Description
Complex plants and investments are commonplace in the Petroleum Supply Chain (PSC), known for its highly automated infrastructures and processes. Expensive equipment items like drilling rigs, offshore platforms, oil tankers, refineries, pipelines, petroleum depots and transport equipment are critical to this industry. The petroleum supply chain appears as a significant risk and high impact industry at the micro and macro economic level.
20th European Symposium on Computer Aided Process Engineering – ESCAPE20
S. Pierucci and G. Buzzi Ferraris (Editors)
© 2010 Elsevier B.V. All rights reserved.
Risk Management Framework for the Petroleum
Supply Chain
Leão J. Fernandes,a,b, Ana Paula Barbosa-Póvoa,b* Susana Relvas, b
a
CLC, EN 366, Km 18, 2050 Aveiras de Cima, Portugal, [email protected]
CEG-IST, UTL, Av.Rovisco Pais, 1049-001 Lisboa, Portugal, [email protected]
*
Corresponding author: [email protected]
b
Abstract
Complex plants and investments are commonplace in the Petroleum Supply Chain
(PSC), known for its highly automated infrastructures and processes. Expensive
equipment items like drilling rigs, offshore platforms, oil tankers, refineries, pipelines,
petroleum depots and transport equipment are critical to this industry. The petroleum
supply chain appears as a significant risk and high impact industry at the micro and
macro economic level. Although, risk management bears prime importance for this
industry, there is notorious absence of quantitative modeling. This paper introduces the
relevance of a systematic approach for the identification, quantification and mitigation
of risk and presents a practical framework for risk management. A PSC example is used
to demonstrate its utilization and the resulting information identifies modeling data for a
PSC risk management tool.
Keywords: petroleum, risk management, uncertainty, framework, roadmap
1. Introduction
The Petroleum Supply Chain (PSC) is a complex assortment of infrastructures and
processes whose mainstream begins with the exploration of crude oil and finalizes with
the delivery of petroleum products to consumers. This industry moves huge quantities
of products and value and is backbone to almost all economic activity. This strategic
sector is highly automated and optimized, so disruptions can rapidly escalate to an
industry-wide or nation-wide crisis. Oil companies, aware of these risks, have put
significant effort in Risk Management, however most of the work is qualitative and is
still at the initial stage. Nevertheless, some advances have been done in quantitative risk
management for pipeline integrity, Muhlbauer (2004) and Alvino (2003). Besides,
considerable research can be observed in Supply Chain Risk Management (SCRM)
which has led to the publication of important reviews. Categorization of these
developments can be found in Tang (2006) and Peidro et al. (2008). However there is
no direct method to identify possible uncertainties, risks and mitigation strategies for a
particular situation.
This investigation builds on earlier research and constructs a framework that is then
tested specifically for the petroleum supply chain. This framework appears as a practical
method that assists in structuring the activities and the information of the risk
management process. The following sections describe the petroleum supply chain,
present the developed framework for risk management, demonstrate its utilization using
a PSC real case, identify modeling directions and finally present the conclusions and
proposals for future research.
L.J.Fernandes et al.
2. Problem Statement and Background
Fig. 1 resumes the petroleum supply chain that divides into two major areas: upstream
and downstream. The upstream comprises of crude oil exploration, production and
transportation. The downstream industry involves product refining, transport, storage,
distribution and retail. These are major activities, which aggregate several hundreds of
processes and thousands of equipment items where availability is of paramount
importance.
Figure 1: The Petroleum Supply Chain
The PSC activities are sequential in nature and as such any failure is critical to the next
stage and more so as this implies huge working capital that is blocked in petroleum
inventories. The prevalent risks in business, operations, finance, environment, safety
and security provide a huge potential for risk optimization. The problem scope will
focus on risk management for the PSC. Hence the problem frontiers include processes,
equipment items, activities and costs while the main drivers are risk sources, impacts
and mitigation strategies.
