Textile: Real-Time Made-to-Order Production Control

TecelFio (real case — data altered under NDA), a textile factory in the Ave Valley with 58 employees, produced garments for European brands on a made-to-order basis. The chronic problem: nobody knew, in real time, the status of each order. When a client phoned to ask "what stage is my order at?", the production director had to walk to the shop floor and ask. We implemented an order tracking system by operation, fabric stock management per roll and automatic cost-per-garment calculation. Delivery delays fell by 52%, fabric waste dropped by 35% and, for the first time, the company knew the exact margin on each order before completing it.

The Scenario Before: Producing Blind

The Portuguese textile industry, particularly in the private label and subcontracting segment for international brands, operates on tight margins and short deadlines. A typical order passes through 8 to 12 operations — from fabric cutting to final packing — and involves dozens of operators in different sections. Without a system tracking each order at each operation, it is virtually impossible to know where the work stands.

At TecelFio, order management was done with paper dockets that physically accompanied the garments. Each section — cutting, sewing, finishing, quality control, packing — stamped the docket upon receipt and upon dispatch. In theory, one only needed to look at the docket to know the status. In practice, dockets got lost, damaged or ended up buried under piles of fabric.

The result was a chronic lack of visibility. Antonio, the production director for 22 years, described the situation with wry humour: "I know how many garments entered the factory and how many left. What happens in between is a mystery."

Fabric stock was another blind spot. TecelFio worked with over 200 different fabric references, stored in rolls. Each roll had a different length, depending on the supplier batch. The warehouse recorded incoming stock in Excel, but outgoing fabric to production was estimated. Nobody knew, with precision, how many metres of each reference were available. The result: orders that stalled mid-way because the fabric ran out, and emergency purchases at inflated prices.

The icing on the cake: TecelFio did not know the actual cost of each order until invoicing — and even then, it was an estimate. Labour time was calculated from historical averages, not actual records. Fabric waste was estimated at 10% (the industry standard), but nobody actually measured it. Result: some orders generated margin, others made a loss, and there was no way to distinguish between them until it was too late.

The Problem in Numbers

• 28% of orders delivered late.
• Zero real-time visibility over production status.
• Estimated fabric waste: 10%, but never accurately measured.
• Actual cost per garment: unknown until end of production.
• Fabric stock: average 15% gap between theoretical and actual stock.
• Emergency purchases: 2–3 per month, with an average 25% surcharge.

The Solution: Integrated Textile Production System

Phase 1 — Order Tracking by Operation (Weeks 1–6)

We installed touch terminals in each factory section (cutting, sewing lines 1 to 4, finishing, quality control, packing). Each order has a unique barcode. When an order enters a section, the operator scans the code. When it leaves, they scan again. The system automatically records: which order, in which section, entry and exit times, how many garments, and who operated.

The result is a real-time dashboard showing where each order is. Antonio, who used to walk the shop floor asking questions, now checks the screen in his office and instantly sees: order #4521 — cutting complete, sewing at 60%, estimated completion Thursday. When a client calls to ask the status, the answer takes 10 seconds instead of 20 minutes.

We implemented automatic alerts for orders at risk of delay. The system knows the delivery deadline and the average speed of each section. When it detects that an order will not meet the deadline at the current pace, it alerts Antonio with enough lead time to redistribute resources — move operators from one line to another, reorder priorities or negotiate the deadline with the client before a breach occurs.

Phase 2 — Fabric Stock per Roll (Weeks 4–8)

Each fabric roll received at the factory is labelled with a unique barcode containing: fabric reference, supplier batch, width, actual length (measured at goods-in) and warehouse location. When a roll is issued for production, the warehouse operator scans the code and associates it with the order. When cutting is done, the system automatically calculates how many metres were used and how many remain on the roll.

The stock dashboard shows, by fabric reference: how many rolls exist, the total available length, projected consumption for open orders and the net balance. When the net balance reaches the reorder point, the system suggests a purchase order to the supplier.

The impact on waste was remarkable. Actual measurement — rather than the assumed 10% estimate — revealed that real waste was 14%. With visibility over the data, the cutting department optimised cutting plans and reduced waste to 9.1% within 4 months — a 35% reduction from the previous actual figure.

Phase 3 — Real-Time Cost per Garment (Weeks 6–10)

The system automatically calculates the cost of each order as it progresses through production, based on three components: raw materials (metres of fabric consumed multiplied by price per metre, plus trims), labour (time recorded at each operation multiplied by the hourly cost of the section) and overheads (apportioned per machine-minute).

Antonio and the management team can see, at any point, the estimated margin on each order in progress. If actual cost is deviating from the initial budget, the system alerts before completion — giving the opportunity to investigate the cause (more expensive fabric, productivity below expectations, too many rejected garments) and take action.

This visibility revealed surprises. Three of TecelFio's 12 clients were generating negative margins — orders had been accepted based on estimated costs that were underestimated. With actual data, management renegotiated prices with two clients and decided to discontinue the third. The factory's average margin rose by 3.2 percentage points from this decision alone.

The Results: Before vs. After

Eight months after implementation:

• Late orders: from 28% to 13.4% (−52%).
• Fabric waste: from 14% (actual) to 9.1% (−35%). Saving of EUR 42,000/year in fabric.
• Theoretical vs. actual stock gap: from 15% to 2.3%.
• Emergency purchases: from 2–3/month to 0–1/month.
• Time to inform client of order status: from 20 min to 10 sec.
• Average margin per order: rose 3.2 percentage points thanks to cost visibility.
• Client satisfaction: significant qualitative improvement — clients praise the responsiveness and transparency.

The Financial Impact

The total investment — system, terminals, roll labelling, integration and training — was EUR 28,000. The estimated annual return: EUR 42,000 in fabric savings, EUR 18,000 in avoided emergency purchases, EUR 22,000 in recovered margin from renegotiated clients, and immeasurable value in client retention thanks to on-time delivery. Payback occurred in under 4 months.

Lessons for Other Textile Factories

1. If you don't measure, you don't control. TecelFio thought it was wasting 10% of fabric — it was wasting 14%. It thought all orders were profitable — three clients were loss-making. Only data reveals reality.

2. Production tracking is not bureaucracy — it is survival. In a sector with 5–10% margins, any invisible inefficiency can be the difference between profit and loss. Knowing where each order is in real time enables action before problems become irrecoverable.

3. Stock per roll is different from stock per reference. In the textile industry, knowing you "have 500 metres of reference X" is not enough — you need to know in how many rolls, what lengths and where they are. Per-roll management is the level of detail required.

Conclusion

The Portuguese textile industry has globally recognised technical competence. What it frequently lacks is real-time information to make better decisions. TecelFio did not change the way it produces — it changed the way it sees what it produces. With per-operation tracking, per-roll stock and cost per garment, the factory went from "producing blind" to "producing with data". And data made all the difference.