14th WCEAM Proceedings

Managers of engineered assets have to make decisions to acquire, utilise, and retire equipment, machinery, and such man-made facilities. On the one hand, manufacturers, suppliers and vendors of machinery and equipment continuously seek to sustain their businesses. On the other hand, asset owners and users seek to achieve better performance of assets. Given the advent of the fourth industrial revolution technologies and globalization 4.0 business models, the question arises as to what level of product-to-service transformation is appropriate in a given user-manufacturer/supplier/vendor setting, and what should be the extent of the corresponding product-service system? This paper discusses an investigation into product-to-service transformation in terms of strategies adopted by equipment and machinery manufacturers, suppliers and vendors on the one hand, and also of asset owners and users on the other hand. Empirical data and information from the respective sides are summarily presented with regard to the effect of product-to-service transformation on the user-side management of engineered assets.

Business models are evolving from selling products towards delivering outcome that may be measured as system performance, capacity, and availability. Novel business models may also include elements of sharing value. Machinery manufacturers and suppliers face a rapidly changing business environment and look for major growth in digital solutions, automation and services. The implementation of advanced technologies and business models call also for novel models of sharing risks and benefits. The literature review on advantages and disadvantages of various business models underlines the investment risk. Modelling of the cash flows of different business models provide examples for leveraging initial investments in assets when applying emerging technologies. The business partners also have to consider how their risk landscape changes and what are the preventable, strategy and external risks of the planned business model. The chosen business model poses requirements to the risk management process and highlights the importance of the collaboration and transparency. This paper focuses in the automation options in the transport sector and uses major port terminal as a case example, but the developed methods are applicable also in other capital intensive industries.

Large organizations, especially those in relatively stable environments like infrastructure managers, are in general managed incrementally with implicit objectives. The rationale for this is quite clear. If the performance of the asset base was adequate in the past, doing more or less the same as last year cannot be that wrong. In volatile times (i.e. the required rate of change exceeds the adaptive capabilities of incremental change) this strategy fails and explicit planning and prioritization (the basic idea of asset management) are needed. A prerequisite for such an exercise is the existence of a shared organizational awareness of what is right (e.g. documented in mission, vision, policy and strategy and the value system as captured in the risk matrix). However, given the incremental past, it is not uncommon to have multiple conflicting versions of the truth. Resolving this directly is almost impossible, as decision makers would (and should) require a reasonable estimate of the consequences for any decision they make. With regard to redefinitions of the value system these consequences are highly uncertain. In this paper, we present an approach which we used to help decision makers specify their value system. We start with modeling the asset base in respect to the dominant change agent (in many cases ageing), so that we can prognosticate cost, performance and risk for diverse concepts of what is right. This helps the decision makers understand what the long term consequences will be of the implicit values they impose on the organization by means of ambitions and objectives, and thus helps them understand if their objectives are right. This approach has been successfully applied in a variety of depths (with associated effort) in several organizations.

Major electrical utilities are capital-intensive organizations. They face the renewal of large parts of their assets that reach the end of their useful life. These companies also operate in an increasingly complex business and operational environment characterized by deep uncertainties. The impact of extreme and rare events is increasingly important, but not sufficiently studied. New methodologies and ways of thinking are necessary to elaborate models which adequately support decision-making process. This paper presents a global asset management approach developed for Hydro-Québec TransÉnergie which is applicable for major electrical utilities. It is part of an overall resilience management strategy and takes into account the context of operational and business complexity.

Research is rich in analyzing performance and quality of city services (e.g., the frequency and timeliness of the), while the quality of experience is usually overlooked. This research combines the quality of service and the quality of experience into a single multi-faceted framework. The proposed framework incorporates visualizations and implements urban analytics to compare QoS and QoE. Discrete event simulations and Markov Chain Models are created to model city services’ behavior and simulate events. At the same time, resilience metrics are used to evaluate the quality of the service. Data mining of social media, natural language processing, and emotion analysis techniques are employed to measure the QoE. The framework is implemented to analyze how services react to different types of service disruptions (e.g., weather, maintenance, attacks) through time and their effect on customers. This research’s main findings are related to the differences in resilience of the services during disruptions, while QoE reveals additional issues associated with their use. The proposed framework is illustrated through transportation services, providing insights on their performability and customer’s perception of the service. Future work involves collecting data on other services to test the framework further, as well as improving the layers of superimposed data for explorations and visualizations, thereby creating a decision-making tool for stakeholders.

