End-of-life of physical assets: A methodology for strategic decision making

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UNIVERSITY OF GRONINGEN – NEWCASTLE UNIVERSITY BUSINESS SCHOOL

End-of-life of physical

assets: A methodology for

strategic decision making

MSc Thesis

Rianne Land – s2060507 (RUG), s130608116 (NUBS) 18/12/2014

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PREFACE

I gratefully acknowledge the assistance provided to this research by Patrick Kos and Jaap van Dalen, of Tata Steel Ijmuiden, in collecting the needed data, linking me to the right persons inside Tata Steel and providing me with feedback. Furthermore, I would like to thank every other person from Tata Steel that directly or indirectly helped me in conducting this research.

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1. INTRODUCTION

Many companies do not know what to do when an asset approaches the end of its life. For instance, Jones & Sharp (2007) argue that most asset managers do not know what is the best decision to make in this case, and that they often do not take the impact on business performance of the organization into account while making a decision. Furthermore, they are not sure whether the theoretical end-of-life is indeed the practical life and what they can do to maximise the life of an asset when it approaches its end-of-life (Li, 2004). This is mostly the case with expensive and unique assets that are very important for the core process of companies. Here, the theoretical end-of-life can be defined as the pre-defined life of the asset, which is often incorporated in the financial administration, while the practical end-of-life can be defined as the time when the asset is found unavailable to perform the required task due to aging failure (Li, 2004). This article focusses on end-of-life-cycle management of physical assets. The aim is to propose a methodology which aids in making decisions in end-of-life-cycle management.

There is much literature available about life cycle management of assets, but there are few articles that really contribute to the end-of-life-cycle knowledge. Most literature focusses on life cycle costing (Gram & Schroeder, 2012; Schuman & Brent, 2005; Taylor & Ccyzty, 1981). However, Schuman & Brent (2005) argue that current asset management models do not address life cycle costs comprehensively, as well as other aspects of sustainable development. There is also much literature available on asset replacement and refurbishment (Mauer & Ott, 1995; Richardson, Kefford, & Hodkiewicz, 2013; Scarf & Martin, 2001; Yatsenko & Hritonenko, 2009). However, there is no literature available that argues which option to choose when an asset approaches the end of its life. For example, if the asset or components of the asset need to be replaced or refurbished.

Since the literature that can help managers with making strategic decisions by choosing the best option is rather scarce, companies are struggling with end-of-life-cycle decisions. This paper will propose a methodology that can help managers with asset end-of-life decision making. This methodology consists of a mathematical model that can model the outcomes of different options.

This research will answer the following research question: “What is a good methodology for strategic decision making regarding assets approaching their end-of- life?”

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2. BACKGROUND THEORY

In this section, the already existing literature will be outlined. First, the asset life cycle will be discussed, after that, literature on the end-of-life determination of assets will be described. Finally, the options to choose from when an asset faces the end of its life, will be discussed.

2.1 Asset life cycle

According to Schuman & Brent (2005), the traditional asset life cycle consists of two main phases, the acquisition phase and the utilisation phase, each containing sub-phases. These phases are shown in figure 1. This proposed research will focus on the utilisation phase, in particular the end of the ‘utilisation and support’ phase and the ‘retirement and disposal’ phase.

Figure 1. Asset life cycle phases (adapted from Schuman and Brent (2005:567))

2.2 End-of-life determination

In order to find out where the ‘retirement and disposal’ phase starts or where the utilization phase can be extended, it is important to determine the end-of-life of assets. Besides the theoretical end-of-life (pre-defined life of the asset) and practical end-of-life (time when the asset is found unavailable to perform the required task due to aging), also another distinction is made between technical and economical end-of-life. The technical end-of-life is defined as the remaining useful life (RUL) left on an asset, which is the length of the current time until the asset’s expected of-life (Si, Wang, Hu, & Zhou, 2011). Economical end-of-life can be defined as the asset lifetime that minimises annual costs, taking into account maintenance cost, new asset cost and salvage value (Yatsenko & Hritonenko, 2011).

Many models exist to measure those four kinds of end-of-life. Those models will be described and analysed later on in this paper.

2.3 Options when an asset faces the end of its life

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6 So, there are actually two choices that need to be made, the first is whether to focus on the whole asset or only on components of the asset and the second is whether to replace (components of ) the asset or to refurbish them.

When the asset is replaced, a new asset needs to be acquired. For acquiring assets, Woodward (1997) introduces life cycle costing. Life cycle costing focusses on maximising value for money in the ownership of physical assets. It takes into account all the costs relating to the asset during its operational life, when requiring a new asset. Though, this model has a major limitation. For computing the life cycle costs (LCCs), it is assumed that the costs associated with operating and maintaining assets are deterministic. However, in reality there are significant uncertainties associated with LCCs, including changes in technology, varied utilisation and operating conditions, economic factors and changes in maintenance practices (Richardson et al., 2013).

Non-deterministic replacement problems of assets have been considered in a number of papers. For example, Mauer & Ott (1995) take tax uncertainty and technological uncertainty into account. Dobbs (2004) estimates the expected machine life under an optimal replacement strategy, where the replacement time is a random variable. He also considers the sensitivity of the optimal replacement point to changes in the basic model parameters. Adkins (2005) shows that the real options approach is superior to the standard Net Present Value approach. Richardson et al. (2013) take into account a long lead-time for new assets in the replacement decision.

Scarf & Martin (2001) argue that the replacement of existing assets will naturally focus on components of the assets, because it is often too complex to consider the whole asset. Refurbishment of the whole asset or components of the asset is also possible to extend the life of the asset (Jones & Sharp, 2007; Scarf & Martin, 2001). Jones & Sharp (2007) argue that the impact on business performance of the organisation needs to be taken into account when selecting an option.

