{"id":46123,"date":"2026-07-24T16:30:54","date_gmt":"2026-07-24T11:00:54","guid":{"rendered":"https:\/\/www.verdantis.com\/?p=46123"},"modified":"2026-07-27T15:50:41","modified_gmt":"2026-07-27T10:20:41","slug":"manufacturing-asset-management","status":"publish","type":"post","link":"https:\/\/www.verdantis.com\/nl\/manufacturing-asset-management\/","title":{"rendered":"Beheer van productiemiddelen: het vakgebied dat uw EAM-software uitvoert"},"content":{"rendered":"
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A production line can carry six figures of spare capacity in one bay and none at all in the next, and most asset registers do not know the difference. Asset management in manufacturing<\/strong> is the discipline that decides where that gap matters and by how much.<\/p>\n

EAM and CMMS software is simply the system that carries the decision out afterward. This piece treats asset management as a practice with its own hierarchy, performance mechanics, and failure modes, distinct from the tools used to run it.<\/p>\n

It is grounded in production equipment, the domain where the discipline is highest stakes and most measurable, and it stays there deliberately.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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In short<\/div>
Asset management in manufacturing is the discipline of deciding how much attention, money, and maintenance rigor a physical asset deserves, based on the actual consequence of it failing, then governing that decision consistently across the asset's working life. It operates in three tiers (strategic, tactical, execution), is measured primarily through OEE, MTBF, and MTTR, and is judged financially by total cost of ownership rather than acquisition price alone. EAM and CMMS software execute this discipline; they do not replace it.<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Why Asset Management in Manufacturing Keeps Getting Reduced to a Software Purchase<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Most guides to this topic collapse two different things into one. Asset management<\/strong> is the strategic practice of getting the most value out of physical assets across their working life. EAM and CMMS software<\/strong> is the system that records, schedules, and tracks the work that practice generates.<\/p>\n

The conflation is understandable. The software is visible every day. The discipline behind it, the classification logic, the maintenance strategy choices, the capital planning horizon, mostly lives in decisions that never touch a screen.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Asset management (discipline)<\/div>
Scope<\/span>Which assets matter, how much, and why<\/div>
Ownership<\/span>Reliability, engineering, and plant leadership<\/div>
Time horizon<\/span>Asset lifecycle: years to decades<\/div>
Output<\/span>Criticality tiers, maintenance strategy, capital plans<\/div><\/div>
EAM \/ CMMS software<\/div>
Scope<\/span>Recording and scheduling the work the practice generates<\/div>
Ownership<\/span>Maintenance planners and IT\/EAM administrators<\/div>
Time horizon<\/span>Work cycle: days to weeks<\/div>
Output<\/span>Work orders, notifications, spares consumption records<\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Neither one works without the other. But a plant can own excellent EAM software and still manage its assets badly, because the classification decisions the software depends on were never made with rigor upstream. That upstream layer is what this article is actually about.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Consider a stamping press nearing the end of its planned service life. The EAM system will happily schedule its next inspection on time, exactly as configured.<\/p>\n

It has no opinion on whether that press should have been reclassified as high-criticality six months ago, after two consecutive near-miss failures, or whether the plant should already be budgeting its replacement. Both are asset management judgment calls. Neither is a software feature, and no CMMS or EAM configuration setting fixes a criticality tier that was never re-evaluated.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The software layer itself is not one thing either. It splits into three overlapping categories, worth naming once before moving past them.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Term<\/th>What it actually covers<\/th><\/tr>
CMMS<\/strong><\/td>Computerized Maintenance Management System. Schedules and records work orders. The narrowest of the three.<\/td><\/tr>
EAM<\/strong><\/td>Enterprise Asset Management. Everything a CMMS does, plus spares inventory, warranties, capital planning, and the full asset lifecycle.<\/td><\/tr>
APM<\/strong><\/td>Asset Performance Management. Adds predictive analytics and condition-based failure prediction on top of EAM data. The newest and most data-dependent of the three.<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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This article stays one layer above all three, at the discipline that decides what any of them should be tracking in the first place. Feature-level evaluation of CMMS, EAM, and APM platforms is covered separately, linked at the end of this piece.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Capital Stakes Behind These Mechanics<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Everything above sounds like a plant-floor concern until the scale of the exposure is visible. It is a capital allocation and risk decision well before it is a maintenance one.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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$1.4 trillion<\/div>
Lost annually to unplanned downtime by the world's 500 largest companies, equal to 11% of their combined revenue<\/div>
Per-hour losses range from roughly $36,000 in fast-moving consumer goods to $2.3 million in automotive<\/div>
Source: Siemens \/ Senseye Predictive Maintenance, The True Cost of Downtime, 2024<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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That figure sits on top of decisions made at the strategic tier described below: which assets get classified as critical, which capital gets approved for replacement versus repair, and which technology investments actually close the gap. None of it is visible from a maintenance work order queue.<\/p>\n

