August 1, 2024
10 Warning Signs Your PLM Implementation Is Failing (And How to Fix It Before It's Too Late)
Your VP of Engineering just told you the PLM implementation is "going fine."
Meanwhile, your manufacturing team is still exporting BOMs to Excel. Quality can't find the latest revision. And purchasing is ordering parts that engineering obsoleted three months ago.
The cost? $2.3M annually for a typical mid-market manufacturer.
We've rescued 47 failed PLM implementations. Here's what we learned: failures are predictable. They follow patterns. And most can be fixed before they become terminal.
This guide covers the 10 warning signs that predict PLM failure—and what to do about each one.
Warning Sign #1: User Adoption Below 40%
The Symptom
Six months post go-live, fewer than half your target users are actually using the system.
How to measure:
Check login frequency (daily active users)
Compare data entry locations (PLM vs. Excel)
Survey actual usage vs. required usage
Track workaround creation (shadow systems)
Why This Matters
Low adoption isn't a "change management issue" to fix later. It's an early predictor of complete failure.
The data:
Implementations with >80% adoption in month 6: 88% long-term success rate
Implementations with <40% adoption in month 6: 12% recovery without intervention
The Real Problem
Users aren't resisting change. They're telling you the system doesn't work for their actual job.
Common causes:
System too complex for daily tasks
Workflows don't match real processes
Takes longer than old method
Can't find what they need quickly
System fights them instead of helping
What to Do About It
Immediate (This Week):
Interview 5 low-adoption users. Don't ask why they don't use it. Ask what they do instead and why that works better.
Identify the biggest friction point. Usually it's one specific workflow that's broken.
Fix that one thing. Not the whole system—just the daily pain point.
Example: Medical device manufacturer found users weren't creating BOMs in PLM because it required 14 clicks and 3 screens. Old Excel template: copy-paste done. We created a simplified BOM template in PLM (5 clicks, 1 screen). Adoption went from 34% to 78% in 6 weeks.
Short-term (This Month):
Create "power user" champions in each department
Document actual vs. ideal processes (don't force ideal on everyone immediately)
Build quick wins that save time, not just "proper process"
Long-term (Next Quarter):
Systematic workflow optimization based on usage data
Progressive training (basic → intermediate → advanced)
Continuous improvement process with user feedback loop
When to Get Help: If adoption is below 40% at month 6 and you don't know why, you need external perspective. Internal teams are too close to see what's broken.
Warning Sign #2: Engineering and Manufacturing Don't Trust the Same Data
The Symptom
Engineering releases a BOM in PLM. Manufacturing doesn't trust it and manually verifies everything before building.
Indicators:
Manufacturing maintains parallel Excel sheets
Weekly "BOM reconciliation" meetings
Constant "which revision is correct?" questions
Shop floor using printed drawings (not system data)
Quality inspecting from different specs than manufacturing builds to
Why This Matters
If departments don't trust the system as single source of truth, you don't have PLM—you have an expensive documentation system.
The cost:
8-12 hours/week reconciling data: $40K-$60K annually
Quality escapes from mismatched specs: $200K-$500K annually
Delayed shipments from wrong parts: $150K-$300K annually
Total: $390K-$860K per year for mid-size manufacturer
The Real Problem
Trust breaks when systems don't talk to each other in ways departments understand.
Engineering's "Assembly-001-Rev B" means nothing to manufacturing if their ERP shows "Part Number 100234-VERSION 2."
Root causes:
No data dictionary translating between systems
Integration only goes one direction (PLM → ERP, but not back)
Changes in one system don't reliably appear in the other
No clear rule for "which system wins" when data conflicts
What to Do About It
Immediate:
Create visual data flow map. Show everyone where data lives, when it moves, who can change it.
Define "single source of truth" per data type. Part numbers: PLM. Cost: ERP. Inventory: ERP. BOM structure: PLM. Write it down. Make it official.
Implement change notifications. When engineering changes PLM, manufacturing gets automatic alert. When ERP cost changes, engineering sees it.
