Planned Maintenance: A TPM Practitioner Guide to Building a Maintenance System That Actually Works
- May 7
- 31 min read
Updated: 11 hours ago
By Allan Ung | Founder & Principal Consultant, Operational Excellence Consulting
Published: 07 May 2026
Allan Ung is the Founder and Principal Consultant of Operational Excellence Consulting (OEC), a Singapore-based management consultancy established in 2009. With over 30 years of experience leading operational excellence and quality transformation across manufacturing, technology, and global operations — including senior roles at IBM, Microsoft, and Underwriters Laboratories (UL) across Asia-Pacific — Allan brings deep shopfloor and strategic expertise to every engagement. He holds the following qualifications and recognitions: Certified Management Consultant (CMC, Japan), Certified Lean Six Sigma Black Belt, JIPM-certified TPM Instructor, TWI Master Trainer, and former National Examiner for the Singapore Business Excellence Award. Allan has designed and facilitated TPM implementations and operational excellence programmes for organisations across semiconductor, automotive, industrial manufacturing, logistics, and public sectors. His clients include Temic Automotive (Continental), Analog Devices, Amkor Technology, STATS ChipPAC, Panasonic, Micron, Lam Research, Infineon Technologies, Dorma, and Tokyo Electron, as well as Singapore government ministries and statutory boards.
What Most Plants Call Planned Maintenance — and Why It Isn't
When I walk into a plant and ask the maintenance manager whether they have a planned maintenance programme, the answer is almost always yes. There is a CMMS system, or at least a spreadsheet. There are service intervals. Equipment gets checked on a schedule. There are work orders, somewhere. And yet when I look at the actual maintenance data — when I sit with the failure logs, the breakdown history, the overtime records, the spare parts consumption — I invariably see something that tells a completely different story. I see a team that spends the majority of its time reacting: responding to breakdowns, chasing urgent repairs, cannibalising one machine to keep another running, and routinely deferring scheduled maintenance because production cannot spare the asset right now.
This is the gap at the heart of Planned Maintenance — not the gap between having a schedule and not having one, but the gap between a maintenance schedule that exists on paper and a maintenance system that is actually executed with discipline, informed by data, and designed around a deliberate strategy for each piece of equipment in the plant. The distinction matters enormously, because a reactive maintenance culture with a preventive maintenance schedule bolted on top does not produce the outcomes that a genuine Planned Maintenance programme delivers. It produces the same firefighting, at greater cost, with the added administrative burden of maintaining records no one has time to analyse.
In the Total Productive Maintenance framework developed by the Japan Institute of Plant Maintenance, Planned Maintenance is one of the eight foundational pillars — and it is the one most frequently misunderstood. It is not, as the name might suggest, simply the practice of doing maintenance according to a plan. In the rigorous JIPM sense, Planned Maintenance is a strategically designed, equipment-specific, condition-informed maintenance system whose goal is to progressively eliminate the need for reactive firefighting — not by intervening in equipment more frequently, but by understanding failure mechanisms deeply enough to intervene at the right time, in the right way, and only where it produces genuine reliability improvement. Understanding what that actually means, and what it requires from both the maintenance function and the wider organisation, is the purpose of this guide.
The Maintenance Strategy Landscape: Understanding What You Are Actually Choosing
Before building a Planned Maintenance programme, it is important to be precise about what the available maintenance strategies actually are and what each one is designed to accomplish. The conventional framing presents these as a progression — reactive is primitive, predictive is advanced — but a mature PM programme does not replace all reactive maintenance with predictive maintenance. It makes deliberate, defensible choices about which strategy to apply to which equipment, based on the consequences of failure, the failure characteristics of the asset, and the cost-benefit arithmetic of different intervention approaches.
Reactive maintenance, or breakdown maintenance (BM in JIPM terminology), means allowing equipment to run until it fails before intervening. This is sometimes the correct choice — specifically for non-critical assets whose failure has negligible production or safety consequences, whose repair is fast and inexpensive, and whose failure mode does not create safety risks or cascade into downstream equipment damage. The mistake is not in using reactive maintenance for these assets; it is in using it by default for all assets, because the organisation has never formally considered the consequences of failure for each piece of equipment and made a conscious decision about how to manage it.
Time-based preventive maintenance (TBM) replaces or services components at fixed intervals regardless of their actual condition, drawing on manufacturer recommendations and accumulated failure history to determine appropriate service frequencies. It is the most widely implemented approach in industry, and for many applications it is entirely appropriate. Its fundamental limitation is that fixed-interval replacement is based on statistical averages of component life. For any individual component, the actual remaining useful life at the point of planned replacement may be far longer than the interval — meaning the plant is paying for maintenance it did not need — or shorter, meaning the component has already entered the wear-out phase and may fail before the scheduled intervention arrives. TBM manages this uncertainty through conservative intervals, which means it is inherently more expensive than it needs to be, and it still cannot guarantee that every individual component will be caught before failure.
