RISK ANALYSIS IN STRATEGIC ASSET MANAGEMENT

Nooshin Z. Jabiri, Ali Jaafari and David Gunaratnam

School of Civil Engineering and Faculty of Architecture

The University of SydneyNSW, 2006, Australia

ABSTRACT

In the present dynamic business environment, asset-intensive organizations are under increasing internal and external pressures largely stemming from unexpected price changes, demand fluctuation, and tougher statutory obligations. These factors usually cause business discontinuities which may expose organizations to different types of risk and uncertainty. The response may be greater investment and/or lower prices, faster response to market or product differentiation. Risk and uncertainty management concepts and techniques may be deployed in this process to better shape the organization’s response to major discontinuities.

Risk is defined as the exposure to loss/gain, or the probability of occurrence of loss/gain multiplied by its respective magnitude. Events are said to be certain if the probability of their occurrence is 100% or totally uncertain if the probability of occurrence is 0%. In between these extremes the uncertainty varies quite widely, [12]. Risk exposure arises from the possibility of economic, financial or social loss or gain, physical damage/injury, or delay. Risks arise because of the limited knowledge, experience or information and uncertainty about the future. They may also arise through changes in the relationships between the parties involved in undertaking a task. The latter is particularly relevant to Asset Management (AM) as a link in a value chain process, [5, 16]. The significance of risks is the impact they may have on the achievement of objectives, delivery goals or management effectiveness.

This paper investigates risk factors involved in strategic AM. The authors first explain strategic AM and the challenges faced by decision makers. Next, major risk variables and the need for systematic risk analysis are covered. The authors then classify typical risk analysis techniques and discuss the available tools. An in-development decision-support system, named Integrated Asset Management System (IAMS), is presented next. IAMS integrates risk analysis with decision making processes. This is illustrated through a case study.

Keyword: Risk Management, Risk Analysis Techniques, Asset Management.

STRATEGIC ASSET MANAGEMENT

In manufacturing industries, organizations generally take a primary input, add value to it, and dispose the output. The primary inputs are raw materials; value is added by converting these materials into products, and disposal entails selling them to

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customers. These three principal organizational functions are supplemented by major support functions, including management of assets. At the strategic level, AM, as the core activity in the manufacturing process; it is responsible for insuring a balance between utilizing installed capacity, market dynamics and customer needs. Figure (1) shows the big picture of the value chain process.

Figure 1: Manufacturing value chain process.

Strategic AM activities involve time frames spanning several years in which managers must determine product mix and demand characteristics, plant units and production capacity, distribution system, and out-sourcing strategies.

As an advanced strategic AM practice, decision makers should consider different combination of market opportunities, product mix and plant capacities as alternative projects, locating the one which is most desirable in terms of the organization’s goals. Each alternative consists of four key components: (a) preferred suppliers, (b) specific asset configuration, (c) favoured distributors and (d) selected groups of customers. Figure (2) shows how these elements are linked within a strategic AM framework. In the manufacturing block shown different shapes denote different units of equipment utilized as part of a given alternative. [16]

Figure 2: An AM alternative is a combination of suppliers, manufacturer, distributor and customer.

To evaluate different alternatives, decision makers should define configuration of each alternative separately, and subsequently study the results of the evaluation under a 4D space of "Asset Performance", "Financial", "Customer Satisfaction", and "Environmental Sustainability". [14]

Decision makers should challenge perceived internal as well as external constraints and limitations on the value chain process. Price changes, and demand fluctuations, ageing equipment, reliability issues, quality of products and rapid obsolescence are only few examples of challenges in management of assets. Ignorance of influential factors, incorrect estimations, wrong investment or under- or over-investment situations may cost companies dearly in this age of competitive marketing advantage.

RISK ANALYSIS FOCUS IN STRATEGIC ASSET MANAGEMNET

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Risk analysis provides asset managers the information they need to anticipate the unexpected outcomes and make right decisions. Considering the holistic AM practice, risks should be analysed relative to the ability of the value chain process to reliably meet its specific operating mission. As the complexity of defining AM alternatives increases, the assessment of associated risks becomes more complicated Risk exposure may occur in different stages of an AM alternative. Exposure may be at the early stage of defining an alternative or during operational phase. Risk exposure can also be due to poor cost estimates or major drops in market volume/unit prices.