Example: Aerospace manufacturer discovered their integration only ran nightly. Engineering released ECO at 9am, manufacturing planned production at 10am from old data, built wrong parts by 2pm. Changed integration to event-driven (instant sync). Trust restored in 3 weeks.
Short-term:
Fix integration gaps (usually bi-directional sync is broken)
Create enterprise data dictionary (translate department languages)
Establish conflict resolution rules (what happens when systems disagree)
Long-term:
True digital thread from design through manufacturing
Real-time validation (system prevents conflicting data)
Automated reconciliation reports (catch discrepancies before production)
When to Get Help: If you have integration but departments still don't trust it, your translation layer is broken. This requires cross-functional expertise in PLM, ERP, and manufacturing processes.
Warning Sign #3: "We'll Add That Feature Later" List Growing
The Symptom
Your "Phase 2" backlog has 40+ items. "Phase 1" shipped 8 months ago. Nothing from Phase 2 has been implemented.
Red flags:
Critical features postponed to meet go-live date
Users requesting same features repeatedly
"Temporary workarounds" becoming permanent
Team says "it works, we just need to..." (followed by long list)
Why This Matters
A growing "later" list means you launched incomplete software that doesn't match how work actually happens.
The psychology:
Week 1: "We'll add that after launch"
Month 3: "We're stabilizing, can't add features now"
Month 6: "Users adapted, maybe we don't need it"
Month 12: "Why is adoption still low?"
The Real Problem
Scope cuts made to hit arbitrary deadlines, not based on "minimum viable product" thinking.
What gets cut (and why it matters):
"Nice to have" search features → Users can't find anything, stop using system
"Optional" reporting → Management can't see value, threatens funding
"Advanced" workflows → These were actually daily tasks, just looked complex
"Phase 2" integrations → Causes the trust problem from Warning Sign #2
What to Do About It
Immediate:
Triage the backlog. Sort into: "Blocking adoption" vs. "Actually nice-to-have"
Implement top 3 blocking items. Not all 40. Just the 3 that would increase adoption most.
Kill the rest. If it's been 6 months and users adapted, they don't need it.
Example: Industrial equipment manufacturer had 52 backlog items. We interviewed users. Only 4 items mentioned repeatedly. Implemented those 4 in 6 weeks. Adoption jumped from 51% to 83%. Deleted the other 48 backlog items.
How to identify blocking items:
What do users complain about weekly?
What causes the most workarounds?
What feature would save the most time?
Short-term:
Implement continuous improvement process
Monthly feature prioritization (based on actual usage data)
Stop accumulating backlog—decide monthly: do it now, do it never
Long-term:
Agile implementation approach (working software over comprehensive documentation)
MVP mindset (minimum viable product that actually gets used)
User-driven roadmap (not vendor-driven or IT-driven)
When to Get Help: If your backlog is over 20 items and growing, your requirements process is broken. You need help separating "must-have" from "nice-to-have" based on actual business impact, not opinions.
Warning Sign #4: Change Management Budget Was First to Get Cut
The Symptom
Your $400K PLM implementation budget includes $350K for software and configuration, $50K for training... and $0 for organizational change management.
Indicators:
Training was two 4-hour sessions
No change champions identified
Communication plan: "announce at town hall"
Assumption: "our people are smart, they'll figure it out"
Why This Matters
The data is brutal:
Implementations with OCM: 88% success rate, 82% adoption
Implementations without OCM: 27% success rate, 39% adoption
Change management isn't a "nice-to-have." It's the difference between success and failure.
The Real Problem
You're not installing software. You're changing how people do their jobs every single day.
What gets missed without OCM:
People don't understand why things are changing
No one explains what's in it for them
Resistance interpreted as stubbornness (actually fear of looking incompetent)
No support structure when people struggle
Early adopters punished (work harder to use new system, get no recognition)
What to Do About It
If you haven't launched yet: STOP. Add OCM budget. Seriously. If you can't afford OCM, you can't afford the implementation.
Minimum viable OCM:
$15K-$25K for mid-market implementation
Change champions in each department (1 per 20 users)
Communication plan (weekly updates, not just "go-live announcement")
Support structure (office hours, not just help desk tickets)
If you already launched without OCM: You can still recover. It's harder, but possible.
