Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol69.Issue3.Feb2001:
Int. J. Production Economics 69 (2001) 287}296
A framework for maintenance and replacement of a network
structured system
Philip A. Scarf!,*, Harry H. Martin"
!Centre for OR and Applied Statistics, University of Salford, Salford M5 4WT, UK
"Department of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
Received 1 February 1999; accepted 27 January 2000
Abstract
The asset management problem of a network owner operating in a regulatory climate is discussed in this paper.
Typically, the networks of interest are water distribution systems, gas pipeline networks, and electricity supply networks.
We look at how network investment projects, relating to refurbishment or replacement of existing assets and network
expansion, may be prioritised under capital rationing. The objectives for prioritising such projects will be in#uenced by
the regulator; the regulator may set future performance targets, and set caps on charges and capital expenditure. The
implications of capital rationing and the relationship between capital investment and future performance and operating
costs are considered through the notion of the marginal `costa of delaying projects. The e!ect of the uncertainty in the
information that the owner currently possesses about the network is also addressed. An example is given to illustrate the
framework presented. ( 2001 Elsevier Science B.V. All rights reserved.
Keywords: Maintenance; Replacement; Networks; Asset management; Capital investment; Regulatory climate
1. Introduction
Network-structured systems, or networks for
short, distribute `productsa such as gas, water, and
electricity. These networks typically consist of
conduit components (pipes and cables) and control
components (pumps, valves, transformers and
switchgear), and they link few producers to
numerous consumers. The network owners in the
UK are retailers of the distributed product, with
the network itself forming part of their retail
infratructure.
* Corresponding author. Tel.: #44-161-295-3817; fax: #44161-295-5559.
E-mail address: p.a.scarf@salford.ac.uk (P.A. Scarf).
Due to their size and age, the investment in the
replacement and maintenance of networks is high.
Therefore, the management of a network requires
a long-term view of issues such as: the consequences
of network failures; progress in network technology; and the development of demand. In many
countries, network owners are now privately
owned, regulated industries. The e!ect of this is
that network owners have to consider their pro"tability in a market with charges and supply-reliability targets set by the regulator. Therefore,
e$cient management of the network and its assets
is crucial for pro"tability. The resulting "nancial
context for the network owner will encompass installation (refurbishment, replacement and expansion) costs; maintenance costs and other
0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 2 8 - 1
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P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
consequences relating to operation of the networks;
the regulated income; and the interests of shareholders.
Since there are many network types (di!ering
technologies, di!ering products) it is natural to "nd
a common approach to network asset management
in which the speci"cs of the networks themselves are
reduced to a minimum. Thus, we will discuss network asset management (NAM) in a general context.
Several levels in the NAM decision-making process
must be recognized; these we will refer to as decision
elements. The decision elements are set out in the
conceptual framework described in the next section.
Many approaches which consider individual decision elements that have some relation to network
asset management in the water industry have been
reported [1}6]; for a recent discussion of asset management in the electricity supply industry see Hosking et al. [7]. However, there has been little work
that considers a consistent and complete framework
of network asset management. For the most part,
this paper concentrates on project planning and
release, and looks at how capital investment can be
managed to maximise share-holder wealth or network performance while meeting certain regulatory
and budgetary constraints. Replacement models for
project planning and release are described in Section
3. These models are discussed in the context of
various decision criteria, and the role of information
uncertainty and investment risk is addressed. In
Section 4 we present an example to illustrate the
framework.
2. The network asset management framework
The ultimate purpose of the network asset management (NAM) framework proposed is to support:
2.1. Component identixcation and evaluation
The refurbishment and replacement of existing
assets will naturally focus on components of the
network. It is expected that a component of the
network will be de"ned in terms of the component
type, its location in the network, and its state, and
that these will need to be identi"able within a network information system (e.g. see Fig. 1). A component type we de"ne as a physical item whose
description is unique when considered outside the
context of the network; thus we think of component
types as items prior to their setting in the network,
e.g. a coil of 100 mm plastic `gas maina. The location of a component will be characterized by the
environmental conditions in which it is installed;
and its logical and geographical location. The state
of a component will be de"ned by its operating
intensity, and its current age and/or condition. This
information for all components will thus form an
evaluation of the `statea of the network. Component identi"cation will be carried out once only
during the life of the component, while component
evaluation may be updated on as as-required
basis.
For certain networks this element of the framework may be trivial } the state of electricity networks is generally well understood by the network
owner otherwise there would be serious concern for
safety. For other networks there may be uncertainty regarding the state of large parts of the newtork
} UK water supply networks originate, for a
large part, from the 19th century and the state
of large parts of the network may be unknown,
certainly in terms of age and condition of components and occasionally in terms of component
type.
2.2. Maintenance concept design
f project planning and release.
The other decision elements in the framework are:
f component identi"cation and evaluation;
f maintenance concept design;
f performance measurement.
In this section we introduce and discuss these elements of the framework.
In general, the maintenance concept [9] for the
network is part of NAM, and for this reason we
discuss it brie#y here. Note that maintenance concept design is carried out over a di!erent timescale
to project planning, and may be determined only
once during the life of a component. However,
changes to the maintenance concept design will
have implications for project planning and release.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
289
of appropriate maintenance rule for a component
failure mode, the level of maintenance intervention,
and the extent to which shared execution of individual maintenance activities is appropriate. Four
elementary maintenance rules can be distinguished:
failure-based maintenance (maintain on failure
only); time-based maintenance (maintain on failure
and at "xed times); use-based maintenance (maintain on failure and when speci"ed levels of component use are reached); and condition-based
maintenance (maintain on the basis of measured
condition). In simple terms, "ve levels of maintenance intervention can be distinguished: inspection;
service (for example, routine adjustment); minimal
repair; refurbishment (overhaul); and replacement.
The various failure modes of components, their
appropriate maintenance rules and the proximity
of components will determine the extent to which
individual maintenance activities can be grouped.
Key components may be subject to condition based
maintenance (CBM) in which the operating intensity and environment will be important factors.
The interaction between the design of the maintenance concept and the identi"cation of network
components will be signi"cant. For example, for
sections of the network on which the network
owner chooses to do only minimal repair on failure,
there is no need to describe, in the framework, these
sections in terms of their components. On the other
hand, use based maintenance (UBM) and condition
based maintenance (CBM) are implemented for
particular components, and therefore, appropriate
subdivision of the network into its components is
necessary.
2.3. Performance measurement
Fig. 1. Typical rural electricity networks (part) drawn by a
network simulator [8]. 500 m squares; (h) infeed; (j) loadpoint;
(£) open point; (***) cable; (- - - -) overhead line. (a) 38.9 km
of 415 V with 124 load points and 2976 consumers. (b) 481 km of
11 kV with 390 loadpoints and 79950 consumers.
