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Article Assets edition May 2019 IAM

The research is focused on the role of Maintenance Management as one of the four fundamental basic principals of Asset Management as defined in the NEN-ISO 55000. The reason is that the ISO 55000 Maintenance Management regarded as one of the four fundamental pillars of Asset Management. Despite the fact that the standard defines a Asset in such a way that it could be anything, it gives no insight into the structures in which Assets are composed. The research aims to provide insight into this through from the inheritance the three types of element structures (Civil, Building and Industrial Process) adding to the paradigm used by the standard and connecting the policy to the smallest Asset component. This linkage is named “The Line Of Sight” and is based on the analogy with the ” Research Onion “. The following considering describes (shortened) this insight so that the degree of influence of failure by the smallest Asset Component definable and/or quantified and is part of a number of related topics. Derived of this consideration is the SAMP/AMP, the definition of the concept Maincontracting and other related topics and objectives of the research.

This article is close related with the article ‘The line of Sight: Deepening the subject”. Click here for the articleFull Width 

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  • Draft Version: 17 April  2019
  • Updated 10 June 2019 (Version 1.0)
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This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options.

Moreover, discerning differences in characteristics between 

crosscomponent and cross-asset interactions, they select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, the authors delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. The authors expecting that the output of this review paper will not only stress research gaps in multi-asset systems, but more importantly help systematise future studies on this aspect.

 

Author:  Sanyang Petchrompo, Ajith Kumar Parlikad; Science DirectManagement Systems

 

1. Introduction

As the industry deploys more technology and equipment for more services to satisfy consumer needs, the field of asset management has piqued the interests of researchers from different disciplines. Since then, a multitude of studies in this field have been conducted, and the scope of the study has rapidly shifted from managing simple repair operations to optimising complex systems of multiple units.

This focus has therefore led to the emergence of the term ‘multi-unit system’ in reliability engineering research. With various interactions among components and assets in a system, optimization modelling for maintenance has become immensely complicated.

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Asset Management Systems: See The Line of Sight-Deepening the subject

On one hand, these intricate interactions require substantial computational resources to deal with operations and maintenance problems. On the other hand, these characteristics also open up opportunities for devising policies that, compared to individual optimization strategies, enhance the efficiency of the overall system. Over the past years, researchers have put forward innovative solution methods for different types of multi-unit systems. Although these studies have greatly contributed to the body of knowledge and benefited the industry, adopters are required to put strenuous efforts to search for models that are appropriate for their specific problems. This is because different researchers have employed different expressions to refer to their systems.

Previous overview articles have made impressive strides in facilitating literature search for readers by categorizing multi-unit systems according to their distinctive features. These classifications are generally based on types of dependence among members of a system. Dekker et al. [1] and Nicolai and Dekker [2] laid the foundations for the three main categories: economic, stochastic, and structural dependence. Structural dependence occurs when an intervention of a component requires that other components be intervened (either replaced or dismantled) at the same time. Stochastic dependence is referred to as failure interactions among components.

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Economic dependence implies that a joint maintenance of several components either negatively or positively affects the overall expense. Recently, Olde Keizer et al. [3] established the concept of resource dependence in which components in a system share common resources such as budget or spare parts. The authors of the paper also extended the definition of structural dependence by further classifying this into technical and performance dependence. A system is considered technically dependent when intervention activities for certain components are restricted by those of other components; the negligence of this dependence inevitably results in an infeasible maintenance strategy. Performance dependence is applied when an intervention activity, deterioration, and failure of a component affects the performance of other components and the overall system. This dependence is also referred to as system configuration in the survey by Wang and Chen [4].

The absence of performance dependence may lead to a feasible policy, but the optimality is not guaranteed. It is evident that previous literature review papers including [1–7] emphasis on single-asset systems comprised of multiple components. However, to the best of our knowledge, a survey that compiles studies on systems of multiple assets has not been carried out. A need for multiasset models emerges as intervention strategies for systems of multiple components are not applicable to systems of multiple assets. Two representative aspects that distinguish a multi-asset system from  multicomponent system are the indistinct asset configuration and the effect of the system reliability on multiple stakeholders. Since the confrontational relationship among components in a multi-component system (e.g. series and parallel) is clearly defined, each component produces a distinct effect on the system output. In contrast, a multi-asset system such as a fleet of vehicles does not require that all assets work collectively in one location. Thus, the contribution of a dysfunctional asset on the output of a multi-asset system cannot be directly measured.