Condition-based maintenance (CBM) shifts the decision trigger from elapsed time to measured equipment condition, using vibration analysis, oil analysis, thermography, ultrasonic inspection, and other diagnostic techniques to monitor actual deterioration and intervene when condition parameters cross defined thresholds. CBM is more economically efficient than TBM for critical assets because it avoids unnecessary replacement while catching genuine deterioration before it becomes a failure. The organisational challenge is that CBM requires both diagnostic capability and the analytical discipline to act on what the data reveals. Many plants acquire the instruments without establishing the processes and skills to interpret the readings and generate timely work orders — and condition monitoring data that sits unanalysed provides no reliability benefit whatsoever.
Proactive maintenance addresses the root causes of deterioration rather than simply responding to it, whether on a schedule or in response to condition signals. It encompasses design improvements that eliminate inherent reliability weaknesses, lubrication and contamination control programmes that extend component life, and failure mode analysis that feeds back into procurement specifications for replacement parts and new equipment. Proactive maintenance is the highest expression of PM maturity, and it is what separates a plant that has eliminated chronic breakdowns from one that is still managing them.
The practical insight for any practitioner building a PM programme is that all four strategies belong in the toolkit. The question is never which strategy is best in the abstract, but which strategy is most appropriate for this specific piece of equipment, given its criticality, its failure characteristics, and the cost and capability required to implement each option. That question cannot be answered without a rigorous equipment register and criticality assessment — which is where a properly constructed PM programme must begin.
The PM-AM Relationship: Why the Foundation Matters Before the Programme
Before going further into the technical architecture of Planned Maintenance, there is a structural relationship that must be understood — the relationship between Planned Maintenance and Autonomous Maintenance. These two pillars are often treated as separate activities implemented on parallel tracks, but in practice they are deeply interdependent, and the failure to understand this interdependence is one of the most consistent causes of PM programme underperformance that I encounter.
The logic runs as follows. Autonomous Maintenance transfers ownership of basic equipment care — cleaning, lubrication, inspection, tightening — from the maintenance department to operators. Through the seven steps of AM, operators develop the skills to detect abnormalities early, maintain basic conditions, and prevent the accelerated deterioration that results from contamination, insufficient lubrication, misalignment, and loose fasteners. When AM is functioning well, the basic condition of equipment is stable. Forced deterioration — the kind caused by neglect, contamination, and inadequate basic care — is controlled at the source.
A Planned Maintenance programme built on a foundation of poor basic equipment condition will always be fighting deterioration it could have prevented. Maintenance technicians who spend their time responding to failures caused by inadequate lubrication or contamination that operators have not been trained to control cannot focus on the higher-value diagnostic and improvement work that a mature PM programme requires. Conversely, when AM is working, the maintenance function is freed from basic failure management and can concentrate on systematic analysis of natural deterioration patterns, selection and optimisation of maintenance strategies, and the development of condition-based monitoring capability.
This is why the JIPM framework positions the maintenance department's role in Step 1 of the PM seven-step progression as providing support and guidance to AM activities — not as a peripheral courtesy, but as a structural prerequisite for everything that follows. In the early stages of a TPM implementation, maintenance engineers must be actively participating in AM step-by-step development: helping operators prepare tentative standards for cleaning, lubrication, and inspection; attending to red-tag abnormalities that operators identify; and using the tagging process to collect baseline data on equipment condition. The maintenance department's investment in AM is not altruistic. It is the investment that creates the stable equipment baseline on which a rigorous PM programme depends.
The Seven Steps of PM: A Structured Development Path
The Japan Institute of Plant Maintenance structures the development of a Planned Maintenance programme into seven sequential steps, each building on the capabilities and data established in the preceding steps. Understanding these steps not as a checklist but as a genuine development pathway — with clear prerequisites, clear deliverables, and clear signals of readiness to progress — is essential to implementing PM in a way that builds sustainable capability rather than simply generating documentation activity.
The first step, providing support and guidance to AM activities, establishes the PM-AM partnership described above. Maintenance technicians participate in tagging sessions, resolve red-tag items that require specialist intervention, help develop one-point lessons for operator use, and collect the first systematic baseline data on failure frequency, MTBF, and MTTR. This data collection is not a bureaucratic exercise. It is the empirical foundation on which every subsequent strategic decision in the PM programme will rest. A plant that skips this step — that moves straight to building a preventive maintenance schedule without first understanding the current failure landscape — builds that schedule on assumptions rather than evidence.
The second step, evaluating equipment and understanding current condition, deepens the diagnostic work begun in Step 1. Maintenance engineers systematically identify and correct existing equipment defects, conduct failure analysis, stratify failures by cause and frequency, and develop a structured understanding of each asset's deterioration mechanisms. Why-why analysis is applied to chronic failures to understand their root causes rather than just their symptoms. The output is not just a list of defects corrected, but a growing body of knowledge about how the plant's equipment actually fails — which components are vulnerable, which failure modes are most costly, and which deterioration mechanisms are driving the majority of downtime.
In the third step, restoring deterioration and correcting weaknesses, the knowledge gained in Step 2 is applied systematically. Beyond simply fixing what is broken, the maintenance team works with operators to establish provisional standards for cleaning, lubrication, and inspection intervals — the tentative standards that AM teams will use as the basis for their own step development. Schedules for time-based maintenance and the first condition monitoring activities are established, based on actual failure data rather than manufacturer defaults alone. The plant begins, for the first time, to have a maintenance system whose parameters are grounded in its own operational reality.