A director or VP reading this is the intended audience for the strategic tier specifically. The tactical and execution detail further down exists so that strategic decisions have somewhere real to land, not so the strategic reader needs to absorb it directly.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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What This Article Covers, and What It Deliberately Leaves Out<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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"Asset" means different things depending on who is using the word. Naming the boundary up front avoids the vague, everything and nothing treatment that dominates search results for this term.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Asset sense<\/th>Scope note<\/th>Coverage<\/th><\/tr>
Production equipment<\/strong><\/td>Machinery, process equipment, rotating assets on the plant floor<\/td>Covered here<\/span><\/td><\/tr>
Facilities \/ infrastructure<\/strong><\/td>Buildings, utilities, site services supporting production<\/td>Acknowledged, not developed<\/span><\/td><\/tr>
IT \/ technology assets<\/strong><\/td>Hardware, software licenses, digital infrastructure<\/td>Out of scope<\/span><\/td><\/tr>
Fixed \/ accounting assets<\/strong><\/td>Capitalization, depreciation, book value treatment<\/td>Deferred elsewhere<\/span><\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Production equipment is the focus throughout, because it is where the strategic, tactical, and execution layers connect most directly to spares, downtime, and cost. The accounting sense of "asset" is a real, separate discipline; see our comparison of fixed asset tracking platforms<\/a> rather than here.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Within production equipment specifically, the physical asset types this discipline governs span several distinct categories, each with its own failure behavior and maintenance strategy.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Category<\/th>Examples<\/th><\/tr>
Rotating equipment<\/td>Pumps, motors, compressors, turbines, fans<\/td><\/tr>
Static equipment<\/td>Tanks, pressure vessels, heat exchangers, piping<\/td><\/tr>
Material handling<\/td>Conveyors, forklifts, cranes, robotic arms<\/td><\/tr>
Process equipment<\/td>Reactors, boilers, extruders, mixers, furnaces<\/td><\/tr>
Instrumentation and electrical<\/td>Sensors, PLCs, switchgear, motor control centers<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Which of these categories dominates a given plant's criticality register depends heavily on industry vertical. The mix of rotating, static, and process equipment shifts significantly across sectors.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Industry<\/th>Typically highest-criticality assets<\/th><\/tr>
Automotive and discrete manufacturing<\/td>Stamping presses, robotic welding cells, paint line ovens, conveyor systems<\/td><\/tr>
Food and beverage<\/td>Retorts, fillers, pasteurizers, refrigeration compressors, packaging lines<\/td><\/tr>
Mining and metals<\/td>Crushers, conveyors, haul trucks, mill drives, dewatering pumps<\/td><\/tr>
Chemicals and process manufacturing<\/td>Reactors, distillation columns, heat exchangers, compressors<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Notably absent from that table is oil and gas, and deliberately so. Asset risk there is regulatory and safety-driven in a way discrete manufacturing is not, which changes the whole risk profile enough to warrant separate treatment.<\/p>\n

Also read: the oil and gas reliability playbook<\/a>.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The Strategic, Tactical, and Execution Hierarchy Behind Every Asset Decision<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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ISO 55000, the international standard for asset management, structures the discipline into tiers without naming them quite this way. In practice, most manufacturers run three layers, whether they have written them down or not.<\/p>\n

Strategic tier.<\/strong> Asset management policy, criticality classification, and the capital planning horizon live here. This is where a plant decides which equipment can least afford to fail.<\/p>\n

Tactical tier.<\/strong> Maintenance strategy selection sits here: the mix of preventive, predictive, and reactive maintenance, and the broader reliability planning that supports it.<\/p>\n