Another distinctive characteristic of a multi-asset system pertains to diverse effects of system reliability on multiple stakeholders. This characteristic is opposed to that of a multi-component system of which the reliability chiefly affects the asset owner. A multi-asset system such as a portfolio of highway assets, which is large in scale as compared to a multi-component system, not only produces an output for the asset owner but also provides direct services to multiple users. Thus, it is a daunting task to choose an appropriate method for enhancing the reliability of the whole multi-asset system. Since the reliability of each asset could contribute differently to the system performance, traditional maintenance policies that consider each asset from a single dimension are not geared to supporting users to make well-informed decisions.

To overcome this limitation, later researchers and practitioners have provided broader views on operating and maintaining physical assets and established the field of asset management. The term asset management has been widely used since the International Organisation for Standardization (ISO) established its definition in ISO 55000 [8].

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Asset management is defined as an integrated activity to realize value from systems of assets. Compared to traditional maintenance engineering, despite the similar aim to enhance the system reliability, asset management extends its focus beyond improving system safety and reducing cost to delivering performance benefits to organisations and their stakeholders [9,10]. Hence, in this integrated view, a decision on which asset and how it should be improved is determined by multiple output metrics, so that the utility of the system reliability is maximized.

The objective of this review paper is therefore to fill the literature gap by giving a review on comprehensive classes of multi-unit systems with an emphasis on multi-asset (fleet and portfolio) systems. To lay the foundations for our new classification, the authors compile different terms employed by previous researchers to refer to different multi-unit systems and examine their specific features (e.g. origin and usage, differences and similarities in intervention options, and asset diversity). Having identified patterned features of these terms, the authors classify multiunit systems into three categories: multi-component, fleet, and portfolio. To explore interactions among assets, the authors  draw three types of potential dependencies – namely performance, stochastic, and resource – and extend these notions in order that they are conducive to all categories.

In addition, they collate studies that fall into these categories and identify possible dependencies that have not been examined in academia. Ultimately, for each multi-asset category, the authors scrutinize modelling approaches employed by previous scholars. Having delved into decision moments, objectives, types of assets, and techniques employed in these studies, the authors elaborate on the state-of-the-art and identify avenues for future research. The remainder of this review paper is organised as follows. Section 2 provides the definitions of related terms and establishes distinct classifications of multi-unit and multi-asset systems. In Section 3, they explore interactions among multiple assets within each of two multi-asset systems. Section 4 examines typical research problems in fleet and portfolio management and classifies these problems into seven decision classes. The authors  also delve into methods for the enhancement of safety, reliability, and other performance measures of multi-asset systems in Section 5. Representative studies are also gathered and exhibited according to solution method, decision class, and asset category. In Section 6, the authors highlight the key gaps in the literature to issue guidelines on potential research areas. Lastly, conclusions are drawn in Section 7.

 

2. Classifications and definitions

In this section, the authors compile previous literature that bolsters the classification and definition of multi-unit systems. The authors review the terms that were employed by previous researchers to refer to these systems. Having filtered out irrelevant contexts, the authors  consider patterns in these terms and establish distinct categories of multi-unit systems.

This section begins with the overview of the usage of the terms ‘multi-unit’ and ‘multi-asset’ systems. Subsequently, after providing our principal classification of these systems, the authors lay an emphasis on how the terms ‘fleet’ and ‘portfolio’ are adopted in asset management research. It is important to note that, in Section 2, the authors focusing on the elementary characteristics of the systems adopted by previous researchers in order to help us primarily define these systems.

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Multi-Unit Systems

The concept of multi-unit asset management emerged when the industry began to step up the production and service with more equipment. With the huge impact of the asset reliability on the industry performance, practitioners and scholars therefore started to delve more deeply into how these assets can be fully exploited. Early academics extended their single-unit models to deal with a two-unit system – the most fundamental scheme for a multi-unit system. Osaki and Asakura [11], Branson and Shah [12], Subramanian and Venkatakrishnan [13], and Kumar [14] pioneered the models for the ‘two-unit standby redundant system’, which can be further categorised into warm-standby and cold-standby systems. As the models imply, th standby system consists of a primary system (an operating unit) and a secondary system (a backup unit).

The major difference between warmstandby and cold-standby systems is the operation of the secondary system. The warm-standby unit runs in the background of the primary system. In contrast, the cold-backup system is called upon only when the primary system fails. Afterwards, these maintenance models were implemented in complex systems with numerous units. The implementation amplified the scope of multi-unit systems to cover a wider range: from a group of multiple assets to an asset with multiple components.

For example, Fard and Zheng [15] considered a policy that minimises the total maintenance cost for a multi-unit system which consists of different types of assets. However, employing the similar term, Park et al. [16] developed a block preventive maintenance policy for a multi-unit system, but this term was used to mention a system of multiple identical assets. The use of the term ‘multi-unit system’ is even more divergent when researchers also applied this term to a single-asset system with various components (e.g. [17]). Khan and Gupta [18] were the early scholars who employed the term ‘multi-component system’ to indicate an asset with a number of components working in series or parallel connections.