The maintenance concept design will comprise
maintenance activities to be carried out on particular components and component groups. The maintenance activities will be de"ned through the choice
The consequences of failure of a component will
clearly depend on the component type and its location in the network. These consequences will
encompass cost, performance and safety. Typically,
annual direct failure costs (cost of repair) for the
network as a whole will be constrained by expenditure on resources for repair (manpower, spares).
Thus failure costs may be "xed, but the consequences of failure on performance, as measured by
the number of interruptions per 100 customers per
annum or minutes lost per customer per annum
290
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
will not. The performance of the network will
impact on costs through the regulation of charges
to the customer; the regulator may impose penalties for poor performance.
To support project planning and release it will
thus be necessary to estimate the performance of
the network before and after the completion of
a project. Thus by performance measurement we
mean calculating, for each potential project, the
performance implications of the project for the
network as a whole. For example, the network
simulator eaNSF developed by Brint et al. [8] does
this in the context of electricity supply networks.
2.4. Project planning and release
This decision element aims to schedule network
projects (expansion, replacement and refurbishment activities) given certain planning constraints.
The objectives for this element will have to be
clearly de"ned by the network owner. Decisions
here may relate to prioritizing of projects, grouping
of projects, shifting the moment of execution of
projects, budget adjustment (e.g. a network owner
may be allowed to alter charges to meet "nancial
demands, or to justify savings or an additional
budget for large planned projects); overlaying of
projects with other network owners (it may be even
worthwhile establishing a specialized institutional
body to coordinate projects with setups common to
other network owners). It will be necessary to re#ect the risk of projects in the capital budgeting and
this can be done through appropriate choice
of discount rates or through portfolio analysis.
Marketing analysis relating to future capacity and
capability would also in#uence the decisions here.
3. Replacement models for project planning and
release
We describe a number of replacement models
that can be used in decision making relating to
project planning and release. We "rst outline
a simple model which will form the basis of the
capital rationing models discussed later. This
model was introduced by Scarf and Hashem [10] in
vehicle #eet replacement. We use the term replace-
ment model throughout because, in the most
simple cases, a project will generally correspond to
the `replacementa of a component.
3.1. A simple replacement model
Consider a "xed horizon of length h over which
we will evaluate the consequences of a potential
project, P. A potential project may be the replacement or refurbishment of a component or group of
components which comprise part of the network,
or a network expansion, or a network re-design.
De"ne f 1 to be the operating cash #ow (or perfort
mance) relating to P in year t after the release of
project P. De"ne f 0 to be the baseline operating
t
cash #ow (or performance) relating to P in year
t (that is, relating to that part of the network to be
replaced or refurbished under P). For network expansion projects f 0"0. Let C ('0) be the capital
t
cost of project P.
Suppose the consequences of project release are
measured in terms of cash#ows, and let l be the
appropriate discount factor. For clarity, we will
take income cash#ows as negative and expenditure
cash#ows as positive, and we will assume that all
cash#ows are incurred at the year end. If project
P is released in year x from now then the total
cash#ow over h years from now will be
A
B
h~x
x~1
+ f 0lt#lx + f 1lt#C .
(1)
t
t
t/0
t/1
If project P is not released then the total cash#ow
over the horizon will be
h
+ f 0lt.
t
t/1
De"ne the `gaina from releasing project P in year
x to be the di!erence between these cash#ows:
h
g (h)" + f 0lt~1
t
P,x
t/1
x~1
h~x
! + f 0lt#lx + f 1lt#C
t
t
t/1
t/0
h
" + ( f 0!f 1 )lt!lxC.
t
t~x
t/x
G
A
BH
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
Release of project P in year x (x(h) will be recommended if g (h)'0. We can optimize project reP,x
lease by choosing that x for which the gain is
a maximum and positive. If g (h))0 for all
P,x
x"1,2, h then release of project P will not be
recommended (over the horizon).
If, alternatively, the consequences of project
release are measured in terms of performance,
then the gain from releasing project P in year x
will be
h
g (h)" + ( f 0!f 1 ).
t~x
t
P,x
t/x
Here we assume that a reduction in the performance measure, f, represents a performance
improvement, which is appropriate when performance is measured, for example, by the number of
interruptions to supply per customer per annum.
Note that the performance (or cash#ow) in the year
in which the project P is released, f 1 , can be used to
0
model reduced performance during execution of P.
Typically, we would expect f 0'f 1 for all t*1
t
t
} otherwise project P would not be considered for
release. Then g (h)'0 for all x } except perhaps
P,x
for x close to h when f 1 is large } and g (h) will be
0
P,x
maximum when x"1. We may even "nd in practice that f 1"f 1 and f 0"f 0 for all t*1; that is
t
t
the release of a project implies a one-o! improvement in performance from a "xed baseline.
For a large system comprising many potential
projects, the outcome of this modelling approach
will depend on how the consequences of project
release are measured. With consequences in terms
of cash#ows, the outcome will be a list of projects
that should be released along with their optimum
release times, and a list of those that should not.
With consequences in terms of performance, the
outcome will be a list of projects that should be
released immediately, and a list of those that should
not. Of course, the release of projects will, in
both cases, be limited by the budget for capital
expenditure.
The measures f 1 and f 0 will be determined essent
t
tially through knowledge of the failure processes
and the maintenance concept. Such knowledge
about the failure processes may be based on objective data, or subjective data or a combination of
291
both. Methods for handling the subjective data of
`expertsa in reliability, and their combination with
objective data, are numerous (e.g. see [11]). In
addition, the `inconvenience costa that makes
a political decision an economic one may be calculated as described in Christer and Scarf [12]. The
consequences of the adoption of a particular maintenance concept may also have to be assessed subjectively. Income associated with the operation of
certain components may be part of cash#ow functions, particularly for network expansion projects.
Where a project release implies that the condition
of some (new) component is monitored, the cost of
monitoring and the monitoring pay-back contribute to future cash#ows or performance. Thus the
use of monitoring will in#uence decision policy
through its consequences.
In the case of investment appraisal for network
expansion, we suppose that f 0"0 for all t. In
t
cash#ow terms, the expansion project would be
released in year x if
h
! + f 1 lt!lxC'0,
t~x
t/x
provided rational economic policy is operating.
For network expansions (and re-design) maintenance concepts for components will need to be determined based on design and environment, and the
revenue cash#ows (operating expenditure and
income) determined. A marketing model must also
be available that can generate a demand function
and the associated revenues; then f 1 for expansion
t
can be estimated, with some uncertainty. Note that
network expansion could not be considered in performance terms in this way (because the gain will
always be negative).