For relatively cheap and easy to replace assets with mostly a relatively short life, Hartman & Lohmann (1997) argue that asset leasing may also be an option. They developed an integer programming model solving the demand constrained finite horizon parallel replacement problem, which minimises the purchase, operating, maintenance and salvage costs of a fleet of assets. This model considers asset replaces, leases and refurbishments as replacement options. However, they focus on assets in the transportation industry, and those assets cannot be compared with relatively expensive physical assets with a long life. Therefore, their method cannot be used for those assets.

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3. RESEARCH METHODOLOGY

In this section, the research methodology will be outlined. The research question that is answered in this research is:

“What is a good methodology for strategic decision making regarding assets approaching their end-of-life?”

This research will propose a methodology that should be followed when a physical asset approaches the end of its life. This methodology will show the steps and decisions that need to be taken and consists of a mathematical model that will model the outcomes of the different options. After having developed the important factors and constraints and the relevant failure mechanisms, the end-of-life of the asset is determined and a mathematical model is to be developed which considers the costs of breakdowns and the risk of breakdowns, based on the failure mechanisms. On the basis of that, the best option can be chosen while taking into account the important factors.

In order to develop the methodology, the following sub-questions need to be answered:

1. a) What are important elements that affect the decision on which option to choose when an asset approaches the end of its life?

This question needs to be answered to find out which variables affect the decision of replacing or refurbishing the asset and the decision of replacing the whole asset or only components of the asset. First, a literature review is done on this subject and after that, interviews were held with a maintenance manager (who is responsible for the maintenance and replacement of many different assets) and a purchase manager (who is responsible for purchasing physical assets for all factories) of Tata Steel Ijmuiden, to validate and extend the literature. The results from this literature research and interviews will be analysed and on the basis of that, some important factors or constraints are being formulated that need to be considered when choosing an option.

1. b) Which components to focus on?

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8 2. How can the actual end-of-life of assets be determined?

This question needs to be answered to determine the end of the life of the asset, in order to map the time that is still available before the asset needs to be replaced. A literature study of models for determining the end-of-life of assets and their advantages and disadvantages is conducted and the results of the literature study are validated and extended by two interviews with managers of Tata Steel Ijmuiden. On the basis of that, the potential practical applicability of the methods described in literature is determined, in order to get an overview of how well these methods can be performed in practice. There is no research available that provides this overview yet.

3. How to design the mathematical model?

The components that turn out to be replaced or refurbished (question 1), will be further analysed. In order to make a well argued decision, the failure mechanisms of the whole asset and the failure mechanisms of the most important components need to be developed. This is done by analysing maintenance and failure data of the specific asset and the most important components, respectively. Then, a mathematical model is made that models the outcomes of each decision by plotting the risks (expressed in money) as a function of time of each option, and by plotting the ratio between the reduction of risk and the costs of implementing the option as a function of time. This is based on the failure mechanisms of the (components of the) asset.

4. How well does the methodology perform in practice?

In order to test how well the methodology performs in practice, it is applied at two identical chimneys at the OSF2-factory at Tata Steel Ijmuiden, The Netherlands.

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4. PROPOSED METHODOLOGY FOR STRATEGIC DECISION MAKING

The proposed methodology for strategic decision making when an asset faces the end of its life is graphically shown in figure 2.

Figure 2. Proposed methodology graphically shown

The first step is to prioritise the important factors to consider, these factors affect the decision on which option to choose. It is important to prioritise these factors, because it not possible to maximise (or minimise) them all. Therefore, a trade-off between the different factors need to be made (Saaty, 1994). Hence, it is needed to prioritise the different factors, in order to make sure that the factors that are most important for the company, are also considered as most important in making a decision. This might be an obvious step, however it is forgotten a lot of times. It is very important to actively think of those factors and prioritise them, in order to make rational choices. Saaty (1994) even argues that, when possible, standards of excellence and poorness should be developed for each factor. Without such standards it is only possible to compare the alternatives and not possible to rate different alternatives.

The second step is to determine the components to focus on. When choosing the option to replace or refurbish components, it is important to know which components to focus on. Therefore, the important components need to be determined.

The third step is to determine the end-of-life. It is important to determine the end-of-life of the asset when considering to replace the asset, first to map the time that is still available for replacing the asset, and second to find out if the asset really needs to be replaced at short notice. Furthermore, the outcome of this step can be used in the next step. Since there are a lot of methods described in literature for determining the technical as well as the economical end-of-life, the potential practical applicability of those methods is compared to show which methods are actually easy to apply in practice.

The fourth step will show the outcomes, the reduction of risk expressed in money as a function of time, of the different options graphically. Furthermore, this step will give an overview on which option reduces the risk relatively the most at the long- and short term.

The last step is to choose the best option. The outcomes of the fourth step in combination with taking into consideration the prioritisation of the factors, will help to choose the best option.

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4.1 Prioritise factors influencing the decision

The first step in the methodology focusses on the prioritisation of important factors that affect the decision on which option to choose. As already described in the background theory, there are several options to choose, for example the options to replace or to refurbish or to focus on the whole asset or only on components of the asset. The important factors that influence the decision which option to choose are summarised in figure 3.

Figure 3. Important factors that influence the decision which option to choose

The factors shown in figure 3 will be described in detail in the remainder of this section, starting with the factor ‘purchase costs’ and then to all other factors in the order of figure 3, going clockwise along. As is described in the research methodology section, a literature review as well as interviews with managers are conducted in order to determine the important factors. Therefore, a theoretical view as well as a practical view will be described for all factors.

