INTRODUCTION

Sustainability has been more and more crucial in the last few decades. The environment, the economy, and society are the three domains in which it is typically discussed [1–3].

Waas et al. (2014) [4] emphasise the importance of using methods for decision-making that promote sustainable development. Martin (2015) [5] suggests techniques for openly integrating values into sustainability decisions, and Zavadskas et al. (2016) [6] evokes that the general sustainability of businesses and organisations can be enhanced by using formal decision-making techniques. Other authors, such as Kamari et al. (2017) [7], Dizdaroglu (2017) [8], Davidson & Venning (2011) [9], reinforce the need to use decision criteria in order to make decisions concerning the sustainability of organizations.

In the context of rising financial constraints, rapid technological obsolescence, and increasing pressure for efficiency in asset-intensive sectors such as healthcare, industry, and public infrastructure, decision-makers face complex choices regarding the acquisition and management of physical assets [10]. These challenges are particularly evident in public health systems, where budget rationality must coexist with the need for continuous technological renewal.

As a result, there is growing interest in alternative financing and asset management models—such as leasing and renting—that allow organizations to ensure operational continuity without heavy upfront capital expenditures. These models must be analysed not only from an accounting or legal perspective, but also in terms of their long-term economic performance, risk exposure, and strategic flexibility.

Although traditional cost-benefit analyses are commonly used in physical assets possession planning, they often fail to incorporate the full financial and operational implications of the asset lifecycle. Furthermore, there is limited comparative research that simulates the long-term outcomes of acquisition, leasing, and renting decisions based on lifecycle models tailored to organizational needs. This study aims to address this gap through quantitative simulation and lifecycle investment modelling.

Framework

For the best possible value and greater sustainability realisation, physical assets must be managed throughout their life cycle.

Businesses must choose between buying physical assets or possessing them by leasing or renting for a certain period of time. The planning, possession, operation, maintenance, and disposal phases of the life cycle are all affected differently by each method [11].

The main concepts used in this paper are the followings (Figure 1): Renting is a financial arrangement where a person (the tenant or renter) pays another person or company (the owner or landlord) to use a Physical Asset (such as a house, apartment, car, or equipment) for a short or flexible period of time, usually for a shorter period compared to Leasing, and often on a month-to-month basis—the renter never owns the asset and must return it after the rental period ends—the owner is typically responsible for maintenance and repairs (especially in property rentals)—Renting has as advantages its maximum flexibility, low commitment, and fast deployment, and as disadvantage a highest per-use cost, limited availability, and less control; Leasing is a financial arrangement where one party (the lessee) pays another party (the lessor) to use an asset—such as a car, equipment, or property, for a specified period of time—Instead of buying the asset outright, the lessee pays regular instalments (usually monthly) to use it—In a Leasing contract, the lessor retains ownership of the asset during the lease term—Leasing typically have a fixed term (e.g., 2–5 years)—At the end of Leasing time, the lessee may: Return the Physical Asset; Renew the Lease; or Purchase the Physical Asset (It is common for organizations to lease physical assets and then purchase them later [12]. In these lease-to-own arrangements, a physical asset is originally accounted as a right to use the physical asset, and, subsequently treated as a fixed physical asset; The transaction is similar to a financed purchase in that one large cash outlay is not required, but rather a series of cash payments over time. This arrangement may be structured as a leasing with a title transfer at the end of the leasing period or with a purchase option that organization intends to make; Usually, the maintenance procedures during the leasing time is of the owner of the physical asset; Leasing as advantages to need reduced capital expenditure, access to newer technologies, off-balance-sheet financing, and as disadvantages to have high long-term costs, contract rigidity, limited customization.

To manage the several options of possession, being the acquisition, renting, leasing or any other, the concepts of Life Cycle help a lot. The Life Cycle concepts used in this paper are: Useful Life Cycle (ULC)—Defines that the life of a physical asset ends when its maintenance costs exceed the maintenance costs plus the capital amortization of an equivalent new equipment; Life Cycle Cost (LCC)—Is a concept that aggregates all costs that an organization will incur over the life of the physical asset. It includes the initial investment (non-recurring expense) and any further investment, such as operating costs, maintenance, and upgrades (recurring expenses); Life Cycle Investment (LCI)—Is a concept that permits to evaluate the Life Cycle of Physical Assets from the point of view of investment, costs, profits, and the global return (Life Cycle with Recovery (LCR)—It is a concept that considers, like LCI, expenses and profits—LCR may correspond to what is usually called Sensitivity Analysis; however, it adds an evaluation about the money that must be earned annually to reinvest in a new Physical Asset to replace the old one; Total Cost of Ownership (TCO)—Is a thorough financial estimate designed to assist owners and purchasers in figuring out the direct and indirect expenses of a system or product over the course of its whole life cycle. TCO is a crucial instrument for supporting strategic choices regarding acquisition, leasing, or renting in the context of physical asset management.