Immediate:
Identify informal leaders (people others go to for help—not always managers)
Make them official champions (training, authority, recognition)
Create visible quick wins (show benefits, not just process compliance)
Example: Automotive supplier launched PLM with zero OCM. 6 months later: 31% adoption, management considering scrapping it. We identified 8 champions, trained them deeply on why the system exists and what problems it solves. Champions became translators between IT and users. Adoption reached 76% in 5 months. Total champion investment: $18K.
Short-term:
Weekly communication (wins, tips, upcoming changes)
Regular feedback loops (listen to frustration, act on it)
Celebrate early adopters (make resistance look like missing out)
Long-term:
OCM becomes standard part of every project
Change champion network maintained (not disbanded after launch)
Continuous improvement mindset (system evolves with business)
When to Get Help: If adoption is low and you skipped OCM, you need external change management expertise. Your IT team can't do this—it requires understanding psychology, organizational dynamics, and adult learning principles.
Warning Sign #5: Pilot Has Been "Almost Ready" for 6 Months
The Symptom
You've been running a "pilot" for 6+ months. It's perpetually "almost ready" to expand to full production. But it never quite gets there.
Red flags:
Pilot keeps finding "one more thing to fix"
No clear success criteria defined
Pilot team says "we're still learning"
No expansion timeline
Management losing patience
Why This Matters
This is pilot purgatory—and it's lethal.
What causes it:
No commitment (pilot was always a hedge, not a real test)
No dedicated resources (pilot team doing this "on the side")
No clear definition of success (so nothing is ever "good enough")
No consequences (pilot can fail without anyone getting fired)
The cost:
Investment without return
Team morale collapse (feels like busywork)
Executive support erodes (becomes a joke)
Competitor advantage (they're implementing while you're piloting)
The Real Problem
Pilots fail because companies use them as safety nets, not real tests.
Successful pilots:
Clear scope (these products, these users, this timeline)
Dedicated resources (pilot is their only job)
Defined success metrics (adoption %, time savings, error reduction)
Hard deadline (decision by Date X, not "when ready")
Executive commitment ("we're doing this, pilot determines how")
Failed pilots:
Vague scope ("let's try it with a few products")
Part-time resources ("fit it in between other work")
Subjective metrics ("when people are comfortable")
No deadline ("until we're confident")
Executive escape hatch ("we'll see how it goes")
What to Do About It
Immediate:
Define success criteria. Quantitative, not subjective. Example: "80% of pilot users logging in daily, BOM creation time reduced 40%, zero quality escapes from pilot products."
Set hard deadline. 90 days maximum. Decision: expand or kill.
Assign dedicated resources. Pilot can't be a side project.
Example: Medical device company ran 9-month pilot. Always "almost ready." We implemented 90-day deadline with clear metrics: 75% adoption, 50% time savings on ECO processing. Week 1: 34% adoption. Week 6: 59%. Week 10: 81%. Expanded to full production week 13. Total pilot time: 12 weeks.
The key mindset shift: Pilots exist to learn fast, not avoid risk. You learn by deciding, not by postponing decisions.
Short-term:
Weekly pilot reviews (progress vs. metrics)
Bi-weekly stakeholder updates (build excitement)
Plan expansion during pilot (don't wait for "perfect")
Long-term:
Agile implementation (working iterations, not big-bang launches)
Fail-fast culture (learn from mistakes quickly)
Decision velocity (speed matters more than perfection)
When to Get Help: If your pilot has been running 4+ months without expansion decision, you need external facilitation to force the decision. Internal teams are too invested in avoiding failure.
Warning Sign #6: IT Owns PLM, But Engineering Uses It
The Symptom
Your PLM system is managed by IT. Configuration decisions made by IT. Troubleshooting handled by IT. But engineering is the primary user.
Indicators:
IT doesn't understand engineering workflows
Engineers submit tickets for things that should be self-service
Change requests take weeks (IT prioritizes based on their understanding, not engineering's)
Engineers build workarounds rather than wait for IT
Why This Matters
IT thinks about systems. Engineering thinks about products. These are different languages.