3.2. Prioritizing project release
We now consider how the simple model relating
to individual projects can be used in planning and
release over the horizon (0, h). Let g (h) be the gain
ij
when project i is released in year j (1)j)h), and
suppose that, in year j, n projects have positive
gains. These projects might be listed in order of
magnitude of their gains. They might also be listed
in order of magnitude of the `pro"tabilitya index,
292
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
g (h)/C , where C is the capital cost of project i. If
ij
i
i
rational decision criteria are to be used to determine policy then projects at the top of the list
should be given priority, since they would be associated with the largest expected gain over the
planning horizon. Under capital rationing with
a "xed budget, this project priority list would indicate which projects can be released in the current
year.
For consequences considered in cash#ow terms,
appropriate discount rates may be chosen to re#ect
the investment risks of projects. A higher required
rate of return (smaller discount rate) might be
imposed on network expansion projects than on
replacement of existing assets. The capital asset
pricing model (e.g. [13]) provides a means for
determining discount rates in these circumstances.
In the case of replacement of existing assets such
variation in the choice of discount rates might
also be used to re#ect uncertainties regarding new
technology.
The capital costs of projects may be formulated
to include shared set-ups, and also the cost (consequences) of a poor replacement. When an opportunity arises to share a set-up cost, then optimal
policy for the network may be re-calculated. This
may change the project priority list. Opportunities
themselves may arise through: the failure of a
component necessitating remedial work; a repair
initiated through condition monitoring; the
combination of projects on the same network; or
combination of projects on di!erent networks.
3.3. A capital rationing model
Suppose the network owner wishes to prioritise
project release over the next k years (k)h). This
problem may be approached using linear programming (LP), similarly to that proposed in vehicle
#eet management by Karabakal et al. [14]. Suppose the capital investment budget for year j is
B ( j"1,2, h). Introduce the indicator variable
j
x which takes the value 1 if project i is released in
ij
year j and 0 otherwise. Then we seek those values of
x (i"1,2, n; j"1,2, k) which maximize the
ij
total gain over (0, h) of all projects released subject
to the constraints that the capital investment
budget is not exceeded in each year. That is
maximize
n k
+ + x g (h)
ij ij
i/1 j/1
subject to
n
+ x C )B for all j"1,2, k;
ij i
j
i/1
k
+ x )1 for all i"1,2, n;
ij
j/1
x "0, 1.
ij
(2)
(3)
Constraint set (2) ensures that the budget for year
j is not exceeded. The annual budgets would re#ect
that capital available once imperative projects have
been "nanced, perhaps for reasons of health and
safety legislation. Constraint set (3) ensures that
project i is released at most once over the planning
horizon. Note that if an individual project has
negative gain whatever its execution time, then the
contribution to the objective function from this
project will be greatest when this project is not
released over (0, k). Tax considerations in particular
contexts will need to be taken into account and
modelled; as these are contextual, discussion is
omitted. Typically, such planning may be informative over the planning horizon, but only decisions
relating to the immediate future (one to two years)
would be acted on. Therefore, policy would be
continually updated, implying a `rolling horizona
approach. Where a network consists of many identical components, the modelling of project planning
may be extended to the case in which a proportion
of `similara projects are released in a given year.
This could be done by formulating the capital
rationing model (CRM) as a mixed programming
problem.
The performance implications of rationing can
be determined by considering optimal performance
for a number of di!erent budgetary constraints:
a plot of performance improvement against annual
budget would be informative to network owners.
An alternative LP problem could be formulated to
determine the release times of projects which minimize capital expenditure while meeting performance improvement constraints.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
3.4. Modelling dependence in replacement costs
While the dependence between the capital costs
of di!erent projects may be considered simply using
the concept of shared set-up, the CRM model
above ignores dependence in operating cash#ows
between components. For example, a major expansion project, while not replacing existing assets,
may have signi"cant operating cost or performance
implications for particular assets: the building of
a large ring main in a water supply network is one
such example.
Essentially, if two projects P and P interact in
1
2
this way, then new projects P@ "(P , not P ),
1
1
2
P@ "(not P , P ), and P@ "(P , P ) would have
12
1 2
2
1 2
to be introduced, along with the constraint to ensure that at most one of P@ , P@ , and P@ is released
12
1 2
over the planning horizon. While this approach
may lead to a signi"cant increase in the number of
`projectsa in the CRM, in principle the solution
procedure would remain unchanged. The existence
of future-cost dependencies between projects would
have to be identi"ed by the network owner. This
may be extremely di$cult in practice. However,
such dependency would very much characterize the
network replacement problem, and therefore, the
approach described is an advance over current
methods. A similar approach has been taken
by Santhanam and Kyparisis [15] in modelling
dependency in the project release of information
systems. It is possible that it may be optimal to
release both P and P during the planning
1
2
horizon, but not simultaneously. This presents
a more di$cult modelling task, without introducing many pseudo-projects, that is. For example, we
could consider: release P at time s and P at time
1
2
t; however, for k"10, say, this would mean the
, for the
introduction of 25 variables, x 1 2
(P P )(s,t)
P , P decision alone!
1 2
293
expenditure; this is the marginal increased revenue
expenditure due to delayed release (see Ref. [12]).
Given the CRM, and focusing on cash#ows, the
operating cost consequences of capital rationing
can be determined by calculating the delay associated with each project as a result of capital
rationing. The revenue cost implications due to this
delay would from expression (1) be
h~xH
h~x{
x{~1
g (h)" + f 0lt# + f 1lt`x{! + f 1lt`xH,
t
t
t
P,x
t/0
t/0
t/xH
where x@ is the execution time for the project under
capital rationing. The marginal increase in revenue
expenditure would be found by summing over all
projects. In a similar manner, the marginal increase
in revenue expenditure due projects delayed in year
j, dc , could be found by summing (x) over all
j
projects with xH"j. Marginal reductions in capital
expenditure under CR, dC , could also be calj
culated.
The capital savings and increased revenue expenditure under CR are in part due to delayed execution and the rest due to non-execution. If the
marginal increase in revenue expenditure in year j,
dc , increases with j then this is indicative of the fact
j
that capital investment is too low to control operating costs in the long term. If the dc are approximj
ately constant with time then this indicates that
capital expenditure is su$cient to maintain the
current level of operating cost. In these circumstances, consideration of dC will indicate how much
j
more capital investment would be required to
reduce revenue expenditure to the optimum level.
When the network operator is interested in
maximising performance, the marginal reduced
performance due to delayed release can only be
considered when comparing alternative budget
contraints.
3.5. Cost consideration of sub-optimal policies
3.6. Choice of decision criteria
For reasons of budgeting constraints or technical
delays, the release of some recommended project at
some optimal execution moment xH may not be
possible. In such cases, it would be informative to
have an indication of the extra cost to be incurred
in revenue expenditure because of lack of capital
The approach discussed in detail in this section
has considered either cash#ows or performance.
