Asset investment planning can make or break your financial goals. Missteps often lead to wasted resources, unexpected costs, and missed opportunities. Here’s a quick look at the seven most frequent mistakes and how to address them:
- Poor Risk Assessment: Relying on outdated methods can lead to costly emergency repairs.
- Disconnected Data: Scattered maintenance records result in inefficiencies and budget overruns.
- Ignoring Carbon Goals: Overlooking energy and emissions targets increases long-term liabilities.
- Short-Term Budgeting: Focusing only on immediate costs leads to higher lifecycle expenses.
- Unreliable Data: Incomplete or outdated asset information disrupts decision-making.
- Initial Cost Focus: Neglecting lifecycle costs causes premature failures and higher expenses.
- Single-Factor Decisions: Overlooking a balanced approach results in fragmented strategies.
Key takeaway: Aligning investments with risk, data, and long-term goals saves money, reduces risks, and improves asset performance. Tools like Oxand Simeo™ can help organizations predict costs, prioritize actions, and optimize budgets effectively.
7 Common Asset Investment Planning Mistakes and Solutions
Mistake 1: Poor Risk Assessment Methods
Many organizations rely on age-based replacement lists or the "worst-first" strategy, focusing only on the most visibly deteriorated assets. While this might seem logical, it overlooks hidden risks, often leading to emergency repairs that are 10 to 15 times more expensive than planned preventive maintenance [5].
The main problem lies in neglecting to assess risk at the component level. Traditional planning methods often rely on an asset’s installation date (its chronological age) rather than evaluating its actual condition, workload, environment, and usage patterns. This approach can lead to costly mistakes – either replacing assets too early and wasting their remaining life or waiting too long and facing failures, penalties, and operational disruptions [2]. Without detailed data, teams fail to identify the "point of no return", when an asset’s remaining life declines rapidly – usually after 15–20 years as deterioration accelerates.
The financial impact of these missteps is staggering. Global infrastructure mismanagement could result in over $1.5 trillion in direct value losses within the next five years [4]. For example, one utility that delayed replacements by reducing its budget by 10% saw its total cost of ownership increase by $4.3 million over five years due to heightened end-of-life risks [2]. It’s also worth noting that construction costs typically account for only 10% to 40% of an asset’s lifetime expenses, while the remaining 60% to 90% stems from operations, maintenance, and renewals [3].
"Replace too early and waste remaining asset life. Replace too late and pay through failures, maintenance, penalties or lost throughput." – Philippe Jetté, Product Manager, Asset Investment Planning, IBM [2]
Without probabilistic aging models, organizations cannot fully grasp how budget cuts or delays will affect long-term costs, often resulting in unexpected financial spikes. The solution lies in adopting proactive, risk-based planning.
Solution: Use Risk-Based Planning Tools
Risk-based planning tools assess both the likelihood of failure (considering factors like condition, age, environment, and usage) and the consequences of failure (impact on safety, service, and finances) [2]. A great example is Oxand Simeo™, which uses 10,000+ proprietary aging models and 30,000+ maintenance laws to predict how components will age, fail, and consume energy throughout their lifecycle.
Simeo™ calculates an asset’s "effective age" by analyzing indicators like vibration, temperature, and inspection results. This allows organizations to prioritize investments based on true risk levels, rather than just addressing what appears to be in the worst condition. For instance, in utility scenarios, increasing budgets by 10% for targeted refurbishments of high-risk assets has been shown to reduce total cost of ownership by 22% over the long term [2].
The platform also supports scenario optimization, enabling teams to test different budget scenarios and evaluate the "cost of deferral" and associated risks. Cities using systematic condition assessments and deterioration modeling have achieved 30% to 40% better resource allocation efficiency, extending infrastructure life by 40% to 60% and cutting total lifecycle costs by 25% to 35% [5].
Oxand’s approach stands out because it is model-driven, not sensor-dependent. While it can integrate IoT data, it doesn’t require extensive sensor networks to deliver results. Instead, it leverages existing surveys, inspections, and asset data, making it accessible for organizations ready to act now rather than waiting years for new sensor deployments. This predictive technology can identify failure patterns 6 to 18 months in advance, enabling timely interventions before disruptions occur [5].
Mistake 2: Disconnected Maintenance Data and CAPEX Planning
When maintenance data is scattered across multiple systems or records, planning capital expenditures becomes a guessing game. In fact, cost and schedule overruns for capital projects often exceed 50% of original estimates [6]. Why? Because decision-makers lack clear visibility into asset conditions and performance. This creates a "black box" scenario, where predicting asset failures or assessing portfolio performance becomes nearly impossible.
