The 5 Hard Problems Engineering and Manufacturing Teams Still Face in 2026

Engineering and manufacturing organizations are entering a new era of complexity.

Products that used to be mostly mechanical now combine mechanical systems, electronics, embedded software, connectivity, and increasingly sophisticated supply chains. Teams are spread across multiple locations, sometimes across multiple companies. Product development cycles continue to accelerate while expectations for quality and responsiveness keep rising.

Yet despite decades of investment in engineering tools, many companies still struggle with very basic operational challenges.

Research from McKinsey & Company shows that knowledge workers spend nearly two hours every day searching for information instead of doing productive work.

Other studies suggest that engineers can spend around 20% of their time simply looking for information or reconciling data across systems, while fragmented knowledge contributes to nearly half of product development delays.

When you think about a typical engineering organization with dozens or hundreds of engineers, this quickly turns into millions of dollars in lost productivity every year.

At the same time, the environment around manufacturing companies has become far more volatile. According to the Deloitte Manufacturing Industry Outlook, global supply chains are facing ongoing disruption due to geopolitical tensions, shifting sourcing strategies, and supplier network complexity.

All of these forces are converging at the same moment when engineering software itself is entering another major transition.

Over the past few decades we have already experienced several major shifts in engineering infrastructure. Engineering tools moved from specialized workstations to personal computers. Later, software moved from desktop installations to cloud-based systems.

Now a new transition is emerging: the shift from cloud software toward AI-driven engineering workflows.

But before companies can fully benefit from this next wave of technology, they still need to deal with a set of stubborn operational problems that have been slowing engineering teams down for years.

These problems are not theoretical. They show up every day in engineering organizations trying to develop and manufacture complex products.

1. Engineering Design Data Still Lives in Files

Despite all the talk about digital transformation, engineering work in most companies still revolves around files.

CAD models, drawings, simulation outputs, documentation, firmware packages, and configuration files are typically stored on engineers’ desktops, shared drives, or network storage systems. Even when companies deploy PDM systems, the underlying structure of the data often remains file-centric.

For many years the industry expected cloud CAD and modern PDM platforms to fundamentally change this situation. While there has certainly been progress, the reality in most manufacturing organizations looks far more hybrid.

Desktop CAD tools remain dominant in many industries, and file-based workflows continue to be the backbone of engineering collaboration.

This creates a familiar set of daily frustrations.

Engineers often spend significant time trying to locate the correct version of a design. Understanding dependencies between components or determining where a specific part is used across multiple assemblies can require manual investigation. Even relatively simple questions about product structure sometimes require digging through folders, spreadsheets, and email threads.

Studies suggest that engineers can spend up to one-fifth of their working time searching for information rather than doing engineering work.

The real problem is not the existence of files themselves. Files are a natural way to store design artifacts.

The deeper issue is that product knowledge remains trapped inside those files instead of being represented as structured, connected data that systems can understand and manage.

As products become more complex, this limitation becomes increasingly visible.

2. Managing Multi-Disciplinary Product Information

Another major shift in modern engineering is the growing multi-disciplinary nature of products.

A few decades ago many products were primarily mechanical systems. Today almost every product contains electronics, firmware, and software.

Mechanical engineers typically work in MCAD environments. Electronics teams use ECAD tools. Software engineers manage code in repositories and application lifecycle management systems.

Each of these disciplines has developed its own tools, workflows, and data structures. While these tools are highly specialized and powerful within their respective domains, connecting them into a coherent representation of the product remains extremely challenging.

In practice, organizations often maintain several parallel views of the same product. Mechanical assemblies are represented in engineering BOMs, electronics are defined in component lists and circuit designs, and software releases are tracked in separate development systems.

These structures evolve at different speeds and are usually stored in different systems.

As teams become more distributed across geographies and partner organizations, maintaining a shared understanding of the product across these disciplines becomes one of the most difficult coordination problems in modern engineering.

3. Supply Chain Complexity Is Now a Design Problem

Supply chain considerations used to be handled mostly by procurement teams after engineering completed the design.

That world no longer exists.

Over the past several years, component shortages, long lead times, and volatile pricing have pushed supply chain considerations directly into engineering decisions.

Today engineers frequently need to ask questions that would previously have been considered procurement concerns. Is a particular component available? What is the expected lead time? Are there viable alternatives if a supplier becomes unavailable? How will cost fluctuations affect the product design?

Unfortunately the information required to answer these questions is rarely integrated into engineering workflows.