Alternative criteria such as safety (e.g. annual
number of fatalities) can be considered in a similar
manner to performance. Such quantities would have
to be determined for existing assets, replacement
294
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
assets and network expansions. All criteria might
be considered using a MCDA approach (e.g. [16]),
or using the "nancial appraisal pro"le
approach of Le#ey and Morgan [17]. They may
also be considered through suitable constraints in
the capital rationing model. A simple approach
would consider, for example, two project priority
lists, one on the basis of cash#ow, the other on the
basis of performance; projects high on both lists
would then be candidates for release.
3.7. Modelling the uncertainty in the `costsa
Uncertainty in the cash#ow/performance model
parameter estimates, re#ecting the extent of currently available information about particular components and potential projects, and the extent of
technological developments (new materials and
techniques), may be propagated through into
uncertainty in the gain function, g(h). This would be
most easily done using the delta method [18]; see
Baker and Scarf [19] for an example of this in
maintenance. The variance of the gain, as well as
the expected gain, may then be used to produce the
project priority list and those projects for which the
expected gain is high and the uncertainty in the
gain (variance of the gain) is low are candidates for
release; these projects would be viewed as sound
investments. Markovitz [20] is the classic reference
here; for a more recent discussion see Booth and
King [21].
Where there is no data regarding a potential
project, there will be no objective basis for determining if and where the project lies on the project
priority list. One possible approach to this problem
would be to use data relating to other projects that
are similar in design. Also subjective data may be
collected, and used to update component data for
the whole network in the manner described in
O'Hagan [22] and Goldstein and O'Hagan [23] in
the context of sewer networks. These methods
are particularly useful for multi-component
systems in which there is only limited data for
a limited number of individual components. On the
other hand, it may be that the income cash#ow may
be deterministic in some situations. For example,
expansion of the network may be initiated by
legislation, and that the compensation for the
investment costs are "xed and predetermined per
customer connection.
How uncertainty in the information we have
a!ects the segmentation of the network will also
require some attention in future work. The e!ect of
such structural uncertainty on the outcome of the
modelling exercise is a topic of signi"cant current
interest in Statistics. A simple approach might consider a simple sensitivity analysis; the more systematic approach of Draper [24], which assigns a prior
distribution over the structural model types, lends
itself more naturally to a knowledge-based
approach within an information system.
4. Example: Electricity distribution networks
The 11 kV overhead networks are a major
contributor to performance of the UK electricity
supply network. The network owner will consider
typical policy alternatives for these networks as
follows:
f Upgrading the construction of 11 kV spurs.
f Upgrading the construction of 11 kV main
branches.
f Removal of the 33 kV network, that is going
straight from 132 to 11 kV.
f Refurbishment of lines, for example: restringing,
replacing insulators, replacing poles.
f Using covered conductors rather than bare wire
for 11 kV.
f Undergrounding 11 kV lines } cables then tend
to be more reliable but have longer repair times.
f Repair team deployment } weather is a signi"cant factor in overhead network performance, as
is time taken to reach a fault. Therefore deployment of teams at high risk periods can provide
bene"ts.
f Alteration to the protection settings or method
of protection.
These alternatives can then form the basis of potential projects for investment, the objective being the
improvement of network performance. Performance is generally measured by the average number of customer-minutes lost and the average
number of customer interruptions per year.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
Following the framework, we would in principle:
(1) identify and evaluate the network in component terms;
(2) design the maintenance concept;
(3) estimate the performance of the network;
(4) identify a potential project;
(5) estimate the performance of the network on
completion of the project;
(6) repeat (4) and (5) for other performance improvement independent projects;
(7) rank projects by their performance improvement characteristics (or set-up and solve the
CRM);
(8) investigate and report the relationship between
annual budget, say, and annual performance
improvement.
For electricity supply networks, (1) and (2) above
will be well established. The network simulation
facility (eaNSF) program [8] has been developed to
do (3) and (5), and calculates expected network
performance indicators and cash#ows over a number of years for speci"c networks. An implementation of the framework would choose between
di!erent policy alternatives, determine which parts
of the network to apply them to, and provide decision support in budget setting.
We anticipate a number of di$culties in this
approach. Information regarding component reliability may be sparse } knowledge about how failure
rates alter if, say, a line is refurbished may be
di$cult to quantify } and although some data may
be available, it may be di$cult to disaggregate
factors such as geographical location and weather
conditions. There will be uncertainty in the network performance indicators; the initial problem is
how to relate interruptions to the number of customer-minutes lost, or to operational costs. This
becomes more di$cult the further into the future
that predictions are required. It may also be necessary to disaggregate the network into regions in
order to keep the number of projects within reasonable limits. With a large number of alternative
projects, a `what}ifa approach may be taken. Also,
constraints must be introduced into the project
release decision-making problem to ensure that
projects which are alternatives (for example, undergrounding a 11kV line and refurbishment of the
same overhead line) cannot be released `togethera.
295
Additionally, the likely degradation in the network
performance during execution of projects would
need to be estimated.
5. Discussion
The companies running the distribution networks are under constant pressure to reduce
operating costs while maintaining or improving
standards. Given the maturity of these industries,
the management of performance and operating
costs rather than carrying out network expansion is
the main goal. As the distribution networks typically form a monopoly resource and will remain so
for the foreseeable future, regulators limit charges
and have introduced penalties for poor network
performance. Hence the main objectives of network
owners are: maximising average network performance; minimising expenditure; bounding yearly
expenditure; avoiding a disastrous performance,
for example, of the type recently experienced by
Yorkshire Water, and with power supply in
Auckland, New Zealand.
This paper attempts to address these issues
through a complete framework for network
asset management, which is the "rst step in the
development of a model for NAM. The framework is
motivated by the need to bridge the gap between
technological reasoning and economic decision
making; that is between engineering judgement,
which provides the basis for maintenance concept
design, performance measurement, and the identi"cation of potential replacement/refurbishment projects, and the economic decision making of the
network owner. The advantage of this framework is
that it takes a broad view of the entire process, is not
motivated by a particular problem, and attempts to
take a top down view of NAM. It is expected that
the framework will be useful for di!erent decisionmakers both within the network owner at di!erent
levels of management, and across di!erent network
owners. The framework would also be useful for: the
valuation of the assets of the network owner; and for
demonstrating that the owner is employing state-ofthe-art management of its assets.
The replacement modelling described in the
framework takes a simple form, with emphasis on
296
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
ease of implementation within a network information system. Publications to date in this area, on the
other hand, deal with particular aspects of network
maintenance and replacement and invariably
describe complex models. Thus, an aim of this paper is to describe simple models in a complex context. The next step is to build a prototype model
that looks at a small scale network using the
elements of the framework outlined here, initially
concentrating on refurbishment and replacement
decision-making.
Acknowledgements
The authors would like to thank colleagues at
KEMA, Transport and Distribution department,
and Tony Christer for his helpful comments on
earlier drafts of the paper. The paper has been
considerably improved through the helpful
comments of the referees. This research has been
supported in part by EPSRC grant number
GR/L20801.
