Explore PLM maintenance challenges and learn how to automate specification updates, property tree changes, and attribute modernization with Migravion.

PLM Maintenance at Scale: Managing Product Data Changes Without Manual Rework  

Many organizations approach Product Lifecycle Management (PLM) initiatives with a clear objective: centralize product information, standardize specifications, and establish a reliable source of truth for product data. Once the implementation is complete and data is loaded into the system, there is often a sense that the most challenging work is behind them.

In reality, implementation is only the beginning as product portfolios evolve. Over time, organizations must adapt to:

• New products entering the portfolio
• Changes to existing formulations and specifications
• Evolving regulatory requirements
• New sustainability and reporting initiatives
• Updated labeling standards across different markets
• Mergers and acquisitions that introduce new data structures and governance models

As these changes accumulate, the underlying product data model must evolve as well. This is where PLM maintenance becomes critical.

While organizations invest significant effort in implementing PLM platforms, many underestimate the ongoing work required to keep product data accurate, consistent, and aligned with changing business needs. Without a structured approach to maintenance, even the most sophisticated PLM systems can become difficult to manage, leading to inefficiencies, data quality issues, and growing technical debt.

In this article, we'll explore:

• Why product data maintenance becomes increasingly challenging as organizations grow.
• The most common obstacles companies face after PLM go-live.
• Practical strategies that help teams manage large-scale updates, evolving data structures, and governance requirements without relying on manual rework.

Why PLM Maintenance Becomes More Complex Over Time

A PLM implementation reflects the business requirements that exist at a specific moment. However, businesses rarely remain static.

As organizations grow, product data environments become increasingly complex. Over time, PLM teams must manage:

• Larger product portfolios and specification libraries
• Increasing numbers of attributes, characteristics, and classifications
• More interconnected product, ingredient, packaging, and material data
• Growing governance and compliance requirements
• Multiple business units, regions, and stakeholders working with the same data

As this complexity increases, maintaining data quality and consistency becomes significantly more challenging.

Over time, organizations often discover that their original product data model no longer fully supports current business needs. Common indicators include:

• Attributes that are no longer used but remain in the system
• New data requirements that existing structures cannot accommodate
• Duplicate or overlapping characteristics created over time
• Classification models that have become difficult to manage at scale
• Product structures that no longer align with reporting or governance objectives

The challenge is not simply managing new data. It is managing change within an existing product data ecosystem without disrupting daily operations.

Organizations that fail to address this challenge often discover that maintaining product information becomes increasingly difficult, even when their PLM platform remains technically sound.

Common PLM Maintenance Challenges

Once a PLM system is in place, the challenge shifts from implementation to ongoing maintenance. Product data must remain accurate, compliant, and aligned with evolving business requirements. However, as product portfolios grow and data structures become more complex, maintenance activities that once seemed straightforward can become increasingly difficult to manage.

Updating large volumes of product data

Business changes rarely affect a single specification. Common examples include:

• New allergen regulations require updates across hundreds of products.
• Supplier changes affect ingredient specifications, sourcing information, and approved materials.
• Packaging redesigns require updates to dimensions, materials, labeling information, and supporting documents.
• Changes to nutritional calculations that impact multiple product formulations.
• New reporting requirements demand updates to product classifications and characteristics.

When these changes affect hundreds or thousands of records, manual updates become time-consuming and difficult to control. The larger the product portfolio, the greater the risk of inconsistencies, missed records, and duplicate effort.

Retiring outdated attributes and data fields

Product data models naturally evolve over time. Attributes that once served a clear purpose may become obsolete as business processes, reporting requirements, or governance standards change.

For example, a manufacturer may replace several region-specific packaging classifications with a single global standard. An old characteristic used to track supplier information may become redundant after the introduction of a centralized supplier management process.

In many organizations, obsolete attributes remain in the system for years because removing them feels risky. As a result, users must navigate increasingly complex data structures that contain a mix of active and legacy fields.