Execution tier.<\/strong> Day to day work order execution, spares consumption, and condition monitoring happen here. It is the tier every EAM system is built to run.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Strategic tier<\/div>
Criticality classification, capital planning<\/div><\/td><\/tr>
↓<\/td><\/tr>
Tactical tier<\/div>
Maintenance strategy mix, reliability planning<\/div><\/td><\/tr>
↓<\/td><\/tr>
Execution tier<\/div>
Work orders, spares consumption, condition data<\/div><\/td><\/tr><\/table>
↑ Feedback loop: execution-level failure data should reclassify strategic-tier criticality, and rarely does in practice<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The mechanism that connects this article to the rest of the maintenance cluster sits in that feedback loop. Criticality classification decided at the strategic tier drives the maintenance strategy chosen at the tactical tier, which in turn drives how spares are stocked and reordered at the execution tier, a policy question covered in full in MRO inventory management.<\/p>\n

Very few plants route the arrow back upward: execution-level failure history rarely reclassifies strategic-tier criticality in practice, a gap covered further on.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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One reason the feedback loop breaks is that no single role is accountable for closing it. Ownership is usually clear within a tier and murky between tiers.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Role<\/th>Strategic<\/th>Tactical<\/th>Execution<\/th><\/tr>
VP Operations \/ C-suite<\/td>A<\/td>I<\/td>-<\/td><\/tr>
Plant manager<\/td>C<\/td>A<\/td>I<\/td><\/tr>
Reliability engineer<\/td>R<\/td>C<\/td>A<\/td><\/tr>
Maintenance planner<\/td>I<\/td>R<\/td>C<\/td><\/tr>
Operator \/ technician<\/td>-<\/td>C<\/td>R<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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No single role is Accountable across all three tiers, including the VP or C-suite reader, whose accountability stops at strategic-tier capital decisions and does not extend down to execution. That gap is exactly where the feedback loop needs an explicit owner, usually the reliability engineer, to actually close.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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The tactical tier's central decision is which maintenance strategy to assign to which criticality tier. That choice trades cost against risk differently for each strategy.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Strategy<\/th>Trigger<\/th>Best fit<\/th><\/tr>
Reactive<\/td>Run to failure<\/td>Low-criticality, low-cost, easily replaced assets<\/td><\/tr>
Preventive<\/td>Fixed time or usage interval<\/td>Predictable wear patterns, moderate criticality<\/td><\/tr>
Predictive<\/td>Condition data crosses a threshold<\/td>High-criticality assets with measurable degradation signals<\/td><\/tr>
Prescriptive<\/td>Algorithmic recommendation from combined data sources<\/td>Highest-criticality assets with rich historical and sensor data<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Assigning a reactive strategy to a high-criticality single-point-of-failure asset, or a predictive strategy to a low-cost, easily stocked part, both waste money in opposite directions. The strategy assignment should follow directly from the criticality tier, not from habit or whichever technician is available.<\/p>\n

A fuller breakdown of how these strategies scale with plant maturity is in mapping strategy choice to operational maturity<\/a>.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Predictive and prescriptive strategies both depend on condition data actually being collected. A handful of sensor-based inspection techniques<\/a> account for most of what execution teams gather.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Technique<\/th>What it detects<\/th><\/tr>
Vibration analysis<\/td>Bearing wear, misalignment, imbalance in rotating equipment<\/td><\/tr>
Thermal imaging<\/td>Overheating connections, insulation breakdown, friction points<\/td><\/tr>
Oil analysis<\/td>Contamination, wear particles, lubricant degradation<\/td><\/tr>
Ultrasonic testing<\/td>Early-stage bearing failure, compressed air leaks, electrical arcing<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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None of these techniques matter if the resulting readings never make it back into the functional location structure described next. Vibration signature analysis<\/a> in particular generates the highest data volume of the four, and is usually the first one worth automating.<\/p>\n

Condition data disconnected from the asset register is just a spreadsheet, not a feedback loop.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Everything above describes what the tiers do and how they connect. The harder question in practice is when a specific event should actually trigger a decision, rather than waiting for a scheduled review.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Trigger event<\/th>Action it should force<\/th><\/tr>
Two or more near-miss failures on one asset within 12 months<\/td>Reclassify criticality tier before the next scheduled review<\/td><\/tr>
OEE consistently below the sector's typical band for a full quarter<\/td>Escalate to a strategic-tier review, not another tactical adjustment<\/td><\/tr>
MTBF declining while MTTR stays flat or rises<\/td>Reassess maintenance strategy fit, not just spares stock levels<\/td><\/tr>
A new production line is commissioned<\/td>Assign initial criticality classification before go-live, not after the first failure<\/td><\/tr>
Line topology changes (new redundancy added or removed)<\/td>Re-score every asset on the affected line, not just the one that changed<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Each of these is a strategic-tier decision by definition, even though the data that triggers it originates at the execution tier. That handoff is the feedback loop from earlier in this article, made operational.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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How OEE Turns Asset Management Practice Into a Single Measurable Number<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Overall Equipment Effectiveness is the manufacturing-specific lens that makes asset management practice visible in a metric, rather than just a policy document.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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OEE = Availability × Performance × Quality<\/div>
Availability: planned production time actually used, net of unplanned downtime. Performance: actual output rate vs. ideal rate. Quality: good units vs. total units produced.<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Asset management practice shows up directly inside each component, not just conceptually. Poor spares availability lengthens unplanned downtime, which lowers Availability. Inadequate maintenance on aging or poorly classified assets increases scrap and rework, which lowers Quality.<\/p>\n

