Service · ARaaS® Toolbox · Lifecycle Economics

The cost of ownership is not
a mystery. It is a calculation.
Run it before you decide.

Asset decisions made without whole-life cost evidence are capital allocation guesswork. Optimal's Asset Lifecycle Analysis & Costing Studies service applies the full ARaaS® Toolbox — availability simulation, maintenance optimisation, Weibull failure data analysis and hierarchical cost modelling — to quantify the true cost of every significant asset decision: repair, replace, defer or invest.

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Study Deliverables

Whole-life cost curve — capital, maintenance, downtime and disposal cost modelled from inception to end-of-life

Replace / repair / defer recommendation — quantified decision with sensitivity analysis and confidence bounds

Optimised maintenance task schedule — ARaaS® Toolbox RCM simulation outputs structured for CMMS integration

Capital investment business case — NPV, IRR and payback analysis supporting CAPEX approval and asset strategy

RAM study — availability, production throughput and production loss cost modelled under alternative strategies

Sensitivity analysis — cost drivers ranked and tested, assumptions made transparent and challengeable

ARaaS® Toolbox · Full Analytical Suite

LCC · Availability Simulation · RCM Optimisation · Weibull · Process Reliability

SAP & Maximo Integration

Oil & Gas · Mining · Power & Utilities · Nuclear · Manufacturing

The Discipline — Life Cycle Costing

"Life cycle costing is a methodology for calculating the whole cost of a system from inception to disposal — every cost element defined, quantified and integrated into a time-dependent model of total ownership cost."

ISO 15663 · Life Cycle Costing for Offshore Facilities · Industry Standard Definition

Acquisition cost is rarely the dominant cost. For most industrial assets, the cost of ownership over a 20–30 year operating life dwarfs the original purchase price. Maintenance, downtime, inspection, energy, environmental compliance and end-of-life disposal collectively represent the majority of total expenditure — but these costs are rarely modelled at the point of investment decision.

The replace/repair/defer decision is an engineering economics problem. Without a quantified cost model, asset replacement decisions default to age, appearance or maintenance management pressure. With a structured LCC analysis, the decision is converted to a comparison of discounted cost streams — repair cost versus replacement cost versus production loss from continued degradation — with an explicit recommendation at each decision point.

Maintenance frequency is a cost optimisation variable. The ARaaS® Toolbox RCM simulation module finds the maintenance task interval at which the sum of preventive maintenance cost plus residual corrective maintenance cost is minimised — replacing calendar-based scheduling with quantitatively optimised intervals derived from real failure data.

Cost modelling integrates with availability modelling. The full ARaaS® Toolbox suite links cost predictions from the RCM and LCC modules directly to availability simulation — so that production throughput loss, spare parts holding cost and maintenance crew utilisation are all quantified as cost consequences within the same unified model, not estimated separately in disconnected spreadsheets.

The Asset Economics Challenge

Decisions made without cost models are not decisions. They are guesses.

Asset-intensive organisations make CAPEX and maintenance investment decisions continuously — replace or refurbish, extend life or retire, increase preventive maintenance frequency or reduce it. In most cases, these decisions are made without a quantified cost model. The consequence is systematic misallocation: assets replaced prematurely, assets run to catastrophic failure, maintenance budgets spent on low-consequence equipment while high-consequence failure modes go unaddressed.

The barrier is not analytical capability — the methodologies for life cycle costing, maintenance optimisation and availability simulation are well established. The barrier is access to the right tools and the expertise to configure them correctly, populate them with defensible failure data and interpret the outputs in the context of the organisation's specific operational and commercial environment.

Optimal removes that barrier. The ARaaS® Toolbox provides an integrated analytical environment — LCC, availability simulation, RCM optimisation, Weibull analysis and process reliability — operated by Optimal's engineers with your asset data, calibrated to your failure history and your cost structure, and delivering findings you can defend to a board, a regulator or an asset owner.