Introducing new product data structures

As businesses grow, they often need to reorganize how product information is structured. Common scenarios include:

• Expanding into new markets and introducing additional product classifications
• Harmonizing specification frameworks following a merger or acquisition
• Standardizing characteristics and attributes across business units
• Redesigning product hierarchies to support new reporting requirements
• Implementing new governance standards for product data management

While creating new structures is relatively straightforward, migrating existing information into those structures is often far more challenging. Product data accumulated over many years must be reviewed, mapped, and validated to ensure that nothing is lost during the transition.

Managing information stored in unstructured formats

Many organizations discover that critical product information is not always stored in structured formats.

Over time, users may begin recording details in notes fields, comments, or free-text descriptions because the appropriate characteristic does not exist or because entering information there is more convenient.

For example, ingredient handling instructions, supplier-specific requirements, packaging details, or regulatory notes may be embedded within text fields, rather than captured as structured data.

While this approach may solve short-term needs, it creates long-term maintenance challenges. Information stored in free text is difficult to search, validate, report on, or migrate into new structures when business requirements change.

Maintaining consistency across environments

Most PLM changes are not implemented directly in production.

New attributes, revised classifications, and updated governance rules are typically introduced and tested in development and quality assurance environments before being promoted to production. Ensuring that both configuration changes and associated data updates remain synchronized across environments can be a significant challenge.

For example, a new product characteristic introduced in a development environment may require existing specification data to be transformed before deployment. If the transformation process differs between environments, teams can encounter unexpected discrepancies, testing delays, and deployment risks.

Maintaining consistency throughout the change lifecycle is essential for ensuring reliable and predictable PLM operations.

The Hidden Cost of Manual Product Data Maintenance

Many organizations continue to rely heavily on spreadsheets, manual reviews, and individual updates to maintain product information.

At first glance, manual product data maintenance may appear manageable:

• A single specification update takes only a few minutes.
• A classification change affects only a handful of records.
• An obsolete attribute can be addressed later.
• A small data correction seems unlikely to create significant risk.

However, the cumulative impact of manual maintenance is often underestimated.

Industry research suggests that manual data entry processes typically produce error rates of around 1%, even under controlled conditions with trained users. Other studies report real-world error rates ranging from 1% to 4%, depending on the complexity of the process and the volume of data being handled.

While these percentages may appear small, they become significant when applied to large product portfolios. A seemingly minor error rate can result in hundreds of incorrect values across specifications, classifications, materials, recipes, or packaging records.

The impact extends far beyond the initial mistake.

Manual product data maintenance often leads to:

• Increased administrative effort spent reviewing and correcting records
• Inconsistent data across specifications and business units
• Delays in regulatory, labeling, or product change initiatives
• Additional testing and validation cycles before deployment
• Reduced confidence in product information
• Greater dependency on spreadsheets and offline tracking mechanisms

Consider this simple example: a company needs to update allergen information across 2,000 product specifications following a regulatory change. Even if each update takes only three minutes to review, modify, and validate, the effort quickly exceeds 100 hours of manual work. Any missed specification or incorrectly updated value can create additional compliance and quality risks.

The challenge becomes even greater when organizations need to restructure data, rather than simply update it. Replacing outdated attributes, introducing new classifications, or standardizing information across business units often requires teams to review and transform large volumes of historical data. When performed manually, these initiatives can consume weeks or even months of effort.

Over time, the consequences become increasingly visible:

• Product data quality gradually declines.
• Maintenance activities take longer to complete.
• Technical debt accumulates within the PLM environment.
• Business users become less willing to trust system data.
• Improvement initiatives become more difficult to execute.

The erosion of trust is often the most costly outcome. When users are uncertain whether information is up-to-date, complete, or consistent, they begin maintaining their own spreadsheets, databases, and workarounds. The organization effectively creates multiple versions of the truth, making future maintenance efforts even more complex.

For this reason, the true cost of manual product data maintenance is not simply the time required to make updates. It is the long-term impact on data quality, governance, operational efficiency, and the organization's ability to adapt to change.

When Product Data Structures Need to Change

One of the most overlooked aspects of PLM maintenance is the need to evolve the underlying product data model itself.

Many organizations assume that once classifications, characteristics, and specification structures have been configured, they will remain largely unchanged. In reality, product data structures must continuously adapt to support new business requirements, governance standards, and operational processes.