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A framework for maintenance and replacement of a network
structured system
Philip A. Scarf!,*, Harry H. Martin"
!Centre for OR and Applied Statistics, University of Salford, Salford M5 4WT, UK
"Department of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, Netherlands
Received 1 February 1999; accepted 27 January 2000
Abstract
The asset management problem of a network owner operating in a regulatory climate is discussed in this paper.
Typically, the networks of interest are water distribution systems, gas pipeline networks, and electricity supply networks.
We look at how network investment projects, relating to refurbishment or replacement of existing assets and network
expansion, may be prioritised under capital rationing. The objectives for prioritising such projects will be in#uenced by
the regulator; the regulator may set future performance targets, and set caps on charges and capital expenditure. The
implications of capital rationing and the relationship between capital investment and future performance and operating
costs are considered through the notion of the marginal `costa of delaying projects. The e!ect of the uncertainty in the
information that the owner currently possesses about the network is also addressed. An example is given to illustrate the
framework presented. ( 2001 Elsevier Science B.V. All rights reserved.
Keywords: Maintenance; Replacement; Networks; Asset management; Capital investment; Regulatory climate
1. Introduction
Network-structured systems, or networks for
short, distribute `productsa such as gas, water, and
electricity. These networks typically consist of
conduit components (pipes and cables) and control
components (pumps, valves, transformers and
switchgear), and they link few producers to
numerous consumers. The network owners in the
UK are retailers of the distributed product, with
the network itself forming part of their retail
infratructure.
* Corresponding author. Tel.: #44-161-295-3817; fax: #44161-295-5559.
E-mail address: p.a.scarf@salford.ac.uk (P.A. Scarf).
Due to their size and age, the investment in the
replacement and maintenance of networks is high.
Therefore, the management of a network requires
a long-term view of issues such as: the consequences
of network failures; progress in network technology; and the development of demand. In many
countries, network owners are now privately
owned, regulated industries. The e!ect of this is
that network owners have to consider their pro"tability in a market with charges and supply-reliability targets set by the regulator. Therefore,
e$cient management of the network and its assets
is crucial for pro"tability. The resulting "nancial
context for the network owner will encompass installation (refurbishment, replacement and expansion) costs; maintenance costs and other
0925-5273/01/$ - see front matter ( 2001 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 2 8 - 1
288
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
consequences relating to operation of the networks;
the regulated income; and the interests of shareholders.
Since there are many network types (di!ering
technologies, di!ering products) it is natural to "nd
a common approach to network asset management
in which the speci"cs of the networks themselves are
reduced to a minimum. Thus, we will discuss network asset management (NAM) in a general context.
Several levels in the NAM decision-making process
must be recognized; these we will refer to as decision
elements. The decision elements are set out in the
conceptual framework described in the next section.
Many approaches which consider individual decision elements that have some relation to network
asset management in the water industry have been
reported [1}6]; for a recent discussion of asset management in the electricity supply industry see Hosking et al. [7]. However, there has been little work
that considers a consistent and complete framework
of network asset management. For the most part,
this paper concentrates on project planning and
release, and looks at how capital investment can be
managed to maximise share-holder wealth or network performance while meeting certain regulatory
and budgetary constraints. Replacement models for
project planning and release are described in Section
3. These models are discussed in the context of
various decision criteria, and the role of information
uncertainty and investment risk is addressed. In
Section 4 we present an example to illustrate the
framework.
2. The network asset management framework
The ultimate purpose of the network asset management (NAM) framework proposed is to support:
2.1. Component identixcation and evaluation
The refurbishment and replacement of existing
assets will naturally focus on components of the
network. It is expected that a component of the
network will be de"ned in terms of the component
type, its location in the network, and its state, and
that these will need to be identi"able within a network information system (e.g. see Fig. 1). A component type we de"ne as a physical item whose
description is unique when considered outside the
context of the network; thus we think of component
types as items prior to their setting in the network,
e.g. a coil of 100 mm plastic `gas maina. The location of a component will be characterized by the
environmental conditions in which it is installed;
and its logical and geographical location. The state
of a component will be de"ned by its operating
intensity, and its current age and/or condition. This
information for all components will thus form an
evaluation of the `statea of the network. Component identi"cation will be carried out once only
during the life of the component, while component
evaluation may be updated on as as-required
basis.
For certain networks this element of the framework may be trivial } the state of electricity networks is generally well understood by the network
owner otherwise there would be serious concern for
safety. For other networks there may be uncertainty regarding the state of large parts of the newtork
} UK water supply networks originate, for a
large part, from the 19th century and the state
of large parts of the network may be unknown,
certainly in terms of age and condition of components and occasionally in terms of component
type.
2.2. Maintenance concept design
f project planning and release.
The other decision elements in the framework are:
f component identi"cation and evaluation;
f maintenance concept design;
f performance measurement.
In this section we introduce and discuss these elements of the framework.
In general, the maintenance concept [9] for the
network is part of NAM, and for this reason we
discuss it brie#y here. Note that maintenance concept design is carried out over a di!erent timescale
to project planning, and may be determined only
once during the life of a component. However,
changes to the maintenance concept design will
have implications for project planning and release.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
289
of appropriate maintenance rule for a component
failure mode, the level of maintenance intervention,
and the extent to which shared execution of individual maintenance activities is appropriate. Four
elementary maintenance rules can be distinguished:
failure-based maintenance (maintain on failure
only); time-based maintenance (maintain on failure
and at "xed times); use-based maintenance (maintain on failure and when speci"ed levels of component use are reached); and condition-based
maintenance (maintain on the basis of measured
condition). In simple terms, "ve levels of maintenance intervention can be distinguished: inspection;
service (for example, routine adjustment); minimal
repair; refurbishment (overhaul); and replacement.
The various failure modes of components, their
appropriate maintenance rules and the proximity
of components will determine the extent to which
individual maintenance activities can be grouped.
Key components may be subject to condition based
maintenance (CBM) in which the operating intensity and environment will be important factors.
The interaction between the design of the maintenance concept and the identi"cation of network
components will be signi"cant. For example, for
sections of the network on which the network
owner chooses to do only minimal repair on failure,
there is no need to describe, in the framework, these
sections in terms of their components. On the other
hand, use based maintenance (UBM) and condition
based maintenance (CBM) are implemented for
particular components, and therefore, appropriate
subdivision of the network into its components is
necessary.
2.3. Performance measurement
Fig. 1. Typical rural electricity networks (part) drawn by a
network simulator [8]. 500 m squares; (h) infeed; (j) loadpoint;
(£) open point; (***) cable; (- - - -) overhead line. (a) 38.9 km
of 415 V with 124 load points and 2976 consumers. (b) 481 km of
11 kV with 390 loadpoints and 79950 consumers.
