MetalForma (real case — data altered under NDA), a metal components factory in Aveiro with 45 employees and 8 production lines, managed its production planning on printed Excel spreadsheets pinned to the shop floor. The result: 34% of orders were delivered late, raw material stockouts halted production for an average of 3 days per month, and quality control was done on paper with no traceability. After implementing a digital planning system, real-time stock management and integrated quality control, delays fell by 60%, material-related stoppages were eliminated and the defect rate dropped from 4.2% to 1.8%.
The Scenario Before: Excel on the Shop Floor
MetalForma produces precision parts for the automotive and domestic appliance industries — components requiring tight tolerances, full traceability and strict delivery deadlines. Its clients are manufacturers operating on a just-in-time basis: one day's delay from MetalForma could mean a production line stoppage at the client's end, with heavy contractual penalties.
Production planning was the responsibility of engineer Carlos, who spent every Friday building the following week's plan in an Excel spreadsheet with 200 rows — one per open order. Carlos manually cross-referenced orders with each machine's capacity, raw material availability (which he checked by phoning the warehouse) and delivery deadlines. The process took 6 to 8 hours.
The resulting plan was printed on A3 paper and pinned up in each section of the factory. Any change during the week — an urgent order, a machine breakdown, a material shortage — required Carlos to redo the plan manually and reprint it. In practice, the printed plan was outdated by Tuesday.
Stock management was another critical issue. The warehouse recorded incoming and outgoing materials in a separate Excel file, updated once a day at end of shift. When production needed to know whether there was enough material for an order, they had to phone the warehouse or go and check physically. Frequently, production started an order and discovered mid-way that a component was missing — halting the line until the next delivery.
The Problem in Numbers
• 34% of orders delivered late.
• 3 days/month of stoppage due to raw material stockouts.
• 6–8 hours/week spent by the engineer on manual planning.
• Defect rate: 4.2% — with no traceability to identify root causes.
• Visibility over order status: zero in real time.
• Late-delivery penalties: an average of EUR 4,800/month in contractual penalties.
The Diagnosis: Three Systems that Did Not Communicate
The root cause was clear: MetalForma had three information systems — planning (Carlos's Excel), warehouse (the warehouse manager's Excel) and quality (paper forms) — that did not communicate with one another. Every decision required manually cross-referencing data from different sources with outdated information.
We identified three modules to implement: digital production planning with an interactive Gantt chart, real-time stock management with replenishment alerts, and digital quality control with batch traceability.
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Phase 1 — Digital Planning (Weeks 1–6)
We developed a production planning module with an interactive Gantt view. Each order is represented by a bar on the diagram, positioned on the timeline based on delivery date, estimated production duration and operation sequence. The system knows each machine's capacity (shifts, speed, setup times) and automatically distributes orders to maximise utilisation and meet deadlines.
When a new order enters, the system automatically checks: is there capacity on the required machines? Is there raw material in stock? If yes, it positions the order in the plan and confirms the delivery date to the sales team. If not, it shows the conflict and suggests alternatives — postpone a less urgent order, subcontract an operation, or negotiate a new date with the client.
The plan is accessible in real time via screens installed in each factory section. Operators see exactly what to produce next, with the technical sheet, quantities and priority. When they complete an operation, they log it with a tap on the screen — and the plan updates automatically across the entire factory.
Carlos, who previously spent 8 hours per week planning manually, now spends 1 hour adjusting and validating the automatically generated plan. The remaining 7 hours have been redirected to continuous improvement and process optimisation.
Phase 2 — Real-Time Stock (Weeks 4–8)
We implemented barcode scanning in the warehouse — every material entry and exit is recorded instantly with a handheld reader. The system updates stock in real time and is integrated with the planning module: when an order is scheduled, the system automatically reserves the required raw material and deducts it from available stock.
We configured reorder points for every material. When stock reaches the defined minimum, the system automatically generates a purchase suggestion with the ideal quantity (calculated based on average consumption and the supplier's delivery lead time). The purchasing manager simply approves — and the purchase order is sent to the supplier by email, already with the correct references and quantities.
The impact was immediate: material-related stoppages, which had cost an average of 3 production days per month, were eliminated in the second month of use. The system anticipates needs before they become urgent.
Phase 3 — Digital Quality Control (Weeks 7–12)
Paper quality control forms were replaced by digital forms on tablets at inspection stations. Every produced batch is traceable: who produced it, on which machine, with which raw material (supplier batch), what the control measurements were and whether it passed or failed.
When a measurement falls outside tolerances, the system automatically blocks the batch and notifies the quality manager. Root cause analysis is done in the system, with immediate access to the full history — enabling comparison of whether the problem is recurrent on that machine, with that operator, or with that raw material batch.
For automotive clients requiring IATF 16949 certification, the system automatically generates statistical process control (SPC) reports, histograms and 8D reports for complaint management. What previously took hours to compile manually is now generated with a single click.
The Results: Before vs. After
Six months after full implementation:
• Late orders: from 34% to 13.6% (−60%). The target is to drop below 5% in the next 6 months with continuous optimisation.
• Material-related stoppages: from 3 days/month to 0 days (−100%).
• Planning time: from 8 hours/week to 1 hour/week (−87.5%).
• Defect rate: from 4.2% to 1.8% (−57%). Traceability enabled the identification and correction of root causes that had been hidden for years.
• Late-delivery penalties: from EUR 4,800/month to EUR 1,200/month (−75%).
• Production status visibility: from zero to real time — any authorised person can see, at any moment, where every order stands.
• Machine utilisation: rose from 68% to 79% thanks to improved sequencing and reduced idle time.
The Financial Impact
The total investment — software, hardware (factory screens, tablets, barcode readers), integration and training — was EUR 38,000. The direct saving in late-delivery penalties (EUR 3,600/month), material wasted through defects (EUR 2,100/month) and production stoppages (estimated at EUR 5,200/month) totals EUR 10,900/month — or EUR 130,800/year. The project payback occurred in 3.5 months.
But the most strategic gain was the increase in client confidence. Two major automotive clients, who had been considering switching suppliers due to the delays, renewed 3-year contracts after verifying the improvement. The value of those contracts exceeds EUR 1.2 million annually.
Lessons for Other Factories
1. Excel is the enemy of production. A spreadsheet is static — the factory is dynamic. Planning needs to keep pace with reality in real time, not remain frozen in a snapshot taken the previous Friday.
2. Real-time stock prevents stoppages. A stockout is not a warehouse problem — it is an information problem. When the system knows what will be produced next week, it can anticipate material needs with precision.
3. Quality without data is guesswork. Digital traceability not only reduces defects but allows you to prove to the client that the process is controlled. It is the difference between "we think it's good" and "the data shows it is within specification".
Conclusion
Portuguese industry has enormous potential to gain from production digitalisation. MetalForma did not invest in robots or artificial intelligence — it invested in information: knowing what to produce, when, with which material and to what quality standard. That information already existed — it was scattered across spreadsheets, paper and people's heads. What we did was organise it, integrate it and make it accessible in real time. The result: a factory that meets deadlines, reduces waste and keeps clients satisfied.
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