Soft Goods Manufacturing — Istanbul

The estimate is the symptom. The system is the case.

I work with manufacturers and engineering teams in soft goods industries — garment, automotive interiors, medical, and technical textiles — to diagnose and resolve spreading, cutting-room, and production-planning problems before they invest in the wrong fix.

Send a production problem →

§ 01 — Problems I encounter

The spreading estimate says 40 minutes. The floor takes 65. Nobody can explain the gap.

Machine data exists in the controller but never reaches planning.

The cutting room is scheduled manually across machines with different fabric-weight and table-length limits.

The system performs in isolation but fails under roll changes, splices, and real operator conditions.

A new system or process change is about to be purchased — but the underlying problem has not been formally defined.

The planning model assumes linear throughput. The machine does not behave linearly.

§ 02 — Method

Observe

Collect facts, constraints, failures, use cases, and unknowns.

Isolate

Separate symptoms from causes.

Model

Turn the problem into a technical, commercial, or operational model.

Test

Validate assumptions with prototypes, calculations, or field evidence.

Decide

Choose the simplest path: stop, redesign, build, or scale.

Develop

Move from diagnosis to practical implementation.

§ 03 — Case files

Case 01 / 2024–2025

Spreading time prediction under real factory conditions

Observed Planning estimates diverged from actual spreading cycles by an unacceptable margin. No reliable model existed to predict machine throughput across material types, speeds, or spreading modes.

Hidden cause Throughput is not a linear function of speed. End-loss geometry, acceleration ramps, and mode-dependent behavior introduce non-linearities that simple estimates ignore.

Intervention Physics-based calculation engine modeling trapezoidal motion profiles, end-loss geometry, and spreading mode logic.

Outcome RMSE 0.52 s validated against 2,500+ real production cycles.

0.52 s

Prediction error (RMSE) — accurate enough to replace manual estimates in production planning

2,500+

Real production cycles validated against the model

Case 02 / 2025–

IoT intelligence layer for legacy spreading machines

Observed Cutting room operators had no real-time data on machine performance, cycle times, or throughput — even on recently manufactured equipment.

Hidden cause Machine electronics generate signals but do not expose them. The gap is not hardware capability — it is data extraction and contextualization.

Intervention ReLay: a retrofit intelligence module that captures machine signals, contextualizes them against the spreading plan, and surfaces actionable throughput data.

Deployment Piloted at a garment manufacturer with production facilities in Turkey and Egypt.

Outcome Spreading intelligence, retrofitted. Cycle-level performance data made visible for the first time. No machine replacement. No workflow change.

Case 03 / 2023–

Cutting room scheduling as a combinatorial optimization problem

Observed Cutting rooms with multiple spreading machines plan job sequences manually — assigning hundreds of lays across machines with different fabric weight capacities, without systematic visibility into total makespan, machine utilization, or order continuity.

Problem class Parallel machine scheduling (Pm ‖ C_max). NP-hard. Constraints include machine-fabric compatibility (GSM), table length, order continuity (lays from the same order should not be fragmented across distant time slots), and realistic job durations that depend on marker geometry, ply count, spreading mode, and motion physics — not flat averages.

Measurement Agent software runs on each machine's on-board PC. It records actual cycle times, roll change durations, defect interventions, splice events, and setup transitions — per ply, per job, per machine. This produces a ground-truth performance dataset that feeds both schedule validation and model refinement.

Intervention GA Planning: a Genetic Algorithm scheduler that encodes the full cutting room schedule as a chromosome. Each gene represents a job-to-machine assignment. The algorithm evolves a population of complete schedules, evaluating each against a composite fitness function that balances makespan, idle time, and order continuity. An event-driven simulator computes realistic timelines — including roll changes and setup — rather than summing nominal durations.

Technical layer

Fitness

F = (T_ref / T_total) − (T_idle / T_ref × 0.1)
With order continuity: F = F_base × 0.6 + S_order × 0.4

Stagnation escape

Adaptive mutation rate — rises automatically when population fitness plateaus. Population restart preserves the elite cohort and regenerates the remainder to break out of local optima.

Machine constraints

Hard-encoded in the chromosome: fabric weight (GSM) determines eligible machine subset. Jobs violating machine capacity cannot appear in a valid schedule — infeasible assignments are rejected at gene level, not penalized post-hoc.

Cost function

Job duration is computed by a physics-based spreading time engine (OCalc) — trapezoidal motion profile, end-loss geometry, mode-dependent behavior — replacing historical averages. This makes the optimizer as accurate as the underlying machine model.

Outcome Near-optimal spreading sequences across a mixed-machine fleet, generated in seconds. Order integrity preserved. Machine utilization made visible and measurable for the first time.

Spreading machines (example config)

18 m

Table capacity modeled

5,000

Generations, parallel fitness calc

§ 04 — How I think

From factory symptoms to technical decisions.

My background is in industrial engineering and manufacturing systems — specifically the pre-cutting stage of soft goods production: spreading, cutting, planning, and process optimization across garment, automotive interiors, medical, and technical textile applications.

I work at the boundary between machine physics, production data, and planning decisions — where a technically correct answer is worthless if it cannot survive the factory floor, the supply chain, or the business model.

My standard for evidence is: would this hold under real operating conditions?

Spreading & cutting room systems Physics-based throughput modeling Production planning & scheduling IoT & machine data R&D strategy & decision support Industrial software development Manufacturing process diagnosis Technical product strategy

§ 05 — Submit a problem

Bring a case.

I take on a small number of consulting and advisory engagements. Send a short description of the problem, the system involved, what has already been tried, and what decision depends on the answer.

You will get a fit/no-fit reply and the first diagnostic questions.

Good fit: spreading and cutting room systems, production planning, machine data, throughput modeling, R&D decisions before tooling investment — across soft goods manufacturing: garment, automotive interiors, medical, and technical textiles.

zia@mac.com