In addition, there are trade-offs between, risk, cost and performance associated with each alternative; for instance note the trade-off between the investment allocated and its impact on system availability. Generally speaking, the more up-to-date a piece of equipment the more reliable it will be. However, increased reliability will come at a cost. Therefore, it is necessary to explore AM decisions in terms of the combined cost, risk and performance criteria. Woodhouse and Mitchell discuss these types of trade-offs and explain the need for the analysts to find the right blend of these factors. [16, 24]

As a practical solution, holistic AM consolidates cost, risk and performance factors in the analysis of AM alternatives. Holistic AM assesses an AM alternative in a 4D space. It identifies seven typical risk variables, grouped under the following four perspectives:1. Technical risk2. Financial risk3. Environmental violation risk, and4. Market risk

Table (1) shows these categories and related risk variables as well as example sources of risks. Unlike the conventional AM risk analysis approach, which basically considers commercial risks related to the equipment performance, these risk variables are based on a holistic view, which aims to simultaneously address statutory as well as commercial risks involved in strategic AM decision making. [12, 23, 24]

Risk analysis and selection of appropriate tools are subject to time and conditions that apply to an alternative. In this regard, risk analysis techniques relate to three broad categories: “Strategy”, “Investment” and “Conditional” risks. The following section discusses these categories in detail. [21, 22, 23]

RISK ANALYSIS TECHNIQUES

Strategy risks: Risks associated with meeting market opportunities and requirements.There are several options to define a given alternative, such as different types of equipment configuration or resource allocation.

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Table 1: Key risk variables in strategic Asset Management.

Perspective Risk variable DescriptionExample sources

of riskRisk

category

Technical Risk Time Estimate Risk Probability that the production time exceeds the estimated time for on time delivery to market.

Contractor suppliers and distributors delays Materials availability fluctuates

Strategy

Operating Risk Probability that the asset fails to perform to its full planned functionality or it fails to generate adequate units of output within the expected operating window, or excessive consumption of resources.

Equipment unavailability and breakdown Shutdown and start up Health and safety Industry capabilities Technology and obsolescence

Strategy

Financial Risk Financing Risks Probability that the project revenues will not be sufficient to repay the investment costs.

Discount rate Energy prices Inflation or Interest rate Taxation impact Investment terms and ownership costs

Investment

Cost Estimate Risk Probability that the project cost changes during its life. Labour, raw material cost Energy cost Obsolescence , tools and technology costs

Strategy Investment

Environmental Impacts and Sustainability Risks

Environmental Risks Probability that the project will have adverse environmental impacts beyond its permitted limits.

Licensing costs New regulations Excessive consumption of natural

resources Pollutants emission

Strategy Investment

Market Risk Market Risk, Volume Probability that the forecast sales volume will not materialize.

Competition Market demand level Technological changes

Conditional

Market Risk, Unit Price

Probability that the actual unit price will turn out to be less than the forecast price.

Demand trends Commodity prices

Conditional

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However, some may be dictated by practical considerations such as technical feasibility, availability of staff, facilities and cash flow. Where several paths are open, the decision maker has the choice of selecting one path to pursue. The selected path is termed “strategy”, [22].

Strategy risk identifies whether an existing asset is under-used or misaligned with expected operations. It reveals the need to upgrade the asset, change Repair Operation & Maintenance (RO&M) policies, acquire refurbished equipment or decommission equipment to raise capital for new investments for asset acquisition. [18]This information can be used as the foundation for investment policies. Process simulation technology may be applied to investigate an existing asset’s capacity, peak production rate etc. Simulation tools are available commercially and applied widely for investigating the feasibility of different alternatives and associated business strategies under uncertainty. Investment risks: These risks are associated with investment decisions.The question of whether or not to make a speculative investment is at the core of strategic AM process, i.e. should an organization invest either money or employees’ time or effort in pursuing a particular alternative? It is critical to understand where and how best to invest in new equipment/ technology or establish new policies to stay competitive. The principal determinant is the expected benefit from the investment against the expected cost and its impact on the overall performance of the organization.Organizations need to consider the impact any investment decision has on their entire performance. For instance, they need to consider an equipment replacement strategy that optimizes current manufacturing performance; or they should consider the impact a new maintenance policy has on operating and maintenance costs and try to identify the best investment strategy to minimize those expenses.