What breaks:
IT optimizes for system stability (few changes, controlled access)
Engineering needs system flexibility (frequent changes, easy access)
IT measures uptime and ticket resolution time
Engineering measures time-to-release and design reuse
IT sees "user error," engineering sees "system doesn't work"
The Real Problem
PLM is not an IT system. It's a business system that IT supports.
The ownership model should be:
Business owner: VP of Engineering (or equivalent)
System admin: Engineering (embedded PLM admin)
IT role: Infrastructure support (servers, network, security)
Not:
Business owner: IT
System admin: IT
Engineering role: Submit tickets and hope
What to Do About It
Immediate:
Identify engineering PLM admin. Someone who understands both the system AND the work. Not an IT person learning engineering.
Transfer configuration authority. IT maintains infrastructure, engineering configures workflows.
Create business owner role. Senior engineering leader owns requirements and prioritization.
Example: Aerospace manufacturer had IT managing PLM. Average configuration change: 6 weeks. Engineers maintained Excel as workaround. We moved PLM admin to engineering (with IT infrastructure support). Average configuration change: 3 days. Excel usage dropped 94%.
Short-term:
Train engineering admin (system configuration, not just system use)
Define IT vs. Engineering responsibilities (clear boundaries)
Implement request triage (engineering handles business logic, IT handles infrastructure)
Long-term:
Engineering-led continuous improvement
IT as enabler, not gatekeeper
Business-driven roadmap (not IT-driven)
When to Get Help: If IT and engineering don't understand each other's priorities, you need a translator who speaks both languages. This is Element's core differentiator—we translate between departments.
Warning Sign #7: Nobody Can Explain the ROI
The Symptom
Six months post-launch, nobody can articulate the return on investment. Not with numbers. Just vague statements like "better data management" and "improved collaboration."
Red flags:
No baseline metrics captured before implementation
No ongoing measurement of benefits
ROI calculated pre-implementation, never validated post-launch
Management asking "was this worth it?" and getting shrugs
Why This Matters
If you can't prove value, you can't get funding for:
Ongoing maintenance
System improvements
Additional modules
Staff training
Integration projects
The death spiral:
Can't prove ROI
Budget gets cut
System deteriorates
Adoption drops
Benefits disappear
ROI gets worse
System gets scrapped
The Real Problem
Teams don't measure ROI because they never defined success in measurable terms.
What should have been measured (before launch):
Time to create BOM (hours)
Time to process ECO (days)
Design reuse rate (%)
BOM errors per month (count)
Cost of quality issues (dollars)
Time searching for data (hours/week)
What to measure (after launch): Same metrics. Compare before vs. after.
What to Do About It
If you haven't launched yet: Capture baseline metrics NOW. You can't prove improvement without baseline.
If you already launched without baseline: You can still build business case, just harder.
Immediate:
Pick 3 measurable outcomes. Focus on what executives care about: time, cost, quality.
Estimate "before" state. Interview users: "How long did this take before PLM?" Document workarounds and their costs.
Measure "after" state. Track same metrics now.
Calculate delta. Show improvement in executive language (hours saved, dollars saved, errors reduced).
Example: Industrial equipment manufacturer couldn't prove ROI 8 months post-launch. We reconstructed baseline by interviewing users about "old way." Found:
ECO processing: 6.2 days → 0.8 days (5.4 days saved × 47 ECOs/month = 254 days saved)
BOM creation: 4.3 hours → 1.1 hours (3.2 hours × 240 BOMs/year = 768 hours saved)
Search time: 8 hours/week → 1.2 hours/week (6.8 hours × 150 users = 1,020 hours/week)
Converted to dollars: $2.1M annual benefit on $400K investment = 525% ROI.
Short-term:
Implement ongoing measurement (monthly metrics review)
Create executive dashboard (visual ROI tracking)
Communicate wins (regular updates showing benefits)
Long-term:
ROI becomes standard language
Benefits measured continuously (not just at launch)
Business case drives continuous improvement
When to Get Help: If you need to prove ROI retroactively or build business case for additional investment, bring in someone who can reconstruct value and communicate it in executive language.
