It’s 3pm on Thursday. Your supplier just flagged a material change on a critical component; something about lead content and new EU regulations. You need to know which assemblies are affected, which customer orders might be delayed, and what the cost impact looks like. And you need to know by tomorrow’s design review.
If you’re reaching for three different spreadsheets, your CAD system, and about to send a dozen Slack messages, you’re not alone. This moment is increasingly common across hardware and industrial machinery companies. Products are more complex, supply chains more fragmented, and regulatory requirements more demanding than ever before. Teams have more data than they’ve ever had, but somehow less clarity when it matters most.
The gap between the information you have and the insights you need isn’t a people problem. It’s a systems problem. And it’s costing more than most companies realize.
Who This Is For
This article is written for the people who live in that gap every day:
Engineering Managers coordinating mechanical, electrical, and software teams who need different information but depend on the same product data. You’re managing complexity that spreadsheets weren’t designed to handle, and you know it.
Product Leaders balancing technical decisions against timelines, costs, and market pressure. You need visibility into what’s actually happening, not just status reports that tell you everything is “on track” until suddenly it isn’t.
Operations Directors connecting design intent to manufacturing reality. You’re translating engineering decisions into production instructions, managing supplier relationships, and ensuring compliance, often with tools that make this harder than it should be.
If you’re at a growing company, somewhere between 20 and 200 people, you’ve likely outgrown spreadsheets but aren’t ready for enterprise software that requires nine-month implementations and dedicated IT teams. You need something purpose-built for how hardware companies actually work.
The Hardware and Industrial Machinery Landscape: What Changed
Let’s start with what makes this industry different in 2025 than it was even five years ago.
The most significant shift is what we call the convergence crisis. A decade ago, an industrial machine was primarily mechanical with some electrical components. Today, that same machine embeds sensors, runs firmware, connects to networks, and generates data. What used to be 200 parts to manage is now 600 parts spanning multiple engineering disciplines. Your mechanical engineers, electrical engineers, and software developers all need to coordinate around the same product, and their changes cascade in ways that aren’t always obvious.
Supply chains have fragmented dramatically. The average industrial machine now sources from 40 or more direct suppliers, up from roughly 25 suppliers a decade ago. Each supplier relationship creates coordination overhead: specs to communicate, changes to manage, quality issues to track. When a supplier discontinues a part or flags a material change, the ripple effects touch multiple assemblies, affect different customer orders, and require decisions from people across your organization.
Then there’s regulatory acceleration. New requirements around digital product passports, carbon tracking, cybersecurity standards, and material compliance are creating traceability demands that simply didn’t exist five years ago. Auditors want to see decision history, approval chains, and change documentation. “We track it in Excel” is no longer an acceptable answer, and “it’s in Bob’s email somewhere” definitely isn’t.
These pressures hit hardest in specific product categories. Industrial automation equipment, production machinery, capital goods, custom fabrication, anywhere products are complex, customizable, and built in lower volumes with higher engineering content. These are the companies where product data management becomes a strategic capability, not just a filing system.
Many teams try to solve this by upgrading their ERP system. But ERP is fundamentally about transactions and resource planning. It’s not designed to manage engineering relationships, revision control, or the iterative nature of product development. You end up forcing engineering workflows into a transactional database, and it doesn’t fit.
What Hardware Teams Actually Do
Let’s talk about the real work, because this is where theory meets reality.
On any given day, engineers are managing BOMs that change 15 to 30 times before production. They’re tracking which revision went to which prototype build, which version the supplier is quoting against, and which one manufacturing is planning to use. Every change creates questions: Does this affect cost? Does it change performance? Do we need to retest? Who needs to approve this?
Drawing approvals happen across email threads, Slack conversations, and design review meetings. Someone asks “Can we substitute this part?” and suddenly you’re checking inventory, verifying compatibility, calculating cost impact, and trying to remember if this component appears in any other assemblies. The information exists somewhere, but gathering it takes hours.
When a supplier discontinues a component, engineers update specifications, check for approved alternatives, notify everyone who needs to know, and document the change for compliance purposes. Except “everyone who needs to know” isn’t always obvious when product data is scattered, and documentation happens after the fact if it happens at all.
Then there’s the firefighting. “Why doesn’t this assembly match the CAD model?” Because someone made a change and it didn’t propagate everywhere it should have. “Which BOM is correct?” Depends on who you ask—engineering has one version, purchasing has another, and manufacturing is working from something they printed three weeks ago.