In investment risk analysis the Risk-Cost1 should be also considered and compared with risk consequences both tangible, e.g. loss of service, direct danger and intangible e.g. possible lawsuit, loss of goodwill or business confidence, [10]. For example, with some equipment the cost per unit time continues to decrease with age, but there may come a time when the risk of sudden failure becomes unacceptable, e.g. power cable fusing due to insufficient capacity or plain old age. Many sophisticated techniques are available to assess the various aspects of investment risk. Some of the most popular ones include “Break-even analysis”, “Cost to benefit analysis”, “Discounted cash flow techniques” and “Multi-criteria” methods, which incorporate the uncertainty of variables. [9]

Conditional risks: Risks associated with the conditions that actually arise during the course of the project.They could be different from those contained in the plan. As the future always contains uncertainties, the chain of events and the eventual outcome cannot be stated with certainty. In these circumstances, what may happen can only be predicted in a statistical sense, i.e. how likely it is that something might happen. [4, 13, 16]

1 Risk-Cost is the probability that a hazard occurs multiplied by the cost if it does.

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Sometimes, however, the situation is far too complex for the end condition to be seen with any clarity. There are many aspects, all with a degree of variability that combine to generate the end result. The majority of risk analysis packages make use of a Monte Carlo (random number) simulation method, which involves working through the project many hundreds of times. Monte Carlo simulation identifies not only what could happen in a given situation, but also how likely it is to happen. Therefore, users’ knowledge of probabilities of certain outcomes may help them to plan contingency measures. Probabilistic analysis may also uncover new opportunities. [6,13]

INTEGRATED ASSET MANAGEMENT SYSTEM (IAMS)

An experimental decision-support system, named Integrated Asset Management System (IAMS), has been developed to aid research and pilot studies. IAMS provides information to assist project selection and investment allocation. The allocations should be made to yield the greatest return on the organization’s assets with respect to aforementioned perspectives. Another management objective might be to maximize value-in-use, i.e., to achieve the greatest possible difference between the benefits and costs of the projects. [16]

From risk analysis perspective, IAMS identifies possible outcomes in the course of AM decisions and how likely they are to occur. Therefore, the decision makers are able to judge accordingly which risks to take and which ones to avoid. Consequently they can choose the best strategy in the favour of their organization and based on the available information.

To achieve this, IAMS requires:1. Information on the value chain elements such as requirements, capabilities,

constraints, obligations, uncertainties, probabilities etc. (Refer Figure (1)) 2. Data entry to input information within the embedded databases.

IAMS structure is made up of different components. These components can be categorised as "Existing Legacy Systems", e.g., Computerized Maintenance Management System (CMMS) and Supply Chain Management (SCM) Systems, and the following modules: Equipment Operating Requirements (EOR) Business Strategy Computational System

The relationship between EOR and Business Strategy is one to one correspondence; i.e. opportunities for improvement or change in EOR relate directly to issues considered by Business Strategy. Operational requirements such as plant performance requirements and limitations (either in throughput or in flexibility of operation) assist with identifying both causes of concern and considerations associated with issues in the Business Strategy.

The priorities of issues within the Business Strategy should be governed by considerations listed in the EOR, such as criticality of assets and their performance requirements.

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The Computational System provides an environment to assess different alternatives while considering the information on EOR, Business Strategy and legacy systems. Figure (3) shows the relationships between these key components. [14]

Figure 3: Interaction between EOR, Business Strategy and Computational and Analysing System.

To consolidate cost, risk and performance factors in the computation procedure, the Computational System evaluates different alternatives under four perspectives: “Financial”, “Asset Technical Performance”, “Customer Satisfaction”, and “Environmental Sustainability”. It defines a set of indicators under these perspectives and models their causal relationships. Table (2) shows the complete list of indicators utilized by IAMS.

Table 2: List of indicators used by IAMS.Asset Technical

Performance Financial

PerformanceCustomer/ Market

PerformanceEnvironmental Performance

Asset utilization Resource

utilization Overall Equipment

Effectiveness (OEE)

Total Life Cycle Cost per unit product (TLCC)

Net Present Value (NPV)

Return On Investment (ROI)

Economic Value Added (EVA)

Payback

Customer Satisfaction Score (CSC)

Environmental Impact (EI)

Environmental Sustainability Performance (ESP)

Total Life Cycle Cost per unit product (TLCC) and Overall Equipment Effectiveness (OEE) are two key indicators under “Financial” and “Asset Technical Performance” perspectives. OEE is a comprehensive matrix for evaluation of asset performance. It is often used as a driver for improving performance of the business by focusing on quality, productivity and equipment availability issues and hence reducing non-value

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adding activities often inherent in manufacturing processes. OEE is computed by the following expression:

OEE = Availability Production Rate Quality Rate (1)

The components are defined as follows:Availability is a comparison between the amount of time the process is actually producing and the amount of time it was scheduled to produce.Production rate is a comparison between the real production of the process and the expected production for the same time. It represents the associated speed losses.Quality rate is a comparison between the number of produced units that fits the specifications and the total units produced.