3. A Hierarchical Framework
Literature on PSC risk management is mainly confined to qualitative approaches
concerning the process of risk analysis and assessment, thereby exposing an absence of
quantitative modeling. This lack of risk structuring and breakdown methodology
provides the motivation to develop a framework that could provide a structured method
for the risk identification, quantification and mitigation process. Research on SCRM
and PSC literature and investigation on PSC risk management (Fernandes et al., 2009)
has lead to the development of a simple framework that assists in capturing and building
quantitative data through a well defined risk management process. Fig. 2 presents a new
risk management hierarchical framework. The framework builds an information model
using two processes: Risk identification process and the Risk mitigation process.
The risk identification process utilizes the framework to identify and hierarchically
relate first the risk agents, second the risk sources, the risk objects and finally the risk
events. Risk elements are identified by asking the following questions: Who initiates the
risk? Risk agent; What are the causes of the risk? Risk source; Which resources are
affected? Risk object; and How does the risk manifest? Risk event. More specifically,
risk agents are the drivers of organizational risks, for example the finance area or the
transport activity. Risk sources are the causes of risks, which provide a negative
impetus to the risk objects thereby generating a risk. An example of a risk source is the
increase in value added tax. Risk objects are resources of an organization, whose
malfunctioning would originate a consequence. An example of risk objects is the
country tax structure. Risk events are the factual occurrence of the risk thereby resulting
in the effective loss, for instance reduced profits. In a nutshell, the framework indicates
Risk Management Framework for the Petroleum Supply Chain
that the risk agent (financial area) includes a risk source (value added tax) which can
affect the risk object (tax structure) thereby generating a risk event (reduced profit).
1. Risk Agent
2. Risk Source
3. Risk Object
4. Risk Event
8.
P
6. Planning Level
7. Mitigation Strategy 5.
Co
ns
e
qu
en
ce
ay
of
fs
s
Figure 2: Risk management hierarchical framework
The above risk identification process culminates into the identification or computation
of the consequence estimated for each risk agent/source/object/event combination. The
framework follows the risk mitigation process to complete the risk information. Each
risk agent initiates a hierarchical identification of a planning level and an appropriate
mitigation strategy that could reduce the potential risks of the risk sources. Planning
level provides a timeframe for the mitigation activity, which could be Strategic or longterm, Tactical in the case of mid-term, Operational or short-term planning and
Contingencial or post-occurrence planning. Mitigation strategies are counter measures
that could reduce the likelihood and the consequences of the risk events triggered by the
risk sources. Expected payoff estimates should be computed for the combination risk
source/mitigation strategy to provide the quantitative data.
Fig. 3 presents an influence diagram of the proposed risk management framework. The
circular chance nodes represent the uncertain events and the square deterministic nodes
represent the decision events. Finally, the diamond-shaped element depicts the result or
the expected payoff for the enterprise. Hence, the risk agent stochastically influences the
risk source and the planning level, which consequently direct the mitigation strategy.
The risk source stochastically determines the risk objects affected which consequently
undermine the occurrence of a specific risk event. The mitigation strategy influences the
risk object and the risk event thereby affecting the risk outcome.
Risk Agent
Risk Source
Planning
Level
Mitigation
Strategy
Risk
Outcome
Risk Object
Risk Event
Figure 3: Risk management framework influence diagram
The risk elements are presented in their aggregated form, however these elements can
be further decomposed, based on their importance and the depth of quantitative
modeling required. Hence a risk agent (Operation) could be sub-divided into risk
agents 1, 2 and 3, more precisely Transport, Storage and Production operations. The
transport operation 1 can be further divided into pure products transfer 11 and
L.J.Fernandes et al.
interface products transfer 12. The pure product transfer operation 11 can be further
sub-divided into 111.. 115, namely, Butane, Propane, Diesel, Gasoline and Jet transfer
operations and so on. The same theory can be applied to risk sources. Analogously, the
risk object storage can be decomposed into individual product subsystem. Each product
subsystem can be decomposed into the dynamic, static and instrumentation equipment.
Static equipment could divide further into tubes, tanks and spheres. Finally risk events
can also be hierarchised, for instance BLEVE(boiling liquid expanding vapor explosion)
can be subdivided in accordance to its intensity, duration and/or time of occurrence.