4.1.1 Purchase costs

Woodward (1997) argues that there is considerable evidence to suggest that many organisations still make acquisitions of assets simply on the basis of the purchase cost. Purchase costs can be defined as the costs of acquiring an asset including the assessment of it and can also be called acquisition costs (Woodward, 1997). It is important to take the purchase costs into account, because these costs require a large investment (Ellram, 1995). However, the asset with the lowest purchase price does not necessarily has the minimal total costs over the whole life of the asset and therefore may not maximise profits. So, it is better to take the life cycle costing into account (Ellram, 1995; Woodward, 1997).

At Tata Steel Ijmuiden, it turns out that the purchase costs need to be taken into account when choosing an option, because there is a fixed budget for acquiring new assets and therefore the options are constraint by this budget for the purchase costs.

4.1.2 Life cycle costing

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11 often used for life cycle costing is ‘total costs of ownership’ (Ellram, 1995). According to Ellram (1995), it is very important to consider the total costs of ownership when acquiring an asset, since it has become increasingly important for organisations to look for ways to better understand an manage their costs. Furthermore, Woodward (1997) also argues that all those costs of the asset need to be taken into account when acquiring a new asset.

It turned out that the purchase manager at Tata Steel Ijmuiden also takes total costs of ownership into account when comparing different options. He also thinks that the total costs of ownership is more important to consider than only the purchase costs of the asset.

4.1.3 Quality and reliability

Besides costs, quality is also very important when considering to replace or refurbish an asset (Pawlina & Kort, 2003). Pawlina and Kort (2003) argue that quality is actually the opposite factor of costs and that, even for relatively cheap assets, often a trade-off between quality and costs need to be made. Lin, Gao, Korinios and Chanana (2007) argue that, especially for assets that are not allowed to fail, quality is a very important factor to consider. Furthermore, reliability is also very important when assets are not allowed to fail (Kariuki & Allan, 1995). According to Kariuki and Allan (1995) reliability can be a determining factor that affects the decision which option to choose.

From practice, it turns out that quality and reliability is very important when acquiring a new asset. Especially at Tata Steel Ijmuiden, downtime of the factory is very costly, so the assets need to be very reliable.

4.1.4 Environment

The environment is a very important factor to consider. Every organisation needs to adapt to her environment in order to remain a viable social system. Organisations need to cope with uncertainty in the organisation’s environment in order to survive (Thompson, 1967). Duncan (1973) argues that amount of influence on the environment is also important. The amount of influence on the environment can be defined as the ability to (i) affect the demands made on it; (ii) affect the expectations of performance made on it; (iii) deal with alternatives to, and (iv) have some control over the factors and components it takes into consideration in the decision making process (Duncan, 1973). The more organisations are able to influence the environment, the less uncertain this factor will be. The environment includes legal rules and laws that must be followed, but also the operating, business and market environment with, for example, customers and competitors (Thompson, 1967). So, it is important to choose an option which fits the environment of the organisation and to manage the uncertainty of the environment.

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12 At Tata Steel Ijmuiden, the environment is also taken into account when an asset needs to be replaced. Mostly because they have to, because of legal rules, and because it will save money. For example, reducing the energy consumption or re-using energy can save Tata Steel a lot of money.

4.1.5 Degree of uncertainty

Since the degree of uncertainty affects actually every decision (Duncan, 1973), it also affects the decision on which option to choose. In decision making, it is often the perceived uncertainty that affects the decision. Perceived uncertainty consists of three components: (i) the lack of information regarding the factors associated with a given decision making situation; (ii) not knowing the outcome of a specific decision in terms of how much the organisation would lose if the decision is incorrect; and (iii) the inability to assign probabilities with any degree of confidence with regard to how specific factors are going to affect the success or failure of the decision unit in performing its function (Duncan, 1973). There can be many uncertain factors, for example the environment (Duncan, 1973), or the market (Pawlina & Kort, 2003), and even all the factors described in this section will have a specific degree of uncertainty. The higher the perceived uncertainty, the more important this factor will be and the more it will influence the decision-making process (Pawlina & Kort, 2003). Managers that make this decision, should be aware of the degree of uncertainty in order to make good decisions (Duncan, 1973; Pawlina & Kort, 2003). At Tata Steel Ijmuiden, uncertainty is also taken into account in decision making, for example by setting safety margins for uncertain factors.

4.1.6 Lead time

Lead time is also an important factor to consider in choosing the option to replace/refurbish (components of) an asset. Lead time can be defined as the time between ordering the asset and the time when the asset can actually be used in the factory. For physical assets, lead times are typically large and uncertain and therefore need to be considered (Richardson et al., 2013). Shahidehpour and Ferrero (2005) argue that lead time could be a big risk factor for asset management, so this factor should be taken into account when deciding which option to choose. They also claim that a proper generation asset planning method should incorporate lead time. Lead time can affect the decision when the RUL of the asset is shorter than the lead time of a specific options. Then, the RUL of the asset can be extended by for example ordering a component with a shorter lead time, so that the RUL can be extended for implementing the option with a long lead time. At Tata Steel Ijmuiden, lead time is also considered in this way.

4.1.7 Downtime of the factory

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4.2 Determine important components

Scarf & Martin (2001) argue that the most important components of an asset are the most critical components, because those components are essential for the whole asset to perform its function. When such a component fails, the whole asset is not capable of performing its tasks. In that case, the best option is to replace the failed critical component (Scarf & Martin, 2001). Furthermore, Her, Kim, Oh, Rhew, and Kim (2007) argue that a distinction needs to be made between functional and non-functional components, in which a non-functional component does not really add value to the process the asset performs, for example a fan that is needed to cool the asset. Her et al. (2007) argue that the most attention needs to be paid to the functional components.