Warning Sign #8: Integrations Keep Breaking
The Symptom
Your PLM-ERP integration worked at launch. Now it breaks weekly. Sometimes PLM data doesn't reach ERP. Sometimes ERP data overwrites PLM. Nobody knows what will break next.
Indicators:
Manual data reconciliation meetings weekly
"Integration failed" alerts ignored (too frequent)
Manufacturing doesn't trust automated data (verifies manually)
IT firefighting integration issues constantly
Why This Matters
Fragile integrations destroy trust faster than no integration.
Users would rather have predictable manual process than unreliable automation.
The cost:
Integration maintenance: $30K-$60K annually (should be $10K-$15K)
Manual reconciliation: 10 hours/week = $40K annually
Error correction: $100K-$300K annually
Lost credibility: Priceless (and project-killing)
The Real Problem
Integrations break because they were built as technical point-to-point connections, not business process automation.
Common causes:
No error handling. Integration fails silently. Nobody knows until manufacturing builds wrong parts.
No validation. Bad data from PLM crashes ERP. Or vice versa.
Hard-coded assumptions. System upgrade changes field name. Integration stops working.
No monitoring. Integration fails Friday night. Discovered Monday morning. Weekend production used wrong data.
No documentation. Only one person understands it. They left.
What to Do About It
Immediate:
Implement monitoring. Alert when integration fails. Not weekly summary—immediate alert.
Add validation rules. Prevent bad data from flowing. Better to reject than to corrupt.
Create error handling. When integration fails, log it, alert humans, retry intelligently.
Example: Automotive supplier had PLM-ERP integration failing 3-4 times/week. Added validation (part numbers must match format XXX-YYYY-ZZ), error handling (retry 3x then alert), and monitoring dashboard. Failures dropped from 3-4/week to 1/month. When they do fail, fixed in hours instead of days.
Short-term:
Document integration logic (so you're not dependent on one person)
Implement automated testing (catch breaks before production)
Create runbook (step-by-step troubleshooting guide)
Long-term:
Robust integration architecture (handles errors gracefully)
Proactive monitoring (catch issues before users report them)
Continuous testing (especially after system upgrades)
When to Get Help: If your integration requires constant firefighting, your architecture is fundamentally flawed. Bring in integration specialists who understand both systems and can rebuild it properly.
Warning Sign #9: Training Was "One and Done"
The Symptom
You trained users once—at implementation. Then stopped. New hires get no formal training. Existing users forget 80% of what they learned. Everyone figures it out through trial and error.
Indicators:
New employees take 3-6 months to become proficient (should be 2-4 weeks)
Users only know 20% of system capabilities
Tribal knowledge ("ask Susan, she knows")
Different departments using system completely differently
Basic features underutilized (system can do it, users don't know how)
Why This Matters
Software training is not event. It's a program.
One-time training = wasting implementation investment.
The math:
Implementation: $400K
Training: $5K (two 4-hour sessions)
Training as % of implementation: 1.25%
Result: $400K system used at 20% capacity = $80K actual value
The Real Problem
Adults don't learn by listening. They learn by doing, repeatedly, with support when they struggle.
Effective training model:
Before launch: Overview and "why this matters"
At launch: Hands-on practice with real data
Week 2: Office hours for questions
Month 1: Advanced features training
Month 3: Optimization and best practices
Ongoing: New hire onboarding, quarterly refreshers, champions program
Not:
Week before launch: 4-hour session
Launch day: Good luck
Forever after: Figure it out yourself
What to Do About It
Immediate:
Assess current proficiency. Survey users: "On scale 1-10, how confident are you with [feature]?" Identify gaps.
Create learning paths. Basic → Intermediate → Advanced (not everyone needs everything)
Build support structure. Champions, office hours, quick-reference guides
Example: Medical device manufacturer trained users once at launch. Six months later: 38% adoption, users calling system "too complicated." We implemented:
Department-specific quick-start guides (5 pages, not 50-page manual)
Weekly 30-minute lunch-and-learn sessions (optional, focused on one task)
Champions program (one per 15 users, available for questions)
Result: Adoption 38% → 79% in 12 weeks. No system changes. Just training.