The ongoing demonstrates that the developed framework supports a flexible and
interactive process for risk identification and risk mitigation information gathering that
could be used to construct a holistic risk management decision tree or a decision matrix.
The following section provides a demonstrative example of the construction of a
detailed risk management decision tree for the PSC using the RM framework.
3.1. A PSC-RM example
As mentioned earlier, the hierarchical framework could be used to construct a decision
tree to guide building of an information database that could drive a quantitative model
such as a mathematical model to optimize the risk management process. A real case
example is used to demonstrate the building of a decision tree using the framework.
Companhia Logística de Combustíveis (CLC) is a strategic lean member of the
Portuguese petroleum supply chain which owns and operates a petroleum products
pipeline and a storage and expedition infrastructure.
Some risks of this organization are visited, though not in a detailed form due to lack of
space, using the simplified decision tree presented in Fig. 4 that was built using the RM
framework. Although the strategic, tactical, operational and contingencial planning
levels are considered, the focus is on the operational risk management. Operational risks
are seen to stem from various risk agents in the PSC, which could generally arise form
the Business, Condition, Operations, Hazard and Finance agents. Each of these risk
agents can be subdivided to observe a detailed view. For instance, the risk agent of
condition could be subdivided into pipeline, storage and bottle filling conditions.
Further exemplification concentrates on the pipeline operation sub-tree.
The pipeline operations are decomposed as pure products and interface operations. Pure
products include the diesel transfer operation where risk sources such as third-parties,
construction, corrosion, ground movement and operator errors are identified. Focusing
on the corrosion risk source, two mitigation strategies are identified, namely product
buffering and risk based inspection. Corrosion affects various risk objects including
inventory, sales, pump station and transport duct resulting in risk events like ruptures,
holes in pipe, cracks and coating damages. These risk events are categorized as ignition,
no ignition, reported and unreported and again subdivided as detonation, high thermal
damage, torch fire, product spill, corrosion leaks and no leaks. As statistical correlation
is observed between the risk elements, a detailed classification although industry
dependent, is crucial to risk quantification. Bayesian theory can be applied to corrosion
determining factors like product, soil, construction material, protection and age to
estimate the probability of the possible risk events and outcomes referred earlier.
The decision tree in Fig. 4 provides a risk profile of a petroleum tank farm and pipeline
company using the new risk management framework. The risk probabilities and
consequence costs are calculated, based on the literature and empirical estimates in the
petroleum sector. Costs include lost sales, product, equipment, environmental fines,
repairs and casualties. For generalization purpose, the monetary unit (m.u.) used equates
Risk Management Framework for the Petroleum Supply Chain
to one day’s EBITDA of the enterprise. This quantification is yet at its early stage and
requires further research. Parameter estimation will be focused in future investigation to
obtain robust generalized measures for risk probabilities, mitigation and consequences.
FALSE
Strategic
+
Risk Agent
-4,07
+
Risk Agent
-5,10
Planning Level
-2,05
FALSE
Tactical
20,2%
Risk Source
+-4,23
Pipeline
Condition
12,12%
Risk Source
+-1,50
Tankfarm
Condition
18,18%
Risk Source
+-1,10
Business
TRUE
Operational
Risk Agent
-2,05
97,5%
Pipeline
0
pure
products
6,34%
Propane
0
transfer
Risk Agent-2
-0,31
0,999%
4,23%
0,6665%
Butane
transfer
0
Gasoline 16,99%
0
transfer
Diesel
transfer
52,54%
0
0,00
0,00