Meet Sarah, a Mechanical Engineering Lead at a 60-person robotics equipment company. Her morning starts with email checking for supplier updates, Slack for any urgent issues, her CAD system to review overnight changes, a spreadsheet to update the project tracker, and another spreadsheet to log yesterday’s design decisions. Before she’s done any actual engineering work, she’s spent 45 minutes just gathering context and syncing information across tools. This is her every day. And she’s good at her job—the problem isn’t her, it’s that her tools force her to be a human API between disconnected systems.
The invisible work compounds. Translating engineering decisions into manufacturing instructions. Maintaining compliance documentation that auditors will actually accept. Onboarding suppliers who need access to specific product data without exposing your entire database. Managing revision history so you can answer “why did we change this?” six months later when the question inevitably comes up.
This work is necessary. But most of it shouldn’t be this manual.
The Weekly and Monthly Rhythm
Zoom out from daily tasks and you see how work compounds at the weekly and monthly cadence.
Design reviews happen every week. Decisions get made, changes get approved, next steps get assigned. But the documentation lags. Meeting notes live in someone’s email, decisions are captured in slides that get filed away, and action items end up in a project tracker that’s only loosely connected to the actual product data. Three weeks later, someone asks “why did we decide to go with Option B?” and the answer requires archaeology.
BOM reviews surface cost surprises too late. You discover that a component costs three times what was estimated, but you’re already two months into the design and changing it now means rework across multiple assemblies. The information was available earlier, but it wasn’t visible at the right time to the right people.
Change approval meetings bottleneck on information gathering. Someone proposes a change, and before anyone can approve it, you need to understand the full impact. Which products are affected? Which customer orders? What’s the cost delta? Does this affect compliance? The meeting turns into “let us research this and reconvene next week” because pulling together impact analysis takes days of manual work.
At the monthly and milestone level, there’s cross-functional alignment on what’s actually locked versus what’s still in flux. Except “locked” means different things to different teams. Engineering thinks a design is locked after the review. Manufacturing thinks it’s locked when they receive final specs. Purchasing thinks it’s locked when they place orders with suppliers. Without a shared definition and shared visibility, these misalignments create expensive surprises.
Risk assessments depend on scattered tribal knowledge. “What are the top risks to our Q2 launch?” Someone makes a list based on what they remember from recent conversations. You’re not getting systematic visibility into open issues, blockers, or dependencies—you’re getting whatever rises to the level of someone’s attention.
Here’s the pattern: Most delays in hardware development don’t come from technical problems. They come from coordination overhead. The engineering work happens, but connecting it—making sure the right people have the right information at the right time—takes twice as long as it should.
What Management Needs (And Why They’re Not Getting It)
Management has reasonable expectations. They want predictable timelines, controlled costs, visibility into risks, and traceability for decisions. The problem isn’t that they’re asking for too much. The problem is that the systems most teams use can’t deliver these things without heroic manual effort.
Let’s be specific about the gap.
Management expects predictable timelines, but gets surprised by last-minute changes. A component that everyone thought was finalized turns out to need rework. A supplier lead time that was supposed to be six weeks is actually twelve. These surprises were predictable—the information existed—but it wasn’t surfaced early enough to adjust plans.
Management expects cost control, but discovers overruns after commitments are made. You’re 70% through the project when you realize the current design is 30% over budget. The costs accumulated gradually through dozens of small decisions, but no one had visibility into the cumulative impact until it was too late to course-correct without significant rework.
Management expects risk visibility, but relies on teams self-reporting what they think is important. This creates two problems: First, teams report what’s visible to them, not what’s actually most critical to the business. Second, by the time something rises to management attention, it’s often past the point where you have good options.
Management expects decision traceability, but audits reveal gaps in documentation. When regulatory questions come up or customers ask why something changed, you’re reconstructing history from email threads and people’s memories. The decisions were made thoughtfully, but the documentation happened poorly or not at all.
The core problem isn’t team performance. Teams are working hard and making good decisions. The problem is that systems don’t surface what management needs to see until it’s too late. Information exists in fragments—some in CAD, some in spreadsheets, some in email, some in people’s heads—and connecting those fragments to answer strategic questions requires time you don’t have.
Real questions leaders ask that spreadsheets can’t answer quickly:
“How many open issues are blocking our Q2 release, and how many are actually critical versus nice-to-have?”
“Which products use this part we just learned has a 12-week lead time, and what’s the revenue impact if we can’t ship on time?”
“What changed between revision 3 and revision 4, who approved it, and what was the rationale?”
These aren’t unreasonable questions. But answering them without proper systems turns into research projects.
The Spreadsheet Tax: What It Really Costs
Let’s quantify what it actually costs to run product development on spreadsheets, email, and general-purpose tools.