Without centralized data, organizations fall into a reactive cycle often referred to as "infrastructure roulette" [5]. Budgets end up being funneled into emergency repairs rather than strategic, long-term asset management. The financial impact is staggering – emergency repairs can cost 10 to 15 times more than proactive maintenance [5]. Worse yet, deferred maintenance caused by poor data visibility can lead to cascading failures across interconnected systems [9].
Here’s the reality: infrastructure assets consume 80% to 85% of their total lifecycle costs during operations and maintenance [5]. Yet, many organizations launch capital projects without fully understanding the long-term operating costs involved. This disconnect often leads to "stability bias", where underperforming projects continue receiving funding simply because fragmented data conceals their lack of value [6]. This lack of clarity not only obscures project performance but also increases financial risks over time.
"Capital performance is typically a black box. Executives find it difficult to understand and predict the performance of individual projects and the capital project portfolio as a whole." – McKinsey [6]
The problem isn’t small. The United States currently faces a $1 trillion backlog of delayed repairs and maintenance, a crisis built up over 50 years [10]. Breaking out of this reactive cycle starts with centralizing data.
Solution: Centralize Maintenance Data
Centralizing maintenance data is a game-changer, shifting organizations from reactive spending to proactive asset management. By consolidating maintenance history, condition assessments, and inspection data into one unified asset registry, organizations can move from guesswork to predictive modeling. This approach enables smarter, forward-thinking decisions.
Take Sund & Bælt in Denmark, for example. They partnered with IBM to create an AI- and IoT-powered system using IBM Maximo, consolidating maintenance records, 3D models, and real-time sensor data. This allowed them to detect issues like corrosion and cracks early, extending the lifespan of bridges and tunnels while optimizing maintenance schedules [8].
Another example is the Downer Group in Australia, which began using the TrainDNA platform (powered by IBM Maximo) in 2017. By integrating near real-time data, they identified high-energy-consuming systems and optimized fleet usage for over 200 trains. This not only reduced maintenance costs but also lowered their carbon footprint [8].
The financial benefits of preventive maintenance are hard to ignore. Organizations that adopt strategic preventive maintenance can cut total lifecycle costs by 25% to 35% [5]. Leading municipalities often allocate 2% to 4% of an asset’s replacement value annually to preventive maintenance. For every dollar spent on early-stage preventive care, they save $4 to $7 in future rehabilitation or replacement costs [5].
Oxand offers a practical solution with its Simeo Inventory platform. Unlike systems reliant on extensive sensor networks, Simeo organizes existing surveys, inspections, and asset data into formats that feed directly into over 10,000 proprietary aging models and 30,000 maintenance laws. This allows organizations to start making data-driven decisions immediately. The platform calculates risk exposure by multiplying the probability of failure by its consequences, but the accuracy of these results hinges on having centralized, reliable data on asset conditions and criticality [7].
To make this work, organizations should establish stage-gate governance with formal reviews at every project phase. This ensures investment decisions stay aligned with updated maintenance data [6]. Additionally, collaboration between operations, maintenance, and design teams during the planning phase is crucial, as lifecycle costs are largely determined at the design stage [7]. By centralizing data and integrating predictive models, organizations can move from reactive firefighting to strategic asset management.
Mistake 3: Ignoring Carbon and Energy Goals
For many organizations, sustainability still takes a backseat, often seen as an optional add-on rather than a core investment priority. This mindset can lead to serious financial and regulatory consequences. Why? Because focusing solely on upfront capital costs ignores a critical fact: 60% to 90% of an asset’s lifetime expenses come from operations and maintenance[3]. With rising energy costs and stricter carbon regulations, this oversight can quickly escalate into a significant liability.
Relying on fossil-fuel systems like gas-fired heating is a prime example of this misstep. Such investments lock emissions into Scope 1, meaning that no matter how much you improve other areas, a portion of your building’s emissions remains fixed. This not only increases operational costs but also exposes organizations to greater regulatory scrutiny.
"If an AHU continues to rely on gas-fired coils, a significant fraction of building emissions remains locked into Scope 1 regardless of improvements elsewhere." – Mansfield Pollard[11]
Failing to prioritize energy performance goals can also lead to non-compliance with mandatory standards like Ecodesign and EN 1886, or sector-specific requirements such as the NHS Net Zero roadmap and HTM 03-01 for healthcare facilities[11]. Beyond penalties, this can tarnish an organization’s reputation and hinder compliance with frameworks like SECR and GRESB.