This work investigates the commuting habits of young (18–24 year old) drivers, in Duluth, Minnesota (MN), with the goal of encouraging sustainable commuting. Sustainable commuting modes are cleaner, cheaper and healthier. Encouraging younger drivers to increase their use of public transportation would contribute to a more sustainable use of transportation infrastructures. Transportation infrastructure asset management is the allocation of available funds across infrastructure classes (e.g. pavements, bridges, signs) or programs (e.g., maintenance, construction). Sustainable commuting attempts to encourage the more efficient and sustainable use of transportation infrastructures. By focussing on younger drivers, it is hypothesized that early adoption of sustainable habits will lead to long-term sustainable habits. The research questions becomes how to encourage young drivers to reduce their dependence on personal vehicles? This work presents the results of a survey of 370 18–24 year old drivers from the University of Minnesota Duluth (UMD) campus to understand their commuting habits and problems with the current bus system. Approximately 46% of UMD students choose their personal vehicle as their primary commuting option. Among these students, 24% cite “inconvenience of the bus schedule” as the reason that prevents them from riding the bus more often even when using the bus can save them more than $2,000 per semester in vehicle maintenance costs. It seems as though poor marketing of bus routes and lack of education toward using the bus system has led to efficient routes being cancelled and lack of ridership.

We speculate that COVID-19 will drive engineering asset owners to require governance entities such as boards of management (BoM), to elevate engineering asset management (EAM) to being a corporate level strategy and core competence. We sought to understand whether there was a fit-for-purpose conceptualization of EAM as a corporate level strategy.

Our research considered foundational Asset Management (AM) prescriptions finding that the available AM models have distinct gaps as to the roles of governance entities and of CEO’s in asset management where these are actively involved in the policy and practice of asset management. Using the Victorian Government Asset Management Accountability Framework as an example of active top-level involvement we identified a ‘bottom-up’ approach, incomplete integration with the whole organization and positioning of AM planning subsequent to strategic level planning as barriers to effective corporate level asset management strategy implementation. Responding to those findings we offer a program of future research aimed at developing fit-for-purpose strategic EAM tools to assist those BoM’s who seek to elevate EAM to a corporate level strategy.

This chapter explains the process to follow in order to schedule major overhauls and renovation of capital intensive, physical assets. The methodology followed considers the importance of the assets for the business (asset criticality) and the asset condition projections over time and according to existing operational and maintenance records.

The current models for the calculation of Life Cycle Cost analysis (LCC) ignore as the aging of assets, in particular conditions of geographical location, operation and maintenance. That’s why there is a need to identify critical failure modes of the important assets of any organization, and how the condition of the systems and its evolution until the end of its useful life can determine the cost of the life cycle. The contribution of this document consists of the use of criticality analysis and asset health index tools, to determine the life cycle cost analysis, based on the impact of each failure mode and the aging of the assets. The methodology provides an objective point of view in order to justify major overhaul, asset renovation, or the extension of useful life.

The above methodology is applied to assets of an Oil & Gas company. As a result, an economic profile is obtained up to the 2035 horizon of two different strategies, the first one is the result of the LCC standard analysis without taking into account the aging of the equipment, the second one takes into account the methodology proposed in the paper, which it allows to know when to advance or delay major maintenance to minimize the impact caused by the increase in the frequency of failures of assets, linked to aging. The application of the previous model to a series of critical complex assets of an oil and gas company is included.