CAPEX decisions without whole-life cost evidenceInvestment cases built on acquisition cost only — operating cost, maintenance cost, availability loss and end-of-life disposal excluded from the financial model because no tool exists to quantify them before the decision is made.

Repair/replace decisions driven by age, not economicsAsset replacement triggered by years of service rather than quantified cost-of-ownership analysis — assets replaced before economic end-of-life, or assets retained past the point where repair cost exceeds the discounted value of remaining service life.

Maintenance intervals set by OEM, not failure dataTask frequencies copied from OEM manuals without adjustment for actual operating environment, failure history or consequence severity — producing either over-maintenance of non-critical equipment or under-maintenance of high-consequence failure modes.

Production loss cost invisible in asset decisionsFinancial models that capture direct maintenance spend but exclude the cost of production downtime, throughput loss and schedule disruption — systematically undervaluing reliability investment and understating the true cost of reactive maintenance strategies.

Sensitivity and uncertainty unquantifiedPoint estimates presented as reliable forecasts — without confidence bounds, sensitivity analysis or identification of the cost drivers that most influence the outcome — leaving decision-makers unable to assess the robustness of the recommendation or the consequences of assumption errors.

The ARaaS® Toolbox — Analytical Suite

Five integrated modules. One unified cost model.

Optimal's Asset Lifecycle Analysis studies are powered by the full ARaaS® Toolbox analytical suite — five integrated modules that together cover the complete spectrum from failure data characterisation through to whole-life cost prediction. Each module feeds the next, and all cost outputs consolidate into a single hierarchical cost breakdown structure.

ARaaS® Toolbox · Module 1

Life Cycle Cost Analysis

Hierarchical cost breakdown structure (CBS) built through unlimited indenture levels — capital, maintenance, inspection, energy, downtime, environmental and disposal costs defined as time-dependent equations or numerical values. Phase-dependent cost modelling. Global cost variables and intelligent equation recognition. All cost nodes link directly to outputs from the simulation and RCM modules.

Whole-Life Cost Model

ARaaS® Toolbox · Module 2

Availability Simulation

Monte Carlo availability simulation of complex, dependent systems — modelling failure interactions, shared spares, maintenance crew constraints and production throughput loss. Identifies system weak points, quantifies production unavailability cost and models alternative maintenance, operational and spares policies. Feeds simulated cost predictions directly into the LCC cost breakdown structure.

Availability & Production Loss

ARaaS® Toolbox · Module 3

RCM Maintenance Optimisation

Reliability Centred Maintenance simulation that finds the optimal maintenance task frequency for every failure mode — minimising the sum of preventive maintenance cost and residual corrective maintenance cost. Compares the cost-effectiveness of predictive maintenance, planned replacement, inspection and alternative spares holdings. Outputs CMMS-ready optimised task schedules. Integrates directly with SAP and Maximo.

Optimised Task Schedule

ARaaS® Toolbox · Module 4

Weibull Failure Data Analysis

Weibull analysis of historical failure and repair data to produce failure distributions characterising asset degradation behaviour — infant mortality, random failure and wear-out populations each identified and modelled. Failure distributions become the quantitative inputs to the availability simulation and RCM optimisation modules, replacing generic failure rate assumptions with client-specific statistical evidence.

Failure Distribution Inputs

ARaaS® Toolbox · Module 5

Process Reliability

Process-level reliability modelling — combining equipment failure behaviour with process flow interdependencies to quantify the reliability and throughput performance of production systems. Accounts for partial production capacity, process flow restrictions and the reliability contribution of individual asset classes to overall process availability. Essential for production optimisation studies and debottlenecking analysis.

Process Throughput Modelling

Study Deliverables

Five outputs. Each one a decision-ready document.

Every Optimal Asset Lifecycle Analysis engagement delivers a defined set of client-facing outputs — each structured to support a specific category of asset or investment decision, with findings presented at the level of rigour required for board, regulator or asset owner approval.