The maintenance concept design will comprise
maintenance activities to be carried out on particular components and component groups. The maintenance activities will be de"ned through the choice
The consequences of failure of a component will
clearly depend on the component type and its location in the network. These consequences will
encompass cost, performance and safety. Typically,
annual direct failure costs (cost of repair) for the
network as a whole will be constrained by expenditure on resources for repair (manpower, spares).
Thus failure costs may be "xed, but the consequences of failure on performance, as measured by
the number of interruptions per 100 customers per
annum or minutes lost per customer per annum
290
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
will not. The performance of the network will
impact on costs through the regulation of charges
to the customer; the regulator may impose penalties for poor performance.
To support project planning and release it will
thus be necessary to estimate the performance of
the network before and after the completion of
a project. Thus by performance measurement we
mean calculating, for each potential project, the
performance implications of the project for the
network as a whole. For example, the network
simulator eaNSF developed by Brint et al. [8] does
this in the context of electricity supply networks.
2.4. Project planning and release
This decision element aims to schedule network
projects (expansion, replacement and refurbishment activities) given certain planning constraints.
The objectives for this element will have to be
clearly de"ned by the network owner. Decisions
here may relate to prioritizing of projects, grouping
of projects, shifting the moment of execution of
projects, budget adjustment (e.g. a network owner
may be allowed to alter charges to meet "nancial
demands, or to justify savings or an additional
budget for large planned projects); overlaying of
projects with other network owners (it may be even
worthwhile establishing a specialized institutional
body to coordinate projects with setups common to
other network owners). It will be necessary to re#ect the risk of projects in the capital budgeting and
this can be done through appropriate choice
of discount rates or through portfolio analysis.
Marketing analysis relating to future capacity and
capability would also in#uence the decisions here.
3. Replacement models for project planning and
release
We describe a number of replacement models
that can be used in decision making relating to
project planning and release. We "rst outline
a simple model which will form the basis of the
capital rationing models discussed later. This
model was introduced by Scarf and Hashem [10] in
vehicle #eet replacement. We use the term replace-
ment model throughout because, in the most
simple cases, a project will generally correspond to
the `replacementa of a component.
3.1. A simple replacement model
Consider a "xed horizon of length h over which
we will evaluate the consequences of a potential
project, P. A potential project may be the replacement or refurbishment of a component or group of
components which comprise part of the network,
or a network expansion, or a network re-design.
De"ne f 1 to be the operating cash #ow (or perfort
mance) relating to P in year t after the release of
project P. De"ne f 0 to be the baseline operating
t
cash #ow (or performance) relating to P in year
t (that is, relating to that part of the network to be
replaced or refurbished under P). For network expansion projects f 0"0. Let C ('0) be the capital
t
cost of project P.
Suppose the consequences of project release are
measured in terms of cash#ows, and let l be the
appropriate discount factor. For clarity, we will
take income cash#ows as negative and expenditure
cash#ows as positive, and we will assume that all
cash#ows are incurred at the year end. If project
P is released in year x from now then the total
cash#ow over h years from now will be
A
B
h~x
x~1
+ f 0lt#lx + f 1lt#C .
(1)
t
t
t/0
t/1
If project P is not released then the total cash#ow
over the horizon will be
h
+ f 0lt.
t
t/1
De"ne the `gaina from releasing project P in year
x to be the di!erence between these cash#ows:
h
g (h)" + f 0lt~1
t
P,x
t/1
x~1
h~x
! + f 0lt#lx + f 1lt#C
t
t
t/1
t/0
h
" + ( f 0!f 1 )lt!lxC.
t
t~x
t/x
G
A
BH
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
Release of project P in year x (x(h) will be recommended if g (h)'0. We can optimize project reP,x
lease by choosing that x for which the gain is
a maximum and positive. If g (h))0 for all
P,x
x"1,2, h then release of project P will not be
recommended (over the horizon).
If, alternatively, the consequences of project
release are measured in terms of performance,
then the gain from releasing project P in year x
will be
h
g (h)" + ( f 0!f 1 ).
t~x
t
P,x
t/x
Here we assume that a reduction in the performance measure, f, represents a performance
improvement, which is appropriate when performance is measured, for example, by the number of
interruptions to supply per customer per annum.
Note that the performance (or cash#ow) in the year
in which the project P is released, f 1 , can be used to
0
model reduced performance during execution of P.
Typically, we would expect f 0'f 1 for all t*1
t
t
} otherwise project P would not be considered for
release. Then g (h)'0 for all x } except perhaps
P,x
for x close to h when f 1 is large } and g (h) will be
0
P,x
maximum when x"1. We may even "nd in practice that f 1"f 1 and f 0"f 0 for all t*1; that is
t
t
the release of a project implies a one-o! improvement in performance from a "xed baseline.
For a large system comprising many potential
projects, the outcome of this modelling approach
will depend on how the consequences of project
release are measured. With consequences in terms
of cash#ows, the outcome will be a list of projects
that should be released along with their optimum
release times, and a list of those that should not.
With consequences in terms of performance, the
outcome will be a list of projects that should be
released immediately, and a list of those that should
not. Of course, the release of projects will, in
both cases, be limited by the budget for capital
expenditure.
The measures f 1 and f 0 will be determined essent
t
tially through knowledge of the failure processes
and the maintenance concept. Such knowledge
about the failure processes may be based on objective data, or subjective data or a combination of
291
both. Methods for handling the subjective data of
`expertsa in reliability, and their combination with
objective data, are numerous (e.g. see [11]). In
addition, the `inconvenience costa that makes
a political decision an economic one may be calculated as described in Christer and Scarf [12]. The
consequences of the adoption of a particular maintenance concept may also have to be assessed subjectively. Income associated with the operation of
certain components may be part of cash#ow functions, particularly for network expansion projects.
Where a project release implies that the condition
of some (new) component is monitored, the cost of
monitoring and the monitoring pay-back contribute to future cash#ows or performance. Thus the
use of monitoring will in#uence decision policy
through its consequences.
In the case of investment appraisal for network
expansion, we suppose that f 0"0 for all t. In
t
cash#ow terms, the expansion project would be
released in year x if
h
! + f 1 lt!lxC'0,
t~x
t/x
provided rational economic policy is operating.
For network expansions (and re-design) maintenance concepts for components will need to be determined based on design and environment, and the
revenue cash#ows (operating expenditure and
income) determined. A marketing model must also
be available that can generate a demand function
and the associated revenues; then f 1 for expansion
t
can be estimated, with some uncertainty. Note that
network expansion could not be considered in performance terms in this way (because the gain will
always be negative).
3.2. Prioritizing project release
We now consider how the simple model relating
to individual projects can be used in planning and
release over the horizon (0, h). Let g (h) be the gain
ij
when project i is released in year j (1)j)h), and
suppose that, in year j, n projects have positive
gains. These projects might be listed in order of
magnitude of their gains. They might also be listed
in order of magnitude of the `pro"tabilitya index,
292
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
g (h)/C , where C is the capital cost of project i. If
ij
i
i
rational decision criteria are to be used to determine policy then projects at the top of the list
should be given priority, since they would be associated with the largest expected gain over the
planning horizon. Under capital rationing with
a "xed budget, this project priority list would indicate which projects can be released in the current
year.