This definition has been adopted and become more prevalent among later researchers (e.g. [1,19,20]). The review papers on multi-unit systems by Cho and Parlar [5] and Wang [6] also signaled the vast variety of multi-unit systems in the models they considered. Therefore, to establish a general principle for future multi-unit asset management research, the authors define the term ‘multi-unit system’ (see Fig. 1) as follows:

As aforementioned in Section 1, there exist a number of intelligently
 omprehensive literature review papers on ‘multi-component’ system (i.e. [1,5–7,3]). Hence, in our paper, the authors lay an emphasis on the review of ‘multi-asset’ systems.

Multi-Asset System

The usage of the term ‘multi-unit system’ varies considerably. Hence, in following studies, researchers came up with different terms to refer to their specified systems. In the manufacturing engineering context, Trippi and Khumawala [21] examined an investment renewal problem for a system with various assets, initiating the term ‘multi-asset system’.

This term is also borrowed by later researchers to explain a system of serially dependent assets (e.g. [22,23]), that of independently operated homogeneous assets (e.g. [24]), that of heterogeneous assets (e.g. [25,26]), and other systems with multiple units. In the control engineering context, Alj and Hauree [27] began to deal with a system with multiple units for a system comprised of a fixed number of identical elements working under the same conditions.

In the study, the authors called this setting ‘a system of m elements’. The similar expression is also adopted by other contributors such as Knessl [28], Dietz and Rosenshine [29], and Kenne and Boukas [30]. Latterly, Suryanarayan [31] also employed the term ‘fleet’ to refer to the system consisting of a number of similar or identical machines. Multi-asset maintenance problems have become even more complicated when an organisation has to cope with different types of assets at the same time. Zheng and Fard [32] proposed an opportunistic maintenance policy for a system consisting of different types of units and called this system a ‘system of multi-type units’.

Considering the similar context, recent researchers have also adopted the terms ‘network’ (e.g. [33]) and ‘portfolio’ (e.g. [34]) to indicate a system with heterogeneous assets. As previously depicted in Fig. 1, in this paper, the authors further classify multi-asset systems into ‘fleet’ and ‘portfolio’ systems. Fundamental characteristics of these two categories are compared and summarised in Table 1. These features will be discussed in details in the remainder of this section.

Table-1

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3. Portfolio

Originally employed in the financial sector, the term ‘portfolio’ has been used to describe a collection of different investments owned by an entity [66]. This term was subsequently introduced to the field of asset management by the ISO 55000:2014 Asset management – Overview, principles and terminology [8]. The ISO standard generically defines an ‘asset portfolio’ as a group of assets established and assigned for managerial control purposes. Since its introduction, this expression has been widely acknowledged in the context of large assets, especially infrastructure assets. An early example of a physical asset portfolio is a group of buildings (e.g. [67,68]). Other typical examples are portfolios of nuclear reactors (e.g. [34]), bridges (e.g. [69–71]), and plants (e.g. [72]).

 

3.1 Diversity of Assets and intervention options

Since the term ‘asset portfolio’ was introduced, asset management researchers have adopted it to identify systems in their studies. These contributors are apt to apply the term towards its original definition in the finance industry, namely a group of heterogeneous assets. In the existing literature, there are different levels of asset diversity in a portfolio. In this sub-section, the authors confer about two variations. In the first variation, asset managers commonly construe an asset portfolio as a group of assets that belong to the same category but that have different characteristics. In our study, the authors  employ the term ‘singlecategory portfolio’ to refer to this system. The authors  illustrate this point through a case study provided in the study by Morales-Torres et al. [73].

In the study, an organisation aimed at the effective prioritisation of safety investments on a portfolio of 27 dams. Although assets in the portfolio are of the same category (the reservoir), they are significantly different in terms of structure, size, construction year, and reservoir volume. Other proponents of this definition of portfolio are, for example, [67–69,74]. As for the second variation, some researchers pointed out that an organisation can have a problem in maintaining different types of assets and hence considered incorporating these diverse assets into one portfolio. In this paper, the authors establish the term ‘multi-category portfolio’ to explain this variation. Zhang and Wang [75] explained that the determinant of comprehensive assets owned by an organisation ensures the allocation of sufficient funding to sustain assets operational condition.

