufacturing Client (27 Industrial Looms)
What Went Wrong & What Changed

The friction encountered

Friction: Initial algorithm did not account for 15-minute loom warm-up cycles, which caused minor shift-start discrepancies in week one.

Remediation: Added calibration offsets for mechanical warm-up into the dispatch schema.

Post-Mortem Reflection

What I'd do differently

Would have conducted on-site line observation during shift changeovers earlier in discovery.

Engineering Stack: Next.js, PostgreSQL, TypeScript, Tailwind CSS, Docker
Next Steps

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