The fourth step, building an information management system, is where many PM programmes stall — because it requires an investment in data infrastructure that feels administrative rather than operational. But without a functional information management system, the plant cannot trend MTBF and MTTR over time, cannot identify which maintenance interventions are producing reliability improvement, and cannot make rational decisions about how to evolve its maintenance strategy. The information management system also supports operator education and on-the-job training in general inspection, developing the shared knowledge base that makes the AM-PM interface work in practice. Visual controls — colour coding, inspection windows, min-max level markings — are developed and deployed at this step, making abnormal conditions visible without requiring specialist diagnostic expertise to detect them.
The fifth step, building a periodic maintenance system, formalises and optimises the time-based maintenance programme. Standard documents are developed for material selection, spare parts management, lubrication specifications, and replacement intervals. The periodic maintenance schedule is refined based on the failure data accumulated since Step 2, progressively extending intervals where deterioration data supports it and increasing inspection frequency where vulnerability has been identified. The goal is a periodic maintenance system that is as efficient as the available evidence allows — one that spends maintenance resources where they produce the greatest reliability return, rather than applying uniform intervals across all components regardless of their actual failure characteristics.
The sixth step, building a predictive maintenance system, introduces condition-based maintenance for critical assets. Equipment diagnostics technology — vibration analysis, oil analysis, thermographic inspection, ultrasonic measurement — is applied to monitor deterioration trends and identify points of change before failure occurs. This is the step that most meaningfully shifts the maintenance posture from time-based intervention to evidence-based intervention. For it to succeed, technicians must develop genuine diagnostic skills, not just the ability to collect readings. They must understand what vibration spectrum signatures indicate incipient bearing failure, what oil analysis parameters signal lubrication breakdown, and how to translate condition data into a maintenance work order that arrives in the workshop at the right time, with the right parts and information for an efficient repair. The transition from TBM to CBM for specific asset classes is not a single event but a progressive substitution as diagnostic confidence grows and the data record deepens.
The seventh and final step, evaluating the planned maintenance system, is what makes the entire PM programme self-improving. The maintenance function reviews the effectiveness of its strategies across the equipment register, refines standards where experience has revealed improvement opportunities, and prepares an updated PM master plan. The critical discipline at this stage is honesty about what the data actually shows. An organisation that evaluates its PM programme and concludes that everything is working well, based on activity metrics rather than reliability outcomes, is deceiving itself. The meaningful measure of PM programme effectiveness is not how many work orders were completed on schedule — it is whether MTBF has improved, whether breakdown frequency has declined, and whether Availability losses in the OEE data have reduced as a result of maintenance interventions rather than despite them.
Building the Equipment Register and Criticality Assessment
A PM programme that applies the same maintenance logic to every piece of equipment in the plant is not a strategy — it is an avoidance of strategy. The single most important analytical step in building a PM programme is the construction of a comprehensive equipment register combined with a rigorous criticality assessment, because this classification drives every subsequent decision about maintenance strategy, inspection frequency, spare parts stocking, and resource allocation.
The equipment register is a complete inventory of all plant assets, structured to support maintenance decision-making. It must capture, at minimum, equipment identity, location, function, age, manufacturer specifications, historical failure data, and the maintenance activities currently applied to each asset. In most plants, this data exists in fragmented form across CMMS records, maintenance notebooks, vendor manuals, and the memories of experienced technicians. One of the early tasks of a PM programme implementation is to consolidate this information into a structured, accessible, and maintained register — a task that is less glamorous than deploying condition monitoring technology but far more foundational to it.
The criticality assessment evaluates each piece of equipment across multiple dimensions of consequence. Safety is the first and non-negotiable criterion: equipment whose failure creates a risk of injury or environmental harm demands a maintenance strategy that prioritises failure prevention regardless of cost. Production criticality addresses the question of what happens to the overall value stream when this asset fails — is it a single-point-of-failure with no bypass or redundancy, or is it one of several parallel assets where a failure can be absorbed within production targets? Quality criticality considers whether the asset's condition directly affects product quality, and if so, what the cost of quality failure is relative to the cost of maintenance intervention. Finally, the cost of maintenance itself — the investment required to implement time-based or condition-based approaches — must be weighed against the cost of the failures those approaches prevent.
The output of a well-conducted criticality assessment is not a single ranking but a classification matrix that groups equipment into categories, each pointing toward a different maintenance strategy. Critical assets — those whose failure would create safety risk, halt production, or cause significant quality failure — warrant investment in condition-based monitoring, short time-based inspection intervals, and generous spare parts stocking. Non-critical assets — those whose failure is inconvenient but not disruptive, and whose repair is quick and inexpensive — may rationally be managed by run-to-failure, with reactive repair as the planned approach rather than an admission of defeat.