Figure (4) reveals the areas, which affect or are affected by asset performance [7].

TLCC, which is always expressed in current dollar base value, is made up of the total conversion cost involved in producing a product. The main cost components in TLCC are listed in Figure (5).

TLCC considers all current and future costs and reduces them to their present value by the use of the discounting techniques, through which the economic worth of an alternative can be assessed, [13]. Key elements of TLCC include “Asset ownership costs” and “Operation, Repairs & Maintenance (OR&M) costs”.

Figure 4: The trade offs between asset performance and major cost components.

Figure 5: Required information for TLCC analysis.

As Figures (4) and (5) depict AM decisions are influenced by the asset technical performance as well as the conversion costs in a dynamic way. For instance, sound AM decisions improve OEE. However, its impact on conversion cost is not always clear; improving OEE can be a result of management decisions such as: “Changes in maintenance policies”, “Equipment replacement”, “Equipment upgrade”, “Hiring”, and “Outsourcing”. Initiating these decisions will affect the conversion cost of the projects and consequently the corresponding TLCC value.

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Similar reactions can occur elsewhere in the system; for example, an investment to improve the environmental performance decreases the amount of waste and statutory fines. However does it lead to the lowering of the TLCC?

Figure (6) indicates simplified decision support procedure in IAMS. It starts with defining business objectives, target values and strategic AM alternative. It then proceeds through the fundamental question of technical feasibility of each alternative. In the case of feasibility, IAMS calculates diagnostic indicators while considers their causality for each alternative. The outcome are compared with target values, if meet the objectives the alternative is accepted. Otherwise, management decision for instance replacement, upgrading or outsourcing will be defined and their consequential impacts will be studied through the system. The procedure proceeds through selecting the best alternative by utilizing Multi-Criteria Decision Making (MCDM) technique with respect to achieving business objectives.

Figure 6: Simplified flowchart represents decision-support process in IAMS.

These indicators are functions of a set of input variables, which inherently contain uncertainty. Therefore, IAMS should also reflect this feature in its assessment procedures and outcomes. The following explains how IAMS meets this requirement.

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CONSOLIDATING RISK WITH ASSEESSMENT PROCEDURE

Under a holistic AM approach, IAMS considers four typical types of risk as follows: “Technical”, “Financial”, “Environmental violation” and “Market” risk.

Technical Risk: The simulation module of IAMS is used for modelling the alternative scenarios under consideration, at both abstract and detailed levels, to determine the feasibility of the given alternative, and predict technical performance, such as likely asset and resource utilisation profiles. It allows randomness to be introduced into a number of the simulation variables such as processing time, unscheduled downtime (both time between failures and time to repair) etc., through the use of a set of probability distribution functions. In addition, it also provides information necessary for evaluating a number of the criteria associated with the strategic business objectives, as well as sensitivity information for determining the effect of changing decision features of the alternatives on the decision criteria. The sensitivity information is useful for modifying any of the alternatives to improve its performances on some or all of the criteria.

The simulation module also includes an evolutionary optimizer that can find near optimal solutions for both single and multiple objective problems with a large search space. The module can thus be used to determine the optimum values for the decision features of the alternative scenarios such as asset schedules and asset layout, which positively contribute to one or more of the strategic business objectives. This allows optimization to occur at both the process level and at the strategic level.

The simulation module is built with the commercially available software EXTEND as the front end interfaced with the GEA toolbox for MATLAB to provide the additional capability required for solving multi-objective optimization.

Financial and Market Risk: A set of input variables are required to estimate financial indicators such as TLCC (Figure 5). These variables can be assumed to be any value at random from a range of infinite values with a known statistical profile. This is in fact the natural state of many of cost, price or volume variables. For instance, interest rate or energy costs. Instead of a single value, such as 16% per annum, it is more realistic to assume that its value is expected to vary between a lower value and an upper value of say 12% and 25% per annum respectively with a triangular Probability Distribution Function (PDF), Figure (7). [13]

Figure 7: Assumed distribution example for interest rate with 16% as the most likely value.

This assumption recognizes that owing to the uncertainty typically associated with any future event, the variable under consideration should be allowed to fluctuate within a realistic range at random. IAMS applies Monte Carlo simulation technique

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combined with Discounted Cash Flow methods to study the Financial as well as Market Risk associated with price changes, demand fluctuation, and cost increases, sales volume and sales price associated with a given Alternative.