Moreover, assets may consist of few components which are relatively cheap and are often standard components that fit on several assets. Such components could be essential for maintaining the function of the asset. Since these components are quite cheap, it is also quite cheap to keep those components in stock and replace those components when the asset fails (Scarf & Martin, 2001). On the other side, assets may consist of one expensive core component which actually performs the main function of the asset. When such a component fails and the cheap rest of the asset is still in good order, it may be better to only replace or refurbish that one expensive component (Her et al., 2007).

Her et al. (2007) also address that customisability could be a reason to only replace a component. When a slightly different requirement of the asset is required to be applicable to new applications, replacing some components may make the core asset applicable to those applications. According to the purchase manager of Tata Steel Ijmuiden, this is also the main reason for them to only replace components instead of the whole asset. He argues that at Tata Steel Ijmuiden, they often replace the electrical components of assets, instead of mechanical components, because there is much more innovation in the electrical components. Furthermore, the electrical components fail more than the mechanical components and the employees at Tata Steel Ijmuiden are more able to repair the mechanical components than the electrical components. Finally, the purchase manager perceived the trend that now more components are being replaced or refurbished, while in the past, usually the whole asset was being replaced or refurbished.

4.3 Determine the end-of-life

As already described in the theory section, there are four kinds of end-of-life, namely the technical, economical, theoretical and practical end-of-life. As described, the theoretical end-of-life is the pre-defined life of the asset, which is mostly used for the financial administration. Since this kind of end-of-life is pre-defined and hence already determined at for example the purchase time of the asset, this kind of end-of-life will be left out in this section.

The practical end-of-life is defined as the time when the asset is found unavailable to perform the required task due to aging failure (Li, 2004), while technical end-of-life is defined as the RUL left on an asset, which is the length of the current time until the asset’s expected end-of-life (Si et al., 2011). When taking a closer look at this definitions, the practical end-of-life is actually the same as the technical end-of-life. The only difference is that the practical of-life reflects the total life of the asset and the technical end-of-life reflects the life left of the asset.

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14 kind of end of the life of the asset is defined as the time when the asset is found unavailable to perform the required task due to aging failure (Li, 2004).

To recall, the economical end-of-life can be defined as the asset lifetime that minimizes annual costs, taking into account maintenance cost, new asset cost and salvage value (Yatsenko & Hritonenko, 2011). Hence, the economical end-of-life can be much shorter than the technical end-of-life.

This section gives an overview of the models for determining the technical or the economical end-of-life, by listing the strengths and weaknesses of these models. Furthermore, the potential practical applicability of each method is determined. On the basis of this, managers can decide which model is the best model to use. First the technical end-of-life models are listed and after that, there is an overview of the economical end-of-life models. Finally, a practical view on end-of-life determination will be described.

4.3.1 Technical end-of-life models

Si et al. (2011) argue that there are two kinds of data that can be used in technical end-of-life determinations. The first is event data, which can be defined as past recorded failure data. The second kind of data is condition monitored (CM) data, which can be subdivided in direct and indirect CM data. Direct CM data describes the underlying state of the system directly (e.g. wear and crack sizes). Indirect CM data can only indirectly or partially indicate the underlying state of the system (e.g. oil based monitoring). Therefore, when using indirect CM data, event data may be needed to get the picture of the state of the asset. “Only if the CM paths can be modelled properly, one can estimate the RUL directly without the need of failure data” (Si et al., 2011:3).

Based on event data, the most simple way to determine the technical end-of-life is to calculate the average age of assets that are already replaced (Li, 2004). However, most physical assets with a long life have generally very limited end-of-life failure data. Therefore, Li (2004) developed two models to estimate the mean (technical) life and standard deviation with limited end-of-life or aging failure data, one method for the normal distribution and one method for the Weibull distribution.

It is noted that end-of-life or failure data is rare in reality or non-existent at all (Si et al., 2011). Alternatively, models based on directly observed state processes have been developed to estimate the end-of-life of assets with no or limited end-end-of-life data.

The models based on direct CM data can be subdivided into four types. The first type are the regression-based models, while the second type of the models regression-based on direct CM data are Wiener processes. Wiener processes are actually types of regression models, however, they have specific properties, and therefore they are taken separately for end-of-life determining models. The third type of the models based on direct CM data are Gamma processes and the last type of the models based on direct CM data are the Markovian-based models.

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15 All these methods mentioned above are described in appendix 1. The strengths and weaknesses of these methods are summarised in table 1. An explanation of these strengths and weaknesses can be found in appendix 2.

As shown, there are a lot of methods described in literature for determining the technical end-of-life. To add to the existing literature, the potential practical applicability of the methods described above is analysed, in order to give an overview how easily the methods can be applied in practice.

The potential practical applicability is determined by giving each method a score for input data, easiness of computing and the accuracy of the outcome. The scores are on a scale from 1 to 10. The potential practical applicability is the average of these three scores. The input data that is needed for each method and the scores are shown in table 2. On the basis of the information on this table, managers can choose which method to uses based on the required input data, the overall potential practical applicability or the other scores. The argumentation for these scores can be found in appendix 3.

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17 Table 2. Input data and scores for potential practical applicability for technical end-of-life models

4.3.2 Economical end-of-life models

In order to deal with the economical end-of-life, some assumptions about the future need to be made, in particular about the price and salvage value of the future replacements (Amey, 1984). Yatsenko and Hritorenko (2009) developed a model to determine the economical end-of-life.