The organisational implication of this classification is significant. It means that maintenance resources — technician time, diagnostic instruments, engineering bandwidth, spare parts investment — can be concentrated where they produce the highest reliability return, rather than being spread uniformly across the entire asset base. In a plant with two hundred pieces of equipment, it is common to find that thirty critical assets are driving eighty percent of the downtime cost. A PM programme that brings rigorous analysis to those thirty assets will produce far more OEE improvement than a programme that applies moderate effort to all two hundred.
Maintenance Strategy Selection: The Logic Behind the Choice
With the equipment register established and criticality classification completed, the maintenance function can make rational strategy selections for each asset class. The decision logic is not complex in principle, though applying it requires both good failure data and the analytical discipline to use that data honestly.
For assets classified as non-critical, run-to-failure is often the appropriate strategy, provided that the plant has the spare parts and labour capacity to respond promptly when failure occurs and that failure modes are not safety-relevant. The discipline required here is not in implementing a sophisticated maintenance approach but in explicitly making the run-to-failure decision, documenting the rationale, and ensuring that the reactive response capability is actually in place. Many plants that claim to use run-to-failure for non-critical assets have actually made no decision at all — they simply haven't gotten around to maintaining those assets — which is an entirely different posture with entirely different consequences.
For semi-critical assets — those whose failure causes production disruption but not catastrophic loss — time-based preventive maintenance is typically the appropriate starting point, with intervals informed by manufacturer data and refined as the plant accumulates its own failure history. The crucial discipline is to actually analyse that failure history and use it. If MTBF data shows that a component consistently fails at fifteen months but the service interval is set at twelve, the interval may be extended, reducing maintenance cost without increasing failure risk. If components are consistently found in a degraded state at scheduled service intervals, and are failing between services, the interval needs to be shortened or condition monitoring introduced. The interval is a hypothesis; failure data is the experiment that tests it.
MTBF — Mean Time Between Failures — and MTTR — Mean Time To Repair — are the two most immediately informative metrics in a PM programme, and their diagnostic value goes well beyond their face value as averages. MTBF tells you how frequently equipment is failing; more importantly, the variance in MTBF tells you whether the failure pattern is random or systematic. High variance in failure intervals suggests random failures driven by external factors — contamination, operator errors, loading variability — rather than by deterministic wear that a preventive maintenance schedule can reliably address. Low variance suggests a wear-out mechanism whose time to failure is predictable, which is precisely the condition under which time-based maintenance performs well. MTTR tells you how long repairs take, and its practical value is as a measure of maintenance system efficiency — whether the right parts are available, whether technicians have the skills and information to repair quickly, and whether work-order management supports efficient execution.
For critical assets, the goal is to transition progressively from time-based to condition-based maintenance as diagnostic capability develops and the data record deepens. The JIPM framework is explicit on this: the movement from TBM to CBM is a deliberate process that requires the development of equipment diagnostics skills that most maintenance organisations do not currently possess. Building those skills — through formal technical training and the on-the-job experience of interpreting diagnostic data and making maintenance decisions based on it — is as important as acquiring the instruments themselves. A CBM programme that specifies vibration monitoring without investing in the skills to interpret vibration spectra will produce data that is collected but not understood, and findings that are noted but not acted on.
Standards and Visual Management: Making Maintenance Executable
A maintenance strategy that exists as a plan but is not supported by clear, operationally useful standards is a strategy that will erode under the pressure of daily production. One of the patterns I observe most frequently in plants that have invested in PM infrastructure is the gap between what the maintenance schedule specifies and what actually happens in the field — not because technicians are unwilling to follow standards, but because the standards are too vague to act on, too complex to execute under time pressure, or have never been properly communicated to the people performing the work.
Effective PM standards must specify not just what is to be done but how it is to be done, under what conditions, using what tools and materials, to what acceptance criteria, and with what recording requirement. A lubrication standard that says "grease bearing — monthly" is not a standard; it is a reminder. A standard that specifies the lubricant grade, the quantity, the application method, the expected condition of the grease point before and after application, and the recording format is actionable. The difference between these two levels of specificity determines whether a maintenance programme is actually executed or merely scheduled.
Visual management is the mechanism that makes standard maintenance tasks legible in the field without requiring technicians to consult documentation at every step. Colour-coded lubrication points indicate which lubricant type and volume to apply. Min-max level markings on oil reservoirs make inspection results immediately visible and unambiguous. Inspection windows on gearboxes allow condition checking without disassembly. Vibration trend displays at equipment stations communicate condition status without requiring access to the CMMS. All of these visual devices reduce the cognitive and procedural overhead of routine maintenance, which means tasks are more likely to be executed correctly and quickly — and their results are more likely to be recorded accurately.
The connection between PM standards and the operator-level inspection standards developed through Autonomous Maintenance is direct and important. When AM teams work through the general inspection step of the AM seven-step programme, they develop inspection standards that specify what operators check, when they check it, and what constitutes an abnormal condition requiring escalation. These operator standards should be designed in close coordination with the maintenance function, so that operator-level inspection and maintenance-level intervention form a coherent detection and response system rather than two parallel activities that occasionally overlap. Where AM standards identify an abnormal condition, the escalation path to a PM work order should be clear, fast, and reliable — and the maintenance function should be tracking the time from abnormality identification to resolution as a measure of the system's responsiveness.