Infrastructure Networks (INs) have reached a level of complexity where conventional vulnerability analysis methods cannot fulfil the challenges for the management of this increasing complexity. There is, therefore, a need for a complementary approach through which the structural complexity and the level of interaction among the components are studied. In this context, researchers have increasingly looked into graph theory for help in understanding the structure, efficiency, and vulnerability of INs. The desire to employ the graph theory has resulted in the proliferation of a wide range of topological metrics. To gain a better appreciation for how various graph theory quantities have been used for vulnerability evaluation of INs, this chapter documents a variety of concepts from graph theory in one place and presents an overview of the application of graph theory in the field of INs. It also reviews the conceptions of the four widely used vulnerability-averse indicators of INs, which are reliability, resilience, robustness, and redundancy. The conventional graph theory methods are criticized on several grounds and the future evolution of graph theory tools is forecast.

With the increased demands regarding the reliability, availability, safety and affordability of key public infrastructures, it becomes increasingly challenging for asset owners and managers to achieve a proper balance amongst performance, risk and cost. As a response, operators of critical infrastructures need sound solutions to maximize the value of their assets, meanwhile realizing their business values and objectives. This requires well-defined decision-making processes with an adequate risk management concept embedded.

This is particularly true for managing ageing infrastructures. Considering the large amount of assets, this requires a balance between managing reliability/availability, budgets and resources. A traditional replacement approach often involves fixed replacement criteria, which are normally being defined for each specific equipment type. The main drawback of this approach is that no account is made for the differences in stress history or in the impact and thereby the risk of failure: within a specific asset class, critical assets follow the same criteria as non-critical assets, leading to (too) high replacement volumes and (too) early replacements.

In this paper, a risk-based replacement process is presented for prioritization, replacement planning and portfolio management, staying away from fixed replacement criteria. Although the risk-based approach itself is well described in literature, it remains a challenge for asset managers to deal with the large amount of assets. This is here solved by using a two-step approach. First, by using predefined triggers, the assets with a potential risk are identified. Rather than simply replacing these assets, these assets (and only these assets) are subjected to a risk assessment by estimating the probability and impact, using the organization’s risk matrix. Next, the optimum solution for mitigating the risk is defined. This may consist of replacement, but it may just as well be refurbishment, intensification of maintenance, or even accepting the risk.

A replacement decision is only one of the types of decisions to be made by an asset manager based on a common asset management policy and a unified approach towards decision making. This paper presents a real-life case study at a large electrical infrastructure operator, and discusses the opportunities and risks encountered in implementing such a policy revision.

Infrastructure consumes significant financial resources from public budgets, which are often restricted. It is thus increasingly important to optimize and justify long-term investments needs and operational expenses and set prioritization criteria for deteriorating infrastructures competing for funding. In this regard, public infrastructure organizations have been progressively adopting asset management principles and maturing techniques to demonstrate that their asset-related decisions, plans and activities are transparent and efficient. Those principles and techniques involve the balancing of cost, risk and performance and the demonstration that value is being derived from the infrastructure asset base. Risk-based approaches are popular in this regard as they translate in plain terms the societal impacts deriving from infrastructure failures. While some infrastructure organizations exhibit signs of excellence pushing the boundaries of sophistication and innovation, others use less formal approaches while applying and infusing risk-based thinking within the asset management functions of the organization. This paper deals with the application of a common infrastructure risk assessment process in a Portuguese infrastructure organization managing both the national road and railway transportation networks. It includes the presentation of a maturity model and the harmonization efforts towards a common language for the operational risks of bridges, tunnels, pavements, track, signaling and power stations assets. The challenges of establishing a common risk management framework for two very different asset networks are discussed, namely within a pilot-case study covering the maintenance function. The paper also covers the integration of the proposed infrastructure risk assessment process within the overall corporate risk management strategy and the asset management system of the organization.