However, according to Adkins (2005), uncertainty also needs to be taken into account in a replacement decision, because of the fact that new information, which may influence the decision, can become available during time. He claims, as his colleagues Brealey and Myers (1984), that any approach which fails to represent the value or impact of new information on present decisions necessarily fails to represent the role of uncertainty in the replacement decisions. Furthermore, Ridcharson et al. (2013) argue that it fails to have the option to adapt the timing of the replacement and Terborgh (1949) claims that the future course of technological progress needs to be assumed too, because technological progress will lead to the appearance of the improved machines, making existing machines obsolete.

Terborgh (1949) was the first to take technological progress (leading to obsolescence) fully and explicitly into account in analysing the replacement problem. He developed a method for calculating the economical end-of-life while taking into account technological progress. Smith (1961) extended Terborgh’s formula.

Author Method Input data Input Easiness Accuracy Potential

data of of practical

score computing outcome applicability

Li (2004) calculating mean + st dev: ages of in-service components 9,5 7 5 7,166666667

normal distribution ages of retired components

calculating mean + st dev: ages of in-service components 9,5 6,5 5 7

Weibull distribution ages of retired components

Several regression based: condition monitoring data of failure 7 8 6 7

(described in machine learning mechanisms

Si et al. (2011)) regression based: condition monitoring data of failure 7 8 6 7

random coefficient mechanisms

regression method

Cox & Miller Wiener process condition monitoring data of failure 6 5,5 6 5,833333333

(1961) mechanisms + threshold level

Several Gamma process condition monitoring data of failure 6 5,5 6 5,833333333

(described in mechanisms + threshold level

Si et al. (2011))

Several Markovian-based models condition monitoring data of failure 7 5,5 6 6,166666667

(described in mechanisms

Si et al. (2011))

Wang et al. stochastic filtering observed condition related variables + pre- 5 4,5 4 4,5

(1997) determined threshold + failure event data

Banjevic covariate based hazard observed state of covariates that cause 5,5 5,5 5 5,333333333

(2009); model deterioration

Zuashkiani et al. (2009)

Several hidden Markov model observed condition related variables + 4,5 4 4,5 4,333333333

(described in probability measure to represent the

Si et al. (2011)) relationship between the real state of

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18 As already described, Richardson et al. (2013) argue that standard economical end-of-life determination models neglect the value of the option to adapt the timing of the replacement decision. One of the main limitations of most of the replacement policies is that it is assumed that new assets arrive exactly when a replacement is required, which means that no lead-time is considered. This assumption is unrealistic in the context of industries where lead-times are large and uncertain (Richardson et al., 2013). Richardson et al. (2013) developed an order replacement frontier, which graphically shows when an order for a new asset needs to be placed.

The methods mentioned above are described extensively in appendix 1. The strengths and weaknesses of these methods are summarised in table 3. An explanation of these strengths and weaknesses can be found in appendix 2.

As shown, there also are a lot of methods described in literature for determining the economical end-of-life. To add to the existing literature, the potential practical applicability of the methods described above is analysed, in order to give an overview how easily the methods can be applied in practice.

The potential practical applicability is determined in the same way as for the technical end-of-life methods. The input data that is needed for each method and the scores are shown in table 4. On the basis of the information on this table, managers can choose which method to uses based on the required input data, the overall potential practical applicability or the other scores. The argumentation for these scores can be found in appendix 3.

Shortly, the economical end-of-life is the asset lifetime that minimises annual costs, taking into account operating and maintenance cost, new asset cost and salvage value. Yatsenko and Hritorenko (2009) developed a model to determine the economical end-of-life. Terborgh (1949) and Smith (1961), besides the described costs, also take technological progress into account when determining the economical end-of-life while Ridchardson et al. (2013) incorporate the effect of lead-time in their economical end-end-of-life determination model.

4.3.3 Practical view on end-of-life determination

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4.4 Model the outcomes of different options

In order to model the outcomes of the different options, a model is developed that could be used. This model is described in this section. This model models the risks as a function of time (in terms of money) for the different options. This model is developed because it is relatively simple to use. Furthermore, it turns out that at Tata Steel Ijmuiden, the technical end-of-life is mostly used, therefore this model is based on this kind of end-of-life.

The input parameters of the model are:

- Probability distribution function (PDF) of the relevant failure mechanisms

o The relevant failure mechanisms are the failure mechanisms of the important components determined at step 3 of the methodology and the failure mechanism of the whole asset o The failure mechanism could be for example normally distributed

- The effect of the failure mechanisms

o All costs involved with the occurrence of that failure mechanism (e.g. the cost of replacing the failed asset/component and the costs of downtime etc.)

- Costs of implementing the different options - The effect of implementing the different options

o The new PDFs of the failure mechanisms

It is assumed that every asset has one or more failure mechanisms as a function of time, as described in formula (1). This formula actually calculates the cumulative chance of the failure mechanism of an asset (area under the PDF curve). As shown in this formula, the chance of failure becomes larger when time elapses and will finally become 1.

𝑄_{𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚}_𝑗 (𝑡) = ∫ 𝑃𝐷𝐹1 _{𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚}_𝑗(𝑡)𝑑𝑡

0

(1) 𝑗 ∈ 1, 2, … , 𝑛

The risk of each failure mechanism as a function of time (in terms of money) can be described as in formula (2).

𝑅𝑖𝑠𝑘 𝑅_{𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚}_𝑗(𝑡) = 𝑄𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚𝑗 (𝑡) ∗ 𝑒𝑓𝑓𝑒𝑐𝑡𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚𝑗 (2)

𝑗 ∈ 1, 2, … , 𝑛

When there is more than one failure mechanism for the asset, the risks of each failure mechanism can be added together, as in formula (3).

