Traceability sounds like a technical term. In reality, it determines whether companies can prove which batch is affected within minutes in an emergency - or whether there is a risk of production stoppage, recall and audit escalation.
This is because modern production generates enormous amounts of data: Machine parameters, batch numbers, test values, ERP data, MES logs, supplier information and quality certificates. The problem: in many companies, this information is spread across different systems, Excel files and legacy systems. This is exactly where traceability gaps arise.
This becomes a real risk in the event of IATF audits, customer complaints, recalls or regulatory audits at the latest. Without complete traceability, there is often a lack of crucial evidence: Which parts were affected? Which machine was involved? Which parameters applied at the time of production? And is this data even available today in an audit-proof manner?
This article shows which requirements traceability systems will have to meet in 2026, which risks companies often underestimate and how audit-proof traceability can be implemented in a technically sound manner - including MES, ERP and legacy systems.
THE MOST IMPORTANT FACTS IN BRIEFTraceability describes the seamless traceability of products, batches, processes and production data across the entire value chain. In manufacturing today, it is a prerequisite for auditability, quality management and regulatory compliance. Traceability is particularly critical in the event of recalls, complaints or ISO/IATF audits: If data is missing or is not available in an audit-proof manner, there is a risk of production downtime, liability risks and high follow-up costs. Modern traceability systems therefore combine ERP, MES, machine and archive data in a permanently traceable data structure. |
IN BRIEF
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Traceability refers to the ability to clearly trace products, batches, materials, process steps and quality data throughout the entire production and life cycle. The aim is to be able to answer at any time what was produced, when it was produced, under what conditions it was produced and which components are affected.
In modern manufacturing, traceability is no longer just about batch numbers or serial numbers. Today, the complete linking of production, quality and system data is crucial. This includes, among other things:
This traceability is particularly mandatory in regulated industries. The automotive industry requires it via IATF 16949, while pharmaceutical and medical technology companies must fulfill FDA and GMP requirements. Food manufacturers are subject to EU Regulation 178/2002 on traceability. All regulations have one thing in common: companies must keep data complete, traceable and available in the long term.
In practice, however, traceability often fails not because of data collection, but because of data availability. Production information is scattered across MES systems, ERP landscapes, machine control systems or decommissioned legacy systems. There is often a lack of standardized data structures or audit-proof archiving processes. This is precisely what creates dangerous gaps in evidence.
Traceability is therefore not just a quality management issue today. It is a central prerequisite for auditability, product liability, compliance and risk management. Companies not only need access to current production data, but also to historical information from previous years - complete, unalterable and machine-readable.
Traceability requirements are increasing significantly in almost all industries. The reason for this is not just increasing regulation. Supply chains, product liability, cybersecurity requirements and international audit standards are also generating considerably more pressure to provide evidence today than just a few years ago.
This is particularly critical in complex manufacturing processes. Modern production environments consist of ERP systems, MES platforms, IIoT sensors, quality management systems and machine control systems that are sometimes decades old. Each of these components generates relevant data - but often without a central, long-term database.
At the same time, regulatory requirements are tightening the requirements for traceability and data integrity. In the automotive industry, IATF 16949 requires complete traceability of safety-relevant components. In regulated areas, the FDA requires complete audit trails and tamper-proof data storage in accordance with 21 CFR Part 11. In Europe, MDR, GMP and new ESG verification requirements are also increasing the pressure on production companies.
There is also an economic factor: recalls are becoming more expensive and more complex. If there is no precise traceability, companies often have to block entire batches or production periods, even though only individual components may be affected. This results in high costs, production losses and reputational damage.
Cybersecurity is also changing the topic. Production data is increasingly regarded as critical infrastructure. Companies must therefore not only record data, but also ensure that it is permanently available, unalterable and auditable. This is precisely where risks arise in many organizations: Data is stored in proprietary legacy systems, backups are no substitute for audit-proof archiving and historical production data can often no longer be analyzed after a few years.
Traceability is therefore developing from an operational quality function into a strategic infrastructure issue. Companies need systems that keep data consistently available for decades - regardless of software changes, ERP migrations or decommissioned production systems.