Research Question

The main problem discussed in this paper can be synthetized in the next Research Question:

Which is the best possession form for a Physical Asset towards sustainability?

The answer to this Research Question is made through evaluating the life cycle of physical asset based on the several possession models, based on quantitative simulation methods, from which result the financial implications of each model—purchasing, leasing, or renting—taking into account costs, profits, and asset recovery mechanisms over time.

Structure of the Paper

This paper is organized as follow:

THEORETICAL FRAMEWORK

The life cycle of physical assets encompasses all stages from acquisition to disposal, including planning, operation, maintenance, and renewal.

ISO 55000:2024 provides a comprehensive overview of asset management, defining its principles, concepts and terminology. ISO 55001:2024 establishes the requirement for designing, implementing, maintaining and improving an asset management system. The updated ISO 55001:2024 edition places greater focus on integrating knowledge management into asset strategies, enhancing decision-making framework, and aligning organisational goals with the dynamic challenges of global industries. ISO 55002:2024 provides guidelines for applying ISO 55001 in practical contexts [13]. New standards linked to the ISO 5500X family have been written and published, such as:

According to the ISO 55000 standard, life cycle management aims to optimize performance, cost, and risks associated with assets over time, ensuring that they continue to deliver value aligned with organizational objectives. ISO 55001:2024 drives asset management aligned with sustainability, promoting innovative and resilient practices that enable organizations to anticipate future challenges.

Almeida Pais et al. (2021) present a case study to apply new methods of econometric models to the Life Cycle Assessment (LCA) of physical assets, by integrating investments, such as maintenance, technology, sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle Investment (LCI), with emphasis on sustainability.

Sustainability is a recurrent theme of existing studies and will be a concern in coming decades. As a result, equipment with a smaller environmental footprint is being continually developed. This paper presents a method to evaluate asset depreciation with emphasis on maintenance investment, technology depreciation, sustainability depreciation, and technological upgrade investment.

Farinha (2025) [14] presents a general view of Leasing and Renting, as well as the main Terms of Reference for Leasing and Renting.

Raposo et al. (2024) [15] emphasize that life cycle of physical assets analysis is a tool of great importance for asset management and of great use to managers, namely hospital facilities and equipment managers. Furthermore, this is an essential issue in the current macroeconomic economy, being its assertiveness preponderant for the success of any investment. This paper aims to emphasize the importance of investment analysis in the decision-making strategy of any organization, intertwined with the life cycle of the physical asset analysis, framed in a structuring approach of certification by the ISO 55001 standard. For an efficient physical assets management, in general, and within the health sector, in particular, optimizing their maintenance and determining the optimal moment for their withdrawal from operation, or their renewal, are fundamental aspects for its profitability and quality of service to users.

In this context, the selection of an acquisition model—whether outright purchase, leasing, or renting—is a critical strategic decision that significantly impacts both financial planning and operational flexibility.

Asset-intensive sectors, such as healthcare and industrial manufacturing, often rely on life cycle models to guide decisions regarding the most appropriate method of asset acquisition and utilization. Amadi-Echendu et al. (2010) [16] emphasize that managing assets across their life cycle is essential to ensuring that they operate efficiently and economically. This perspective is particularly relevant when dealing with technologically sensitive equipment, where the pace of innovation and the risk of obsolescence must be weighed against cost and functionality.

When evaluating acquisition versus leasing or renting solutions, decision-makers frequently resort to financial methodologies such as Total Cost of Ownership (TCO) and Net Present Value (NPV) to compare long-term implications. Brealey (2022) [17] highlights that direct acquisition is generally more cost-effective in the long term for assets with extended useful lives and high utilization rates. Conversely, leasing and renting arrangements tend to be more advantageous in environments characterized by high uncertainty or rapid technological change, as they require lower upfront capital and provide greater flexibility.

