A practical path to modernize process simulation without disrupting delivery

Posted: September 28, 2026

Many industrial organizations know their current process simulation approach is holding them back. Models are hard to re-use. Engineering data gets duplicated across tools. Validation is time-consuming. And each new project feels like it starts from scratch.

Yet even when the case for modernization is clear, change can stall for one reason: risk.

• Risk of schedule disruption mid-project
• Risk of rework and model rebuild costs
• Risk of losing hard-earned engineering knowledge embedded in legacy models
• Risk of stakeholders losing trust in results during a transition

Many teams take a phased approach for a reason: across AVEVA’s user community, successful transitions are treated as change programs, not just software rollouts. These companies consistently emphasize clear training paths, physical property governance, and selective model transfer as the levers that reduce disruption and speed up adoption.  

This is why successful modernization programs don’t start with “replace tool X with tool Y.” They start with a transition plan that protects delivery while building a stronger foundation for long-term performance.

This article outlines a practical, phased approach that engineering leaders can use to modernize simulation workflows, without betting the project on a single change.

Why legacy simulation workflows struggle under today’s constraints

Process simulation has always been central to safe, profitable design and operations, but the expectations have changed. Teams are now asked to:

• Evaluate more scenarios, earlier (feedstock variability, emissions constraints, new process routes)
• Deliver faster convergence across larger, more distributed teams
• Create models that are not just “study files,” but reusable assets across lifecycle phases
• Support governance and traceability (what changed, why it changed, and what it impacted)

In many environments, the limiting factor is not engineering capability; it is workflow fragmentation: models, assumptions, and engineering data are split across disconnected tools and files. That fragmentation is what drives rework, delays, and inconsistent results.

What experienced teams prioritize first

Organizations modernizing process simulation repeatedly highlight three adoption foundations:

• A structured training plan (so adoption scales beyond a few experts)
• A physical property strategy (so models are consistent and comparable)
• A focused model transfer scope (start with the models that matter most)

LANXESS’ transition program puts these foundations into practice through training, physical properties, and model transfer.

Moving to a more modern approach typically means adopting a simulation environment that supports rigorous first-principles modeling, structured scenario work, and maintainable model governance. These capabilities are core to AVEVA™ Process Simulation and the broader AVEVA simulation portfolio.

A pragmatic modernization path: Transition in phases, prove value early

Below is a phased approach designed for broad applicability, whether you are an owner-operator, an EPC, or a hybrid engineering organization.

1) Start with a model inventory and classify by business value
Most organizations have more simulation models than they think, but only a subset are truly worth migrating or modernizing. The objective is to avoid migrating models that are not actively providing value in your organization.

A simple classification that works well:

• Business-critical & recurring: models used for frequent studies, debottlenecking, revamps, or operational support
• Project-critical: models tied to active projects or near-term FEED/front-end decisions
• Historical/archival: models that are rarely reused and provide limited ongoing value

A useful refinement is to define transfer scope rules early. For example, LANXESS describes focusing on transferring relevant process models and prioritizing bigger plant models first, while simultaneously starting new models in the new environment to avoid prolonging the legacy tool tail.

BASF adopts a similar strategy: maximize the benefit of process simulation while minimizing overlap of tools, with emphasis on where a modern environment creates additional opportunities in engineering and operations support, and where dynamic simulations become a key driver of value.

Your early modernization work should focus on the first business-critical category, because it’s where improved reuse and faster iteration create measurable impact.

2) Define “success” before you migrate: Acceptance criteria and scenario library
One of the fastest ways to lose stakeholder confidence is to migrate a model and then argue about whether the new results are “right.”

Instead, define acceptance criteria up front:

• What KPIs must match (e.g., key product properties, yield, energy/utility consumption, constraint limits)?
• What operating envelope must be covered (not just one base case)?
• What level of deviation is acceptable and why?

Then create a small, structured scenario library that represents how the plant or project is challenged:

• Nominal case (base)
• Feed/spec variability cases
• Constraints case (utilities, emissions, throughput limits)
• Operability case (turndown, ramping, key upset condition where relevant)

Several organizations also call out the importance of repeatable scenario management during transition. LANXESS, for example, highlights the ability to save multiple cases as snapshots as part of its rationale for switching. This helps teams validate and compare scenarios without scattering assumptions across copies. BASF similarly emphasizes scenario discipline (including standby vs steady operation snapshots for very large models) as a practical way to build confidence and repeatability as adoption expands.