2,6772%
0,00
49,0% Mitigation Strategy
Third
0
+-0,74
party
Risk Source
-0,60
Project
16,0%
0
Risk Source-1
+-0,50
FALSE
Product
-5
Buffer
Corrosion 14,0% Mitigation Strategy
-0,74
0
Risk Object
-15,00
+
Inventory 13,02%
TRUE
Risk
0
based
Inspection
Risk Event
-0,14
+
Risk Object
-0,74
Sales
Pump
Station
3,26%
Risk Event
-0,48
39,17%
Risk Object-1
+ -0,01
+
0
Rupture 1,0%
0
upto 16"
5,0%
Hole in
0
pipe
Risk Event-1
+ -132,78
Risk Event-1
-8,39
+
Detonation
Ignition
4,0%
Crack
0
Coating
damage
90,0%
0
3,2%
Risk Event-1
-0,73
No
ignition
96,8%
Reported
60,0%
BlockValve
Station
4,21%
Remote
Monitoring
Station
Intake
Station
7,89%
0
0
0
0,55%
0
-38,39
3,94E-06
-8,20
7,81E-07
-1,54
99,0%
Product
-0,52
Spill
Risk Event-2
-0,52
0,0142%
1,0%
Increase
-0,17
feed
pressure
Risk Event-2
-0,44
1,43E-06
Corrosion 32,5%
-1,2
leak
Risk Event-2
-0,50
0,0433%
67,5%
0,0898%
-0,16
-0,16
-0,52
-0,17
+
40,0%
No leak
31,9%
8,05E-09
-38,39
Risk Event-1
-0,47
Unreported
Transport
Duct
0,17%
Risk Event-2
-7,15
High
83,33%
thermal
-8,2
damage
16,5%
Torch
-1,54
fire
-1,20
Risk Event
-2,20
Risk Object-1
+ -0,01
Risk Object-1
+ -0,01
Risk Object-1
+ -0,01
7,0%
Risk Object
Ground
0
+-0,20
movement
Operator 14,0% Mitigation Strategy
0
+-0,28
error
JET
transfer
Pipeline 16,16%
Operation
Tankfarm 24,24%
Operation
9,0%
Finance
0,1%
Hazard
FALSE
Contingencial
+
19,89%
0
3,1342%
0,00
Risk Agent-1
-0,33
2,5%
Pipeline
Interfaces 0
Risk Agent-1
+-0,99
Risk Agent-2
-1,00
+
Risk Source
+-1,99
Risk Source
-340,00
+
Risk Agent
-2,67
Figure 4: Framework decision tree for the Petroleum Supply Chain
4. Modeling approaches
The framework presented in earlier sections assists us in structuring the risk sources and
mitigation information. Nevertheless the development of a risk management model is of
L.J.Fernandes et al.
prime importance in order to generate maximum potential from this information. The
framework may require enhancement to feed appropriate modeling techniques which in
turn should be selected taking the framework into consideration.
Various modeling approaches can be found in the literature that model supply chain and
risk management problems. Stochastic dynamic programming, two-stage multiobjective modeling, decomposition models, mixed integer linear and non-linear
programming, scenario simulation, genetic algorithms, decision tree analysis, and agentbased and artificial intelligence are seen to have potential for risk modeling. More
specifically the agent-based modeling used in Adhitya et al. (2007), the multiobjective
model used in You et al. (2009) and the model predictive control from Puigjaner (2008)
have special interest for the PSC risk management due to their proved applicability in
the process industry.
The presented work will evolve from the translation of the presented framework into a
quantitative model approach. Further investigation and testing is required to develop
this framework methodology into a functional model.
5. Conclusions and future work
The current investigation adds to earlier research on methodologies and models on
supply chain risk management which now results in the development of a new SCRM
framework. The framework is used to define a decision tree for the deployment of a risk
methodology. The framework is demonstrated using typical risk and mitigation
scenarios from the petroleum supply chain. Potential modeling directions are considered
for the quantitative risk management model.
Future research will focus on building an elaborate risk profile for the PSC. Major
impact agents such as business, operations and finance risk areas will be targeted. This
effort is directed to developing and case testing an integrated model for risk
management in the petroleum supply chain.
6. Acknowledgements
The authors thank Fundação para Ciência e Tecnologia (FCT) and Companhia Logística
de Combustíveis (CLC) for supporting this research.
References
A. Adhitya, R. Srinivasan, I.A. Karimi, 2007, Heuristic rescheduling of crude oil operations to
manage abnormal supply chain events, AIChE Journal, Vol. 53, No. 2, 397-422.