Recent approaches have expanded traditional life cycle cost models by incorporating investment recovery perspectives. Concepts such as Life Cycle Investment (LCI) and the sensitivity analysis through the Life Cycle with Recovery (LCR) allow decision-makers not only assess cost accumulation and depreciation but also model reinvestment strategies for asset renewal. These models are particularly relevant when the financial sustainability of asset replacement is critical to service continuity, as in the case of public healthcare infrastructures.

Tlili et al. (2024) [18] propose a planned maintenance strategy to be adopted by the lessor during the lease period. They also refer that the greater the quantity of consumables used by the customer, the more the equipment will deteriorate. They add that the objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables; so, the lessor can maximize their profit. As an example, these authors refer the X-ray machines. In some countries, this equipment is provided with free of charge to healthcare institutions on the condition that they purchase a predetermined minimum quantity of certain films during the lease period.

From an accounting perspective, the International Financial Reporting Standard IFRS 16 (https://www.ifrs.org/issued-standards/list-of-standards/ifrs-16-leases/, accessed on 2025 May 16) has reshaped how organizations record leasing obligations, reducing the distinction between finance and operating leases. This shift underscores the need for decision-making models that incorporate both financial and regulatory implications.

To navigate the complexity inherent in evaluating such models—where multiple qualitative and quantitative factors must be considered—researchers have increasingly adopted fuzzy logic and other soft computing techniques. Zimmermann (2001) [19] notes that fuzzy set theory enables the modelling of linguistic variables and imprecise data, thereby supporting structured decision-making in scenarios where uncertainty is significant. Kahraman et al. (2003) [20] demonstrate that fuzzy logic is particularly effective when applied to Multi-Criteria Decision-Making (MCDM) problems involving supplier selection, a framework that can be extrapolated to leasing and acquisition decisions, especially when factors such as service quality, technological obsolescence, and maintenance requirements are considered.

While NPV and TCO remain central to acquisition analysis, their applicability is constrained when qualitative factors, such as supplier reliability, technological interoperability, or strategic alignment, are pivotal. In such cases, hybrid approaches combining soft computing (e.g., fuzzy logic) with financial modelling provide a more accurate representation of real-world complexity.

Within the healthcare industry, the application of simulation and optimization models further supports the lease-versus-buy decision process. According to Leite et al. (2020) [21], simulation models applied to healthcare asset management demonstrate that leasing strategies can facilitate the adoption of advanced technologies without large upfront investments. This approach is particularly effective in environments characterized by budgetary restrictions and constant technological evolution.

Thus, the theoretical framework underscores that asset acquisition decisions should not rely solely on cost-minimization principles. Instead, they must integrate long-term strategic, operational, and contextual variables, including technological renewal cycles, organizational mission, and sustainability objectives.

In summary, theoretical models of asset life cycle management offer a multidimensional lens through which organizations can evaluate acquisition strategies. By integrating financial analyses with qualitative decision-support systems such as fuzzy logic, these models facilitate comprehensive, context-aware decision-making that aligns operational needs with long-term strategic goals.

LIFE CYCLES MODELS FRAMEWORK

In his book, Farinha (2025) [14] are presented several models to analyses the life cycle of physical assets. Some of the most relevant concepts presented are summarized in the next sections.

This paper focuses on the assessment of the purchasing, leasing, and renting options what adds value to the cited previous publications.

Fisher Equation

It is important to define the way the Apparent Rate (i_A) is calculated and influences the results of the models, because it is used in all of the models. In this paper, not only was the Real Interest Rate, Inflation Rate and Profit Rate introduced but also the Risk Rate. The relationship among these rates is described using Fisher’s equation (Equation (1)) [14]:

$\left(1+i_A\right)=\left(1+r\right)\times \left(1+h\right)\times \left(1+R\right)$ (1)

where:

Life Cycle Investment

The Life Cycle Investment Method not only considers expenses and taxes but also profits. This method allows for an evaluation of the right time as to when an investment does not return a positive value. Equation (2) defines this approach:

$GR{}_n=\sum _{j=0}^n\frac{B_j}{(1+i_A){}^j}-\sum _{j=0}^n\frac{F_j}{(1+i_A){}^j}-\sum _{j=0}^n\frac{M_j}{(1+i_A){}^j}$ (2)

where:

Life Cycle of Physical Assets with Recovery

The value of the Physical Asset over time may be seen from two perspectives: Devaluation, taking into account the Apparent Rate; and the value of a new asset, taking into account the Apparent Rate.