Many companies associate traceability mainly with batch numbers or serial numbers. However, this is not enough for reliable traceability. The decisive factor is the linking of all production-relevant data - across systems, locations and years.
In practice, traceability gaps often arise precisely where data is only stored in isolation: in the MES without an ERP reference, in machine control systems without long-term archiving or in legacy systems without export options.
The following overview shows which data sources are typically part of a complete traceability architecture:
| Data area | Typical content | Critical risk in the absence of traceability |
|---|---|---|
| ERP data | Orders, batches, material movements, suppliers | No clear assignment of affected products |
| MES data | Production steps, line assignment, process history | Lack of traceability of production processes |
| Machine & IIoT data | Temperature, pressure, torque, sensor values | Root cause analysis for quality problems impossible |
| Quality data | Test values, SPC data, approvals, complaints | Audit and verification problems |
| User & audit trails | Changes, approvals, operator actions | No security against manipulation |
| Documents & certificates | Test reports, factory certificates, compliance documents | Missing regulatory evidence |
| Supplier & material data | Batch origin, incoming goods, components | Recall expansion to unnecessarily large areas |
| Archive & legacy system data | Historical production data, legacy systems | Historical evidence no longer available |
It becomes particularly problematic with long product life cycles. In industries such as automotive, pharmaceuticals or medical technology, production and quality data often has to remain available for 10 to 30 years. However, many operational systems are not designed for this. ERP or MES changes mean that although historical information still exists technically, it can no longer be evaluated in practice.
This is precisely why audit-proof long-term archiving is increasingly becoming part of modern traceability strategies. Companies not only need real-time data for ongoing production, but also permanent, auditable access to historical production information - regardless of the original source system.
Most companies today have no problem recording production data. The actual weak point lies elsewhere: although data is available, it is incomplete, not linked or can no longer be analyzed in an emergency.
This is precisely what becomes critical during audits, complaints or recalls. Traceability only works if information remains consistently available for years - regardless of which system it originally came from.
The most common problems are not caused by a lack of software, but by IT landscapes that have evolved over time and isolated data storage.
| Typical traceability gap | What happens in practice | The risk |
|---|---|---|
| Data islands between ERP and MES | Batch information is not fully synchronized | Traceability ends at system boundaries |
| Production data only in the old system | Historical data can no longer be read after system changeover | Loss of audit and verification |
| Lack of audit-proof archiving | Data has been changed or overwritten | Compliance violation |
| Backup instead of archiving | Data exists technically, but is not auditable | GoBD/FDA/IATF risk |
| No linking of quality and production data | Causes of errors cannot be properly analyzed | High recall costs |
| Missing audit trails | Changes to data records are not traceable | Suspicion of manipulation |
| Different data formats | Historical data cannot be analyzed automatically | Audit and audit problems |
| Manual Excel traceability | Information is incomplete or inconsistent | High susceptibility to errors |
This is particularly dangerous in the case of long-term verification obligations. Many production systems are replaced or modernized after a few years. Theoretically, the data is retained, but in practice there is often no technical means of fully evaluating it at a later date.
A typical example is decommissioned MES or CAQ systems. While operational processes have long since been migrated to new platforms, historical quality and production data is still stored in the proprietary format of the old system. In the event of an audit, this creates a massive problem: although the information exists, it can no longer be provided in an audit-proof manner.
What's more, many companies still see traceability purely as a quality management issue. In reality, however, it now also affects product liability, cybersecurity, supply chain verification and regulatory compliance. Accordingly, the requirements for data integrity, long-term availability and auditability are constantly increasing.
Modern traceability therefore means more than just data collection. The ability to keep production and quality data permanently traceable, unchangeable and available across systems is crucial.
Traceability is no longer a voluntary quality measure in many industries. Today, regulatory requirements demand traceable, tamper-proof and long-term documentation of production and quality data. Companies must not only record data, but also be able to prove at any time how products were manufactured and which processes were relevant.
In regulated industries in particular, simple batch lists or Excel documentation are no longer sufficient.