A.E.I. Alvino, 2003, Aplicação da Lógica Nebulosa ao Modelo Muhlbauer para análise de risco
em dutos, Doctoral Thesis, Pontfícia Universidade Católica do Rio de Janeiro.
L.J. Fernandes, A.P. Barbosa-Póvoa, S. Relvas, 2009, Risk Management in Petroleum Supply
chain, in Barbosa-Póvoa A.P., Salema M.I., (Eds.), Proceedings of the 14th Congress of
APDIO, Vencer Novos Desafios nos Transportes e Mobilidade, 59-66.
W.K. Muhlbauer, 2004, Pipeline risk management manual ideas techniques and resources (3rd.
Edition), Elsevier.
D. Peidro, J. Mula, R. Poler, F. Lario, 2008, Quantitative models for supply chain planning under
uncertainty: a review, Int. J. Adv. Manuf. Tech.,Vol. 43, No. 3-4, 400-420.
L. Puigjaner, 2008, Capturing dynamics in integrated supply chain management. Comput. Chem.
Eng., 32, 11, 2582-2605.
C.S. Tang, 2006, Perspectives in supply chain risk management, Int. J. Prod. Econ., Vol. 103,
Issue 2, 451-488.
F. You, J.M. Wassick, I.E. Grossmann, 2009, Risk Management for a global supply chain
planning under uncertainty: Models and Algorithms, AIChE Journal, 55, 4, 931–946.
doc_557100829.pdf
Complex plants and investments are commonplace in the Petroleum Supply Chain (PSC), known for its highly automated infrastructures and processes. Expensive equipment items like drilling rigs, offshore platforms, oil tankers, refineries, pipelines, petroleum depots and transport equipment are critical to this industry. The petroleum supply chain appears as a significant risk and high impact industry at the micro and macro economic level.
20th European Symposium on Computer Aided Process Engineering – ESCAPE20
S. Pierucci and G. Buzzi Ferraris (Editors)
© 2010 Elsevier B.V. All rights reserved.
Risk Management Framework for the Petroleum
Supply Chain
Leão J. Fernandes,a,b, Ana Paula Barbosa-Póvoa,b* Susana Relvas, b
a
CLC, EN 366, Km 18, 2050 Aveiras de Cima, Portugal, [email protected]
CEG-IST, UTL, Av.Rovisco Pais, 1049-001 Lisboa, Portugal, [email protected]
*
Corresponding author: [email protected]
b
Abstract
Complex plants and investments are commonplace in the Petroleum Supply Chain
(PSC), known for its highly automated infrastructures and processes. Expensive
equipment items like drilling rigs, offshore platforms, oil tankers, refineries, pipelines,
petroleum depots and transport equipment are critical to this industry. The petroleum
supply chain appears as a significant risk and high impact industry at the micro and
macro economic level. Although, risk management bears prime importance for this
industry, there is notorious absence of quantitative modeling. This paper introduces the
relevance of a systematic approach for the identification, quantification and mitigation
of risk and presents a practical framework for risk management. A PSC example is used
to demonstrate its utilization and the resulting information identifies modeling data for a
PSC risk management tool.
Keywords: petroleum, risk management, uncertainty, framework, roadmap
1. Introduction
The Petroleum Supply Chain (PSC) is a complex assortment of infrastructures and
processes whose mainstream begins with the exploration of crude oil and finalizes with
the delivery of petroleum products to consumers. This industry moves huge quantities
of products and value and is backbone to almost all economic activity. This strategic
sector is highly automated and optimized, so disruptions can rapidly escalate to an
industry-wide or nation-wide crisis. Oil companies, aware of these risks, have put
significant effort in Risk Management, however most of the work is qualitative and is
still at the initial stage. Nevertheless, some advances have been done in quantitative risk
management for pipeline integrity, Muhlbauer (2004) and Alvino (2003). Besides,
considerable research can be observed in Supply Chain Risk Management (SCRM)
which has led to the publication of important reviews. Categorization of these
developments can be found in Tang (2006) and Peidro et al. (2008). However there is
no direct method to identify possible uncertainties, risks and mitigation strategies for a
particular situation.
