In the realm of Total Productive Maintenance (TPM), the most powerful maintenance intervention may occur before a machine reaches the factory floor.
Design-out maintenance: often called Maintenance Prevention or Early Equipment Management (EEM): uses operating and maintenance knowledge to eliminate failure modes during equipment design, specification, procurement, or major refurbishment. Instead of asking maintenance teams to become faster at repairing recurring problems, it changes the equipment so those problems are less likely to occur.
This is a strategic, ROI-focused pillar of TPM. It improves Mean Time Between Failures (MTBF), reduces Mean Time To Repair (MTTR), increases Overall Equipment Effectiveness (OEE), and lowers total lifecycle cost.
The fundamental purpose is simple: do not build tomorrow’s maintenance burden into today’s equipment.
Design-Out Maintenance Is Different from Autonomous and Planned Maintenance
These TPM pillars work together, but they solve problems at different points in the equipment lifecycle.
| TPM approach | Primary focus | Typical activities | Main question |
|---|---|---|---|
| Autonomous Maintenance | Operator-led equipment care | Cleaning, inspection, lubrication, basic adjustment | How can operators detect and prevent deterioration early? |
| Planned Maintenance | Structured maintenance execution | Preventive, predictive and corrective maintenance | When and how should maintenance be performed? |
| Design-Out Maintenance | Equipment and process design | Simplification, modularity, accessibility, diagnostics and error-proofing | How can we prevent the failure mode from being created? |
Autonomous maintenance improves the daily relationship between operators and equipment. Planned maintenance creates a disciplined schedule based on failure data and equipment condition. Design-out maintenance goes further upstream by changing the underlying design.
For example, an operator may inspect a difficult-to-reach lubrication point every shift. A planned maintenance engineer may schedule bearing replacement every 3,000 hours. A design-out team asks whether the bearing can be sealed for life, relocated for access, or replaced with a more robust standard component.
That distinction matters. Maintenance routines manage exposure to failure; design-out thinking reduces the exposure itself.
The Cost-of-Downtime Case: Why Design-Out Thinking Pays
Consider a packaging line operating two shifts per day for 300 days per year. The line generates an estimated contribution margin of $1,850 per production hour.
The maintenance team records the following annual costs:
- 210 hours of unplanned production loss
- $96,000 in emergency labour, contractors and spare parts
- $42,000 in scrap, rework and retesting
- $24,000 in overtime and expedited logistics
The annual downtime-related cost is therefore:
| Cost category | Annual cost |
|---|---|
| Lost contribution from 210 hours of downtime | $388,500 |
| Emergency labour and spare parts | $96,000 |
| Scrap, rework and retesting | $42,000 |
| Overtime and expedited logistics | $24,000 |
| Total annual loss | $550,500 |
A cross-functional TPM team reviews the failure history and invests $145,000 in a design-out project during a planned refurbishment. The project includes a modular drive assembly, improved guarding access, alignment features, condition sensors and redesigned cable routing.
After implementation, the expected annual benefits are:
- Downtime reduced from 210 to 72 hours
- Emergency maintenance cost reduced from $96,000 to $53,000
- Scrap and rework reduced from $42,000 to $25,000
- Overtime and expedited logistics reduced from $24,000 to $10,000
The resulting annual benefit is approximately $329,300.
That produces:
- Payback period: $145,000 ÷ $329,300 = 0.44 years, or approximately 5.3 months
- First-year net benefit: $329,300 − $145,000 = $184,300
- First-year ROI: ($184,300 ÷ $145,000) × 100 = 127%
This is why design-out maintenance should be treated as a business investment, not merely an engineering preference. Use your organisation’s actual failure, downtime and rework data to build the business case. The Cost of Poor Quality Calculator can help quantify downtime, failure and rework costs that are often distributed across different budgets.
Worked Example: Engineering Maintainability into a Drive System
A packaging machine experiences repeated stoppages at its conveyor drive. The historical data shows three dominant failure modes:
- Coupling misalignment caused by difficult installation and insufficient alignment references.
- Bearing contamination caused by exposed seals and poor cleaning access.
- Long fault diagnosis because the vibration sensor is located behind fixed guarding.
The existing design requires two technicians, multiple special tools and approximately 3.8 hours to isolate and repair a typical drive failure.
The TPM design-out team applies a failure mode and effects analysis, then introduces the following changes:
| Failure mode | Existing condition | Design-out response | Expected effect |
|---|---|---|---|
| Coupling misalignment | Manual alignment with inconsistent reference points | Alignment dowels and a standardized modular coupling | Fewer installation-related failures |
| Bearing contamination | Exposed bearing seals and difficult cleaning access | Sealed bearing housing and improved washdown protection | Lower contamination-related failure rate |
| Slow diagnosis | Sensor hidden behind guarding | External diagnostic point and vibration sensor connected to the control system | Faster fault isolation |
| Long repair | Drive components replaced individually in confined space | Quick-change drive cartridge with standardized fasteners | Shorter repair duration |
| Maintenance exposure | Full guard removal required | Hinged, interlocked access panel | Safer and faster maintenance |
Before the redesign, coupling misalignment occurred approximately 8 times per 1,000 operating hours, while contamination-related failures occurred 5 times per 1,000 hours. The redesigned system targets fewer than 1.5 combined events per 1,000 hours.