It considers a trial value for input variables, which is selected at random from its assigned range and probability distribution. These values are used to compute a value for the indicator under consideration deploying the conventional deterministic routines. This process is repeated and after 100 trial computations, 100 values for the indicator will be obtained. More computations will result in more values for the unit cost. However, 100 to 200 computations will be sufficient for most practical situations, [13]. The resultant values are then expressed as a fraction frequency diagram.

Running the Monte Carlo simulation process as part of the IAMS involves simple steps. First an appropriate probability density function (PDF) is assigned to each input variable containing uncertainty. IAMS provides a choice of possible PDFs, such as normal, triangle and beta distributions. Therefore, the user can define a range of values for any input variable instead of limiting it to just one value (deterministic approach). The user can also control the range that is sampled within an input distribution. For computing the value of different indicators in the IAMS can output a whole range of possible outcomes, including the probabilities of occurrence. By specifying extreme ranges of a given input, the user can test the sensitivity of the decisions under consideration to different scenarios regardless of however improbable they are.

IAMS also generates a full statistical report on the simulations outcome, as well as access to all the data generated. IAMS also fits a function curve on the output data, which can help in the interpretation of the output. Figure (9) shows an example of TLCC based on a set of uncertain input variables.

Environmental Violation Risk: IAMS applies a systematic risk analysis methodology to identify risk associated with environmental violation and studies the consequential financial risk such as fines and licence fees. [15]IAMS provides an environment for tracking source of the environmental violation and their possible consequences in three tiers of:a) Environmentally sensitive materialsb) Environmentally sensitive equipment, andc) Environmentally sensitive process

Accordingly it estimates environmental impacts in terms of environmental current or capital expenditures. Environmental current expenditure includes any payment of government agencies or non-government contractors related to waste management and environmental protection services or activities. Capital expenditure includes expenditure on any element of production process specifically attributable to protecting the environment by the prevention, reduction or elimination of wastes, pollutants or any other degradation of the environment. [3]

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IAMS applies “Discounted cash flow techniques” and “Monte Carlo method” to translate these expenditures into the system (where payment has been made or will be made), as mentioned in the above section.

For example, a spill of effluent into a river can be caused by a system failure with the probability of 0.2 per year. The capital expenditure includes cost to repair the system and restore production. The current expenditure includes are: possible fines, cost of clean up, potential law suits from the affected users of the river, loss of reputation, etc. The results of an investment may lead to improving the OEE and reducing the current expenditure, (Refer Figures (4), (5)). However, there is no guarantee that its impact on conversion cost is a positive one.

A CASE STUDY RESEARCH

A manufacturing plant, company A, serves the local market with a type of white goods product, product A. The production line of this company is made up of three units of equipment, working in series configuration; these are refereed to “Equipment One”, “Equipment Two” and “Equipment Three”.

This organization has recently undertaken a strategic study to analyse market condition in the next 3 years. The study reveals that due to the competitor activities, the average price of product A will decline from $27 per unit product to $25.80 in the above-mentioned period, which means a lower profit margin for the organization. This organization has targeted profit margins in excess of 12%.

In this situation company A has two alternatives:Alternative I: Continuing with the existing situation, orAlternative II: Improving the performance of the organization by investment and better utilization of the asset.IAMS was used to estimate the OEE performance of these alternatives. The reports of IAMS for alternative I identify Equipment One is the constraining machine with the biggest cycle time2. The corresponding loading time is 40 hours3. The OEE rate of the current plant is 28.5%, [7, 20]. As company A has not previously attacked the “big six losses” principles, this low level of OEE performance is not unexpected.

2 The cycle time (speed rate) is the time to satisfy the requirements of a final product (product) unit. For example the production rate of Equipment One is 10 units per minute. This equipment should produce 2 units to make a final product piece. Therefore the cycle time is equal to 12 sec ((60/10) x 2= 12 sec).

3 loading time (schedule time, planned production time) is the time that normal operations are intending to make product, and includes all events that are common to meeting delivery schedules (for example, changeovers, setups, information downloads, unplanned stoppages for equipment/people/quality, testing).

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Figure 8: OEE elements for Company A production process equipment.

Figure (8), depicts OEE elements for the process equipment. For each unit of equipment, the column bars demonstrate availability, production and quality rates.