Time costs show up in every engineer’s calendar. Four to eight hours per week per engineer just finding and verifying information. Not doing engineering work—just figuring out what’s current, where it lives, and whether it’s trustworthy. For a team of ten engineers, that’s 40 to 80 hours per week spent on information archaeology. Two to three days to prepare for design reviews because product data lives in six different places and needs manual compilation. Change requests that take five days to approve because impact analysis requires checking multiple systems and coordinating across teams.
Scale that across a year and you’re looking at 10-15% of your engineering capacity spent on coordination overhead instead of product development.
Risk costs are harder to measure but easier to feel. When the wrong BOM gets sent to manufacturing, you scrap parts. Real numbers from companies in this space: $15,000 to $50,000 per incident, and it happens more often than anyone wants to admit. Compliance gaps discovered during audits lead to project delays, rushed documentation efforts, and potential penalty exposure. Engineering changes made without proper supplier notification result in delivery delays when the supplier quotes and builds against outdated specs.
A 75-person industrial controls manufacturer we spoke with discovered they had three different BOMs for the same product—one in their CAD system, one in their ERP, one in spreadsheets. The reconciliation effort took two people three weeks of full-time work, and they still weren’t completely confident they’d caught everything. This wasn’t a mistake—it was the inevitable result of managing complex product data in tools that weren’t designed for it.
Scaling costs compound as you grow. Each new hire takes four to six weeks to understand “how we track things here.” Not because your people are bad at explaining, but because the system is implicit knowledge distributed across tools and tribal wisdom. You can’t take on more complex products without proportionally more coordination overhead, which eventually becomes the constraint on your growth. And when experienced people leave, knowledge walks out the door because it lives in their heads rather than in accessible systems.
The spreadsheet tax isn’t just about efficiency. It’s about what you can’t do because your current systems can’t support it. You can’t confidently answer customer questions about product history. You can’t quickly analyze change impacts. You can’t provide suppliers with clear, controlled access to the information they need. You can’t scale your product complexity without scaling coordination overhead proportionally.
How PLM Actually Works
Product Lifecycle Management systems get explained through feature lists, which makes them sound abstract. Let’s instead show PLM in motion through scenarios you’ll recognize.
Scenario 1: A supplier change happens
Without PLM, here’s the sequence: Supplier emails that they’re discontinuing a component. Email gets forwarded to engineering. Engineering checks where the part is used by searching through CAD files and spreadsheets. They find three assemblies that use it, but aren’t confident they found everything. Email chain starts to discuss alternatives. Someone updates a spreadsheet. Someone else updates a different spreadsheet. Purchasing gets looped in five emails deep. Manufacturing finds out about it two weeks later when they go to order parts. Throughout this, there are 47 emails, three meetings, manual BOM reviews, and no real confidence that nothing was missed.
With PLM, here’s what happens: Supplier change gets logged in the system. System automatically identifies all assemblies where this part is used—not through search, but through explicit relationships that were defined when the product was designed. Change workflow triggers, routing to relevant stakeholders with full context: what’s affected, what the proposed alternative is, what the cost and schedule impact looks like. Approvals happen with audit trail. When approved, changes propagate to BOMs, supplier gets notified through the system, manufacturing sees updated specs automatically. Complete visibility, complete traceability, minimal coordination overhead.
Scenario 2: A design review needs preparation
Without PLM, preparation means gathering files from various locations, checking if they’re current or if someone made changes since you last looked, compiling redlines and open issues from email and meeting notes, and hoping everyone has enough context to make informed decisions. The meeting itself starts with 15 minutes of “wait, which version are we reviewing?”
With PLM, all stakeholders access the same current state in one view. Change history is visible. Open issues are linked to affected components. Decisions from previous reviews are documented and accessible. The design review focuses on actual decisions rather than information gathering, and what gets decided is immediately captured in the system with appropriate approvals.
These scenarios reveal PLM’s core capabilities through the problems they solve:
Single source of truth means no more “which version is right?” Product data lives in one system with clear revision control, and everyone sees the same information.
Structured relationships enable instant impact analysis. When something changes, you know what’s affected because the relationships between parts, assemblies, documents, and specifications are explicit rather than implicit.
Controlled workflows ensure that approvals happen, get documented, and stay traceable. Change management becomes a process you can rely on rather than something you hope people remember to do.
Lifecycle visibility gives you understanding of where each component stands in its journey from concept through design, validation, production, and eventual phase-out. You know what’s locked, what’s still in flux, and what’s been released to manufacturing.
PLM isn’t magic. It’s structure applied to information that previously lived in fragments. That structure enables teams to work faster, make better decisions, and scale without proportionally increasing coordination overhead.
