Quantifying the Uncertainty Associated with Long Term Maintenance Contracts
Date
2003
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
Type
Degree Level
Doctoral
Abstract
Long term maintenance contracts are emerging as an alternative for state agencies to man
age their infrastructure assets. For the owner, long term maintenance contracts establish
a deterministic schedule for maintenance costs over a fixed time horizon. It has been documented that contractors are willing to accept the risks associated with long term maintenance contracts when provided with the information necessary to assess the potential
risk and the freedom to provide innovative solutions to address these risks.
The objective of this research was to develop a generic framework to quantify the
financial risk faced by a contractor in bidding a long term maintenance contract for public sector infrastructure. To accomplish this goal, a methodology was developed to take
generic infrastructure asset performance curves, maintenance treatment costs, and mini
mum performance criteria as inputs to calculate the present value of the expected maintenance costs for a long term maintenance contract. The probability distribution associated
with these predicted costs can then be applied by a contractor (in conjunction with their
risk tolerance) to establish the appropriate tender price. By adjusting the input parameters, the contractor can determine the sensitivity of the optimal maintenance strategy
to model inputs. The sensitivity analysis allows the contractor to determine the inputs
that must be controlled to ensure success as well as to identify the areas which could potentially provide the greatest opportunity for savings. The framework developed in this
research is a generic mathematical methodology, applicable to all forms of public sector
infrastructure. To illustrate its application, a roadway pavement management problem
was selected.
The methodology to accomplish the research objective was quite straight forward.
The first step in the process was to generate transition probabilities from infrastructure
asset performance curves. These transition probabilities provided a mathematical representation of asset deterioration and the effects of maintenance and rehabilitation activities
throughout the term of the contract. From the transition probabilities, an optimal maintenance strategy was determined. The optimal maintenance strategy was modelled over a
ten year time horizon (the typical length of a long term maintenance contract). From the
ten year model, expected costs, variance, and in tum the risk associated with a project (individual maintenance segments in a contract) were determined. The sum of the expected
project costs is equal to the expected total cost of the long term maintenance contract. The
variance of expected costs of a long term maintenance contract can be determined in a
similar manner. Thus, if the risk associated with individual maintenance segments can
be determined, the risk associated with a long term maintenance contract can be deter
mined. A series of sensitivity studies were also included to determine the sensitivity of
the optimal maintenance strategy to changes in asset performance, maintenance costs, or
performance constraints.
In general, the methodology performed well. It was observed, as would be expected,
that reducing the rate of asset deterioration reduced maintenance costs. Similarly, in
creasing treatment effectiveness resulted in a decrease in overall maintenance costs. The
maintenance strategies for each scenario were quite similar. The only real change between
scenarios was the frequency with which the treatments were applied.
A limitation to this study was the use of a Markov process to create numeric representations of asset deterioration. The Markov process overestimated early deterioration and
underestimated deterioration late in the lifecycle of the asset. It is suggested that a semi
markov model would be better suited to model performance curves with the geometric
characteristics of the performance curves included in this research; curves with little or
no slope for the first few years of the asset's design life.
Description
Keywords
Citation
Degree
Doctor of Philosophy (Ph.D.)
Department
Civil Engineering
Program
Civil Engineering