The most important requirements at a glance:
| Standard / Regulation | Industry | Relevant traceability requirement |
|---|---|---|
| IATF 16949 | Automotive | Traceability of safety-relevant components and production parameters |
| ISO 9001 | General industry | Traceability of processes, tests and quality measures |
| FDA 21 CFR Part 11 | Pharmaceutical / medical technology | Tamper-proof electronic records and audit trails |
| EU GMP guidelines | Pharmaceuticals | Complete batch documentation and long-term archiving |
| EU MDR | Medical technology | Traceability over the entire product life cycle |
| REGULATION (EC) 178/2002 | Food industry | Traceability of raw materials and batches |
| ISO 27001 | IT / Production | Protection and integrity of critical production data |
These regulations have three core requirements in common:
Companies must ensure that all relevant production and quality data is available without gaps. Missing data records are often considered missing evidence in the audit - even if the process was actually carried out correctly.
Many regulations require that archived data cannot be subsequently manipulated. FDA 21 CFR Part 11 and GMP in particular define clear requirements for electronic records and audit trails.
Production data must often remain available for much longer than operational systems exist. This is precisely why risks arise during ERP, MES or CAQ migrations. If historical data can no longer be read after a few years, traceability is effectively interrupted.
In practice, many companies underestimate the last point in particular. Operational production often functions without any problems - until an audit demands historical evidence from a decommissioned system. Then it becomes clear whether traceability was really designed for the long term or only works within the current system landscape.
Modern traceability strategies therefore combine operational data acquisition with audit-proof long-term archiving. This is the only way to ensure that production data remains available for auditing even after software changes, system shutdowns or regulatory changes.
Functioning traceability is not created by individual software solutions alone. The ability to permanently link production, quality and system data with one another is crucial - regardless of which system they originate from or how long they need to be stored.
This is precisely where many existing architectures fail. ERP, MES, CAQ, machine control systems and IIoT platforms often work in isolation from one another. Historical data remains in legacy systems or is only saved via backups. This is not sufficient for audits or recall analyses.
An audit-proof traceability architecture therefore requires several technical levels at the same time:
| Technical requirement | Goal | Critical benefit |
|---|---|---|
| Central data consolidation | Consolidate data from ERP, MES, machines and QM systems | Complete traceability |
| Audit-proof archiving | Protection against subsequent manipulation | Compliance & auditability |
| Audit trails | Logging changes and accesses | Verifiability |
| Long-term availability | Keep data available regardless of the original system | Protection against legacy system risks |
| Standardized data formats | Ensure machine evaluability | Fast audits & analyses |
| Role and access concepts | Meet GDPR and compliance requirements | Controlled data access |
| Interface capability | Integrate ERP, MES and IIoT systems | End-to-end data chain |
The separation between operational systems and long-term data storage is particularly important. ERP or MES systems are primarily designed for ongoing processes - not for decades of audit-proof archiving. This is precisely why traceability gaps often arise during migrations or system shutdowns.
Modern companies are therefore increasingly relying on so-called application retirement strategies. This involves extracting historical production and quality data from legacy systems and transferring it to an independent long-term archive. The original software can be shut down while the data remains available for auditing.
There is also another factor: data integrity. Traceability data must not only be available, but must also remain verifiably unchanged. Many standards therefore require technical mechanisms such as unchangeable storage structures, logging of all changes and traceable access histories.
In practice, this means that a company must be able to provide evidence even years later,
Only this combination of data availability, integrity and long-term archiving makes traceability truly auditable.
The biggest challenge of modern traceability rarely lies in new systems. It lies in the existing ones.
Many companies today work with historically grown IT landscapes: several ERP generations, different MES systems, local databases, machine control systems and isolated quality solutions.
The problem with this is that each platform stores production-relevant information differently.
Without a central data strategy, this creates gaps in traceability
. A typical example:
However, in the event of an audit or recall, this information must be able to be evaluated together.
This is exactly where many companies fail.
Decommissioned or discontinued systems are particularly critical.
Production data often remains stored there for years - albeit in proprietary data formats or on platforms that can hardly be operated anymore.