Solution: Include Sustainability in Planning
The key to avoiding these risks? Make sustainability a central part of your planning process. Organizations need to shift their perspective, treating environmental performance as a measurable goal alongside cost and risk. This means moving away from outdated age-based replacement strategies and adopting a lifecycle value approach. By setting clear CO₂ reduction targets tied to specific asset attributes, organizations can prioritize investments that deliver both financial and environmental benefits[2].
Take Transport for London (TfL) as an example. By November 2025, TfL plans to centralize its maintenance efforts using IBM Maximo software, enabling it to manage critical infrastructure while reducing carbon emissions from public transport[2]. Similarly, VPI, an energy company, uses the same software to monitor its asset fleet and guide its journey toward net-zero emissions and renewable energy goals[2].
The financial case for sustainability-focused planning is hard to ignore. For instance, switching from gas heating to a heat pump with a COP of 3.5 can cut carbon emissions by about 75% compared to a standard gas coil[11]. Adopting a total cost of ownership (TCO) approach, which factors in energy efficiency, can lower lifecycle costs by 20% to 40%[3]. For organizations managing large estates, targeting critical asset upgrades can deliver quick carbon reductions without disrupting operations[11]. Upgrades like improved fans and controls often pay for themselves within three years[11].
Tools like Oxand Simeo™ make this transition easier. Its sustainability modules allow organizations to model CO₂ reduction pathways and energy performance at a portfolio level. With access to over 10,000 aging models and 30,000 maintenance laws, Simeo enables planners to simulate multiple scenarios, balancing budget, energy, and carbon goals. This "what-if" analysis ensures that the chosen investment path aligns financial performance with environmental objectives. A rolling 12- to 18-month planning cycle further refines strategies based on actual asset performance, energy use, and evolving regulations[2].
Mistake 4: Short-Term Budgeting Without Testing Scenarios
Focusing solely on the current year’s budget can lead to major blind spots for decision-makers. Why? Because initial costs are often just a small slice of the total lifetime expenses. When annual capital expenditures (CapEx) and operations and maintenance (O&M) budgets are planned in isolation, without a long-term view, unexpected costs creep in. These surprises can mask the true financial impact over time. For instance, infrastructure projects like roads, bridges, and railways built on tight upfront budgets often wear out faster than anticipated, triggering costly renewal programs far earlier than planned. Similarly, real estate and data centers constructed with minimal initial investments can become financial burdens due to high operating costs down the line [2][3].
At the heart of the problem lies the high cost of manual scenario planning, which makes it difficult for teams to evaluate investment choices under real-world limitations [2]. Without exploring alternatives, organizations risk two extremes: over-investing by replacing assets prematurely, wasting their remaining useful life, or under-investing, which leads to failures, penalties, and reduced performance [2]. A high-speed rail operator provided a powerful example in December 2025. By adopting a Total Cost of Ownership (TCO) strategy for fleet procurement, they slashed lifetime costs by nearly $5 billion, thanks to optimized maintenance schedules, energy efficiency measures, and renewal planning [3].
Solution: Test Multiple Scenarios
The key to avoiding these pitfalls? Testing alternative budget scenarios. Asset managers should analyze at least three budget levels – a flat budget, a 10% increase, and a 10% decrease – to fully understand how funding levels interact with long-term risks [2]. Take this example: In November 2025, a utility company used scenario testing to assess its budget options. They found that cutting the budget by 10% would increase their total cost of ownership by $4.3 million over five years due to higher failure risks. On the flip side, increasing the budget by 10% enabled them to lower TCO by 22%, thanks to strategic refurbishments [2].
Tools like Oxand Simeo™ make this type of analysis feasible on a large scale. Its scenario simulation features allow teams to evaluate multiple investment strategies under various constraints [2]. For instance, planners can calculate the "cost of deferral", clearly showing how postponing investments today can lead to far higher maintenance and failure costs in the future [2]. By using a rolling 12–18-month cycle, recalibrated quarterly, organizations can ensure their plans stay aligned with actual asset conditions [2]. One global mining company saw the benefits firsthand, saving $100 million annually by implementing a standardized TCO framework across $800 million worth of capital equipment procurement [3].