Industry research suggests that nearly 70% of manufacturers still rely heavily on spreadsheet-driven workflows for sourcing and cost analysis, which makes it difficult to maintain consistent and up-to-date information about suppliers and components.

When supply chain problems appear, they often show up late in the development process, forcing engineering teams to redesign products, find substitute components, or renegotiate supplier relationships under time pressure.

In modern product development, supply chain information has effectively become part of engineering data.

4. ERP Integration Remains a Long-Standing Friction Point

For decades, engineering and ERP systems have operated in parallel worlds.

Engineering tools focus on product definition and design structures. ERP systems focus on procurement, manufacturing planning, inventory management, and operational execution.

Connecting these two environments has always been difficult.

In many organizations, product data still travels from engineering systems to ERP platforms through spreadsheet exports, manual data entry, or custom integration scripts. Even when automated integrations exist, maintaining them can be fragile and expensive.

When engineering changes occur—as they inevitably do during product development—keeping engineering data synchronized with ERP systems becomes an ongoing challenge.

The financial impact of these data problems is significant. Research suggests that poor enterprise data quality costs organizations an average of $12.9 million per year, reflecting the cumulative effects of incorrect data, manual reconciliation, and operational inefficiencies.

Despite decades of investment in PLM and enterprise integration initiatives, many companies still struggle with maintaining reliable data flows between engineering and operational systems.

5. Collaboration with Contract Manufacturers Is Still Painful

Manufacturing today is rarely confined to a single organization.

Many companies rely on contract manufacturers, suppliers, and specialized partners to produce components and assemblies. This distributed manufacturing model allows companies to scale production and leverage specialized expertise, but it also introduces new coordination challenges.

Sharing product information with external manufacturing partners is often surprisingly manual.

Engineering teams frequently exchange data through email attachments, spreadsheets, PDF documents, and static BOM exports. Each organization may maintain its own version of the product information, and keeping these versions synchronized becomes difficult as designs evolve.

When issues arise—whether related to manufacturability, component substitutions, or quality problems—communication cycles between engineering teams and manufacturing partners can become slow and fragmented.

Disconnected data systems also create traceability challenges in distributed manufacturing networks, which can lead to production delays and quality risks.

As manufacturing ecosystems become more distributed and product complexity continues to increase, this collaboration problem becomes even more visible.

Why These Problems Persist

If these problems are so widely recognized, it is reasonable to ask why they have not been solved already.

Over the past twenty years, companies have invested heavily in PDM, PLM, ERP integrations, and various cloud platforms. Yet many of the operational challenges described above continue to appear across industries.

One reason is that most engineering systems were originally designed around documents and applications, not around connected product data.

CAD systems manage design files and geometry. ERP systems manage transactions, materials, and production planning. Supply chain systems manage vendors and logistics. Software development environments manage code and releases.

Each of these systems represents the product from a different perspective.

What is often missing is a shared data foundation capable of connecting these perspectives into a unified information model.

Without such a foundation, organizations rely on exports, spreadsheets, and integration scripts to bridge the gaps between systems. As products become more complex and teams become more distributed, those gaps become increasingly difficult to manage.

Conclusion: The Next Step for Engineering Systems

None of these problems are new.

Engineering teams have been dealing with fragmented design data, supply chain uncertainty, ERP integration challenges, and collaboration issues with manufacturing partners for many years. What has changed is the scale and complexity of modern products.

Mechanical systems are now deeply intertwined with electronics and software. Supply chains span global networks of suppliers and contract manufacturers. Engineering teams operate across multiple organizations and locations.

The traditional tools used to manage product development were not designed for this level of complexity. Many of them evolved around managing files and documents rather than organizing connected product information.

As a result, engineering organizations spend significant time reconciling data between systems instead of using that data to make better decisions.

According to Boston Consulting Group, manufacturers increasingly rely on data platforms and analytics to optimize value chains and improve product development productivity.

This shift points toward a new generation of engineering systems that focus less on managing files and workflows and more on organizing connected product data that can flow across engineering, supply chain, and manufacturing environments.

At the same time, a new transition in software is emerging. Just as cloud computing reshaped enterprise applications over the past decade, AI-driven workflows are beginning to transform how engineering teams interact with product data.

When product information becomes structured, connected, and accessible across systems, AI tools can help engineers navigate complexity, automate routine tasks, and support better decision-making throughout the product lifecycle.

The companies that succeed in the coming years will not simply adopt new tools. They will rethink how product information is organized, shared, and used across the entire engineering and manufacturing ecosystem.

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Best, Oleg