This investigation builds on earlier research and constructs a framework that is then
tested specifically for the petroleum supply chain. This framework appears as a practical
method that assists in structuring the activities and the information of the risk
management process. The following sections describe the petroleum supply chain,
present the developed framework for risk management, demonstrate its utilization using
a PSC real case, identify modeling directions and finally present the conclusions and
proposals for future research.
L.J.Fernandes et al.
2. Problem Statement and Background
Fig. 1 resumes the petroleum supply chain that divides into two major areas: upstream
and downstream. The upstream comprises of crude oil exploration, production and
transportation. The downstream industry involves product refining, transport, storage,
distribution and retail. These are major activities, which aggregate several hundreds of
processes and thousands of equipment items where availability is of paramount
importance.
Figure 1: The Petroleum Supply Chain
The PSC activities are sequential in nature and as such any failure is critical to the next
stage and more so as this implies huge working capital that is blocked in petroleum
inventories. The prevalent risks in business, operations, finance, environment, safety
and security provide a huge potential for risk optimization. The problem scope will
focus on risk management for the PSC. Hence the problem frontiers include processes,
equipment items, activities and costs while the main drivers are risk sources, impacts
and mitigation strategies.
3. A Hierarchical Framework
Literature on PSC risk management is mainly confined to qualitative approaches
concerning the process of risk analysis and assessment, thereby exposing an absence of
quantitative modeling. This lack of risk structuring and breakdown methodology
provides the motivation to develop a framework that could provide a structured method
for the risk identification, quantification and mitigation process. Research on SCRM
and PSC literature and investigation on PSC risk management (Fernandes et al., 2009)
has lead to the development of a simple framework that assists in capturing and building
quantitative data through a well defined risk management process. Fig. 2 presents a new
risk management hierarchical framework. The framework builds an information model
using two processes: Risk identification process and the Risk mitigation process.
The risk identification process utilizes the framework to identify and hierarchically
relate first the risk agents, second the risk sources, the risk objects and finally the risk
events. Risk elements are identified by asking the following questions: Who initiates the
risk? Risk agent; What are the causes of the risk? Risk source; Which resources are
affected? Risk object; and How does the risk manifest? Risk event. More specifically,
risk agents are the drivers of organizational risks, for example the finance area or the
transport activity. Risk sources are the causes of risks, which provide a negative
impetus to the risk objects thereby generating a risk. An example of a risk source is the
increase in value added tax. Risk objects are resources of an organization, whose
malfunctioning would originate a consequence. An example of risk objects is the
country tax structure. Risk events are the factual occurrence of the risk thereby resulting
in the effective loss, for instance reduced profits. In a nutshell, the framework indicates
Risk Management Framework for the Petroleum Supply Chain
that the risk agent (financial area) includes a risk source (value added tax) which can
affect the risk object (tax structure) thereby generating a risk event (reduced profit).
1. Risk Agent
2. Risk Source
3. Risk Object
4. Risk Event
8.
P
6. Planning Level
7. Mitigation Strategy 5.
Co
ns
e
qu
en
ce
ay
of
fs
s
Figure 2: Risk management hierarchical framework
The above risk identification process culminates into the identification or computation
of the consequence estimated for each risk agent/source/object/event combination. The
framework follows the risk mitigation process to complete the risk information. Each
risk agent initiates a hierarchical identification of a planning level and an appropriate
mitigation strategy that could reduce the potential risks of the risk sources. Planning
level provides a timeframe for the mitigation activity, which could be Strategic or longterm, Tactical in the case of mid-term, Operational or short-term planning and
Contingencial or post-occurrence planning. Mitigation strategies are counter measures
that could reduce the likelihood and the consequences of the risk events triggered by the
risk sources. Expected payoff estimates should be computed for the combination risk
source/mitigation strategy to provide the quantitative data.
Fig. 3 presents an influence diagram of the proposed risk management framework. The
circular chance nodes represent the uncertain events and the square deterministic nodes
represent the decision events. Finally, the diamond-shaped element depicts the result or
the expected payoff for the enterprise. Hence, the risk agent stochastically influences the
risk source and the planning level, which consequently direct the mitigation strategy.