For many companies, it is important to create financial reserves that aim to be financially available to renew a physical asset or buy a new one at the end of its economic life cycle. Because of this, it is important to know when these two lifecycle times occur.

The preceding perspectives are opposites and are evaluated as follows:

The devaluation of the Physical Asset, taking into account the Apparent Rate is given by Equation (3):

$C_{0D}^t=C_{0D}^{t-1}\times i_A$ (3)

where,

The value of a new Physical Asset, taking into account the Apparent Rate (Equation (4)):

$C_{0N}^t=C_{0N}^{t-1}/{(1+i_A)}^i$ (4)

where,

The Total NPV, considering the devaluation of the Physical Asset and taking into account the Apparent Rate, is given in Equation (5):

${\mathrm{<i>NPV</i>}}_{\mathrm{<i>TD</i>}}={\mathrm{<i>NPV</i>}}_{\mathrm{<i>FpD</i>}}+{\mathrm{<i>NPV</i>}}_{\mathrm{<i>Fe</i>}}$ (5)

where,

The Total NPV, considering the value of a new Physical Asset and taking into account the Apparent Rate, is given in Equation (6):

${\mathit{NPV}}_{\mathit{TN}}={\mathit{NPV}}_{\mathit{FpN}}+{\mathit{NPV}}_{\mathit{Fe}}$ (6)

where,

Leasing and Renting Models

By evolving the models presented in the previous sections, new Leasing/Renting models can be developed, as will be seen below.

The new Leasing/Renting models are proposed through Equation (7):

${Rt}_n=\frac{{Rt}_i}{{(1+i_A)}^i}$ (7)

where,

Leasing Considering a New Physical Asset

If it was considered the acquisition of a new asset after the leasing/renting time, it must be considered an additional amount to be possible to save money for that time. With this objective, below will be presented a model to support this objective.

It can be considered, in a first step, to make a Leasing/Renting and, after accumulating the necessary budget, to buy the Physical Asset. Equation (8) gives the way to calculate the Rent to reach this objective:

${Rt}_n=\sum _{i=1}^n(\frac{{Rt}_i}{{(1+i_A)}^i}\ast Inc)$ (8)

where,

Return On Investment with and without a New Physical Asset

It is very relevant to know when the Return On Investment (ROI) happens. Equation (9) presents the most usual way to evaluate the Return On Investment (ROI):

$ROI=\frac{\sum _{j=1}^n{CF}_j}{II}$ (9)

where,

However, the ROI can be evaluated considering the accumulation of a budget to by a New Physical Asset; this new approach can be done through Equation (10):

${ROI}_N=\frac{\sum _{j=1}^n{CF}_j}{({II}_{NPV}+{II}_N)}$ (10)

where,

BASE CASE AND SENSITIVITY ANALYSIS

The simulation models developed in this study and presented in this section are directly grounded in the theoretical foundations discussed in Section “Theoretical Framework”. Financial indicators such as Net Present Value (NPV), Return on Investment (ROI), and Total Cost of Ownership (TCO) were applied to compare acquisition, leasing, and renting strategies over a 25-year horizon. Moreover, the simulation incorporates life cycle investment principles (LCI and LCR), allowing the quantification of asset recovery strategies and replacement planning.

The values inside the Table 1 are all theoretically; however, in real analysis there are main general indicators that may be used, like the following:

About Renting by activity sector, some general indicators are the following:

About Leasing by activity sector, some general indicators are the following:

Usually, lato sensu, the values for Renting varies between 1.5% and 10%, depending on the type of Physical Asset to rent: Heavy construction equipment, between 5% and 10% of purchase cost (Excavators, loaders, cranes); Industrial equipment, between 4% and 8% (Compressors, generators); IT/office equipment between 3% and 6% (Servers, photocopiers); Medical equipment, between 2% and 5% (High-value devices usually have a lower %); Vehicles (fleet), between 1.5% and 3% (Long useful life with lower rate).

The simulations presented next are based on the following data, to which must be added the annual values inside Table 1

It is important to emphasize the relation between production and the Availability of the Physical Assets, through the MTBF and MTTR and the Equation (11).

$A=\frac{MTBF}{MTBF+MTTR}$ (11)

Table 1 shows the simulated data relating to the accumulated financial results over the life cycle of the asset, considering different acquisition and recovery strategies. The negative values mean expenses; the positive values mean positive incomes.