𝑅(𝑡) = ∑ 𝑅𝑓𝑎𝑖𝑙𝑢𝑟𝑒 𝑚𝑒𝑐ℎ𝑎𝑛𝑖𝑠𝑚𝑗(𝑡)

𝑛 𝑗=1

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21 With this model the risks (as a function of time) of the different options can be graphically modelled and can be compared by the risk of doing nothing. When these risks are plotted graphically, they are quite easy to compare. Furthermore, the ratio between the reduction of the risk and the costs of implementing the option, as a function of time, see formula (4), can be used to find out which option relatively decreases the risk the most at the long- and short term. In formula (4), Ri reflects the (new) risk after implementing

option i and Ci reflects the costs of implementing that option.

𝐷_𝑖(𝑡) = 𝑅 𝑑𝑜𝑛𝑜𝑡ℎ𝑖𝑛𝑔 _𝐶(𝑡) − 𝑅 𝑖(𝑡)

𝑖 (4)

𝑖 ∈ 1, 2, … , 𝑛

4.5 Choose the best option

The last step of the proposed methodology is to choose the best option, based on the outcomes of the previous steps. As described before, the first step makes managers actively think of which factors that affect the decision are the most important and which factors are of less importance. Knowing this is important for making the right decision.

At the second step, the most important components are determined, so it can be analysed whether these components are critical or functional and hence how they affect the function of the asset.

At the third step, the actual life is determined, which may influence the decision when the end-of-life is approached quickly or is not yet approached in the near future. As described before, this step gives insights in the time available to choose an option. Furthermore, a distinction can be made between technical and economical end-of-life.

The fourth step models the outcomes of different options and gives an overview of which option relatively decreases the risk (expressed in terms of money) the most at the long- and short term.

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22

5. APPLICATION OF THE METHODOLOGY

In order to test how well the proposed methodology performs in practise, it is applied at two identical chimneys of the OSF2-factory (an oxygen steel making plant) of Tata Steel Ijmuiden. A drawing of the chimneys can be found in appendix 4. The chimneys are built in 1991. This research only focusses on the higher part of the chimneys (from the blue stripe in appendix 4 up), because this is the most complicated part of the chimney. The factory has a continuous production, and when one of the chimneys fail, the process of the factory is disturbed. All steps of the methodology, except the last one, were gone through and are described in this section.

5.1 Prioritise factors influencing the decision

Together with the maintenance manager of the OSF2-factory, the factors described in section 4.1 are discussed and prioritised. The prioritization of the factors can be found in table 5. The argumentation is described in appendix 5.

Prioritisation

(1=most important, 7=least important)

Factor

1 Quality and reliability

2 Life cycle costing

3 Downtime of factory

4 Environment

5 Purchase costs

6 Lead time

7 Degree of uncertainty

Table 5. Prioritisation of factors influencing the decision

It turned out that it is indeed very important to actively think about this prioritisation, because it is hard to really focus on what is the most important for the organisation. This step forces decision makers to choose between factors that may have been valued equally before actively thinking about it.

5.2 Determine the important components

At this step, the important components are determined with the maintenance manager, the technical manager and a technical support employee of the OSF2-factory. It turned out that several components are important for the chimney. Her et al. (2007) argue that a distinction needs to be made between functional and non-functional components, in which a non-functional component does not really add value to the process the asset performs. The important components, including their functionality and the reason why they are important, are shown in table 6. All these components are critical, so these components are essential for the whole asset to perform its function (Scarf & Martin, 2001).

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23 Although the measuring venturi fails often, this component is less important than the other components, because it can be easily repaired and failure of it often does not cause any interruption or delay of the process.

Table 6. The important components of the chimney including their functionality and the reason why they are important

Some pictures of failure of the stairs and platforms and the mantelshelf can be found in appendix 6.

5.3 Determine the end-of-life

For the end-of-life determination of the chimney, one method for determining the technical end-of-life is used and one method for determining the economical end-of-life is used. In section 4.3, the potential practical applicability of the end-of-life models described in literature is determined. On the basis of this, the method that is actually applied is chosen.

The method with the highest potential practical applicability is chosen, but all the individual scores need to be sufficient (higher than 5,5). Therefore, a regression based method is chosen for determining the technical end-of-life and the method of Smith (1961) is chosen for determining the economical end-of-life. The application of these methods is described in this section.

5.3.1 Technical end-of-life

For determining the technical end-of-life, a regression based method is used. It is assumed that the degradation of the mantelshelf and the stairs and platforms are representative for the status of the chimney. However, there is a lot of preventive and corrective maintenance performed at the mantelshelf and the stairs and platforms, through which the degradation is restored or reduced. The technical manager of the OSF2-factory claims that the deterioration of the mantelshelf and the stairs and platforms can be restored for ever, so that the chimney will never reach the aging failure and hence the technical end-of-life. This is also what is actually happening now. Only, the costs of the maintenance to restore the deterioration become higher when time increases. The maintenance costs per year for one chimney are shown in figure 4. What is especially shown in figure 4, is that the maintenance costs from 2013 are relatively high. This is because of the fact that corrective maintenance was needed 6 times in 2013 and 5 times in 2014, while in the other years, corrective maintenance was only needed once or not at all.