Short-term:
Develop role-based training materials
Implement new hire onboarding program
Create video tutorials for common tasks
Long-term:
Learning management system (track training completion)
Continuous improvement based on usage patterns
Advanced training for power users
When to Get Help: If users are struggling with system and your training was minimal, bring in instructional design expertise. Technical trainers teach tools. Learning experts teach humans.
Warning Sign #10: Executive Sponsor Has Moved On
The Symptom
The VP who championed PLM implementation left the company. Or got promoted. Or just stopped caring. New leadership doesn't understand the system. Asks "why did we buy this?"
Indicators:
PLM never mentioned in executive meetings
Budget requests declined ("we already spent $400K")
Team feels unsupported
Implementation treated as "IT project" not "business initiative"
No strategic direction
Why This Matters
PLM without executive support dies slowly but inevitably.
The death timeline:
Year 1: Implementation launches with fanfare
Year 2: Budget cut, no improvements, team frustrated
Year 3: Adoption drops, workarounds grow, people leave
Year 4: New executive: "Why are we paying for system nobody uses?"
Year 5: System scrapped, start over with different vendor
The Real Problem
PLM is not a project with an end date. It's a capability that requires ongoing investment.
Executive sponsor responsibilities:
Communicate strategic value (why PLM matters to business)
Secure ongoing funding (maintenance, improvements, training)
Resolve cross-departmental conflicts (break down silos)
Hold teams accountable (for adoption, data quality, process compliance)
Celebrate wins (make PLM success visible)
When sponsor is gone:
Nobody communicates value (system becomes invisible)
Budget gets cut (seen as cost, not investment)
Conflicts fester (departments work around system)
Accountability disappears (adoption optional)
Wins go uncelebrated (success feels like failure)
What to Do About It
Immediate:
Identify new sponsor. Must be VP-level or higher. Must have authority over multiple departments.
Educate new sponsor. Don't assume they understand. Show ROI, strategic value, business impact.
Create sponsor visibility. Regular executive briefings (monthly), not just "it's working fine."
Example: Aerospace manufacturer lost PLM sponsor when VP of Engineering retired. New VP questioned investment ("$400K for database?"). We created executive briefing:
Strategic value: Single source of truth enabling digital transformation
ROI: $2.1M annual benefit (time saved, errors reduced, quality improved)
Risk: Competitors moving to digital thread, we'd fall behind without PLM
Future: PLM foundation for AI/ML, advanced analytics, supplier collaboration
New VP became champion. Secured $80K improvement budget. System thrived.
Short-term:
Make PLM value visible (executive dashboard, quarterly wins summary)
Connect PLM to business strategy (how it enables strategic goals)
Build executive coalition (not just one sponsor—multiple champions)
Long-term:
PLM governance board (executive + operational leadership)
Strategic roadmap (5-year vision, annual milestones)
Business case refresh (update ROI annually, show ongoing value)
When to Get Help: If you've lost executive support and can't get it back internally, bring in external perspective. Sometimes executives need to hear "this is valuable" from neutral third party, not from internal team seen as protecting their project.
The Pattern: What All 10 Warning Signs Have in Common
These aren't random failures. They're symptoms of the same root cause:
PLM treated as technology project, not business transformation.
When you think it's technology:
IT owns it
Go-live is success
Training is one-time event
Users should adapt to system
ROI is assumed, not measured
Integration is technical problem
Adoption is change management issue (nice-to-have)
When you treat it as business transformation:
Business owns it (IT supports)
Go-live is beginning, not end
Learning is ongoing program
System adapts to work
ROI is measured continuously
Integration is translation problem
Adoption is success metric (must-have)
The mindset shift: You're not implementing PLM. You're changing how your company creates products.
How to Know If You Need a Rescue
Answer these 5 questions:
Is adoption below 60%? (Yes = rescue needed)
Do departments maintain parallel systems? (Yes = integration broken)
Can you prove ROI with numbers? (No = value not realized)
Do users complain weekly about the same issues? (Yes = fundamental problems)
Has executive support weakened? (Yes = strategic risk)
Scoring:
0-1 Yes: You're okay. Normal post-implementation optimization.
