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 Sydney
NSW, 2006, Australia
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, .
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
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
Raw Materials Finished Goods
Supplier Manufacturer Distributor Customer
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. 
Suppliers Manufacturer Disrtributors Customer
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". 
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
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,
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
1. Technical risk
2. Financial risk
3. Environmental violation risk, and
4. 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.
Table 1: Key risk variables in strategic Asset Management.
Example sources Risk
Perspective Risk variable Description
of risk category
Technical Risk Time Estimate Risk Probability that the production time exceeds the estimated Contractor suppliers and distributors delays Strategy
time for on time delivery to market. Materials availability fluctuates
Operating Risk Probability that the asset fails to perform to its full planned Equipment unavailability and breakdown Strategy
functionality or it fails to generate adequate units of output Shutdown and start up
within the expected operating window, or excessive Health and safety
consumption of resources. Industry capabilities
Technology and obsolescence
Financial Risk Financing Risks Probability that the project revenues will not be sufficient Discount rate Investment
to repay the investment costs. Energy prices
Inflation or Interest rate
Investment terms and ownership costs
Cost Estimate Risk Probability that the project cost changes during its life. Labour, raw material cost Strategy
Energy cost Investment
Obsolescence , tools and technology costs
Environmental Environmental Risks Probability that the project will have adverse Licensing costs Strategy
Impacts and environmental impacts beyond its permitted limits. New regulations Investment
Sustainability Excessive consumption of natural resources
Risks Pollutants emission
Market Risk Market Risk, Volume Probability that the forecast sales volume will not Competition Conditional
materialize. Market demand level
Market Risk, Unit Probability that the actual unit price will turn out to be less Demand trends Conditional
Price than the forecast price. Commodity prices
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”, .
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. 
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
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, . 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. 
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]
Risk-Cost is the probability that a hazard occurs multiplied by the cost if it does.
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. 
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
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)
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
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. 
EOR Business Strategy
Features Opportunities for Issues with the
Criticality Improvement Asset
Production Requirements Cause of the
Considerations Priority of Works
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 Financial Customer/ Market Environmental
Performance Performance Performance Performance
Asset utilization Total Life Cycle Customer Environmental
Resource Cost per unit Satisfaction Score Impact (EI)
utilization product (TLCC) (CSC) Environmental
Overall Equipment Net Present Value Sustainability
Effectiveness (NPV) Performance
(OEE) Return On (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
adding activities often inherent in manufacturing processes. OEE is computed by the
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 .
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, . Key elements of TLCC include “Asset ownership
costs” and “Operation, Repairs & Maintenance (OR&M) costs”.
Equipment Failure Initial Capital Cost
Setup & Adjustment Asset Ownership Cost
Idling & Minor Stoppages Affect
Production Asset Maintenance Cost
Reduced Speed Rate
Defects in Process Operating Cost
Reduced Yield Inventory Cost
Production Process Duration Energy Cost
Equipment Usage Waste Cost TLCC
No of Sellable Product
Asset Environmental Liabilities Cost
Performance Safety Liabilities Cost
Repair & Maintenance Cost
Operating Cost Education and training Cost
Inventory Cost Administrative Cost
Figure 4: The trade offs between asset Figure 5: Required information for TLCC
performance and major cost components. 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.
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.
- Define Business Objectives
- Define Target Values
- Define production chain
- Define asset configuration
- Define available resources/ constraints
- Define funds and policies
Simulate production process
Calculate Diagnostic Indicators
YES Is there
NO Satisfy Target
any improvement YES
NO Accept the alternative.
Multi Criteria Decision Making
(Considering Business Objectives)
Disregard the alternative. Select the best alternative
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.
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). 
12 16 25
Optimistic Probable Values Pessimistic
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
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, . The resultant values are then expressed as a fraction frequency
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
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. 
IAMS provides an environment for tracking source of the environmental violation and
their possible consequences in three tiers of:
a)Environmentally sensitive materials
b)Environmentally sensitive equipment, and
c)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. 
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, or
Alternative 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 hours 3. 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.
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=
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,
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, . 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 Figure 10: TLCC elements contribution to the
alternative I. 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
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.
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
3. 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
The total cost involved with this decision is between $3,300 and $4700,
considering rectangular distribution.
Figure 11: TLCC cumulative distribution for Figure 12: TLCC elements contribution to the
Alternative II. 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,
comparison of Figures (10) and (12) show a rise in manufacturing cost compared to
Alternative 1. However, this is justified when viewed holistically.
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
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.
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
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