Deliverable 01

Whole-Life Cost Curve Report

A time-resolved cost model tracing every cost element from acquisition through operations, maintenance, inspection, regulatory compliance, life extension and disposal — presented as cumulative and annual cost curves for the full asset life. Cost breakdown by category, by phase and by subsystem. The foundation document against which all other study deliverables are evaluated.

Hierarchical cost breakdown structure — unlimited indenture levels

Time-dependent and phase-dependent cost equations

Capital, maintenance, downtime, inspection and disposal costs integrated

Cost driver ranking — identify the 20% of elements driving 80% of total cost

Deliverable 02

Replace / Repair / Defer Recommendation

A quantified asset decision recommendation — comparing the discounted whole-life cost of repair, refurbishment, replacement and continued operation under each scenario. Sensitivity analysis tests the recommendation against key assumption uncertainties. The output is a defensible, evidence-based asset decision with explicit cost consequences for each option and a documented rationale for the recommended course of action.

Scenario modelling — repair, refurbish, replace, defer compared on common basis

Discounted cost comparison — NPV of total ownership cost per scenario

Sensitivity analysis — assumptions ranked by influence on recommendation

Explicit recommendation with confidence statement and decision criteria

Deliverable 03

Optimised Maintenance Task Schedule

A CMMS-ready maintenance task schedule produced by the ARaaS® Toolbox RCM simulation — every task interval set at the frequency that minimises total maintenance cost for that failure mode, validated against actual failure data. The schedule replaces OEM-default or calendar-based intervals with quantitatively optimised frequencies, with the cost justification for each interval documented and auditable.

Task interval optimisation — preventive cost vs residual corrective cost minimised

Failure mode coverage — every significant failure mode addressed

CMMS-structured output — ready for direct upload to SAP PM or Maximo

Predictive vs planned vs run-to-failure task type justified per failure mode

Deliverable 04

Capital Investment Business Case

A structured investment business case supporting CAPEX approval — quantifying the financial benefit of investment in terms of reduced maintenance cost, avoided production loss, improved availability and deferred end-of-life expenditure. Includes NPV, IRR, payback period and cost-benefit analysis. Structured for presentation to board, asset owner or lender audiences, with assumptions, data sources and analytical methodology documented.

NPV and IRR calculation — discounted financial benefit quantified

Production uplift and availability improvement monetised

Payback analysis — simple and discounted payback periods

Board/lender-ready presentation format with documented assumptions

Deliverable 05

RAM Study

A Reliability, Availability and Maintainability study using Monte Carlo simulation — modelling system availability and production throughput under current and alternative asset strategies. Quantifies production loss cost as a function of asset reliability, spares holding and maintenance policy. Identifies the system bottleneck and the intervention that delivers the greatest improvement in production availability per unit of maintenance investment.

Monte Carlo simulation — full system availability and throughput modelled

Bottleneck identification — weak point ranked by production loss contribution

Spares optimisation — holding levels modelled against availability impact

Strategy comparison — current vs optimised maintenance policy availability delta

The Analytical Foundation — Failure Data & Simulation

Cost models are only as reliable
as the failure data behind them.

The accuracy of any lifecycle cost analysis is determined by the quality of the failure distributions that drive it. Optimal's ARaaS® Toolbox Weibull module analyses historical failure and repair records from the client's CMMS or ERP system — fitting statistical distributions to real operational data and identifying whether assets are in infant mortality, random failure or wear-out behaviour regimes.

These failure distributions — not generic handbook values — become the inputs to the availability simulation and RCM optimisation modules. The result is a cost model calibrated to how your assets actually fail, in your operating environment, under your maintenance regime. Industry failure rate databases (OREDA, CCPS, nuclear T-Book) are used where client data is insufficient — but always with explicit acknowledgement of the data source and its applicability to your asset class.

For organisations with SAP PM or IBM Maximo as their CMMS, the ARaaS® Toolbox connects directly via the SAP Portal and Maximo Portal modules — pulling current maintenance plans and performance history for analysis, and pushing optimised task schedules back into the system without manual re-entry.