Why Nora IPLM Exists
We built Nora IPLM because we saw a gap that existing tools weren’t filling.
Enterprise PLM systems are over-engineered for growing hardware companies. Nine-month implementations, consultant dependency, rigid processes designed for organizations with 1,000 engineers, feature bloat that adds complexity without adding value, and pricing models that assume enterprise budgets. If you’re a 50-person company trying to get control of your product data, enterprise PLM is like buying a semi-truck for your daily commute.
On the other end, spreadsheets and general project management tools weren’t built for engineering data relationships. They can store information, but they can’t model the relationships between parts and assemblies, can’t manage revision control properly, can’t enforce workflows, and can’t provide the traceability that hardware development requires.
Growing hardware companies needed something purpose-built but adoptable. Something that understood how hardware actually gets developed but could be implemented in weeks rather than months. Something powerful enough to handle complex products but accessible enough that teams would actually use it.
That’s why Nora IPLM exists. Here’s what makes it different:
Built for real workflows, not theoretical processes. We designed around how engineers actually work rather than imposing a rigid methodology. The flexible object model adapts to your product structure, your processes, and your terminology. You’re not configuring yourself to match the software—the software configures to match how you work.
Strong on what matters most. We went deep on BOM management, change control, and traceability because these are the capabilities that make the biggest difference in hardware development. Rather than spreading effort across 100 features, we focused on doing the critical things exceptionally well.
Task and issue visibility built in. PLM shouldn’t just store data—it should help you execute. Tasks, issues, and dependencies are first-class objects in Nora, not afterthoughts. You can see what’s blocking progress, who’s responsible, and how it connects to the product data it affects.
Cloud-based and accessible. Your team can access what they need from anywhere without VPN complications. Suppliers and contract manufacturers can be given controlled access to specific product data without exposing your entire system. Modern infrastructure that works the way people actually work.
Designed for SMB reality. Faster adoption with less overhead, pricing that scales with you, and no dependency on consultants to make changes. You should be able to get value within weeks, not quarters.
Let’s make this concrete with a workflow example.
When a component change request comes in at 4pm, here’s what happens in Nora IPLM:
First, the engineer creates a change request directly in the system, linking it to the affected component and any related assemblies. The system automatically identifies downstream impacts—which products use this component, which customer orders might be affected, what the cost implications look like based on current BOMs and pricing data.
The approval workflow routes to relevant stakeholders with full context visible in one place. The mechanical engineer sees the technical impact. The purchasing manager sees the supplier and cost implications. The program manager sees the schedule impact. Everyone can add comments, ask questions, and provide their approval or concerns without leaving the system or sending emails.
When the change is approved, it propagates automatically. BOMs get updated to reflect the new component. The affected supplier receives notification with the updated specifications they need. Manufacturing sees the change reflected in their production data. A complete audit trail captures who decided what, when they decided it, and what information informed that decision.
In spreadsheets and email, this same process takes two to three days and lives in scattered email threads that become impossible to reconstruct later. In Nora IPLM, it’s clear, traceable, and done by end-of-day.
Real Teams, Real Improvements
Let’s talk about what actually happens when teams adopt better product data management.
A custom machinery builder that was managing change requests through email and spreadsheets reduced their approval cycle from five days to eight hours. The time savings came from eliminating information gathering—all the context approvers needed was already in the system, so they could make decisions immediately rather than waiting for research.
An industrial automation company cut their new engineer onboarding time by 60% after centralizing product data. New hires could find current BOMs, understand change history, and see how products were structured without relying on senior engineers to explain tribal knowledge. The documentation was there, accessible, and trustworthy.
A production equipment manufacturer reduced BOM errors by 80% in their first quarter after implementing proper change management workflows. Errors didn’t disappear because people suddenly got better at their jobs—errors disappeared because the system prevented the scenarios that caused errors in the first place.
Common adoption patterns we see: Teams typically start with BOM management and change control because these deliver immediate value. Value becomes clear within the first month as teams experience faster approvals, fewer errors, and better visibility. Expansion happens organically as teams realize that task management, issue tracking, and supplier collaboration all benefit from being connected to the same product data.
What customers tell us they didn’t expect: How much time they got back from not hunting for information. How much faster decisions happen when context is visible rather than scattered. How easy it becomes to onboard suppliers and contract manufacturers when you can give them controlled access to exactly what they need without exposing everything.
The pattern across these improvements is similar: Better systems don’t make people work harder, they remove the friction that was slowing people down. Teams were already making good decisions—they just couldn’t execute on those decisions as fast as they should have been able to.
