{"id":26970,"date":"2026-08-06T15:45:43","date_gmt":"2026-08-06T13:45:43","guid":{"rendered":"https:\/\/www.regestaitalia.eu\/metal-data-production-and-traceability-in-the-industrial-supply-chain\/"},"modified":"2026-09-01T15:52:50","modified_gmt":"2026-09-01T13:52:50","slug":"metal-data-production-and-traceability-in-the-industrial-supply-chain","status":"publish","type":"post","link":"https:\/\/www.regestaitalia.eu\/en\/metal-data-production-and-traceability-in-the-industrial-supply-chain\/","title":{"rendered":"Metal: Data, Production, and Traceability in the Industrial Supply Chain"},"content":{"rendered":"

In the metalworking industry, managing data means managing production<\/strong>. Raw materials, castings, batches, orders, machine parameters, quality controls, certifications, and shipments generate a continuous stream of information. <\/p>\n

When this process remains fragmented across ERP systems, spreadsheets, shop floor systems, and document repositories, the company loses speed and control. When, on the other hand, data is integrated<\/strong>, contextualized, and available in real time, it becomes a driver for improving efficiency, quality, traceability, and resilience.<\/strong> <\/p>\n

For a company in the metal industry, digital transformation<\/strong> cannot simply be the installation or adoption of new technology. It involves establishing a continuous flow of information that tracks materials from procurement to delivery<\/strong>, linking every physical event to its digital representation.<\/p>\n<\/div>

The Metalworking Industry in Numbers<\/h2>\n

The sector continues to play a key role in the Italian economy. According to Federmeccanica\u00b9<\/strong>, the national metalworking industry employs about 1.7 million people, generates approximately 150 billion euros in value added, and exports goods worth about 280 billion<\/strong>. Sixty percent of production consists of capital goods, 36% of intermediate goods, and the remaining 4% of consumer goods. <\/p>\n

The data published by the association are based on the statistical data available for the period 2000\u20132022 and should therefore be interpreted as indicators of the sector\u2019s structural characteristics, not as current economic indicators. The most recent picture, however, reveals significant pressure. Federmeccanica\u2019s 177th Economic Survey\u00b2 shows that in 2025, Italian metalworking production decreased by an average of 0.9% compared to 2024.<\/p>\n<\/div><\/div><\/div>

\"SAP<\/span><\/div><\/div><\/div><\/div>

This figure comes amid a period marked by global uncertainty, supply chain tensions, energy costs, and volatility in demand.<\/p>\n

These figures underscore the<\/strong> urgent need to improve production efficiency<\/strong>, identify the reasons for losses, pinpoint the causes of inefficiencies, and respond quickly. When volumes and margins are under pressure, simply generating more data isn\u2019t enough\u2014it must be transformed into operational decisions. <\/p>\n

The Main Digital Challenges Facing the Metalworking Industry<\/h2>\n

The complexity of the metal industry stems primarily from the variability of its processes. Production can be organized by order, series, batch, or continuous flow. A single plant may combine different processes, such as casting, rolling, cutting, heat treatment, welding, machining, assembly, and testing. <\/p>\n

Added to this diversity are machines from different generations, inconsistent protocols, and systems developed to meet local needs. The result is often a siloed architecture: the ERP system handles orders and costs, the MES manages production, the machines generate technical parameters, the lab stores test results, and logistics uses additional applications. <\/p>\n

The main challenges can be categorized into five areas:<\/strong><\/p>\n

    \n
  • Data Integration and Quality<\/strong>: Fragmentation among management, production, quality, and logistics data; inconsistent master data; manual data collection; difficulties in linking machine parameters to quality results.<\/li>\n
  • Supply Chain Traceability and Visibility<\/strong>: Inconsistent batch identification; limited visibility into suppliers, materials in transit, and actual availability.<\/li>\n
  • Legacy infrastructure and systems<\/strong>: the presence of legacy facilities and systems, resulting in difficulties with integration and technological upgrades.<\/li>\n
  • Production variability and skills<\/strong>: high product variability and inconsistent digital skills, which make process standardization more complex.<\/li>\n
  • Security and Compliance<\/strong>: The Need to Protect OT Environments and Manage Ever-Evolving Document and Regulatory Requirements.<\/li>\n<\/ul>\n