Spare Parts and Maintenance Resources: The Infrastructure That Makes PM Possible
A well-designed maintenance strategy is only as good as the infrastructure that supports its execution. Across the semiconductor, automotive, and industrial manufacturing facilities I have worked with in Asia-Pacific, spare parts management is among the most consistently underinvested dimensions of PM programmes — and the consequences are predictable. When a critical asset fails and the required replacement part is not in stock, MTTR extends dramatically. Emergency procurement is expensive, expedite fees compound the already-high cost of unplanned downtime, and maintenance engineers spend their time on supply chain management rather than reliability improvement.
The link between maintenance strategy and spare parts decisions is direct. A run-to-failure strategy for non-critical assets requires on-hand stocking of the parts most likely to be needed for reactive repair, in quantities sized to support a response time consistent with the production consequence of that asset's failure. A time-based preventive maintenance programme requires consumables and replacement components in quantities aligned with the service schedule, with lead-time management ensuring that parts arrive before they are needed rather than being ordered when the work order is generated. A condition-based programme requires parts to be available on short notice when diagnostic data indicates that a failure point is approaching — which means either stocking critical components or maintaining supplier agreements that guarantee fast delivery when monitoring signals the need.
The pathologies of spare parts systems are remarkably consistent across plants and industries. Excess stock of slow-moving parts for non-critical equipment ties up capital and occupies storage space, while critical components for high-consequence assets are out of stock at precisely the moment they are needed. This inverse relationship between stocking levels and actual criticality is not accidental — it typically reflects a spare parts system built through historical ordering patterns rather than through systematic analysis of failure risk. Correcting it requires the same criticality-driven logic that governs maintenance strategy selection: concentrate investment where the cost of unavailability is highest, and reduce investment where the consequence of waiting is manageable.
Beyond spare parts, the human resource dimension of PM must be addressed honestly. A maintenance organisation that is chronically understaffed relative to the workload its equipment demands will always revert to reactive behaviour, regardless of how well its PM programme is designed. Planned maintenance work requires scheduled access to assets — which requires coordination with production planning, advance notice of planned shutdowns, and the organisational authority to hold maintenance windows against production pressure. In many plants, this coordination is the hardest problem in the entire PM implementation, not because the technical challenges are insurmountable but because the organisational dynamics consistently prioritise short-term output over planned equipment care. The education and training of maintenance staff — which the JIPM PM pillar assessment explicitly examines — must also be treated as a resource investment, not an operational overhead. Technically competent maintenance engineers are the single most important asset in a PM programme, and their skills in failure analysis, condition monitoring interpretation, and improvement methodology directly determine the ceiling of PM performance the programme can reach.
PM and OEE: Reading the Data as a Maintenance System Signal
The connection between Planned Maintenance and Overall Equipment Effectiveness is specific and direct. OEE is composed of three factors — Availability, Performance, and Quality — and Availability is the factor most directly driven by maintenance system effectiveness. Every unplanned breakdown that stops production reduces Availability. Every planned maintenance shutdown, if poorly scheduled or extended beyond its planned duration, also reduces Availability. And many of the minor stoppages that appear as Performance losses in OEE data originate in equipment that is in a deteriorated condition but not formally broken down — a deterioration that a functioning PM programme, combined with AM inspection, should be catching before it manifests as repeated stops.
The practical discipline of connecting PM to OEE data is to use Availability loss analysis as the primary input for PM prioritisation. When OEE data is disaggregated by loss type and the breakdown losses are stratified by equipment, the result is a Pareto distribution of downtime sources that should directly inform where PM investment is concentrated. If three assets account for sixty percent of breakdown downtime, those three assets must be the focus of failure analysis, root cause investigation, and maintenance strategy review — not as a general improvement initiative but as a specific, data-driven PM activity. The failure data from these assets should be examined in detail: what components are failing, at what frequency, after what operating intervals, and under what conditions? The answers to these questions drive maintenance strategy adjustments that produce measurable OEE improvement.
MTBF and MTTR trend data are the maintenance system's internal equivalent of the OEE trend — they tell the maintenance function whether its programme is actually working. If MTBF is increasing over time for a critical asset class, the PM programme is succeeding at extending equipment reliability. If MTBF is flat or declining despite an active preventive maintenance programme, something is wrong — either the maintenance activities being performed are not addressing the actual failure mechanisms, or the basic equipment conditions are deteriorating faster than the PM programme can compensate, which is the signal that the AM foundation is not yet adequate. If MTTR is high and not improving, the maintenance system's ability to execute efficient repairs is limiting recovery from whatever failures do occur, pointing to spare parts availability, skills gaps, or work management process problems.