Changes to the data model are often driven by the following initiatives:

• Expanding into new markets with different regulatory requirements
• Introducing new product categories or business lines
• Standardizing data structures across regions or business units
• Supporting mergers and acquisitions
• Improving reporting and analytics capabilities
• Strengthening product data governance

For example, a food manufacturer may decide to replace multiple regional allergen attributes with a single global standard. A consumer goods company may need to harmonize classification structures after acquiring another brand. A global organization may redesign its specification framework to improve consistency across business units.

These initiatives can significantly improve data quality and governance. However, they also create a new challenge: existing information must be moved, transformed, and validated within the new structure.

Common restructuring activities include:

• Replacing obsolete attributes with new characteristics
• Consolidating duplicate or overlapping data fields
• Moving information from legacy structures into standardized frameworks
• Reorganizing classification hierarchies
• Introducing new specification templates and governance models
• Retiring outdated product data structures

While creating a new structure is relatively straightforward, migrating years of accumulated product information into that structure is often far more complex. Existing data must be reviewed, mapped, transformed, and validated to ensure that information remains accurate, complete, and usable.

Without a controlled approach, these initiatives can become lengthy manual projects that consume significant resources and introduce unnecessary risk.

Organizations that successfully manage product data recognize that restructuring is not an exceptional event. It is a natural part of maintaining a mature PLM environment. Rather than treating each change as a standalone project, they establish repeatable processes that allow product data structures to evolve alongside the business.

This approach enables continuous improvement of the PLM environment, while preserving data quality, governance, and operational stability.

What PLM Maintenance Looks Like in Practice

PLM maintenance is not a single activity; it encompasses a broad range of initiatives aimed at keeping product data accurate, compliant, and aligned with evolving business requirements. While the specific priorities vary between organizations, maintenance efforts typically fall into several recurring categories:

• Bulk specification creation and updates: As new products are introduced and existing products evolve, organizations must create and update large volumes of ingredient, packaging, recipe, and finished product specifications. Activities may include updating allergen declarations, nutritional information, classifications, material assignments, or supporting documentation across hundreds or even thousands of records.
• Recipe and formulation maintenance: Product changes often require updates to recipe data, ingredient compositions, and related specifications. These changes may be driven by supplier substitutions, cost optimization initiatives, reformulation projects, or regulatory requirements.
• Attribute lifecycle management: Product data models are never static. Organizations regularly introduce new characteristics, retire obsolete attributes, consolidate duplicate fields, and update governance standards to ensure product information remains relevant and manageable.
• Property tree restructuring: As business requirements evolve, organizations frequently need to redesign parts of their property tree structure. For example, a company may standardize classifications across regions, replace legacy attributes with globally governed characteristics, or move information from deprecated property trees into newly configured structures. These initiatives help improve consistency and support long-term data governance.
• Converting legacy information into structured data: Valuable product knowledge is often stored in notes fields, spreadsheets, and other unstructured formats. Maintenance initiatives frequently involve identifying this information and converting it into governed attributes that can be searched, validated, reported on, and maintained more effectively.
• Data extraction, analysis, and validation: Before making significant changes, organizations need visibility into the current state of their product data. Extracting information from specifications and recipes supports impact assessments, data quality reviews, governance initiatives, audit preparation, and validation of maintenance outcomes.

Individually, these activities may appear manageable. Collectively, however, they illustrate why PLM maintenance becomes increasingly complex as product portfolios grow and data models evolve. The challenge is not simply performing these tasks; it is executing them consistently, efficiently, and at scale.

A Real-World Example of PLM Maintenance at Scale

The maintenance activities discussed throughout this article rarely occur in isolation.

In one SAP PLM engagement, a manufacturer needed to execute several large-scale maintenance initiatives simultaneously, including:

• Bulk creation of ingredient, packaging, and recipe specifications
• Large-scale updates across specification attributes and characteristics
• Creation of recipes and plant recipes linked to materials and specifications
• Retirement of obsolete properties and data structures
• Information stored in legacy notes migrated into governed attributes
• Movement of data between old and newly configured property tree structures
• Extraction and validation of specification and recipe data for analysis and quality assurance

Managing each of these activities manually would have required significant effort, extensive validation, and substantial business involvement. More importantly, the initiatives were interconnected: changes to data structures affected specifications, governance requirements influenced attribute design, and validation activities were required throughout the process.