The result:
This effectively interrupts traceability - even though the data still technically exists
. This risk will increase further in 2026 because many companies are currently carrying out ERP transformations, SAP migrations or MES modernisations.
Without a long-term archiving strategy, the very historical information that is needed years later for audits or product liability cases is often lost.
Modern traceability concepts therefore rely on a system-independent data level between operational systems and long-term archiving
. The goal:
production data remains permanently available - regardless of whether the original system still exists.
The architecture typically consists of four levels:
| Level | Task | Example |
|---|---|---|
| Operational systems | Ongoing production and data acquisition | ERP, MES, CAQ, IIoT |
| Integration layer | Consolidation and structuring of data | APIs, ETL, data platform |
| Audit-proof archive | Long-term, unalterable storage | CHRONOS |
| Audit & analysis access | Evaluation of historical data | Audit export, reporting |
The decoupling of data and application is particularly important here. Companies must not make historical evidence dependent on the continued operation of old ERP or MES systems.
CHRONOS was developed precisely for this scenario: the long-term storage and analysis of business-critical production and quality data - regardless of the original system.
Many companies assume that their traceability works - until an audit, a complaint or a recall shows otherwise. The following questions help to reveal typical weaknesses in the existing traceability architecture.
☐ We can trace which batches, materials and production parameters were used for each product.
☐ Historical production data from the last 10-15 years can still be fully analyzed today.
☐ Our traceability also works when an ERP, MES or CAQ system is switched off.
☐ Quality data, machine parameters and ERP information are linked across systems.
☐ Changes to production or quality data are seamlessly logged.
☐ Archived data is audit-proof and cannot be subsequently manipulated.
☐ An auditor can obtain historical production records for machine evaluation within a few hours.
☐ Our production data is not stored exclusively in proprietary legacy systems.
☐ Recall analyses can be limited to individual batches or production periods.
☐ Our traceability strategy takes into account regulatory requirements such as IATF, FDA, GMP or GoBD.
The more points that remain open, the greater the risk that traceability will only work partially in an emergency. This is particularly critical in the case of long-term verification obligations or decommissioned systems. This is because missing data is usually only noticed when it is urgently needed.
Traceability describes the complete traceability of products, batches, materials, production steps and quality data. Companies must be able to trace when a product was manufactured, which components were used, which machines were involved and under which process conditions production took place. The aim is a complete and auditable chain of custody across the entire product life cycle.
Audits require reliable evidence of production and quality processes. Missing or non-evaluable data is often considered missing evidence - even if processes have been carried out correctly. Standards such as IATF 16949, FDA 21 CFR Part 11 or GMP in particular require complete documentation, audit trails and long-term data availability.
Modern traceability includes data from several systems at the same time. These include ERP systems, MES platforms, CAQ solutions, machine control systems, IIoT sensors and quality and archiving systems. It is critical to link these data sources so that information remains traceable across systems.
The retention period depends on the industry and regulation. Tax-relevant production data is often subject to 10-year retention obligations in accordance with AO and GoBD. In regulated industries such as pharmaceuticals, medical technology or automotive, however, the obligation to provide evidence can be 15 to 30 years. It is crucial that data remains readable and auditable throughout the entire period.
No. A backup is primarily used for the technical recovery of data, but usually does not meet the requirements for audit compliance, audit trails or long-term evaluability. For compliance requirements such as GoBD, FDA or GMP, companies need audit-proof archiving with unchangeable data storage and a traceable access history.
A lot of historical production data is stored in proprietary ERP, MES or CAQ systems. After migrations or system shutdowns, this information is often no longer fully readable or analyzable. This results in traceability gaps, even though the data still technically exists. Modern long-term archiving prevents precisely this problem.
The more precise the traceability functions, the more accurately affected batches or products can be identified. If this transparency is lacking, companies often have to block or recall larger production quantities than are actually necessary. Good traceability therefore significantly reduces costs, liability risks and production losses.
CHRONOS archives production, quality and ERP data in an audit-proof and system-independent manner. This means that historical information remains available for long-term auditing even after ERP, MES or system migrations. Companies can provide production records, audit trails and quality data in a traceable manner at any time - regardless of the original source system.