The relationship between PM infrastructure maturity and Availability outcomes is not theoretical. In a benchmarking study I facilitated in 2014 across three Asia-Pacific semiconductor manufacturers — conducted using the Xerox benchmarking methodology and including site visits to each facility — the differences in PM capability produced measurably different Availability results that illustrated this pattern with unusual clarity. One manufacturer operated systematic MTBF and MTTR tracking programmes, using the data to identify technical gaps, reduce failure recurrence, and drive maintenance skills improvement. A second had comparable analytical depth through an in-house system. The third tracked neither MTBF nor MTTR and had no computerised maintenance management system in place. The Availability outcomes reflected this gradient directly, with a spread of more than ten percentage points between the most and least PM-mature operation — the least mature recording the lowest Availability of the three.
What made the finding particularly instructive was the equipment age data. The plant with the most advanced PM infrastructure was running test assets averaging eight years old. The plant with the newest equipment — averaging two to five years — recorded comparable but not superior Availability. Despite operating assets that were three to four times older on average, the more PM-mature plant consistently outperformed on equipment uptime. The conclusion was direct: equipment age is not a significant driver of OEE Availability performance. Maintenance strategy, data discipline, and PM infrastructure are. A plant that tracks MTBF systematically, manages spares against a criticality framework, and executes a structured PM programme on ageing equipment will consistently outperform one that relies on reactive intervention with newer assets — because the performance differential comes from how equipment is maintained, not from when it was purchased.
The interaction between PM and the other TPM pillars in driving OEE is important to understand clearly. Focused Improvement (Kobetsu Kaizen) applies structured problem-solving to individual loss themes, and its effectiveness in addressing breakdown losses depends on the quality of failure data that the PM information management system produces. A PM programme that records breakdowns carefully, stratifies their causes systematically, and feeds that analysis into focused improvement projects creates a virtuous cycle of reliability improvement. Autonomous Maintenance prevents the forced deterioration that, if unchecked, overwhelms the PM programme's capacity to manage natural deterioration. These pillars are not independent. They are expressions of a common operating discipline applied to different loss categories, and their combined effect on OEE is substantially greater than any one of them applied in isolation.
PM Maturity and the JIPM Standard: What World-Class Actually Looks Like
The JIPM TPM Excellence Award self-assessment provides one of the most useful external references available for understanding where a PM programme sits on the maturity spectrum and what closing the gap to world-class actually requires. The assessment criteria for the Planned Maintenance pillar operate across three dimensions — failure management and MTBF/MTTR improvement, the rationality of maintenance method selection, and the efficiency of maintenance resources management — and they describe, in precise terms, the progression from a reactive maintenance posture to a genuinely proactive, systems-based maintenance capability.
At the entry level of PM maturity, failure data and equipment downtime hours are recorded but not systematically analysed. Maintenance activities are triggered reactively, and TBM has been implemented for some equipment — often for the most visible or noisiest assets — without a criticality assessment to guide which equipment should receive it. Spare parts and consumables may have received basic organisational attention, but there is no systematic management system linking stocking decisions to maintenance strategy and failure risk. The maintenance department is busy, but its activity is not producing measurable reliability improvement because there is no systematic connection between what it does and what the equipment needs.
At the intermediate level, MTBF and MTTR data is actively managed, failures are categorised by type and cause, and recurrence prevention measures are implemented for identified failure modes. Criticality assessment has been conducted for the asset base, and TBM has been firmly established for high-criticality equipment. CBM has been introduced in specific applications — perhaps vibration monitoring for critical rotating equipment or oil analysis for key hydraulic systems — and the first tangible results from condition monitoring are visible in extended MTBF for monitored assets. A spare parts management system is in place, with designated supervisory responsibility for its maintenance and a system for managing lubricants, blueprints, maintenance tools, and cost records. The improvement is real, but the PM programme is still largely executing according to a predetermined schedule rather than responding intelligently to what condition data and failure analysis are revealing.
At the advanced level that the JIPM recognises as consistent with TPM Excellence Award standards, the maintenance programme has evolved into a fully integrated equipment management system. Preventive and predictive maintenance approaches are firmly established and working in combination, with CBM progressively replacing TBM across the critical asset base as the diagnostic data record deepens and confidence in condition-based decisions grows. Maintenance costs are actively managed and tracked as a business metric. Maintenance staff have received education and training that builds genuine technical depth — not just procedural competence in executing work orders but the engineering understanding of failure mechanisms that enables them to contribute to proactive design improvement. MTBF improvement activities are operating plant-wide, and horizontal deployment of failure prevention measures ensures that a solution developed for one asset or one line is systematically applied wherever similar conditions exist. The overall maintenance system — its strategies, standards, data infrastructure, and human capability — is subject to periodic review and continuous improvement, with the PM master plan serving as the integrating document that keeps the entire programme aligned with evolving equipment and production requirements.
What the JIPM standard reveals most clearly, when applied honestly to a real maintenance programme, is that the majority of plants are operating at the entry to intermediate level — with good intentions and some useful data infrastructure, but without the analytical discipline to use failure data for strategic decisions, without the diagnostic capability to implement genuine condition-based maintenance, and without the organisational authority to protect planned maintenance windows against production pressure. The gap between this reality and the world-class standard is not primarily a technical gap. It is an organisational and management gap. The techniques of PM are well understood and well documented. The challenge is building the leadership commitment, the cross-functional disciplines, and the performance management systems that make those techniques part of the way the plant actually operates, rather than aspirational standards that erode under daily operational pressure.