This is precisely the type of challenge Migravion helps organizations address. Rather than treating specification maintenance, property tree restructuring, attribute modernization, and data validation as separate projects, Migravion provides a structured approach for managing large-scale PLM maintenance initiatives. Organizations can execute product data changes more efficiently, while maintaining consistency, traceability, and data quality.

The result is not simply faster execution; it is the ability to continuously evolve the PLM environment as business requirements change — without relying on spreadsheets, manual rework, or one-off migration efforts.

A Scalable Approach to PLM Maintenance

As the previous example demonstrates, PLM maintenance extends far beyond routine data updates. Organizations must continuously manage changing specifications, evolving product data models, governance initiatives, and regulatory requirements, while maintaining data quality and operational continuity.

The challenge is performing maintenance activities efficiently and consistently as product portfolios grow. Organizations that successfully scale their PLM environments typically follow several key principles:

• Minimize repetitive manual work: Many maintenance challenges originate from processes that require users to update records one at a time. While this approach may be manageable for a handful of specifications, it becomes challenging when changes affect hundreds or thousands of records. For example, updating allergen declarations across 2,000 specifications manually may require hundreds of hours of effort and extensive validation. The larger the product portfolio, the more important it becomes to reduce dependence on repetitive manual activities.
• Manage product data structures proactively: Product data structures should be treated as evolving business assets. Regular reviews can help identify obsolete attributes, overlapping characteristics, outdated property tree structures, and opportunities to improve consistency. For example, a global manufacturer may discover that different regions use separate attributes to capture similar information. Consolidating these structures can simplify reporting, improve governance, and reduce maintenance effort.
• Establish clear governance for product data changes: Organizations should define clear ownership for product data changes and establish standards for introducing, modifying, and retiring data structures. This may include approval processes for new characteristics, rules for attribute naming, procedures for deprecating obsolete properties, and standards for documenting data model changes.
• Validate changes before they reach production: Even relatively small changes can have broader consequences across specifications, recipes, reports, integrations, and downstream business processes. Effective validation typically includes extracting and reviewing affected data, identifying impacted records, testing changes in non-production environments, and verifying outcomes after implementation.
• Treat maintenance as a continuous discipline: Product data environments are constantly evolving. New products are introduced, regulations change, governance standards mature, and business requirements shift. Organizations that continuously review data quality and refine governance practices are better positioned to support future growth and transformation initiatives.

Ultimately, scalable PLM maintenance is not defined by a specific tool, project, or methodology. It is the result of establishing repeatable processes that enable organizations to manage product data consistently as business requirements evolve. By reducing manual effort, governing change effectively, and planning for continuous improvement, organizations can maintain high-quality product data, while ensuring that their PLM environment remains adaptable, efficient, and ready for future growth.

Conclusion

Many organizations view PLM maintenance as a series of isolated tasks, such as updating specifications, correcting data, or responding to regulatory changes as they arise. In reality, maintenance plays a much broader role in the long-term success of a PLM environment.

As product portfolios expand, business requirements evolve, and data models become more complex, organizations must continuously adapt their product information. This includes maintaining existing specifications and recipes, as well as evolving property trees, modernizing data structures, improving governance practices, and ensuring that product information remains accurate and accessible.

Organizations that treat PLM maintenance as an ongoing discipline are better positioned to maintain data quality, support compliance initiatives, and respond to change without creating unnecessary complexity. Rather than becoming a source of technical debt, the PLM environment remains a strategic asset that evolves alongside the business.

Whether the goal is to update thousands of specifications, restructure product data models, retire obsolete attributes, or improve governance processes, success depends on having the right processes and tools in place. By approaching PLM maintenance strategically, organizations can reduce manual effort, improve consistency, and ensure that their product data continues to support business growth long after the initial implementation is complete.

Ready to modernize your PLM maintenance processes? Contact the Migravion team to learn how we can help you manage product data changes faster, more accurately, and at scale.