Complex asset systems are essential to the functioning of our society. They include but are not limited to: oil platforms, transportation networks, water and gas distribution systems, nuclear power plants, the stock exchange system, communication networks and electrical power utilities. Electrical power generation, transport and distribution utilities such as Hydro-Québec (HQ) have significant asset portfolios. In every step of the chain of production, electrical utilities must know the condition, location and availability of their assets to maximize productivity, reduce service interruptions and ensure the safety of operations and users. When their assets are managed effectively, they can conduct monitoring, obtain vital information and stimulate performance to improve it. This integrated management, commonly known as asset management, allows them to fully exploit the potential of their assets and to convert asset management into a powerful competitive advantage. There are many asset management challenges for electrical utilities: infrastructure maintenance and updating, the obligation to adapt and prepare for all types of changes (standards, types of client, environmental, etc.), new market development, etc. Asset management is considered an efficient approach available to electrical utilities to make better investments in their assets. In this regard, R&D and innovation are crucial. For this reason, HQ, in partnership with UQTR and NSERC, developed and launched a research chair on asset management. The Chair’s research will be primarily focused on developing asset management concepts, optimization algorithms and generic models, as well as adapting and transferring results to HQ and other major Canadian electrical utilities. One of the unique features of this Chair is the development of a new integrated, up-to-date and comprehensive asset management paradigm. This paper introduces the background, the key issues and the detailed research proposal for this Chair. One of the leading research projects for this Chair, regarding the development of a resilience management framework, is presented. A resilience case study is presented as an example at the end of this paper.

Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of water distribution systems (WDS), i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, graph-theoretical metrics have been proposed. In this study, a promising approach is first physically derived analytically and then applied to assess the resilience of the WDS for a district in a major German City. The topology based resilience index computed for every consumer node takes into consideration the resistance of the best supply path as well as alternative supply paths. This resistance of a supply path is derived to be the dimensionless pressure loss in the pipes making up the path. The conducted analysis of a present WDS provides insight into the process of actively influencing the resilience of WDS locally and globally by adding pipes. The study shows that especially pipes added close to the reservoirs and main branching points in the WDS result in a high resilience enhancement of the overall WDS.

Despite standards such as International Standards Association (ISA-95) and Manufacturing Enterprise Solutions Association (MESA) International guiding operational process, asset automation and control for some time, the integration challenge remains for Industrial Internet of Things (IIoT) that the strategies of converging Information Technology (IT) and Operational Technology (OT) domains remain disparate. IT systems prioritize data governance and reliability to ensure the needs of enterprise users are met while OT systems prioritize human safety and asset reliability to ensure that the production process remain safe and effective. These differing priorities lead to greater complexities in the design of convergent systems necessary to progress Industrial Internet of Things, coined more recently as Industry 4.0.

The gaps are between IoT computing layers and alignment to the ISA-95 automation layers plus the evolving constructs of Industry 4.0 and how in the future un/trusted data moves seamlessly between converged OT and IT domains. In doing so, the information governance links between technology, organizational processes, and people (TOP) across the ISA-95 and Manufacturing Operations Management (MOM) System stacks must be considered to provide true secure convergence of the OT and IT domains. This paper explores and builds upon the growing industrial manufacturing OT/IT/IoT convergence literature [1, 31], extending exploration of the role of TOP governance factors to RAMI4.0, suggesting a single OT/IT strategy for organizations to achieve Industry 4.0 initiatives.

The case study research method provides insights into emerging industry and standards convergence practices. Applying these standards and aligning the interdependence between OT and IT strategies with TOP constructs, are explored in the context of achieving Industry 4.0 objectives robustly and resiliently through applying a holistic Data Infrastructure Platform (DIP) in process-centric organizations.

Manufacturing networks contain vast amounts of data over the whole lifecycle of assets. There is much talk of data based services and business models but limited number of success stories from the industry. Valuation of data is identified as a major obstacle for successful service business. Actors operating in the value network may have different perspectives about the value of data and do not necessarily see the opportunities that the data offers to other actors in the network. Often the concept of data value may be limited to a single dimension such as quality or security. This paper aims at providing a more holistic perspective on the value of data in manufacturing asset management. The paper presents dimensions of data value based on a literature review. Additionally, future research needs regarding value of data in managing manufacturing assets are identified.