    The level of digital maturity remains uneven. In 2024, only 55% of EU manufacturing firms with at least ten employees had reached at least a basic level of digital intensity\u00b3. This figure is lower than the overall average for European companies, which stands at 59%, and far below the 91% recorded in the information and communication sector. <\/p>\n

    Data throughout the entire production cycle<\/h2>\n

    Data-driven management begins with procurement. The order placed with the supplier specifies the material, alloy, dimensions, quantity, terms of trade, and requested delivery date. Upon receipt, this information must be compared with the actual weight, lot number, certificate, country of origin, and results of the incoming inspections. <\/p>\n

    During production, the data changes in nature.<\/strong> It becomes a sequence of operations, a machining center, a tool, an operator, cycle time, temperature, pressure, speed, energy consumption, quantity produced, scrap, and downtime. The raw data acquired from the machine only becomes meaningful when it is associated with an order, a material, a phase, and a time interval. <\/p>\n

    In terms of quality<\/strong>, the system must link the control plan, the characteristics to be measured, the results, the tolerances, the instrument used, and the affected lot. A nonconformity can thus be analyzed by tracing back to the materials used, the recorded parameters, and other products that may be involved. <\/p>\n

    Finally, the warehouse and logistics operations<\/strong> must maintain this identity all the way through delivery. Location, unit of load, packaging, weight, shipping document, carrier, and destination complete the information chain. The customer does not simply receive a product; they receive an industrial item accompanied by its verifiable history. <\/p>\n

    What Is Traceability in the Metal Industry?<\/h2>\n

    Traceability is the ability to trace the origin, processing steps, inspections, and destination of a material or product<\/strong> through associated identifiers and records.<\/p>\n

    Upstream traceability<\/strong> allows you to trace the finished product back to the raw material, the supplier, the casting, and the related certificates. Downstream traceability allows you to identify all products, orders, and customers associated with a specific batch. Internal traceability<\/strong>, on the other hand, documents what took place at the plant: the machines used, the operations performed, process parameters, inspections, and rework. <\/p>\n

    This capability narrows the scope of a recall, speeds up root-cause analysis, and strengthens the handling of complaints. If a material is found to be noncompliant, the company can isolate only the items actually involved rather than indiscriminately halting production and deliveries. <\/p>\n

    To achieve this result, the identifier must follow the material<\/strong> even when it is transformed, divided, or aggregated. A sheet of metal may be cut into multiple components; materials from different lots may be combined into an assembly; a semi-finished product may undergo external processing. The data model must represent these genealogies without losing the relationships between objects. <\/p>\n

    Certifications, Compliance, and New Disclosure Requirements<\/h3>\n

    In the metal industry, traceability and compliance are closely linked.<\/strong> Material certificates, declarations of conformity, laboratory test results, and documents relating to substances, origin, and performance must be linked to the correct item and made available to authorized parties.<\/p>\n

    This issue takes on even greater significance with the Carbon Border Adjustment Mechanism<\/strong>(CBAM). As of January 1, 2026, the CBAM entered its final phase and applies, among other sectors, to imports of iron, steel, and aluminum. Even the rules governing the transitional phase already required extensive data on imported shipments, including, for example, the country of origin and the identification number of the steel mill associated with the batch, when available. This demonstrates how compliance and traceability depend on information that flows across various organizations and systems. <\/p>\n

    Another development is the Digital Product Passport\u2074<\/strong> provided for in the European regulation on the eco-design of sustainable products. Iron and steel are among the priority groups in the ESPR 2025\u20132030 work plan. Any binding requirements will be defined by specific delegated acts, with a clear objective: to make structured information available on identity, composition, recycled content, sustainability, compliance, and traceability throughout the product lifecycle. <\/p>\n

    How to Integrate ERP, MES, and Factory Systems<\/h2>\n

    End-to-end integration requires a clear allocation of responsibilities. The ERP system manages demand, purchasing, master data, planning, orders, inventory, sales, and cost accounting. The MES translates the plan into operational tasks, tracks progress, and connects people, materials, and resources. Supervision systems and machines generate high-frequency technical data. Quality management applications handle inspection plans, measurements, and nonconformities. WMS, TMS, and supply chain platforms complete the logistics and forecasting picture. <\/p>\n

    An effective architecture combines transactional integrations and event streams.<\/strong> The former ensure process consistency: order issuance, material reservation, production reporting, scrap recording, and transfer to inventory. The latter provide real-time signals, such as machine status, alarms, threshold exceedances, or the completion of a phase. <\/p>\n