The Organisational Reality: Why PM Programmes Stall and How to Prevent It
Understanding the technical architecture of a Planned Maintenance programme is necessary but not sufficient for building one that works in practice. The organisational dynamics that cause PM programmes to stall or revert are as important to understand as the technical methodology — and in my experience, they are the more frequent cause of PM underperformance.
The most common failure mode is the deferral trap. Planned maintenance activities are scheduled, but when the window arrives, production has a delivery commitment that cannot be missed. The maintenance window is deferred — just this once — and the asset continues running. When the rescheduled window arrives, the same pressure recurs. Over time, the actual execution rate of the planned maintenance schedule declines to a fraction of the nominal schedule, maintenance intervals are effectively randomised by repeated deferral, and the equipment begins operating in a chronically degraded condition. When breakdowns eventually occur, they are attributed to equipment age or operator error rather than to the systematic deferral of maintenance that made them inevitable.
Preventing the deferral trap requires something that is fundamentally a management decision rather than a technical one: the authority for the maintenance function to hold planned maintenance windows, and the production scheduling discipline to build those windows into the capacity plan rather than treating them as discretionary time. This means that the maintenance plan must be integrated into the production schedule from the outset, not bolted on as an afterthought. It means that the cost of deferred maintenance — in terms of increased breakdown risk, expected downtime cost, and OEE impact — must be made visible to production management in terms that connect to their own performance metrics. And it means that senior plant leadership must be prepared to hold the line on maintenance windows when production pressure pushes back, because without that leadership signal, the maintenance function does not have the organisational standing to protect its own programme.
A second organisational failure mode is the activity metric trap — measuring PM programme performance by input metrics (number of work orders completed, percentage of scheduled tasks executed, hours spent on PM activities) rather than by output metrics (MTBF, MTTR, breakdown frequency, Availability losses, maintenance cost per unit of output). Input metrics are easier to collect and less uncomfortable to report, because they can show a well-functioning maintenance department even when the underlying equipment reliability is not improving. Output metrics are harder to collect, require more analytical effort to interpret, and can reveal uncomfortable truths about whether the maintenance programme is actually producing reliability improvement. The discipline of reporting both and holding the maintenance function accountable to the output metrics is what separates a PM programme that produces results from one that produces reports.
The third organisational trap is the skills gap — the tendency to assume that the transition from reactive to planned maintenance, and from time-based to condition-based maintenance, can be accomplished with the existing skills of the maintenance workforce without deliberate investment in capability development. Condition-based maintenance requires vibration analysis skills, oil analysis interpretation, thermographic inspection technique, and the engineering understanding of failure mechanisms that allows a technician to translate a reading into a maintenance decision. These skills are not acquired incidentally; they require structured training programmes and the on-the-job mentoring time to build practical confidence. A PM programme that specifies CBM activities without investing in the skills to execute them correctly will produce condition data that is collected but not interpreted, instruments that are operated but not calibrated, and maintenance intervals that are adjusted arbitrarily rather than systematically.
From Firefighting to Proactive: What the Transition Actually Requires
A plant that is genuinely ready to move from a reactive maintenance culture to a Planned Maintenance programme aligned with TPM principles needs to be honest about where it is starting. The starting point is not a technical assessment of equipment condition — though that matters — but an honest appraisal of the organisational conditions that will support or undermine the programme. Is senior leadership prepared to commit maintenance windows in the production schedule and hold to them under pressure? Is the maintenance department resourced adequately, with both the time to do planned work and the tools and skills to do it competently? Is there a functioning information management system, or at minimum a systematic approach to recording failure data that will support the strategic decisions the PM programme requires? And critically, is Autonomous Maintenance sufficiently developed that basic equipment condition is stable enough for a PM programme to build on?
If the honest answer to any of these questions is no, the PM implementation plan needs to address the enabling conditions first, not as preliminary steps before the real work begins, but as integral elements of the PM programme itself. Starting a PM programme without management commitment to maintenance windows is building on sand. Starting one without failure data is building blind. And starting one without AM progress means building on a foundation that is actively deteriorating.
The seven-step structure that JIPM provides for PM development is valuable precisely because it sequences these prerequisites correctly. It begins with foundational work — AM support, equipment evaluation, deterioration restoration — before progressing to the periodic and predictive systems that most practitioners think of as PM's core activities. Plants that shortcut this sequence, jumping to CBM technology deployment before they have reliable failure data or stable basic equipment conditions, consistently find that the advanced techniques fail to deliver their promised benefits because the foundational conditions for their effectiveness are absent.
The goal of a mature Planned Maintenance programme, fully achieved, is a plant where breakdowns are rare enough to be genuinely abnormal events rather than routine occurrences, where the maintenance function spends the majority of its time on planned work rather than emergency response, where MTBF trend data reflects a continuously improving equipment reliability profile, and where the maintenance cost per unit of production is declining because resources are applied with precision rather than dispersed reactively across every failure that arises. That goal is achievable. I have seen it achieved in semiconductor fabrication plants, in automotive component manufacturing, in precision instrumentation production across Asia-Pacific. It does not require exotic technology or unlimited resources. It requires the discipline to follow the methodology, the management commitment to protect the conditions the programme needs, and the patient accumulation of the data and skills that genuine PM maturity demands.