For consequences considered in cash#ow terms,
appropriate discount rates may be chosen to re#ect
the investment risks of projects. A higher required
rate of return (smaller discount rate) might be
imposed on network expansion projects than on
replacement of existing assets. The capital asset
pricing model (e.g. [13]) provides a means for
determining discount rates in these circumstances.
In the case of replacement of existing assets such
variation in the choice of discount rates might
also be used to re#ect uncertainties regarding new
technology.
The capital costs of projects may be formulated
to include shared set-ups, and also the cost (consequences) of a poor replacement. When an opportunity arises to share a set-up cost, then optimal
policy for the network may be re-calculated. This
may change the project priority list. Opportunities
themselves may arise through: the failure of a
component necessitating remedial work; a repair
initiated through condition monitoring; the
combination of projects on the same network; or
combination of projects on di!erent networks.
3.3. A capital rationing model
Suppose the network owner wishes to prioritise
project release over the next k years (k)h). This
problem may be approached using linear programming (LP), similarly to that proposed in vehicle
#eet management by Karabakal et al. [14]. Suppose the capital investment budget for year j is
B ( j"1,2, h). Introduce the indicator variable
j
x which takes the value 1 if project i is released in
ij
year j and 0 otherwise. Then we seek those values of
x (i"1,2, n; j"1,2, k) which maximize the
ij
total gain over (0, h) of all projects released subject
to the constraints that the capital investment
budget is not exceeded in each year. That is
maximize
n k
+ + x g (h)
ij ij
i/1 j/1
subject to
n
+ x C )B for all j"1,2, k;
ij i
j
i/1
k
+ x )1 for all i"1,2, n;
ij
j/1
x "0, 1.
ij
(2)
(3)
Constraint set (2) ensures that the budget for year
j is not exceeded. The annual budgets would re#ect
that capital available once imperative projects have
been "nanced, perhaps for reasons of health and
safety legislation. Constraint set (3) ensures that
project i is released at most once over the planning
horizon. Note that if an individual project has
negative gain whatever its execution time, then the
contribution to the objective function from this
project will be greatest when this project is not
released over (0, k). Tax considerations in particular
contexts will need to be taken into account and
modelled; as these are contextual, discussion is
omitted. Typically, such planning may be informative over the planning horizon, but only decisions
relating to the immediate future (one to two years)
would be acted on. Therefore, policy would be
continually updated, implying a `rolling horizona
approach. Where a network consists of many identical components, the modelling of project planning
may be extended to the case in which a proportion
of `similara projects are released in a given year.
This could be done by formulating the capital
rationing model (CRM) as a mixed programming
problem.
The performance implications of rationing can
be determined by considering optimal performance
for a number of di!erent budgetary constraints:
a plot of performance improvement against annual
budget would be informative to network owners.
An alternative LP problem could be formulated to
determine the release times of projects which minimize capital expenditure while meeting performance improvement constraints.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
3.4. Modelling dependence in replacement costs
While the dependence between the capital costs
of di!erent projects may be considered simply using
the concept of shared set-up, the CRM model
above ignores dependence in operating cash#ows
between components. For example, a major expansion project, while not replacing existing assets,
may have signi"cant operating cost or performance
implications for particular assets: the building of
a large ring main in a water supply network is one
such example.
Essentially, if two projects P and P interact in
1
2
this way, then new projects P@ "(P , not P ),
1
1
2
P@ "(not P , P ), and P@ "(P , P ) would have
12
1 2
2
1 2
to be introduced, along with the constraint to ensure that at most one of P@ , P@ , and P@ is released
12
1 2
over the planning horizon. While this approach
may lead to a signi"cant increase in the number of
`projectsa in the CRM, in principle the solution
procedure would remain unchanged. The existence
of future-cost dependencies between projects would
have to be identi"ed by the network owner. This
may be extremely di$cult in practice. However,
such dependency would very much characterize the
network replacement problem, and therefore, the
approach described is an advance over current
methods. A similar approach has been taken
by Santhanam and Kyparisis [15] in modelling
dependency in the project release of information
systems. It is possible that it may be optimal to
release both P and P during the planning
1
2
horizon, but not simultaneously. This presents
a more di$cult modelling task, without introducing many pseudo-projects, that is. For example, we
could consider: release P at time s and P at time
1
2
t; however, for k"10, say, this would mean the
, for the
introduction of 25 variables, x 1 2
(P P )(s,t)
P , P decision alone!
1 2
293
expenditure; this is the marginal increased revenue
expenditure due to delayed release (see Ref. [12]).
Given the CRM, and focusing on cash#ows, the
operating cost consequences of capital rationing
can be determined by calculating the delay associated with each project as a result of capital
rationing. The revenue cost implications due to this
delay would from expression (1) be
h~xH
h~x{
x{~1
g (h)" + f 0lt# + f 1lt`x{! + f 1lt`xH,
t
t
t
P,x
t/0
t/0
t/xH
where x@ is the execution time for the project under
capital rationing. The marginal increase in revenue
expenditure would be found by summing over all
projects. In a similar manner, the marginal increase
in revenue expenditure due projects delayed in year
j, dc , could be found by summing (x) over all
j
projects with xH"j. Marginal reductions in capital
expenditure under CR, dC , could also be calj
culated.
The capital savings and increased revenue expenditure under CR are in part due to delayed execution and the rest due to non-execution. If the
marginal increase in revenue expenditure in year j,
dc , increases with j then this is indicative of the fact
j
that capital investment is too low to control operating costs in the long term. If the dc are approximj
ately constant with time then this indicates that
capital expenditure is su$cient to maintain the
current level of operating cost. In these circumstances, consideration of dC will indicate how much
j
more capital investment would be required to
reduce revenue expenditure to the optimum level.
When the network operator is interested in
maximising performance, the marginal reduced
performance due to delayed release can only be
considered when comparing alternative budget
contraints.
3.5. Cost consideration of sub-optimal policies
3.6. Choice of decision criteria
For reasons of budgeting constraints or technical
delays, the release of some recommended project at
some optimal execution moment xH may not be
possible. In such cases, it would be informative to
have an indication of the extra cost to be incurred
in revenue expenditure because of lack of capital
The approach discussed in detail in this section
has considered either cash#ows or performance.
Alternative criteria such as safety (e.g. annual
number of fatalities) can be considered in a similar
manner to performance. Such quantities would have
to be determined for existing assets, replacement
294
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
assets and network expansions. All criteria might
be considered using a MCDA approach (e.g. [16]),
or using the "nancial appraisal pro"le
approach of Le#ey and Morgan [17]. They may
also be considered through suitable constraints in
the capital rationing model. A simple approach
would consider, for example, two project priority
lists, one on the basis of cash#ow, the other on the
basis of performance; projects high on both lists
would then be candidates for release.