Component Functionality Why important

pilot flame functional need a lot of maintenance in order to avoid failure stairs and platforms non-functional degrades a lot, needed to access pilot flames mantelshelf functional holes are arising due to corrosion

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24 Figure 4. Maintenance costs per year for one chimney

Since the condition of the chimney stays approximately the same, because of the corrective maintenance, it will probably never reach a specific threshold value. Therefore, a regression analysis is not performed on the CM data, but on the cumulative costs of maintenance. Then a threshold value can be set for these costs. In this case, the asset will reach its technical end-of-life when the cumulative maintenance costs have reached the threshold value. When at this point, maintenance is not performed anymore, the asset will eventually fail.

Regression applies formula (5), which means that the unknown variable (Y) is a function of the known variable (x) and an unknown parameter (U).

𝑌 = 𝑓(𝑥, 𝑈) (5)

In order to determine how the unknown variable (in this case the cumulative maintenance costs) correlates with the known variable (in this case the time), maintenance data from 2004 until 2014 is analysed. To control for the unknown parameter, data of two (identical) chimneys is used and the average maintenance costs per month of those two chimneys are used to model the relationship. The relationship is shown in figure 5. In MS Excel, a trend line is fitted to the real data and it appears that this trend line follows formula (6). As can be seen as in figure 5, the trend line does not exactly fit the real data. Therefore, this relationship should be updated regularly since new information comes available as time passes.

𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡𝑠 𝑌 = 37026𝑒0,0068𝑡₍₆₎ 0,00 5.000,00 10.000,00 15.000,00 20.000,00 25.000,00 30.000,00 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 M a inte na nce co st s (€ ) Year

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25 Figure 5. Relationship between cumulative maintenance costs and time

In order to determine the technical end-of-life, a threshold level for the cumulative maintenance costs should be set and formula (6) should be solved for this value. In this case, 25% of the acquisition costs of one chimney is used as the threshold level, which is €140.250. When solving the equation for Y = 140.250, it shows that the time is 195,85 months, which corresponds with 16 years and 3 months. This implies that the chimneys reach their technical end-of-life at March 2020, since t0 = January 2004.

It must be noted, however, that a threshold level of 25% the acquisition costs of an asset is relatively high for such expensive assets. Therefore, this result should be interpreted with caution. Formula (6) can be solved for any other threshold level.

5.3.2 Economical end-of-life

For determining the economical end-of-life, the method of Smith (1961) is used. This method is described in appendix 1. The method of Smith (1961) uses formula (7). In this formula, L* is the economical life of the asset, W represents the acquisition costs of the asset, S is the salvage value of the asset, α is the annual obsolescence costs (opportunity costs of failing to replace the current asset each year with the best (newly) available asset) and β is the annual increase in operating and maintenance costs of the asset with age.

𝐿∗ _{= √}2(𝑊 − 𝑆) 𝛼 + 𝛽 (7) 0 20000 40000 60000 80000 100000 120000 0 20 40 60 80 100 120 140 Cum ula tiv e m a inte na nce co st s (€ ) Time (months) 0 = January 2004

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26 The acquisition costs of the asset are €561.000 and the salvage value is 0, because the chimney cannot be used or sold anymore when they are replaced. Since the chimney is a relatively simple asset with simple components, there is relatively little technological progress for this asset. Therefore, the annual obsolescence costs are relatively low. These costs are assumed to be €100. The operating costs of the chimney stay the same during its lifetime, but the older the asset gets, the more it fails, so the maintenance costs do increase with age. This is already shown in figure 4. From January 2004 until December 2014 (10 years), the cumulative maintenance costs have increased with €106.715. So the annual increase in operating and maintenance costs is €10.671,50.

When all these values are filled in in formula (7), it appears that the optimal economical life is 10 years and 2 months. Since the chimney is already built in 1991, this means that the chimney was already at the economical end of its life in 2002. However, this result is quite pessimistic, because the cumulative maintenance costs can be described with an exponential function, which means that the increase (derivative) of the cumulative maintenance costs is also exponential, while a fixed cost rate per annum is assumed. Furthermore, only data from 2004 is used, so the average of the latest 10 years is used to determine the annual increase in maintenance costs, while the increase in maintenance costs for the first 13 years of the asset were probably much lower.

5.3.3 Overview

Even though the outcomes of the end-of-life methods used may not be very accurate, what strikes is the large difference between the technical and the economical end-of-life. This means that the economically optimal life (with the lowest life cycle costs) is much shorter than the technical end-of-life. Though, as already mentioned before, Tata Steel replaces her assets at their technical end-of-life, because there is not enough budget available to replace the assets already on their economical end-of-life. This sounds quite illogical, since replacing the asset at its technical end-of-life will cost more than replacing it at their economical end-of-life, especially when considering the fact that the maintenance costs increase exponentially with age. Therefore, it is recommended to replace assets at their economical end-of-life instead of their technical end-of-life.

5.4 Model the outcomes of different options

On the basis of the important components determined in section 5.2, the relevant failure mechanisms can be determined. The pilot flame does need a lot of maintenance, but when that maintenance is performed, the flame has a negligible chance of failure. Therefore, this failure mechanism is not taken into account in the model. Also, the failure mechanism of the measuring venturi is not taken into account, because this component can be easily repaired without interrupting the process of the factory.

The chimney actually consists of three core components, which are the mantelshelf, the stairs and platforms and the top. The failure mechanisms of these components are determined and are graphically shown in appendix 7. On the basis of that, the risk of each failure mechanism is calculated by multiplying the failure mechanism with the effect of the failure mechanism. The effects and risks of each failure mechanism can be found in appendix 8.

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27 When the mantelshelf is replaced, the stairs and platforms also need to be replaced. For refurbishing a mantelshelf or top of a chimney, the component needs to be removed from the factory, which means that the process of the factory is disturbed. Therefore, it is not possible to refurbish the two chimneys. Only for option 5, the two chimneys are treated differently. For the other options applies that the option is performed at both the chimneys.