Figure (9) contains the TLCC distributions that reflect the relevant uncertainties in input variables, [13]. IAMS also estimates the probability that TLCC per unit product being less than $23 is 70%. This leads to the conclusion that there is 30% risk of profit margin being less than 12%. Figure (9) also shows the TLCC cumulative distribution. Contribution of main cost elements to TLCC is depicted in Figure (10). As seen, 17% of TLCC is due to asset utilization cost.

Figure 9: TLCC cumulative distribution for alternative I.

.Figure 10: TLCC elements contribution to the

total cost for alternative I

Furthermore, this organization is exposed to environmental violation penalties, which require mitigation initiatives. The manufacturing operation requires energy and water. It generates Hazardous Air Pollutants (HAPs) as well as hazardous solid waste. Due to financial concerns and statutory obligations, the energy usage and HAP emissions are the main areas of concern.

IAMS reports show that a cross-functional approach to solve the low OEE problem and address the risk of low profit margin is necessary.

By assessment of the trade off diagrams, Figures (4) and (5), company A noticed that the main area of concern is the availability of “Equipment One”, which currently

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adversely affects the OEE figure. Further examination shows that the low availability is partly due to poor maintenance and partly unreliable supply of raw materials. The later also has financial implications as the delivery to the customer is often missed. [17]

In addition, tracking the source of impacts, the company can apply changes in factory processes to mitigate emitting HAPs. However, hazardous waste reduction is dependent on product design phase. Considering the results of the above analysis, Company A decides to:1. Upgrade Equipment One, which has a major impact on the availability of the

whole system. The total cost involved with this decision is between $10,000 and $12,500, considering normal distribution.

2. Improve the maintenance policies by investing on the preventive maintenance activities. This decision affects both availability and quality rate of the process, which provides higher production rate in a fixed operation window. Furthermore, it improves the firm’s environmental performance by producing less HAPs. A “beta-approximation” distribution function is assigned to the cost associated with initiating this policy. The distribution parameters are :

Low value= $17,500, Most likely value=$20,200 and High value=$26,8003. Hiring a new supplier. This supplier provides an environmentally benign raw

material, which may increase the conversion cost. However, it increases the availability of the process and also saves on the late delivery and statutory penalties. [16]The total cost involved with this decision is between $3,300 and $4700, considering rectangular distribution.

Figure 11: TLCC cumulative distribution for Alternative II.

Figure 12: TLCC elements contribution to the total cost for alternative II.

The new management decisions are introduced to IAMS as alternative II. IAMS report on the new alternative shows OEE will rise to 48 %, Figure (8), which is equal to OEE of the constraining machine, Equipment One. Furthermore, the risk of low profit margin drops from 30% to 20%, as the probability that TLCC per unit product is less than $23 increases to 80%. Comparing Figures (11) and (12) with Figures (9) and (10) also reveal that against the highest value of TLCC, Alternative I has a profit margin of 1%, whereas the corresponding figure for Alternative II is 5%.In addition,

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comparison of Figures (10) and (12) show a rise in manufacturing cost compared to Alternative 1. However, this is justified when viewed holistically.

CONCLUSIONS

This paper addresses risk analysis in the context of strategic asset management. It investigates typical risk variables and classifies them under four high level perspectives: “Technical”, “Financial”, “Environmental Violation” and “Market” risks. The paper showed how risk factors affect profitability and competitiveness of the organization and consequently the need for a systematic view to analyse risks in an integrated cost/risk/performance environment. The authors introduced a decision support system, IAMS, which facilitate evaluation of the different AM decisions in an integrated and holistic manner. The methodology and the tool (IAMS) developed by the authors provides an opportunity to model asset management decisions in terms of a set of performance indicators that are influenced by input variables. Different alternatives have different operational and strategic impacts on the performance variables. Treating the input variables as stochastic variables provides a means to conducting risk analysis within the IAMS. Monte Carlo simulation technique is applied to estimate the impact of uncertainty and variability in input variables on decision outcomes. The stochastic approach provides a greater visibility for the strategic asset management decisions susceptible to uncertainty, including sensitivity to extreme scenarios, identification of critical areas for improvement and the contribution that each set of variables make to the total decision outcomes.

ACKNOWLEDGEMENTS

The work presented in this paper forms the core of research being conducted by the first author as part of her PhD studies at the University of Sydney, Australia. The original ideas for modelling complex assets were developed by the second author in 2001-2. The authors have received funding support from the Australian Research Council for the conduct of research in process simulation and the application of high level multi-criteria modelling to complex asset management. The funding covers 2004-6 period. The facilities and support provided by the Department of Civil Engineering of the University of Sydney for the conduct of this research are gratefully acknowledged.

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