2-3 Yes: Warning signs. Address immediately.
4-5 Yes: Critical. You need rescue.
What Rescue Actually Looks Like
It's not starting over. (Most systems can be saved)
Element's rescue approach:
Phase 1: Diagnosis (Week 1-2)
Usage analysis (who's using what, and how)
Workflow observation (watch people work, not how they say they work)
Pain point identification (what's actually broken vs. what people complain about)
Quick win identification (what small fix would have biggest impact)
Phase 2: Stabilization (Week 3-6)
Fix top 3 pain points
Restore user confidence (show you hear them and can fix things)
Implement basic monitoring (catch issues before users report them)
Establish support structure (champions, office hours, communication)
Phase 3: Optimization (Week 7-12)
Workflow improvements (system adapts to work, not vice versa)
Integration fixes (if needed)
Training program (ongoing, not one-time)
Measurement implementation (prove value with numbers)
Phase 4: Transformation (Month 4-6)
Executive re-engagement (show value, secure commitment)
Long-term roadmap (continuous improvement, not maintenance mode)
Capability building (make team self-sufficient)
Change management program (if skipped originally)
Typical rescue timeline: 4-6 months
Typical rescue cost: $60K-$120K (vs. $400K+ starting over)
Success rate: 87% (based on 47 rescue projects)
The Most Important Question
"Should we rescue or start over?"
Start over only if:
Data structure is fundamentally wrong (can't be fixed, only rebuilt)
System was wrong choice for your needs (rare—usually implementation is problem, not product)
Customization so extensive it's unmaintainable (need to return to out-of-box)
Company has changed dramatically (acquired, merged, pivoted) and requirements are completely different
Everything else can be rescued.
Most "failures" are actually:
Poor implementation (fixable)
Weak change management (fixable)
Integration problems (fixable)
Training gaps (fixable)
Wrong ownership model (fixable)
The decision framework:
Is the core system capable of meeting your needs? (Usually yes)
Is your data structure salvageable? (Usually yes)
Can users be re-engaged? (Usually yes, if you fix pain points)
Is executive support recoverable? (Yes, if you prove value)
If yes to all four: rescue. If no to multiple: consider restart.
Next Steps
If You Recognize 1-2 Warning Signs:
You're in optimization territory. Not critical yet, but don't ignore.
Action: [Download our PLM Health Assessment] (5 minutes, scores your implementation 0-100)
If You Recognize 3-4 Warning Signs:
You're in warning territory. Needs attention soon.
Action: [Schedule 30-minute consultation] (We'll review your situation, identify biggest risks, recommend focus areas)
If You Recognize 5+ Warning Signs:
You're in rescue territory. This needs immediate action.
Action: [Request rescue assessment] (We'll audit your implementation, deliver detailed diagnosis, recommend recovery plan)
If You're About to Implement PLM:
Learn from others' mistakes.
Action: [Download implementation guide] (How to avoid all 10 warning signs from day one)
About Element Consulting
We've implemented 127 PLM systems. Rescued 47 failed implementations. Helped 89 companies optimize existing systems.
Our core differentiator: We translate between departments. Engineering speaks engineering. Manufacturing speaks manufacturing. We speak both—and connect them.
Specializations:
PLM rescue and optimization
ERP-PLM integration
Cross-functional process design
Change management and adoption
Digital thread implementation
Industries:
Aerospace & Defense
Automotive
Medical Devices
Industrial Equipment
Partners:
PTC Windchill
Leading ERP platforms
MES and CPQ systems
[See our case studies] | [Read client testimonials] | [Learn our methodology]
The Bottom Line
Your PLM implementation is failing if:
Users don't use it consistently
Departments don't trust the same data
You can't prove ROI
Adoption stays below 60%
Executives question the investment
The good news: Most failures are reversible. The patterns are predictable. The fixes are known.
The bad news: Ignoring warning signs doesn't make them go away. It just makes recovery harder and more expensive.
The choice: Address warning signs now (when fixable) or restart later (when unavoidable).
We've seen both paths. Early intervention is always cheaper, faster, and less painful.
What's your next move?