Tools matter here: a modern simulator such as AVEVA Process Simulation helps teams run and compare scenario sets in a disciplined way, keeping assumptions tied to the process model rather than scattered across spreadsheet copies.

One reason modernization efforts stall is that adoption remains limited to a few specialists. In contrast, companies that scale usage treat training as a structured workstream.

LANXESS’ transition approach, for example, explicitly calls out:

• Training of key users first, supported by AVEVA
• Development of in-house training materials by those key users
• A phased rollout: train power users first, then casual users
• A baseline curriculum for all users, with advanced modules for power users (e.g., dynamic modeling and model writing)

This kind of role-based progression helps reduce disruption: power users become multipliers, and casual users gain enough confidence to use models consistently, without overloading the central expert team.

3) Choose a “low-disruption pilot” that still proves real value
A good pilot is not the smallest model; it’s the smallest model that matters.

Look for a pilot that:

• Has clear stakeholders (users and approvers)
• Has known pain (slow iteration, heavy rework, frequent requests)
• Requires scenario work (so productivity gains are visible)
• Can be completed in weeks, not quarters

The goal is to prove that modernization improves outcomes (speed, repeatability, trust), not just that a model can be recreated somewhere else.

4) Build governance so models become assets, not one-off files
Modernization pays off when models are reusable and maintainable. That requires lightweight governance:

• Ownership: who maintains the model and approves changes?
• Versioning: how do you track revisions across phases and stakeholders?
• Change log: what changed, why, and what did it impact?
• Assumption register: key assumptions, basis, and validity window (“sale-by date”)

The payoff is practical: models built in AVEVA Process Simulation can be maintained as a reusable basis for future studies and project phases, reducing rebuild cycles and supporting more consistent decision-making.

5) Scale with standards: Templates, review checklists, and repeatable workflows
Once a pilot proves value, scaling becomes a methodology exercise:

• Standard model templates for common units and study types
• A review checklist that enforces scenario discipline and traceability
• Shared naming conventions and documentation expectations
• Automated reporting where possible (so engineers spend time analyzing, not formatting)

This is also where capability-building matters: when teams share the same approach, simulation results become comparable across projects, regions, and engineering partners.

Phasing is also reflected in how teams move from evaluation to broad usage. For example, Bayer describes an AVEVA Process Simulation test phase followed by user adoption, expanding into applications such as distillation (continuous and batch), property data analyses, waste gas processes, recycling processes, and online monitoring/simulation use cases. This “test → adopt → expand use cases” pattern is a practical way to prove value early and reduce risk as more stakeholders rely on the model.

What modernization unlocks

A well-managed transition delivers benefits that resonate with both technical teams and business stakeholders:

• Faster time to decision: scenario-driven evaluation becomes routine rather than exceptional
• Reduced rework: fewer late-stage changes driven by missing constraints or inconsistent assumptions
• Improved trust: results are repeatable, traceable, and easier to audit
• Lifecycle reuse: models evolve with the project rather than being rebuilt at every stage
• Better collaboration: consistent workflows support distributed engineering and shared accountability

Most importantly, modernization shifts simulation from a specialist activity into a decision backbone, supporting design choices that hold up under change.

Questions to ask before you modernize your simulation workflow

If you are considering a transition, these questions help clarify the most practical first step:

• Which models are truly business-critical and reused (or should be re-used)?
• What scenario set represents your real operating/design challenges?
• What does “validated enough” mean for your stakeholders?
• Who will own model quality and governance after the pilot?
• What is the smallest pilot that proves measurable value quickly?

The organizations that answer these questions early tend to modernize faster—because they reduce uncertainty and make progress visible.

Ready to evaluate whether it’s time to move beyond legacy process simulation? Join our webcast with Hydrocarbon Processing on 27 October: “Replacing legacy Process Simulation: A practical evaluation guide for EPCs and Operators in Chemicals and Oil & Gas projects.” We will share a practical checklist for comparing simulation tools and proven steps to de-risk a transition (pilot, validation, training, governance).