Conclusion: The Discipline That Makes the Difference
Planned Maintenance is not a programme that can be installed and left to run. It is a management system that requires continuous analytical attention, organisational discipline, and the willingness to let data — not habit, not history, and not production pressure — drive maintenance decisions. The distinction between a maintenance schedule and a maintenance system is not semantic. A schedule is a list of activities with dates attached. A system is an integrated set of processes — equipment classification, strategy selection, standards development, work management, information management, performance measurement, and continuous improvement — that work together to produce reliable equipment and measurable OEE improvement.
Every plant that has successfully built a world-class PM programme has done so by following the same fundamental logic: start with an honest assessment of where the programme is today, ground every strategic decision in failure data and criticality analysis, protect the AM foundation on which PM depends, invest in the skills and tools that condition-based maintenance requires, and hold the organisation accountable to output metrics that reveal whether reliability is actually improving. The technical methodology is established. What distinguishes plants that achieve it from those that don't is the organisational will to follow it consistently — through production pressure, through resource constraints, and through the inevitable setbacks that accompany any serious improvement effort.
If you are starting a PM implementation, or revitalising one that has stalled, the place to begin is not with the CMMS or the condition monitoring instruments. It is with an honest conversation about whether the organisational conditions for PM success are in place, and a clear plan for establishing them. Everything else builds from there.
About the Author
Allan Ung is the Founder and Principal Consultant of Operational Excellence Consulting, a Singapore-based management training and consulting firm established in 2009. With over 30 years of experience leading operational excellence and quality transformation in manufacturing-intensive environments, Allan's expertise spans Lean Thinking, Total Quality Management (TQM), TPM, TWI, ISO systems, and structured problem solving.
He is a Certified Management Consultant (CMC, Japan), Lean Six Sigma Black Belt, JIPM-certified TPM Instructor (Japan Institute of Plant Maintenance), TWI Master Trainer, ISO 9001 Lead Auditor, and former Singapore Quality Award National Assessor.
During his tenure with Singapore's National Productivity Board (now Enterprise Singapore), Allan pioneered Cost of Quality and Total Quality Process initiatives that enabled companies to reduce quality costs by up to 50 percent. In senior regional and global roles at IBM, Microsoft, and Underwriters Laboratories, he led Lean deployment, quality system strengthening, and cross-border operational transformation.
Allan has facilitated TPM, OEE and Lean programmes for organisations including Temic Automotive (Continental), Analog Devices, Amkor Technology, STATS ChipPAC, Infineon Technologies, Panasonic, Micron, Lam Research, Tokyo Electron, Dorma, and NEC. He holds a Bachelor of Engineering (Mechanical Engineering) from the National University of Singapore and completed advanced consultancy training in Japan as a Colombo Plan scholar.
His philosophy: "Manufacturing excellence is achieved through disciplined systems, capable leadership, and sustained execution on the shopfloor."
His practitioner-led toolkits have been utilized by managers and organizations across Asia, Europe, and North America to build Design Thinking and Lean capability and drive organizational improvement.
For enquiries about Planned Maintenance, TPM, or operational excellence consulting, visit www.oeconsulting.com.sg or contact us directly through the OEC website.
Related Articles in the TPM Practitioner Guide Series
Total Productive Maintenance (TPM): A Practitioner's Guide — The hub article covering TPM philosophy, the eight pillars, the implementation roadmap, and what separates plants that achieve TPM excellence from those that don't.
Autonomous Maintenance: A Practitioner's Guide — The seven-step AM development pathway, the AM-PM partnership that makes both pillars work, and the practitioner discipline required to build genuine operator ownership of equipment condition.
Focused Improvement (Kobetsu Kaizen): A Practitioner's Guide — The methodology for targeting, analysing, and eliminating the specific equipment and process losses that hold OEE back.
Quality Maintenance (Hinshitsu Hozen): A Practitioner's Guide — The eight-step methodology for achieving zero defects by establishing and maintaining the precise 4M conditions required to prevent defect generation at the source.
Overall Equipment Effectiveness (OEE): A Practitioner's Guide — The definitive practitioner reference for calculating, interpreting, and improving OEE — including how Availability losses connect directly to PM system effectiveness.
OEE Benchmarking: A Practitioner's Guide — How to use OEE benchmarking data intelligently, what world-class OEE performance looks like by industry, and how to avoid the benchmarking traps that produce misleading comparisons.
TPM Self-Assessment and the TPM Excellence Award: A Practitioner's Guide — How to conduct a JIPM-aligned TPM self-assessment, what the award criteria reveal about PM and AM maturity, and how to use self-assessment findings to drive a structured improvement roadmap.
OEC TPM Maturity Diagnostic: A Practitioner Guide — Bridges implementation gaps with a four-level maturity model based directly on JIPM award checklists, translated into practical descriptors that make the assessment entirely actionable for practitioners.
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