Table 7. Description of five possible options

Table 8. Costs of implementing the options

For these five options, the outcomes are modelled. The outcomes are shown in figure 6. In figure 6, the outcomes in terms of risks are shown for the two chimneys together. When applying option 5, first one chimney is replaced, while the other chimney is not. It takes some time to refurbish the old chimney, therefore the second chimney is replaced after one year. This declares the large reduction of risk between the 1st and the 2nd year for the fifth option.

It appears that at the short term, all options reduce the risk a lot. However, at the long term, there is a large difference in the reduction of risk of the different options. In order to compare which option relatively decreases the risk the most, the ratio between the reduction of the risk and the costs of implementing the option is determined, see formula (4). This ratio is shown in figure 7. It appears that, except for the first year, option 5 relatively decreases the risk the most.

Option Description 1 Replace stairs

Refurbish platforms

2 Replace stairs and platforms 3 Replace mantelshelf

4 Replace mantelshelf and top

5 Replace mantelshelf and top of one chimney

Refurbish the old mantelshelf and top of that chimney

Replace the other chimney with the refurbished mantelshelf and top

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28 Figure 6. Outcomes of different options for two chimneys

Figure 7. Ratio between reduction of risk and costs of implementing different options

0 2000000 4000000 6000000 8000000 10000000 12000000 14000000 16000000 18000000 20000000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Ris k ( €) Time (years) 1 = 2015

Outcomes of different options for two

chimneys

Donothing Option 1 Option 2 Option 3 Option 4 Option 5 0 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Time (years) 1 = 2015

Ratio between reduction of risk and costs

of implementing different options

Relative decrease in risk option 1

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29

5.5 Choose the best option

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30

6. DISCUSSION

Although the proposed methodology in this research can be very helpful in strategic decision making regarding assets that approach their theoretical end-of-life, this research and this methodology also have some limitations.

First, all the methods for determining the end-of-life of an asset described in this paper rely on a number of assumptions. All these assumptions need to be fulfilled in the real life situation in order to get a reliable end-of-life out of these methods. Furthermore, the methods need a lot of input data, which may not always be available or may not be of good quality. A pitfall of this is that the outcomes of the methods are as precise as their input data, so when the input data is of bad quality, the outcome of the method will be of bad quality too.

Moreover, the fourth step on the methodology is based on the technical end-of-life of an asset, since only this kind of end-of-life is used in the researched practice. However, as also described in this paper, some authors argue that it is better to use the economical end-of-life, in order to minimise costs. Focussing only on the technical end-of-life at that step of the methodology will also make the methodology less generalizable.

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7. CONCLUSION AND RECOMMENDATIONS

This research proposes a methodology that can help with strategic decision making regarding physical assets approaching their end-of-life. To choose the best option, all the steps of the proposed methodology are advised to be followed to make a well-argued strategic decision.

The first step makes managers actively think of which factors that affect the decision are the most important and which factors are of less importance. Knowing this prioritisation is important for making the right decision. It turns out that for Tata Steel Ijmuiden, quality and reliability is the most important factor, while lead time is considered to be less important.

At the second step, the most important components are determined, so it can be analysed whether these components are critical or functional and hence how they affect the function of the asset. The chimney at the OSF2-factory at Tata Steel has four important components, namely the pilot flame, the stairs and platforms, the mantelshelf and the measuring venturi. It appeared that these components were not equally important, because the stairs and platforms and the mantelshelf are the most important components, because of their failures.

At the third step, the actual life is determined, which may influence the decision when the end-of-life is approached quickly or is not yet approached in the near future. As described before, the first step gives insights in the time available to choose an option. Furthermore, a distinction can be made between technical and economical end-of-life. It turns out that the economical end-of-life (which minimises costs) can be much shorter than the technical end-of-life.

The fourth step models the outcomes of different options and gives an overview of which option relatively decreases the risk (expressed in terms of money) the most at the long- and short term. There are five options described for replacing the chimneys at the OSF2-factory at Tata Steel Ijmuiden. Based on this step, it turns out that the option of replacing one chimney and then refurbishing that old chimney and replacing the other chimney with the refurbished chimney relatively decreases the risk the most at the long- and short term (except for the first year).

The last step of the methodology is to choose the best option based on the outcomes of the previous steps. It appears that the methodology can be performed relatively easy in practise and provides clear insights that are useful for making the decision on which option to choose.

Therefore, it is recommended to managers to use this methodology for strategic decision making regarding assets approaching their end-of-life. The outcomes of the different steps of the methodology give important information needed for choosing the best option. On the basis of the outcomes of the different steps, managers can make the decision which option to choose.

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32 Furthermore, some future research is recommended. First, it is recommended to test how well the methodology performs in other cases by doing another (multiple) case study applying the model on other physical assets. This can improve the generalizability of the model, since the case used in this research might not be representative for other organisations or assets.

Moreover, it is recommended to check if the findings from the interviews at Tata Steel Ijmuiden are representative for the larger population by doing that part of the research again at for example another organisation from another industry. This can also improve the generalizability of the methodology.

Also, as described in the discussion, the fourth step of the methodology is based on the technical end-of-life, since that kind of end-of-life is used in the researched practice. However, this may not be suitable for other practical settings. So in order to make the model more generalizable, it is recommended to extend the methodology by developing a method that will model the economical end-of-life. In that way, the methodology will be more complete and hence can be used in more contexts.

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End-of-life of physical assets: A methodology for strategic decision making