Weibull Analysis — failure distribution fitting from CMMS/ERP historical dataWeibull

Monte Carlo Simulation — system availability modelled across 10,000+ iterationsMonte Carlo

SAP PM Portal — live integration with SAP Plant Maintenance maintenance plansSAP PM

IBM Maximo Portal — live integration with Maximo work order and asset historyMaximo

Engagement Methodology

How Optimal conducts
an Asset Lifecycle Study.

Every Asset Lifecycle Analysis and Costing Study follows a structured engagement methodology — from data collection and model scoping through to findings presentation and CMMS integration. The study is conducted by Optimal's engineers, using the ARaaS® Toolbox, with your operational data as the primary input. The client receives findings, not a software licence and a training requirement.

Step 01 — Scope Definition & Data CollectionDefine the system boundary, asset register scope and study objectives. Extract failure history, maintenance records, cost data and production throughput data from the client's CMMS or ERP. Identify data gaps and agree substitution approach where failure data is insufficient.

Step 02 — Weibull Analysis & Failure Distribution FittingApply Weibull analysis to historical failure records — fitting failure distributions by asset class and failure mode. Identify infant mortality, random and wear-out populations. Supplement with industry failure rate database references where operational data is limited. All distributions documented with source, confidence and applicability assessment.

Step 03 — Model Build: Availability Simulation & RCM OptimisationBuild the availability simulation model — system architecture, dependencies, shared resources, spares policy and maintenance crew allocation. Run RCM optimisation to determine cost-minimising task intervals for every failure mode in scope. Validate model outputs against known operational performance before cost integration.

Step 04 — Life Cycle Cost Model ConstructionBuild the hierarchical cost breakdown structure — linking simulated maintenance and downtime costs from the availability and RCM modules to capital, inspection, environmental, compliance and disposal cost equations. Validate total cost against current expenditure baseline. Define scenarios for repair, replace, refurbish and defer options.

Step 05 — Scenario Analysis & Sensitivity TestingRun and compare cost scenarios — quantifying the whole-life cost differential between options. Sensitivity analysis identifies which input assumptions most influence the recommendation. Monte Carlo sensitivity sweeps where uncertainty is high. Cost driver ranking isolates where investment generates greatest return.

Step 06 — Findings Report & CMMS IntegrationFindings presented in a structured study report — cost curves, scenario comparisons, recommendation with supporting rationale and sensitivity analysis. Optimised maintenance task schedule formatted for direct CMMS upload (SAP PM or Maximo via portal integration). Presentation to asset owner, engineering team or board as required.

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Engagements · Evidence

Asset lifecycle analysis delivered in practice.

Selected Optimal engagements where lifecycle cost analysis, availability simulation and maintenance optimisation were applied to support asset investment decisions across oil & gas, mining, power and water sectors.

Oil & Gas · North Sea · Offshore

Major Offshore Operator — Two-Platform Engagement

Rotating Equipment — Replace vs. Refurbish LCC Analysis

Life cycle cost analysis and RCM maintenance optimisation covering compressor and pump assets across two North Sea platforms — whole-life cost modelling used to determine the economic crossover point between continued refurbishment and full replacement. Weibull analysis applied to platform CMMS failure history to calibrate the cost model to actual degradation rates.

Offshore platforms — compressor, pump and gas turbine assets within study boundary

LCC

Whole-life cost model built from CMMS failure history — refurbish vs replace recommendation with quantified crossover point

CMMS

Optimised maintenance task schedule delivered — direct SAP PM integration, replacing OEM-default intervals

Mining · Multi-Commodity · Global

Global Mining Group — Group-Wide Programme

Diamond, Platinum & Coal — Equipment Lifecycle & RAM Study

RAM study and lifecycle cost modelling across diamond, platinum, coal and iron ore operations — Monte Carlo availability simulation used to quantify production throughput loss by asset class and identify the maintenance investment delivering the greatest improvement in process availability. Findings structured as capital investment business cases for group CAPEX approval.