Interesting examples of this form of portfolio are demonstrated in [76,77]. The former considered an approach to the management of a portfolio of various assets (i.e. buildings, facilities, and airfield pavements) under the control of the US Air Force. The latter developed strategies to maintain a portfolio of diverse assets (i.e. cargos, sea walls, roads, navigation aids, and marine and berth structures) owned by the Newcastle Port Operation. Apropos the intervention diversity, the heterogeneity of assets in a portfolio undoubtedly leads to different asset-specific intervention actions. McKoy [70] explained how the London Underground (LU) managed its portfolio of approximately 8000 bridges.

These bridges substantially differ in material profiles, structures, and functionalities (i.e. footbridges, overbridges, and underbridges). As a consequence, LU engineers had to apply specific assessments to definite groups of assets. For instance, while underbridges subject to rail vehicles loading had to be assessed for Railways London (RL) loading standard, the assessmen of pedestrian footbridges was carried out to the 20 kN concentrated loading standard.

 

Definition

In this study, the authors enhance the definition provided by the ISO 55000 [8] and define the term ‘portfolio’ in asset management as follows:

  • i. An asset portfolio is a collection of assets under the control of an organisation.
  • ii. Assets in a portfolio do not necessarily operate at one location (e.g. buildings that are located in different places but that are owned and managed by an organisation).
  • iii. An asset portfolio is comprised of assets with different characteristics. Assets in a portfolio can be either:
  • iv. An intervention option that can be applied to an asset
  • v. An asset portfolio is comprised of assets with different characteristics. Assets in a portfolio can be either:

 

A. Assets that fall into the same category but that have different features (e.g. a portfolio of heterogeneous bridges). the authors establish the term ‘single-category portfolio’ to refer to this type of portfolio.

B. Assets that fall into different categories (e.g. a portfolio of diverse assets managed by a city council). They establish the term ‘multi-category portfolio’ to refer to this type of portfolio.

Related terms

Asset management TB Systeem 0.04

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Based on the aforementioned definition, the following expressions
also fall into the category of portfolios:

  • A system of multi-type units (e.g. [32,78])
  • A multi-unit/multi-asset system of heterogeneous assets (e.g.[79,80])
  • A serially dependent production system of different machines (e.g.[22,23])
  • A network of heterogeneous assets (e.g. [33,81])

 

 

4. Conclusions

The authors reviewed the literature on multi-unit asset management systems with an emphasis on fleets and portfolios. They began with collating previous studies on systems with multiple units to investigate how the term ‘multi unit system’ and its related expressions are adopted in different contexts. In accordance with essential features such as the diversity of system members and intervention options, multi-unit systems are primarily categorised into multi-component and multi-asset systems. The authors further classified the latter into a fleet – a system of homogeneous assets – and a portfolio – a system of heterogeneous assets. The authors demonstrated that models that are considered effective in a multi-component system may not be applicable to a multi-asset system due to some dissimilar characteristics of these categories. To highlight dissimilarities among categories, they drew three types of previous multi-component dependencies – performance, stochastic, and resource dependence – and revised these notions in order that they are applicable to multi-asset systems. The authors successfully identified potential niches in both fleet and portfolio research.

Their findings revealed that performance and stochastic interactions are more apparent in multi-component systems than in multi-asset systems and showed the opposite for resource dependencies. Furthermore, the authors scrutinized research contributions made by previous researchers who focused on multi-asset systems. Typical problems pertaining to these systems were categorized into seven decision classes. The authors also gathered state-of-the-art references that belong to each decision class and discussed how problems in each asset category were dealt with in previous research. The authors  found that the majority of multi-asset problems stem  from the shared facility (intervention scheduling), resource (spare parts management), and budget (MR&R project selection, asset prioritisation, and budget allocation). More importantly, the authors also investigated various solution methods applied in fleet and portfolio studies. It is apparent that decisionmaking at the multi asset level affects a wider range of stakeholders and involves more criteria when

criteria when compared to that at multi-component level. Factors considered in multi-asset problems include both direct technical measures (e.g. reliability, safety, and cost) and indirect functional measures (e.g. production output and other user-based performance metrics). Hence, methods for the enhancement of safety and reliability of multi-asset systems are not only complex models that focus on a single objective, but also frameworks that incorporate multiple criteria from different dimensions. Thus, besides single-objective optimisation methods, various MOO and MCDA methods applied in previous studies were also investigated. To facilitate the selection of conductive techniques to readers’ interests, The authors also explicated the mechanisms behind these techniques along with their advantages and drawbacks. Lastly, the authors  also provided our insights into current trends in asset management research and offered potential research avenues.

 

The findings of the authors review of asset dependencies pointed out possible types of dependencies that could exist in reality, but have not been examined in existing studies. In addition, the authors also identified the shortcomings of current modelling approaches. These findings enable us to put forward suggestions on the inclusion of different value components and the deployment of data driven approaches to mitigate the subjectivity in decision-making in fleet and portfolio asset management.

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