    From Smart Manufacturing to Real-Time Decision-Making<\/h2>\n

    Smart Manufacturing is a production model in which machines, people, materials, and systems share data to make processes measurable, adaptive, and coordinated.<\/p>\n

    Real-time means that information is available in time to influence the outcome of the process<\/strong>, though decision-making does not necessarily have to be instantaneous. A temperature anomaly may require an immediate response; a decline in OEE can be analyzed at the end of the shift; a deviation in consumption can inform the weekly review of parameters. <\/p>\n

    The selection of indicators must follow the same criterion: the<\/strong>data must make it possible to distinguish the symptom from the cause\u2014a drop in performance may be due to machine speed, material quality, micro-stops, logistics delays, or the order sequence.<\/p>\n

    An example of an integrated process<\/h2>\n

    Let\u2019s consider a manufacturer of metal components<\/strong> that receives coils accompanied by casting certificates. Upon receipt, the supplier\u2019s batch is recorded in the ERP system and linked to the digital document. The material is identified by a code that can be read by both operators and the warehouse system. <\/p>\n

    When an order is issued<\/strong>, the MES receives the bill of materials, production cycle, and expected quantity. Before production begins, it verifies that the coil is approved for that job order. During cutting and forming, the production line records speed, stoppages, and quantity; the quality control system collects the measurements specified in the inspection plan. <\/p>\n

    If a batch exceeds the tolerance limit, production is suspended or the batch is quarantined in accordance with established rules.<\/p>\n

    At the end of the production process, the quantity produced, scrap, lead times, and material consumption are reported back to the ERP system. The finished product retains its link to the coil, certificate, machine, shift, and inspections. <\/p>\n

    In the event of a dispute, the company can trace the lineage in just a few steps and verify whether other components share the same material or the same processing conditions.<\/p>\n

    The Benefits of a Data-Driven Strategy<\/h2>\n

    A data-driven strategy creates value when it improves measurable operational decisions. In terms of efficiency, it helps identify inefficiencies and deviations from expected processes. In terms of materials, it allows for the comparison of yield, scrap, waste, and rework by batch, supplier, or product family. <\/p>\n

    Quality improves because quality controls can be linked to production conditions<\/strong>. Cost control becomes more accurate by linking consumption, time, energy, and materials to the actual job order. Maintenance also evolves, shifting from exclusively periodic interventions to decisions based on the condition of the assets. <\/p>\n

    There is also a less obvious but crucial advantage: the speed of analysis. Consistent data allow us to understand more quickly why an indicator has changed, which products are involved, and where to take action. <\/p>\n

    Data and Supply Chain Resilience<\/h2>\n

    A resilient supply chain is not one that is free of disruptions. It is a chain capable of recognizing a change, assessing its impact, and reorganizing itself in a timeframe consistent with the required service. <\/p>\n

    In the steel and metallurgy sectors, this requires visibility into open orders, production capacity, available materials, lead times, supplier dependencies, and logistical constraints. A delay in the delivery of a specific alloy can hold up multiple orders; a breakdown can alter the workload at plants and subcontractors; a change in demand can render priorities set just a few days earlier obsolete. <\/p>\n

    Planning must therefore be coordinated with execution.<\/strong> If the MES detects a decline in capacity, the planning system must be able to evaluate alternative scenarios. If a delivery is delayed, the sales and logistics teams must understand its impact on promised delivery dates. If a critical raw material is available only in limited quantities, the company must decide which orders to prioritize based on agreed-upon priorities. <\/p>\n

    Toward a Connected, Measurable, and Transparent Metal Supply Chain<\/h2>\n

    The digitization of the metal industry centers on data continuity. ERP, MES, factory systems, quality, and supply chain must all contribute to a consistent representation of the process.<\/strong>
    \nTraceability makes the product\u2019s history verifiable. Real-time data allows for intervention while the process can still be corrected. Integrated planning improves the response to fluctuations in demand. The proper correlation between materials, consumption, and emissions also prepares the company for compliance requirements that are increasingly based on structured information. <\/p>\n

    Building this continuity requires expertise in process management, integration, and data management. Since 2007, Regesta has been supporting manufacturing companies<\/strong> in connecting management systems with the factory floor, in smart manufacturing, and in the design of end-to-end digital processes.<\/p>\n

    The goal is not simply to observe what is happening more closely, but to turn every piece of reliable information into a faster, more accurate, and more informed business decision.<\/p>\n<\/div>