Commodity sectors — availability simulation model built for each, with group-level cost comparison

RAM

Monte Carlo simulation — production throughput and availability modelled under current and optimised maintenance strategies

CAPEX

Investment business cases produced for group board approval — NPV and payback analysis per site

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Public Sector · Water Treatment · South Africa

Major District Municipality — KwaZulu-Natal

Water Treatment Infrastructure — Asset End-of-Life & Cost Study

Whole-life cost analysis and replace/repair/defer assessment for water treatment infrastructure serving four local municipalities — lifecycle cost modelling applied to 33 critical asset types to determine the economically optimal intervention strategy, prioritise capital expenditure allocation and produce a defensible business case for infrastructure investment approval.

Critical asset types analysed — whole-life cost model built for mechanical, electrical and instrumentation systems

Municipalities served — one integrated LCC study covering regional water utility capital investment decisions

Repair/
Replace

Replace/repair/defer recommendations produced per asset class — with quantified cost consequences for each option

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Nuclear · Decommissioning · UK

Major Nuclear Decommissioning Facility

End-of-Life Asset Strategy — Lifecycle Cost & Maintenance Optimisation

Lifecycle cost analysis and maintenance task optimisation for a nuclear decommissioning facility — assets approaching end-of-design-life requiring rigorous cost and risk justification for continued operation. RCM simulation used to optimise maintenance intervals in the decommissioning phase, reducing maintenance expenditure while maintaining safety-classified system reliability.

Nuclear

Safety-classified asset lifecycle analysis — T-Book failure rate data applied, regulatory justification documented

RCM

Maintenance task optimisation — decommissioning-phase intervals derived, OEM schedules replaced with cost-justified frequencies

Decom

End-of-life cost model aligned to decommissioning milestones — capital and maintenance expenditure profiled to programme

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The ARaaS® Toolbox

Asset lifecycle analysis inside a continuous reliability programme.

Asset Lifecycle Analysis and Costing Studies are not one-time exercises. The cost model, the failure distributions and the optimised task schedule all need to be updated as operating experience accumulates, as assets age and as the commercial environment changes. Within the ARaaS® framework, Optimal maintains the analytical model continuously — updating Weibull distributions as new failure data is generated, re-running the optimisation as cost inputs change, and producing refreshed business cases at the decision points your organisation requires.

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Module 1

Life Cycle Cost — Hierarchical CBS, Cost Equations, Phase Modelling

Module 2

Availability Simulation — Monte Carlo, Spares, Production Throughput

Module 3

RCM Optimisation — Optimal Task Frequency, CMMS-Ready Output

Module 4

Weibull Analysis — Failure Distribution Fitting from CMMS/ERP History

Module 5

Process Reliability — Throughput Modelling, Debottlenecking

Integration

SAP PM Portal · IBM Maximo Portal · Direct CMMS Connectivity

Related Services

Where lifecycle cost analysis connects.

Engage Optimal

The asset decision you are facing has a cost model answer.

Whether you are evaluating a major capital replacement, trying to justify a maintenance budget increase, preparing an asset for life extension or optimising maintenance intervals across a fleet — the ARaaS® Toolbox analytical suite provides the quantified foundation your decision requires. Optimal runs the study. You receive the findings. Start with a scoping conversation.

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The cost of ownership is nota mystery. It is a calculation.Run it before you decide.

Decisions made without cost models are not decisions. They are guesses.

Five integrated modules. One unified cost model.

Five outputs. Each one a decision-ready document.

Cost models are only as reliableas the failure data behind them.

How Optimal conductsan Asset Lifecycle Study.

Asset lifecycle analysis delivered in practice.

Asset lifecycle analysis inside a continuous reliability programme.

Where lifecycle cost analysis connects.

The asset decision you are facing has a cost model answer.

The cost of ownership is not
a mystery. It is a calculation.
Run it before you decide.

Cost models are only as reliable
as the failure data behind them.

How Optimal conducts
an Asset Lifecycle Study.