Enterprise Asset Management (EAM) is used to support strategic planning and budgeting, maintenance, repair and operations of organisational assets throughout the asset lifecycle. The purpose of EAM is to improve the assets’ reliability and performance without compromising health, safety and the environment, where Internet of Things (IoT) devices are useful to sense, monitor, respond to, or report asset conditions in real-time with minimum human intervention. Typically, IoT is adopted or integrated into an existing business process in an EAM organisation without reengineering the overall business process. As IoT technologies are usually not custom made for specific business process, adequate governance is critical to the success of IoT adoption. In this paper, adoption of IoT in EAM organisations and its challenges will be discussed from the perspective of data management. Literature reviews and academic case studies are also included to help identify the research gaps and data governance challenges related to adopting IoT technologies into EAM organisations. The factors influencing the IoT adoption will be presented from a technology, organisational, and people perspective. Different categories of challenges have been identified, including standardisation, security and privacy, data quality, organisational culture, business continuity and resilience. The paper concludes with some recommendations to improve IoT adoption in EAM organisations, as well as suggestions for future research directions.

The family of Standards ISO 55000 on Asset Management aims to support a kind of management oriented to obtain value from assets. Besides these standards, other frameworks have help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. With this purpose, the present paper links a Maintenance Management Model well known in this area, with the publication of ISO 55000. As a consequence, justifies that a proper implementation of the Maintenance Management Model fulfils the requirements stated by the Standards ISO 55000, improving consequently the decision making, costs reductions, quality of the operations and increasing business profitability and users satisfaction.

The paper describes an evidence-based construct hierarchy for engineering asset management based organisations to cost effectively aggregate operational (OT), information technologies (IT) and Industrial Internet of Things (IIoT) to enable automated data driven decision making event triggers between assets, robotics, sensors, business systems and people. The research followed an interpretive epistemology conducted as seven case studies across mining, utility and defence based organisations, a Delphi survey with over fifty Technology and Engineering practitioners to confirm baseline TOP constructs and subsequent application of open, federated data architecture in Australian water and defence industries. The research findings and industry application suggests a cost effective, benefits realization, vendor agnostic way to connect and automate often soiled important data types for reuse and first level automated decision making, improving data quality, integrity, asset reliability and organizational trust in data and automation initiatives. Digital transformation and safety goals can be achieved by people becoming second point decision makers, informed by the automated aggregated data across the organization, providing a valuable extension to the reliance on traditional machine learning and business analytics involving data warehouses and business system data to the exclusion of asset performance. The research extends the application of ISA95/MES to holistic organizational wide emerging frameworks such as RAMI4.0 and

Mining industry is highly dependent on the state of its assets, and it is also a significant water user and producer of wastewater. Thus, the management of water-related assets and operations is a critical business issue and has an effect both on the growth and profitability in mining companies. Moreover, digitalization influences asset management processes, and implementation of novel IIoT technologies for water balance management are vital for sustainable mining business. However, investments in water-related IIoT technologies must be carefully planned and evaluated to deliver immediate and long-term benefits, to enhance the asset utilization, and to reduce the environmental risk. This research presents an asset management framework and related tool: Framework for assessing Economic, Environmental and Social Value of Monitoring System (FEES). FEES defines the customer value of the water balance system and supports the economic, environmental and social impact and risk assessment in mining sector, and demonstrates the costs and risks as well as the benefits achieved by a potential customer. The paper contributes to the asset management research field by providing a multidimensional approach to assess water-related investments in mining industry. From a long-term perspective, applying a multidimensional approach to the assessment generates several benefits, including advancing the ability to communicate the value of water-related investments to business ecosystem, local societies, people, investors and other stakeholders as well as to support the goal of sustainable growth. The proposed model provides practitioners with the opportunity to adopt a proactive approach to assess water balance system’s value.

It is well understood that the success in managing assets depends on well-established engineering principles, techniques and processes. Nevertheless, socio-organisational factors also play important roles in asset management systems and asset management activities. Their influence expands well beyond the engineering aspects. This chapter focuses on the discussions on the potential to take into account the social complexity and Systems Intelligence in asset management systems development. It suggests that the momentum to bring engineering and governance together to create the conditions for achieving a successful asset management system is Systems Intelligence, as our ability to behave intelligently in the context of complex systems involving interactions, dynamics and feedbacks is insufficient. This chapter presents eight dimensions of Systems Intelligence within the context of the asset management system, discusses the asset management system as a socio-technical system and as a ‘system of systems’ and applies basic principles from systems theory as a way to improve our Systems Intelligence to be better prepared to deal with social complexity. It shows how Systems Intelligence can be applied to increasing the chances of success in managing assets. On the basis of these discussions, the future research directions are identified.