The 85% "world class" OEE benchmark traces back to Seiichi Nakajima's Total Productive Maintenance framework, built from three component targets multiplied together: roughly 90% availability, 95% performance, and 99.9% quality. Most discrete manufacturers operate well below it.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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A hypothetical worked example makes the multiplication concrete. A line running at 88% Availability, 92% Performance, and 97% Quality does not average those three numbers; it multiplies them: 0.88 &#215; 0.92 &#215; 0.97, for a composite OEE of roughly 78.5%, several points below world class despite every individual component looking reasonable in isolation.<\/p>\n

That compounding effect is why a plant can feel like it is performing well on every dashboard metric individually while its composite OEE tells a different story.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Composite OEE by performance band<\/div>
85%<\/span><\/div>
World-class<\/div>
Nakajima \/ TPM benchmark<\/div><\/td>
60%<\/span><\/div>
Typical<\/div>
Common discrete-manufacturing range<\/div><\/td>
<40%<\/span><\/div>
Poor<\/div>
Unaddressed downtime and quality loss<\/div><\/td><\/tr><\/table><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Composite OEE hides which lever actually moved it. The chart below breaks the same causal chain down by component, and it is a conceptual illustration rather than a benchmark figure.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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How asset management quality moves each OEE component (illustrative pattern, not a cited statistic)<\/div>
High<\/span><\/div>
Availability<\/div>
Most exposed to spares stockouts<\/div><\/td>
Moderate<\/span><\/div>
Performance<\/div>
Degraded assets run below rated speed<\/div><\/td>
High<\/span><\/div>
Quality<\/div>
Poor condition drives scrap and rework<\/div><\/td><\/tr><\/table><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Nakajima's TPM framework breaks the three OEE components down further into six specific loss categories, which is where corrective action actually starts.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Loss category<\/th>OEE component affected<\/th><\/tr>
Breakdowns<\/td>Availability<\/td><\/tr>
Setup and adjustment<\/td>Availability<\/td><\/tr>
Small stops<\/td>Performance<\/td><\/tr>
Reduced speed<\/td>Performance<\/td><\/tr>
Startup rejects<\/td>Quality<\/td><\/tr>
Production rejects<\/td>Quality<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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The asset register and criticality gaps described elsewhere in this article show up specifically as breakdowns and startup rejects, the two categories most directly tied to how well spares and condition data are managed.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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OEE measures effectiveness in the moment. Mean Time Between Failures and Mean Time To Repair measure reliability and maintainability over a longer window, and both depend on the same notification and downtime-code data that feeds the asset register.<\/p>\n

The related metric for non-repairable components is MTTF<\/a>, and the two get conflated more often than practitioners would like to admit.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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MTBF = Total uptime ÷ Number of failures<\/div>
MTTR = Total repair time ÷ Number of repairs<\/div>
MTBF rising and MTTR falling together are the clearest sign that criticality classification and spares strategy are actually working.<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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A Simple Maturity Model: From Reactive to Prescriptive<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Everything covered so far, criticality, maintenance strategy, condition monitoring, and OEE, maps onto a maturity progression most plants recognize once it is laid out.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Reactive<\/div>
Run to failure, no criticality tiering<\/div><\/div>
Preventive<\/div>
Scheduled maintenance, basic criticality tiers<\/div><\/div>
Predictive<\/div>
Condition monitoring, criticality tied to topology<\/div><\/div>
Prescriptive<\/div>
Closed feedback loop, criticality reclassified continuously<\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Most manufacturers sit somewhere between preventive and predictive. Very few operate the closed feedback loop described earlier as standard practice, which is precisely why it shows up as a named failure mode later in this article rather than as an edge case.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\tSee what your own OEE and criticality data actually says<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Run this hierarchy against your own functional location structure instead of a generic template.<\/p><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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