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Mistake 5: Incomplete or Unreliable Asset Data
When asset data is incomplete or unreliable, it throws a wrench into every decision-making process. The culprits? Integration hiccups, inconsistent workflows, outdated systems, and the natural aging of data over time [15]. In fact, poor data quality costs organizations an average of $15 million annually [15]. Here’s a striking example: about 40% of email users change their addresses every two years [15]. This highlights just how fast data can become outdated.
Faulty data doesn’t just waste money – it hides critical issues like corrosion, rust, cracks, and stress, all of which can lead to asset failures [13]. It also makes calculating key metrics like Mean Time Between Failures (MTBF) and Mean Time to Repair (MTTR) nearly impossible, leaving organizations in the dark about reliability [12]. On top of that, poor data quality can derail compliance efforts, opening the door to audit failures, legal troubles, and financial penalties [15]. Even the best reporting tools are powerless to deliver accurate insights without a solid foundation of dependable data [12][15].
Organizations often pour resources into new technologies only to find their strategic goals misaligned, knowledge gaps widening, and stakeholder support dwindling during the creation of Strategic Asset Management Plans [14]. Inaccurate lifecycle modeling further compounds the problem, making it difficult to evaluate equipment performance and lifecycle costs [12]. These challenges underscore the critical need for a trustworthy data foundation.
Solution: Build a Reliable Asset Database
To overcome these challenges, building a strong, reliable asset database is essential. Accurate and current data is the backbone of effective risk assessment and maintenance planning. Start by applying the five key principles of data quality:
- Accuracy: Data should reflect reality.
- Completeness: No missing fields.
- Consistency: No contradictions between records.
- Uniqueness: Eliminate duplicates.
- Timeliness: Keep information up to date [15].
These principles ensure audit-ready reporting and enable sound investment decisions.
A centralized asset inventory is a great starting point. Include unique identifiers, precise locations, condition ratings, and performance metrics [16]. If your data quality is currently lacking, prioritize building a reliable database for your most critical assets first, then expand gradually [14]. Assign clear ownership of data to ensure accountability and continuous updates [15]. Standardizing formats for names, dates, and addresses can also reduce duplication and improve searchability [15].
Tools like Oxand Simeo Inventory simplify the process by offering standardized data classification, mobile app-based digital inspections, and built-in validation rules to catch errors, duplicates, and gaps [16]. A centralized database serves as a single source of truth, eliminating the need to sift through multiple systems. It also provides stakeholders with clear, objective data, enabling quicker and more informed decisions. Regular data audits are essential for identifying issues like missing fields, duplicate records, and outdated information, ensuring compliance and keeping your data in top shape [15].
Mistake 6: Focusing Only on Initial Costs
When organizations concentrate solely on upfront costs, they risk missing the broader financial implications. Initial construction or acquisition costs typically only account for 10% to 40% of an asset’s total lifetime cost, while the remaining 60% to 90% comes from long-term operations, maintenance, and eventual disposal [3]. This narrow focus often masks future liabilities, leading to budget overruns, emergency repairs, and premature asset failures.
Skipping preventive maintenance to save on initial costs can backfire dramatically. Emergency repairs often end up being 10 to 15 times more expensive than planned, proactive maintenance [5]. Similarly, opting for cheaper construction methods may result in assets aging faster than anticipated, forcing costly renewal programs much earlier than expected [3].
"Capital expenditures typically account for just 10% to 40% of an asset’s lifetime costs; the other 60% to 90% of costs reside in long-term operations, maintenance, and other expenses."
- Santiago Ferrer, Amir Ganaba, Thomas Eisenhart, Roya Noorbakhsh, Alex Vickers, and Khaled Naja, BCG [3]
The numbers make a strong case for adopting a lifecycle perspective. For every dollar spent on preventive maintenance, organizations save $4 to $7 in future costs [5]. Leading cities allocate 2% to 4% of their asset replacement value annually to preventive maintenance, effectively reducing the frequency of emergency failures [5].
Take the example of a high-speed rail operator that, in December 2025, implemented a Total Cost of Ownership (TCO) strategy for fleet procurement. By optimizing maintenance, energy consumption, and renewal schedules, they managed to cut lifetime costs by about $5 billion [3]. Similarly, a global mining company introduced a TCO framework for $800 million worth of capital equipment purchases, achieving $100 million in annual savings through vendor consolidation and better asset management [3].
Solution: Model Full Lifecycle Costs
To avoid falling into cost traps, organizations must assess the full lifecycle costs of their assets. Building on insights from risk and maintenance data, evaluate the Total Cost of Ownership (TCO) from the very beginning. By incorporating TCO as a key metric during the planning phase, infrastructure owners can reduce lifecycle costs by 20% to 40% [3]. This involves analyzing not just the purchase price but also energy use, maintenance needs, potential failures, and disposal costs over the asset’s lifespan.