3.7. Modelling the uncertainty in the `costsa
Uncertainty in the cash#ow/performance model
parameter estimates, re#ecting the extent of currently available information about particular components and potential projects, and the extent of
technological developments (new materials and
techniques), may be propagated through into
uncertainty in the gain function, g(h). This would be
most easily done using the delta method [18]; see
Baker and Scarf [19] for an example of this in
maintenance. The variance of the gain, as well as
the expected gain, may then be used to produce the
project priority list and those projects for which the
expected gain is high and the uncertainty in the
gain (variance of the gain) is low are candidates for
release; these projects would be viewed as sound
investments. Markovitz [20] is the classic reference
here; for a more recent discussion see Booth and
King [21].
Where there is no data regarding a potential
project, there will be no objective basis for determining if and where the project lies on the project
priority list. One possible approach to this problem
would be to use data relating to other projects that
are similar in design. Also subjective data may be
collected, and used to update component data for
the whole network in the manner described in
O'Hagan [22] and Goldstein and O'Hagan [23] in
the context of sewer networks. These methods
are particularly useful for multi-component
systems in which there is only limited data for
a limited number of individual components. On the
other hand, it may be that the income cash#ow may
be deterministic in some situations. For example,
expansion of the network may be initiated by
legislation, and that the compensation for the
investment costs are "xed and predetermined per
customer connection.
How uncertainty in the information we have
a!ects the segmentation of the network will also
require some attention in future work. The e!ect of
such structural uncertainty on the outcome of the
modelling exercise is a topic of signi"cant current
interest in Statistics. A simple approach might consider a simple sensitivity analysis; the more systematic approach of Draper [24], which assigns a prior
distribution over the structural model types, lends
itself more naturally to a knowledge-based
approach within an information system.
4. Example: Electricity distribution networks
The 11 kV overhead networks are a major
contributor to performance of the UK electricity
supply network. The network owner will consider
typical policy alternatives for these networks as
follows:
f Upgrading the construction of 11 kV spurs.
f Upgrading the construction of 11 kV main
branches.
f Removal of the 33 kV network, that is going
straight from 132 to 11 kV.
f Refurbishment of lines, for example: restringing,
replacing insulators, replacing poles.
f Using covered conductors rather than bare wire
for 11 kV.
f Undergrounding 11 kV lines } cables then tend
to be more reliable but have longer repair times.
f Repair team deployment } weather is a signi"cant factor in overhead network performance, as
is time taken to reach a fault. Therefore deployment of teams at high risk periods can provide
bene"ts.
f Alteration to the protection settings or method
of protection.
These alternatives can then form the basis of potential projects for investment, the objective being the
improvement of network performance. Performance is generally measured by the average number of customer-minutes lost and the average
number of customer interruptions per year.
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
Following the framework, we would in principle:
(1) identify and evaluate the network in component terms;
(2) design the maintenance concept;
(3) estimate the performance of the network;
(4) identify a potential project;
(5) estimate the performance of the network on
completion of the project;
(6) repeat (4) and (5) for other performance improvement independent projects;
(7) rank projects by their performance improvement characteristics (or set-up and solve the
CRM);
(8) investigate and report the relationship between
annual budget, say, and annual performance
improvement.
For electricity supply networks, (1) and (2) above
will be well established. The network simulation
facility (eaNSF) program [8] has been developed to
do (3) and (5), and calculates expected network
performance indicators and cash#ows over a number of years for speci"c networks. An implementation of the framework would choose between
di!erent policy alternatives, determine which parts
of the network to apply them to, and provide decision support in budget setting.
We anticipate a number of di$culties in this
approach. Information regarding component reliability may be sparse } knowledge about how failure
rates alter if, say, a line is refurbished may be
di$cult to quantify } and although some data may
be available, it may be di$cult to disaggregate
factors such as geographical location and weather
conditions. There will be uncertainty in the network performance indicators; the initial problem is
how to relate interruptions to the number of customer-minutes lost, or to operational costs. This
becomes more di$cult the further into the future
that predictions are required. It may also be necessary to disaggregate the network into regions in
order to keep the number of projects within reasonable limits. With a large number of alternative
projects, a `what}ifa approach may be taken. Also,
constraints must be introduced into the project
release decision-making problem to ensure that
projects which are alternatives (for example, undergrounding a 11kV line and refurbishment of the
same overhead line) cannot be released `togethera.
295
Additionally, the likely degradation in the network
performance during execution of projects would
need to be estimated.
5. Discussion
The companies running the distribution networks are under constant pressure to reduce
operating costs while maintaining or improving
standards. Given the maturity of these industries,
the management of performance and operating
costs rather than carrying out network expansion is
the main goal. As the distribution networks typically form a monopoly resource and will remain so
for the foreseeable future, regulators limit charges
and have introduced penalties for poor network
performance. Hence the main objectives of network
owners are: maximising average network performance; minimising expenditure; bounding yearly
expenditure; avoiding a disastrous performance,
for example, of the type recently experienced by
Yorkshire Water, and with power supply in
Auckland, New Zealand.
This paper attempts to address these issues
through a complete framework for network
asset management, which is the "rst step in the
development of a model for NAM. The framework is
motivated by the need to bridge the gap between
technological reasoning and economic decision
making; that is between engineering judgement,
which provides the basis for maintenance concept
design, performance measurement, and the identi"cation of potential replacement/refurbishment projects, and the economic decision making of the
network owner. The advantage of this framework is
that it takes a broad view of the entire process, is not
motivated by a particular problem, and attempts to
take a top down view of NAM. It is expected that
the framework will be useful for di!erent decisionmakers both within the network owner at di!erent
levels of management, and across di!erent network
owners. The framework would also be useful for: the
valuation of the assets of the network owner; and for
demonstrating that the owner is employing state-ofthe-art management of its assets.
The replacement modelling described in the
framework takes a simple form, with emphasis on
296
P.A. Scarf, H.H. Martin / Int. J. Production Economics 69 (2001) 287}296
ease of implementation within a network information system. Publications to date in this area, on the
other hand, deal with particular aspects of network
maintenance and replacement and invariably
describe complex models. Thus, an aim of this paper is to describe simple models in a complex context. The next step is to build a prototype model
that looks at a small scale network using the
elements of the framework outlined here, initially
concentrating on refurbishment and replacement
decision-making.
Acknowledgements
The authors would like to thank colleagues at
KEMA, Transport and Distribution department,
and Tony Christer for his helpful comments on
earlier drafts of the paper. The paper has been
considerably improved through the helpful
comments of the referees. This research has been
supported in part by EPSRC grant number
GR/L20801.
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