A spreadsheet can’t tell you when your pump is about to evaporate, when a capacity expansion will create pressure imbalances across your network, or whether your system can handle next year’s load. For engineers managing complex fluid systems in power generation, water and wastewater, chemical processing, and data center infrastructure, these aren’t hypothetical risks. They’re daily operating realities. This is why, for industries like power generation, water and wastewater, chemical processing, and data center infrastructure, modeling and simulation software is increasingly crucial.
However, recent research shows that not all tools are delivering on expectations. The Smart Manufacturing 2026: Agile Leaders Confront the AI Skills Gap report highlights why manufacturers are investing more heavily in modeling and simulation, and where many organizations still struggle. These findings are especially relevant for engineering managers, senior engineers, and IT leaders who rely on a mix of spreadsheets, CAD tools, and legacy systems to manage complex fluid systems across design, operation, and expansion.
The research makes one thing clear: modeling and simulation are no longer optional engineering aids. They are becoming core infrastructure for controlling cost, minimizing risk, and supporting scalable operations.
Let’s look into the research to discuss some of the pervasive challenges engineers in these industries face, and how a fluid modeling and simulation solution like PIPE-FLO can help.
Design engineers remain the primary users of modeling and simulation software, and for good reason. According to the research, top use cases include performance testing (43%), design verification (39%), and manufacturing process simulation (36%). These activities are critical in fluid-intensive systems, where small design errors can cascade into pressure imbalances, energy waste, or premature equipment failure.
Yet many engineering teams still rely on Excel spreadsheets and manual calculations during early design and modification phases. In a water utility managing an aging distribution network or a manufacturing plant retooling for new capacity, one engineer’s spreadsheet often becomes the de facto system model, with no practical way to validate assumptions against how the system actually behaves. While familiar, these approaches make it difficult to maintain consistency across projects, validate assumptions, or understand how changes affect the system as a whole.
Integrated fluid system modeling addresses this gap by replacing fragmented calculations with a unified system view. For example, PIPE-FLO allows engineers to visually model piping networks while applying verified flow calculations and OEM pump data behind the scenes. This helps engineering managers standardize design practices and reduce reliance on individual spreadsheets that are difficult to audit, reuse, or scale.
The research also highlights the growing importance of advanced visualization (37%) and real-time simulation (34%). These capabilities are not just about engineering precision. They are about communication.
In many organizations, engineering insights must be shared with operations, maintenance, project management, and IT stakeholders who may not work directly in simulation software. For a data center scaling cooling infrastructure across multiple facilities, or a power generation team defending a design decision to plant operations, this disconnect between engineering detail and stakeholder communication creates real project risk. When results are buried in dense reports or disconnected files, decision-making slows and confidence erodes.
Visual system modeling helps bridge this gap. By representing fluid systems schematically, engineers can clearly show how flow, pressure, and equipment performance interact across the network. For engineering managers and project leads, this improves alignment during design reviews, capacity planning discussions, and failure investigations, all without requiring every stakeholder to interpret raw calculations.
Despite increasing adoption, the research shows that manufacturers still struggle to embed modeling and simulation software into day-to-day workflows. Thirty-four percent cite difficulty integrating simulation into workflows, while 33% report challenges integrating with existing systems. Data accuracy concerns (31%) further complicate adoption.
This challenge is especially acute in organizations that rely on a combination of AutoCAD, Plant 3D, ERP systems, and maintenance platforms. In oil and gas or chemical processing environments, where those systems may span multiple sites and decades of infrastructure, the cost of a disconnected simulation model is compounded every time it falls out of sync with reality. When simulation tools operate in isolation, models quickly become outdated or disconnected from operational reality.
Simulation platforms that support continuity across the system lifecycle are better suited to these environments. PIPE-FLO enables teams to carry a fluid system model forward from design through operation and modification, supporting what many organizations describe as a digital twin approach. For IT and operations leaders, this reduces risk by minimizing duplicate data entry, preserving model integrity, and avoiding unnecessary disruption to existing infrastructure.
Organizations using modeling and simulation report measurable benefits, including improved product quality (44%), increased design accuracy (42%), and faster time-to-market (36%). However, these outcomes depend on tools that support scenario-based analysis rather than static, one-time calculations.
One of the most important takeaways from the Smart Manufacturing 2026 research is that modeling and simulation are becoming operational standards, not niche engineering tools. As manufacturers invest more heavily in digital transformation, expectations for these platforms are rising.
For engineering-led organizations, the most effective solutions share several characteristics: they replace fragmented spreadsheets with standardized system models, integrate into existing toolchains, support clear communication across teams, and enable scenario-driven decision-making.
The Smart Manufacturing 2026 findings reinforce what many engineering and IT leaders already know: simulation delivers the greatest value when it reflects real-world systems, fits naturally into engineering workflows, and supports decisions across the system lifecycle. As manufacturers continue to modernize, those that adopt modeling tools aligned to these realities will be best positioned to reduce risk, control costs, and scale with confidence.