Oxand’s predictive modeling offers a practical approach, combining 10,000+ proprietary aging and performance models with 30,000+ maintenance laws developed over two decades of projects. This system helps organizations pinpoint the best times for interventions, leading to 10–25% savings on targeted components. Instead of guessing when maintenance might be needed, this method enables decision-makers to visualize how different investment scenarios could play out over 5, 10, or even 30 years, all while accounting for budget constraints.
Bringing operations and maintenance teams into the design and procurement process early on is another critical step. Maintenance experts can provide valuable feedback to ensure designs prioritize long-term efficiency [3]. For instance, simulating a ±10% budget adjustment can reveal how those changes affect reliability and overall costs [2]. A petrochemicals company applied this approach to its $1 billion annual capital expenditure. By using a standardized project-definition taxonomy and economic analysis, the company achieved 22% savings on in-year capital expenditures and boosted portfolio net present value by over 70% within just 12 months [1]. Tracking return on investment not only at project approval but also during post-completion reviews ensures investments meet expectations over time [1].
| Infrastructure Type | Annual Maintenance (% of Replacement Value) | Preventive ROI | Lifespan Extension Potential |
|---|---|---|---|
| Roadway Pavement | 2.5% – 4.5% | 1:5 to 1:8 | 15-25 years [5] |
| Bridge Structures | 1.5% – 3.0% | 1:6 to 1:12 | 20-40 years [5] |
| Water Distribution | 3.0% – 5.5% | 1:4 to 1:7 | 25-50 years [5] |
| Public Buildings | 1.8% – 3.2% | 1:5 to 1:9 | 20-30 years [5] |
Mistake 7: Single-Factor Decision Making
When organizations base investment decisions solely on upfront costs, they risk creating blind spots that can derail their entire portfolio strategy. Each department tends to focus on its own priorities – finance looks at cash flow, engineering zeroes in on failure modes, and sustainability prioritizes carbon targets. Without a shared framework to unite these perspectives, the result is a fragmented approach that hinders effective asset planning.
"Without a common model, everyone is partially right – and collectively wrong."
- Philippe Jetté, Product Manager, Asset Investment Planning, IBM [2]
The financial consequences of this disconnect are far-reaching. Ignoring factors like energy performance, regulatory compliance, and service levels leads to measurable financial setbacks. For example, organizations often lose up to 35% of potential warranties simply because they fail to accurately track asset repair costs and lifecycle data [17]. Decisions made in isolation also risk poor timing, which can result in wasted asset life or expensive failures [2] [18].
This issue isn’t just about immediate costs. When planning processes rely on manual methods, conducting thorough scenario analyses becomes too time-consuming and costly. This often leads to missed opportunities to test budget constraints or explore alternative strategies [2]. Without the right analytics, assets can turn into budgetary sinkholes, with managers failing to see that replacing an asset might offer better returns than continuing to spend on maintenance [18]. Delaying replacements to save short-term budgets often backfires, increasing preventive maintenance needs and elevating end-of-life risks. Ultimately, these choices shift higher long-term costs onto customers or stakeholders [2].
Solution: Balance Multiple Priorities
To address the disconnect between different performance metrics, organizations need to adopt a multi-criteria decision-making approach. This shift moves away from single-factor thinking and incorporates frameworks that evaluate risk, lifecycle costs, CO₂ impact, and regulatory compliance together. By leveraging advanced analytics for capital planning, organizations can achieve portfolio savings of 5% to 15% [18]. A critical part of this approach is treating risk as a measurable cost, factoring in acquisition, maintenance, and end-of-life expenses as part of a total lifecycle analysis [2].
Tools like Oxand Simeo™ make this possible by integrating multiple factors into a unified decision-making framework. With 10,000+ proprietary aging and performance models and 30,000+ maintenance laws, the platform enables organizations to conduct multi-scenario optimizations. These tools allow decision-makers to compare budget levels and visualize long-term trade-offs. For instance, in one utility scenario, a +10% budget adjustment for targeted refurbishments reduced the total cost of ownership by 22% over time [2]. Instead of guessing which projects are most critical, managers can now quantify the impact of each investment on reliability, costs, energy use, and carbon emissions over 5, 10, or even 30 years – all while staying within real-world budget and resource limits.