The design does not simply make repairs easier. It changes the conditions that created the failures:
- Alignment is controlled mechanically rather than relying entirely on technician technique.
- Contamination resistance is improved through component selection and enclosure design.
- Diagnostic information is available without extensive disassembly.
- The failed module can be removed and replaced without dismantling the entire drive train.
That is the essence of design-out maintenance: translate recurring operational pain into permanent design requirements.
Measuring the Result: MTBF, MTTR and OEE
A design-out project should have measurable performance targets. MTBF indicates reliability, while MTTR indicates maintainability. OEE then shows how equipment performance translates into productive output.
The following figures represent an illustrative 12-month comparison after redesign:
| Metric | Before design-out | After design-out | Improvement |
|---|---|---|---|
| MTBF | 160 hours | 420 hours | 162.5% increase |
| MTTR | 3.8 hours | 1.4 hours | 63.2% reduction |
| Availability | 97.7% | 99.7% | 2.0 percentage points |
| Performance | 67.0% | 78.0% | 11.0 percentage points |
| Quality | 96.0% | 98.9% | 2.9 percentage points |
| OEE | 62.9% | 76.8% | 13.9 percentage points |
The availability improvement comes directly from fewer and shorter failures. Performance improves because the redesigned drive experiences fewer minor stops and speed losses. Quality improves because stable equipment conditions reduce defects generated by inconsistent motion, tension or alignment.
This is where TPM connects naturally with Lean Six Sigma. A Green Belt or Black Belt team can use Pareto analysis, trend charts, FMEA, hypothesis testing and process capability analysis to identify which design changes will deliver the greatest financial impact.
A Kaizen Sequence for Introducing Design-Out Thinking in an Existing Plant
Design-out maintenance is not restricted to new equipment. Existing plants can introduce the approach through a focused, evidence-based kaizen sequence.
1. Establish the loss baseline
Select one asset family or production constraint. Collect at least three to six months of:
- Breakdown frequency
- Downtime duration
- MTBF and MTTR
- Spare-part consumption
- Emergency labour cost
- Scrap and rework
- OEE loss categories
Separate true equipment failures from waiting, changeovers, material shortages and operating errors.
2. Rank the largest failure opportunities
Use a Pareto chart to identify the small number of failure modes creating the largest financial and operational losses. Prioritise issues using a risk score based on frequency, severity, detectability and downtime cost.
Do not begin with the easiest redesign. Begin with the failure mode that creates the greatest business exposure.
3. Observe the work at the equipment
Conduct a structured observation with operators, technicians, engineers and safety representatives. Record actual task times, access limitations, special tools, lifting requirements, diagnostic delays and repeated adjustments.
A process mapping guide can help visualise the current maintenance process and distinguish value-adding repair activity from waiting, searching and unnecessary motion.
4. Convert observations into design requirements
Translate recurring problems into specific engineering requirements, such as:
- Maximum access time of 10 minutes
- No special tools for routine module replacement
- Standardised fasteners across the asset family
- External diagnostic test points
- Safe one-person component handling
- Visual identification of lubrication, inspection and isolation points
A requirement is stronger than a general instruction to “improve maintainability.”
5. Pilot one controlled modification
Test the proposed change on one machine or one shift. Measure the outcome against the baseline. Confirm that the intervention does not introduce new safety, quality or performance risks.
6. Standardise and control
Once validated, update the equipment specification, maintenance standard, spare-parts list, training material and control plan. Feed the learning into future capital projects and refurbishment decisions.
Make Design-Out Maintenance a Management System
Design-out maintenance succeeds when it becomes part of governance rather than a one-time engineering event. Include maintenance, operations, quality, procurement, finance and safety in equipment design reviews. Require lifecycle cost estimates instead of evaluating equipment only on purchase price.
Track leading indicators such as:
- Percentage of new equipment with completed maintainability reviews
- Number of inherited failure modes eliminated before commissioning
- Early-life failure rate
- Time required for routine maintenance tasks
- Maintenance cost per unit produced
- OEE during the first 90 days of operation
The most mature TPM environments create a closed learning loop: autonomous maintenance identifies abnormalities, planned maintenance quantifies recurring failures, and design-out maintenance prevents those failures from being repeated in the next equipment generation.
Build the capability to lead this work with Lean 6 Sigma Hub’s CSSC-accredited Black Belt Online Training. The self-paced programme covers FMEA, process mapping, advanced statistics, root-cause analysis, designed experiments, SPC and control planning: practical skills for converting maintenance losses into measurable business results.
Pursue your Lean Six Sigma certification and learn to design reliability, maintainability and continuous improvement into every process.
Kaizen. Kai-Care. Kai-Done. ( Lean Six Sigma)