Figure 2 shows the cumulative evolution of total positive and negative flows over 25 years, with Sensitivity Analysis:

As can be seen in the figure the green line shows an interesting positive result for the option of a rent during 16 years.

The option to earn money to buy an equipment only gives a positive result between years 6 and 13 but it permits to buy an asset after that time as alternative to make a new Rent.

The graphic of Figure 3 compares LCI reduced to NPV over a 25-year horizon for two scenarios:

From the Figure 3, it can be seen that with rent the positive results happen during 17 years, while considering an annual earning the positive results happen only during16 years. However, if the percentage of annual earnings increases, obviously the positive interval decreases.

The graph of Figure 4 compares the Net Present Value (NPV) over 25 years for different scenarios of acquiring and financing a new asset (purchase, purchase with recovery, rental and NPV with new/depreciated assets)—This graph highlights some results shown in Figure 2:

Figure 4 reinforce the idea that the Rent option gives a more enlarged positive period of positive return; however, this option does not give the solution to own the equipment.

The graph of Figure 5 shows the cumulative return on investment (ROI) where can be identified the Payback time for two scenarios: purchase with recovery (blue line) and ROI based on II (dark grey line).

As can be seen in Figure 5, it is reinforced the idea that with Rent the ROI and corresponding Payback, with the simulation values considered are better than with investing previously in a Physical Asset.

CONCLUSIONS

This study presented an in-depth analysis of lifecycle models for physical assets, with a focus on comparing acquisition, leasing and rental alternatives, using simulations based on financial indicators such as Net Present Value (NPV), Return on Investment (ROI) and accumulated financial movements. The results show that the decision on how to obtain assets should be guided by an integrated view of the life cycle, taking into account not only initial costs, but also operating costs, maintenance strategies, residual value and potential recovery gains, also taking sustainability into account, building a decision-making tool to managers.

From a methodological point of view, the use of life cycle simulation models makes it possible to explore different scenarios and support decisions based on quantitative evidence, reinforcing the importance of a systemic and forward-looking approach to physical asset management. It is this aspect that is highlighted, i.e., all options of possessing ought to be evaluated but the choosing must be according the one that gives the better life Cycle for the investment.

The integration of variables such as depreciation, planned maintenance costs and the benefits of asset recovery is fundamental to building more realistic models that are applicable to complex contexts such as the health sector or heavy industry.

In practical terms, this work provides a solid basis for supporting public and private decision-makers in defining more sustainable, efficient and economically viable investment strategies. Future research could explore the application of fuzzy models, multi-objective optimization and risk assessment, incorporating variables such as technological obsolescence, environmental regulations and social impact.

The conclusion is that life cycle analysis of physical assets, supported by robust analytical tools, is essential for maximizing the value of investments over time, ensuring decisions that are more in line with the strategic objectives of organizations and current sustainability requirements.

Furthermore, the study highlights the strategic relevance of integrating lifecycle-based decision-making into public procurement policies. By aligning financial planning with technological renewal cycles, organizations—particularly in the public healthcare sector—can enhance resilience, accountability, and service quality. The simulation approach developed also offers a transferable framework for application in other infrastructure-intensive domains, such as transportation, education, or energy. As a scientific contribution, this research advances the field by combining empirical simulation with theoretical lifecycle principles, reinforcing the applicability of quantitative decision-support tools in complex, high-stakes environments.

Finally, the math models developed prove to be robust enough to support the decision about the most adequate form of possession for physical life cycles that organization needs. In this way, they contribute to better management of their physical assets and, in turn, to the sustainability of organizations.

DATA AVAILABILITY

No restrictions apply to the availability of the data used in the paper. Data will be made available on reasonable request.

AUTHOR CONTRIBUTIONS

JTF: Conceptualization, Data curation, Formal analysis, Investigation, Project administration, Resources, Validation, writing–original draft, Writing–review & editing. IS: Validation, visualization, writing–review & editing. HR: Conceptualization, Validation, writing–original draft, writing–review & editing. JEd-A-e-P: Validation, Visualization, Writing–review & editing.

CONFLICTS OF INTEREST

The authors declare that they have no conflicts of interest.

FUNDING

This work was supported by the i2A Applied Research Institute, Polytechnic University of Coimbra, RCM²⁺ Research Center for Management Engineering and Asset Systems.