Conclusion
Planning asset investments doesn’t have to feel like navigating a maze of uncertainty. The seven pitfalls we’ve discussed – from inadequate risk assessment to overlooking carbon goals – share a common root cause: disconnected processes and incomplete data. By adopting a risk-based, data-driven approach, organizations can step back, see the bigger picture, and make well-informed decisions that balance cost, performance, and sustainability.
The financial benefits of getting this right are hard to ignore. Organizations that implement a total cost of ownership strategy can achieve life-cycle cost reductions of 20% to 40%[3]. As we’ve seen, strategic budget adjustments can dramatically cut ownership costs, translating into millions of dollars saved and better-performing assets.
Industry experts highlight the importance of this shift:
"Infrastructure is built to serve generations. It’s time that planning, construction, and management practices embody that same long-term vision." – Boston Consulting Group[3]
Advanced tools are making this vision a reality. Solutions like Oxand Simeo™ simplify the process by integrating proprietary models into a unified decision-making framework. Instead of relying on fragmented spreadsheets and siloed priorities, organizations can model lifecycle costs, test real-world scenarios under budget constraints, and align capital planning with carbon reduction goals – all in one platform. The result? A portfolio that’s more cost-efficient, environmentally conscious, and resilient.
Moving from reactive, short-term fixes to proactive, long-term strategies isn’t just about avoiding missteps. It’s about creating a repeatable, auditable process that transforms asset data into a competitive advantage. Organizations that take this approach aren’t just solving today’s challenges – they’re building infrastructure that will serve generations to come, all while meeting today’s financial and environmental demands.
FAQs
How do risk-based planning tools help improve asset investment decisions?
Risk-based planning tools take uncertainties and transform them into actionable, data-driven insights. They empower organizations to make smarter investment choices by quantifying both the likelihood and financial impact of risks such as equipment failures, extreme weather events, or market shifts. This approach helps decision-makers align projects with their risk tolerance and focus on investments that provide the best risk-adjusted value.
Take this example: these tools can simulate various budget scenarios to uncover the most cost-effective strategies. A utility company using a risk-based approach discovered that increasing its budget by 10% to refurbish high-risk transformers led to a 22% reduction in long-term ownership costs. On the flip side, cutting the budget by 10% would have resulted in millions of dollars in future expenses. With this level of precision, risk-based tools not only improve planning and ROI but also support the creation of stronger, more resilient assets.
Why is it important to centralize maintenance data for better asset management?
Centralizing maintenance data means having all essential information – like maintenance records, inspections, and repair costs – stored in one dependable location. This setup makes it easier for asset managers to monitor infrastructure condition and performance, pinpoint high-risk assets, and plan capital expenditures (CAPEX) using accurate, real-time data instead of relying on scattered or disconnected systems.
A unified system also empowers decision-makers to conduct scenario analyses, such as reallocating budgets or prioritizing specific repairs. This helps quantify cost savings, enhance reliability, and minimize risks. Additionally, it facilitates integration with financial tools, aligns with sustainability objectives, and meets regulatory requirements, paving the way for smarter investments and better long-term asset performance.
By turning fragmented records into actionable insights, centralizing maintenance data helps U.S. infrastructure owners make better decisions, cut costs, and achieve greater returns on their investments.
How does including sustainability in asset planning reduce long-term costs?
Incorporating sustainability into asset planning – such as addressing climate risks, prioritizing investments that align with carbon reduction goals, and using maintenance data in capital planning – shifts the focus from initial costs to the total life-cycle cost of an asset. This approach evaluates the complete cost of ownership, factoring in design, construction, operations, maintenance, and eventual disposal. The result? Organizations can achieve significant long-term savings.
Take this for example: allocating just 10% of a budget toward sustainability-driven refurbishments can cut total ownership costs by more than 20%, all while boosting reliability. Research highlights that upfront construction costs usually represent only 15%–30% of an asset’s total expenses, with a hefty 70%–85% tied to operations and maintenance. By embedding sustainability into the planning process early on, organizations can sidestep expensive emergency repairs, lower operational costs, and cultivate a more predictable and efficient asset portfolio over time.
Related Blog Posts
- Infrastructure Asset Management: A Risk-Based Approach for Multi-Year CAPEX Planning
- Asset Investment Planning 101: How to Decide What to Invest in, When and How Much
- Extending Asset Lifespan Without Blowing the Budget: Lifecycle Investment Strategies
- Financing the Green Transition: How to Use Your Investment Plan to Access Sustainable Funding