Autonomous machines are complex systems that are able to perform independent decision-making and to operate without operator’s continuous activities. Increasing autonomy and system complexity create new challenges to safety engineering. In this article, we introduce and discuss about a safety engineering approach that is being developed in VTT for autonomous work-machine applications in close collaboration with machine manufacturers and system suppliers. The main motivation has been to support the early development phases of novel automation solutions and system operating concepts. The system approach focuses on system-level safety issues arising from the shift from manual mobile machines to autonomous machinery systems. It utilizes elements from system safety engineering methods and the latest safety standards for autonomous machinery, as well as the goal-based safety case approach to support the management of safety goals and requirements. The approach and methods are being applied to identify and analyze autonomy related safety risks in several industrial cases.

Contemporary organizations function in a complex business and operational environment composed of closely interdependent systems. They are also complex by their internal structure, management and deployed modern technologies. This complexity is not always well understood, and cannot be efficiently controlled. As the complexity and interdependencies increase, man-made systems become more unstable creating conditions for cascading, system-level failures causing serious threats to both themselves and society in general. Such breakdowns may consist of a) serious physical damages and destruction of their physical assets (caused by natural disasters, extreme weather phenomena and climate change, malicious human actions, etc.), and/or b) large functional disruptions with no physical damages of assets (caused by major organization’s internal disturbances, market crashes, pandemics, disruptions of supply chains, etc.). Those sources of risks are basically external to organizations. They are unable to control them, but are deeply affected by those risks.

The latest case of the COVID-19 pandemic demonstrates the above. It is affecting both all sectors of life and businesses worldwide. It convincingly shows that we need to think, plan and act globally in order to deal with such situations that will also take place in the future. Thus, organizations have to find ways of coping with this reality to remain economically viable. We are of opinion that the concepts of structured Asset Management (AM) and resilience put together may provide an efficient framework in this regard.

Two case studies in a major North American electrical utility demonstrate the applicability of this approach: i) during an exceptional ice storm with significant damages of its physical assets, and ii) coping with challenges of COVID-19 with no destruction of its physical assets.

Uncertainty and its profound impact on the management of infrastructure project portfolios is introduced. The advent of COVID-19 is depicted as a defining event in the management of infrastructure. This study applies the Black Scholes option valuation model and real options analysis to determine value for money in front-end engineering for Australian megaprojects. The findings indicate that despite the fact all current Australian infrastructure projects examined had a positive net present value and a benefit to cost ratio greater than one, almost sixty percent of the planned expenditure was for projects for which real options analysis indicated the engineering as being poor value for money. The paper concludes with recommendations to manage the national portfolio of infrastructure projects as a pipeline of carefully chosen pre-engineered options, some of which are constructible projects and others being non-asset, demand management solutions.

In this Chapter, several efforts that the SIM Research Group, from the University of Seville, accomplished during the Covid-19 pandemic lockdown in March-June 2020, are introduced. These efforts had an important impact on media and were mainly concern with the pandemic recovery phase.

When confinement ends, recovery phase must be accurate planned at a local level. Health System (HS) capacity, specially ICUs and plants capacity and availability, would remain the key stone in this pandemic life cycle phase.

This Chapter describes: First the important of the action plans design by local level, while a unique decision-making center is considered by country; Second, a management tool based on a ICUs and plants capacity model to estimate ICUs and plants saturation, and with these results, set new local and temporal confinement measures. The tool allows a dynamic analysis to estimate maximum Ro saturation scenarios; Third, a practical management tools to tackle the deconfinement strategy design problem. A proper control system to follow the course of action, especially in a scenario with unprecedent uncertainty, is developed.

In all cases, it is remarked the importance of R (the pandemic basic and effective reproductive number), first as a variable to monitor and control the pandemic, to ensure its decline; second as a target to score risks associated to start certain activities over, after confinement. Reducing the potential increase in the value of R, when any type of activity is re-opening, guides the strategy.

One common objective in these initiatives: Applying asset management principles to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk.