A Fuzzy DSS for Contractual Risk
Allocation in Bioenergy Projects
Daniel Wright1,*, Prasanta Dey1, John Brammer1
1
Aston University, Birmingham, UK
* E-mail: [email protected]
Track: Operations, Logistics and Supply Chain Management
Developmental (Discussion) Paper
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
Summary: The aim of this research is to design a decision support system (DSS) for the
allocation of contractual risk in bioenergy projects. The paper explores the established body
of risk analysis and allocation research in the general construction and project management,
then develops on the existing models to create a novel methodology for contractual risk
allocation in the bioenergy industry. The paper concludes that the rapid developing bioenergy
industry in Europe could greatly benefit from a contract and risk allocation support system.
Keywords: Bioenergy, Decision Support System, Risk Allocation and Contract Design
Purpose
Bioenergy project development requires large volumes of information to be processed by
developers to successfully progress through the necessary developmental stages in a project
(Mitchell, 2000). Academia has shown great enthusiasm in aiding the developer with decision
support systems (DSS1) that target the complexities of feedstock supply chain design (Ayoub
et al., 2009, 2007, 2006) and facility location for maximum bioenergy resource exploitation
(Freppaz et al., 2004). These issues are confined only to bioenergy projects not any other
renewable energy technology. This is a possible explanation why all of the current modelorientated decision support systems in the bioenergy industry show short-sightedness in only
supporting the decision-maker in the developmental phase of the bioenergy project lifecycle.
Carlos and Khang (2009) identified in their research that there are three clearly defined phases
in lifecycle of a bioenergy project: development, construction and operation.
The construction phase of any project is known to be inherently risky; resulting in over four
decades of risk analysis research in construction and project management literature (Laryea
and Hughes, 2008). Bioenergy projects, in particular, with their varying levels of
technological risk compiled with the possible lack of contractor expertise as this is a new
industry, must be classified as some of the riskiest projects to handle during this phase.
Therefore, it is surprising that there is a clear lack of research in this area: bioenergy projects
and renewable energy in general. It is hypothesised that risk analysis and allocation in this
phase of a project lifecycle is conducted in the industry, but is likely commercially sensitive.
Rapid drives to increase the development of bioenergy projects of all sizes to meet the
European and member state country targets has led to many small to medium sized enterprises
(SME) entering this potentially lucrative market. With the varying abilities and expertise
levels of the industry decision-makers, there can only be an ever increasing need for a support
tool to assist in this construction phase.
Objectives
The research objectives are to:
- Investigate the most suitable risk analysis and allocation methodology
- Construct a framework of how this decision support system would function
1
The DSS acronym represents both the singular and plural of decision support system
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
Contribution
The research contributes as it is the first paper to develop a model-orientated DSS
methodology for analysing and allocating risk in bioenergy projects.
Decision Support Systems in the Bioenergy Industry
There are many model-orientated DSS research papers over the past 20 years, these papers
attempt to represent or optimise a most often specific non-generalisable issue. A timeline of
all the relevant research papers over this period is shown in figure 1.
BEAM: Originally developed to
solve the problem of storing data
from a series of trials of harvesting,
storing and drying wood fuel. Later
versions, investigated the
relationship between feedstock
supply and conversion on energy
production cost.
A biomass supply chain
configuration DSS model.
This analysed supply chain
configuration and its effect
on delivered cost.
Cundiff et
al. (1997)
Nagel
(2000)
DSS to optimise the
district heating pipe
network in an isolated
case study area.
1990s
A DSS for representing
agricultural biomass
residues and power
plant location.
IBSAL Model: A supply
chain simulation model,
which tracks material flow
from field to biorefinery.
gBEDS: A two-level DSS for
planning, implementing and
optimising bioenergy
projects in Japan.
2000
2001
Voivontas et al.
(2001)
EDSS: The environmental
DSS model analyses
location planning and
technology configuration to
optimise decision.
A supply chain configuration
model for cotton biomass in
Greece.
Tatsiopoulos and Tolis
(2003)
Mitchell (2000)
1980s
BSM: A techno-economic
facility siting and supply
chain configuration DSS for
California in 2015.
2002
2003
Frombo et al. (2009a;2009b)
Stokhansanj
et al. (2006)
2004
2005
Freppaz et al. (2004)
Location planning DSS
for maximising forest
biomass capture in Italy.
Ayoub et al. (2009;2007;2006)
2006
2007
2008
Tittman et al.
(2010)
2009
A DSS for exploiting renewable
energy sources (including biomass) in
Taiwan. Analysing cost, incentives
and return on investment.
2010
Rentizelas and Tatsiopolous
(2010) and Rentizelas et al. (2009)
van Dyken et
Analyses both location and
al. (2010)
supply chain decisions for
eTransport is a DSS
district heating and cooling
currently under
networks in Greece. the
development. The DSS
financial return.
aids in planning and
configuring supply chains.
Yue and Yang (2007)
Fig. 1. Annotated timeline of model-oriented bioenergy DSS research.
The timeline highlights 13 DSS models created for developing bioenergy projects, primarily
spanning two decades, with most models being created in the last 10 years. The timeline gives
a brief overview of each DSS and shows the publication lag, or when explicitly stated in the
article, when research began.
Risk Analysis and Allocation in the Construction Industry
It is common knowledge that a multitude of qualitative and quantitative risk analysis or
management techniques exist. As Flanagan and Norman (1993:69) state, the two broad
categories are: deterministic and stochastic or probabilistic. It is also well known that risk
analysis techniques are common place in the construction and project management industries.
However, a reliance on qualitative techniques persists in the general construction industry
with namely “intuition, judgment and experience” having the highest level of use (Akintoye
and MacLeod, 1997). It is assumed that the risk management methods utilised by the
bioenergy project developer are similar to what was found in Akintoye and MacLeod’s
contractor’s perspective research. Although, this experience based method is easy to apply
and useful, in its own right is likely to lead to sub-optimum risk analysis, especially in the
context of this research with non-expert developers.
There are four well defined and understood actions for responding to risk:
- Reduction: focuses on reducing risk causes and risk effects
- Avoidance: is simply about getting rid of the risk
- Retention: is accepting the risk
- Transfer: changes the responsibility and ownership of the risk
(van Staveren, 2006:140-141)
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
The transfer or allocation of risk is controlled by the developer. If a developer is unwilling to
carry a project risk or believes that it could be better managed, they can transfer or share it
with the contractor. Effective risk transfer is an important decision-making process that
increases project success (Lam et al., 2007) and reduces the overall cost (Zaghloul and
Hartman, 2003). When transferring risks the contractor has the option to add a premium to the
contract price, to enable them to ‘manage’ the risk. The risk premium incurred by the
developer affects whether it is worthwhile transferring the risk. Contractors most commonly
add risk premium in the form of a contingency margin within the total project cost (Akintoye
and MacLeod, 1997). Though, it is argued that the risk premium incurred by the client is often
subjectively calculated by the contractor, with Perry and Hayes (1985) arguing that premium
is often, simply, an arbitrarily chosen percentage of capital expenditure, because of a lack of
objective risk analysis techniques being applied by contractors or project management
consultants.
Methodology
By developing on the available qualitative and quantitative techniques available for the
analysis of general construction risk and risk allocation, the research explores the use of two
fuzzy logic methodology papers, in the bioenergy industry: Carr and Tah (2001) and Lam et
al. (2007).
Fuzzy set theory is one of the newer conceptual approaches to risk analysis and allocation,
which has seen a recent flurry of activity in the construction industry (Gao and Jiang, 2008,
Carr and Tah, 2001, Lam et al., 2001, Lam et al., 2007, Tah and Carr, 2000, Tah et al., 1993),
this is not a new topic, as was first proposed by L. A. Zedah in 1965, who is regarded as the
forefather of the theory. Fuzzy set theory is a useful tool in a lot of areas, but especially risk
analysis and allocation where there is “…decision-making in an environment of uncertainty
and incompleteness of information” (Zadeh, 2002:ix). The reason being, the theory is
different to the traditional probabilistic techniques as it does not require exact values to be
attributed to variables; their value can be ‘fuzzy’, creating an ideal methodology for risk
where there is great uncertainty in the perception of risk. As it is not possible to rely on the
dominant ‘expertise and judgement’ method for risk analysis (Akintoye and MacLeod, 1997)
as this is a new industry and expertise is in short supply and often carries a high premium. It
was necessary to select the most appropriate method to maximise benefit for the, likely, ‘nonexpert’ decision-maker. The justification for selecting fuzzy logic for risk analysis and
allocation is the subjective (fuzzy) probability estimations was considered more suitable as
they accommodate a level error in the decision-makers weighting and are excellently suited to
areas with uncertainty or incompleteness of information (Zadeh, 2002:ix). Hence, the more
precise numerical input required for other probability models such as Monte Carlo simulation
or sensitivity analysis meant that they were not suitable for this type of user. Moreover, other
popular techniques such as analytical hierarchy process (Eom, 2007) are generally useful
tools for prioritising risks based on a decision-maker(s) allocated criteria, but as the model
users are potentially not experts, it seemed unwise to allow them this amount of freedom.
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
Expert Systems
Expert systems (ES) are a sub-set of the DSS discipline, which incorporate knowledge and
expertise to operate as an ‘artificially intelligent’ support tool. Typically, ES comprise of
three major components, as shown in the figure below:
Fig. 2. Overview of an Expert System. (Darlington, 1999:24)
Firstly, the knowledge base is a database of expertise rules and heuristics, gathered from
primary research with both contractors and developers of bioenergy projects in the UK.
Secondly, the inference engine interacts with the knowledge base, interpreting the rules in a
specific order to reach a conclusion. It is possible in the inference engine to integrate fuzzy
logic to adapt the ES to the research problem. Finally, the user or developer in this instance is
able to interact with the user interface to solve problems; which in this case, is the most
suitable allocation of contractual risk in a bioenergy project.
A flow chart of the proposed stages in the final models and their interaction with the DSS
knowledge base can be seen in figure 3:
Lam et al.’s (2007) adapted
model
Carr and Tah’s (2001) adapted
model
Model
Identification of significant risk
factors
Knowledge base
Pre-identified construction
risks for small-scale bCHP
projects
Assessment and characterising
risk likelihood and severity in
linguistic terms
Analysis of risk factors allows
for prioritisation in terms of
magnitude and importance
Pre-defined base rules
from expert’s knowledge
Identify input variable and
linguistic terms
Pre-defined risk allocation
criteria for bCHP projects
Conduct fuzzification - convert
linguistic terms into
membership functions
Examine the relevant base rules
in the Inference Engine
Pre-defined base rules
from expert’s knowledge
Apply fuzzy mathematic
operations to obtain the
membership values of
inferences
Determine final risk allocation
decision
Stored project risk
allocation decisions
Most suitable standard contract
type for client risk and
flexibility appetite
Pre-defined standard
contract types and client
risk base rules
Fig. 3. Flow chart of construction risk analysis models
The flow chart illustrates how the stages in the two existing fuzzy risk analysis and allocation
models have been adapted for the research problem. In addition, based on the overall
A Fuzzy DSS for Contractual Risk Allocation in Bioenergy Projects
bioenergy project risk allocation output distribution of risks, it is possible to suggest a suitable
standard form of contract. This is not attempted in the Lam et al. (2007) risk allocation model,
which only analyses risk on an individual basis. As it is likely that a ‘non-expert’ developer
will rely on a standard form of contract, it is advantageous to add this to the construction risk
model. It is possible to achieve this by the decision-maker entering their required project
flexibility and risk taking disposition in to the model, for the final stage. For example, if the
majority of the risks entered into the model were suggested to be retained by the developer, as
long as the decision-maker approved of risk taking, then the model would suggest that a
higher risk, low premium ‘cost contract’ is preferential over a high premium, lump sum
contract. Figure 4, illustrates how the standard contract forms are related to the overall risk
transfer and premium levels:
Fig. 4. Risk allocation model standard contract form output. (adapted from Pan, 1996)
Developments for the BAM 2011 Conference
As this is still only a developmental paper, there have been targets set which will be
integrated into the presentation of this paper by September 2011, as long as the research
remains on schedule:
- A partially built knowledge base and fuzzy logic based inference engine, and;
- A demonstration of an early version of the DSS tool.
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