Where the Industry Is Heading
Looking ahead, several trends are reshaping hardware and industrial machinery development.
Immediate trends over the next 12 to 24 months:
Mechatronics is becoming the default rather than the exception. Even traditionally “simple” industrial products are embedding software, electronics, and connectivity. This isn’t just adding complexity—it’s fundamentally changing how products get developed because mechanical, electrical, and software engineering need unified management. Design decisions in one domain cascade into the others, and managing those dependencies with disconnected tools becomes increasingly untenable.
Traceability is shifting from competitive advantage to table stakes. Digital product passports, supply chain transparency regulations, and cybersecurity requirements are making “we track it in Excel” legally and commercially unacceptable. Companies that can demonstrate complete traceability from requirements through design, validation, and production will win contracts. Companies that can’t will lose them.
Distributed collaboration is the norm, not the exception. More contract manufacturing, more supplier partnerships, more outsourced engineering services. This means more people outside your organization need controlled access to specific product data. The old model of “everything is internal and locked down” doesn’t work when your supply chain is your competitive advantage.
Medium-term shifts over the next two to five years:
AI-assisted impact analysis will predict change ripple effects before you make them. Instead of discovering that a component change affects twelve assemblies after you’ve already committed to it, AI will model the implications and surface risks during the decision-making process. This doesn’t replace engineering judgment, but it makes that judgment better-informed.
The industry is moving from document-centric to data-centric workflows. Traditional PLM stored PDFs of drawings and specifications. Modern PLM stores structured relationships and generates documents from that data when needed. This shift enables better analysis, better collaboration, and better automation.
Cloud-first PLM adoption will accelerate, particularly among SMBs. Companies won’t wait for IT infrastructure buildouts or capital budget cycles. They’ll adopt tools that work today, scale as they grow, and don’t require dedicated IT staff to maintain. The consumerization of enterprise software has reached PLM.
What won’t change:
The fundamental need for structure, traceability, and single source of truth isn’t going anywhere. Products will get more complex, regulations will get stricter, and supply chains will stay distributed. The coordination layer becomes more important, not less.
The coordination overhead of multi-disciplinary engineering won’t disappear with better tools—but better tools can reduce that overhead from a strategic constraint to a manageable operational reality.
The cost of getting product data wrong will continue to be expensive. Wrong BOMs lead to scrapped parts, missed delivery dates, and unhappy customers. Good product data management remains a strategic capability that directly impacts bottom-line results.
Conclusion: Structure Is a Competitive Advantage
Here’s the core truth: Your engineering team is already doing great work. The question is whether your systems help or hinder that work.
As products get more complex, suppliers more distributed, and compliance requirements more demanding, the coordination layer becomes your constraint. The teams that master this ship faster, scale easier, and make better decisions because their people spend time on engineering problems rather than information archaeology.
PLM isn’t optional infrastructure anymore—it’s a competitive advantage. Companies that treat product data as a strategic asset rather than just files to store will outpace those still coordinating through spreadsheets and email. Not because their engineers are more talented, but because their systems multiply that talent rather than diminish it.
The shift is already happening. While some companies debate whether they need PLM, their competitors are shipping products faster, onboarding new engineers more efficiently, and responding to customer changes with agility that fragmented systems can’t match.
If your team is evaluating PLM solutions, ask vendors these three questions:
- How long until our team sees value, not just setup completion? Implementation timelines tell you whether the vendor understands that your goal is better product development, not just software deployment.
- Can your system adapt to our workflow, or do we adapt to yours? Flexibility matters because your processes reflect your competitive advantages. Tools should enhance those advantages, not force you into generic best practices.
- What happens when we need to change how we work six months from now? Your business will evolve. Can the system evolve with you, or will you be locked into decisions you made during initial setup?
Platforms like Nora IPLM were built to answer these questions differently than traditional tools. We designed for adoption speed, workflow flexibility, and the reality that growing companies need power without complexity.
Your product data is the foundation everything else builds on. Get that foundation right, and everything becomes easier. Learn more about how Nora IPLM supports hardware and industrial machinery teams at
Explore More from Nora IPLM
Discover more ways Nora IPLM simplifies product development and innovation. Explore related topics, use cases, and platform features that help your teams work smarter and bring better products to market faster.
Advanced BOM Management Module
Enhance your engineering efficiency with Nora IPLM’s Bill of Materials (BOM) Management tools.
Product Lifecycle Management
Efficiently manage, collaborate, and innovate across every stage of your product lifecycle.
Revolutionizing BOM Management
Explore the use case to see how Nora IPLM’s BOM Management helps you take full control of your product data.