The risk source stochastically determines the risk objects affected which consequently
undermine the occurrence of a specific risk event. The mitigation strategy influences the
risk object and the risk event thereby affecting the risk outcome.
Risk Agent
Risk Source
Planning
Level
Mitigation
Strategy
Risk
Outcome
Risk Object
Risk Event
Figure 3: Risk management framework influence diagram
The risk elements are presented in their aggregated form, however these elements can
be further decomposed, based on their importance and the depth of quantitative
modeling required. Hence a risk agent (Operation) could be sub-divided into risk
agents 1, 2 and 3, more precisely Transport, Storage and Production operations. The
transport operation 1 can be further divided into pure products transfer 11 and
L.J.Fernandes et al.
interface products transfer 12. The pure product transfer operation 11 can be further
sub-divided into 111.. 115, namely, Butane, Propane, Diesel, Gasoline and Jet transfer
operations and so on. The same theory can be applied to risk sources. Analogously, the
risk object storage can be decomposed into individual product subsystem. Each product
subsystem can be decomposed into the dynamic, static and instrumentation equipment.
Static equipment could divide further into tubes, tanks and spheres. Finally risk events
can also be hierarchised, for instance BLEVE(boiling liquid expanding vapor explosion)
can be subdivided in accordance to its intensity, duration and/or time of occurrence.
The ongoing demonstrates that the developed framework supports a flexible and
interactive process for risk identification and risk mitigation information gathering that
could be used to construct a holistic risk management decision tree or a decision matrix.
The following section provides a demonstrative example of the construction of a
detailed risk management decision tree for the PSC using the RM framework.
3.1. A PSC-RM example
As mentioned earlier, the hierarchical framework could be used to construct a decision
tree to guide building of an information database that could drive a quantitative model
such as a mathematical model to optimize the risk management process. A real case
example is used to demonstrate the building of a decision tree using the framework.
Companhia Logística de Combustíveis (CLC) is a strategic lean member of the
Portuguese petroleum supply chain which owns and operates a petroleum products
pipeline and a storage and expedition infrastructure.
Some risks of this organization are visited, though not in a detailed form due to lack of
space, using the simplified decision tree presented in Fig. 4 that was built using the RM
framework. Although the strategic, tactical, operational and contingencial planning
levels are considered, the focus is on the operational risk management. Operational risks
are seen to stem from various risk agents in the PSC, which could generally arise form
the Business, Condition, Operations, Hazard and Finance agents. Each of these risk
agents can be subdivided to observe a detailed view. For instance, the risk agent of
condition could be subdivided into pipeline, storage and bottle filling conditions.
Further exemplification concentrates on the pipeline operation sub-tree.
The pipeline operations are decomposed as pure products and interface operations. Pure
products include the diesel transfer operation where risk sources such as third-parties,
construction, corrosion, ground movement and operator errors are identified. Focusing
on the corrosion risk source, two mitigation strategies are identified, namely product
buffering and risk based inspection. Corrosion affects various risk objects including
inventory, sales, pump station and transport duct resulting in risk events like ruptures,
holes in pipe, cracks and coating damages. These risk events are categorized as ignition,
no ignition, reported and unreported and again subdivided as detonation, high thermal
damage, torch fire, product spill, corrosion leaks and no leaks. As statistical correlation
is observed between the risk elements, a detailed classification although industry
dependent, is crucial to risk quantification. Bayesian theory can be applied to corrosion
determining factors like product, soil, construction material, protection and age to
estimate the probability of the possible risk events and outcomes referred earlier.
The decision tree in Fig. 4 provides a risk profile of a petroleum tank farm and pipeline
company using the new risk management framework. The risk probabilities and
consequence costs are calculated, based on the literature and empirical estimates in the
petroleum sector. Costs include lost sales, product, equipment, environmental fines,
repairs and casualties. For generalization purpose, the monetary unit (m.u.) used equates
Risk Management Framework for the Petroleum Supply Chain
to one day’s EBITDA of the enterprise. This quantification is yet at its early stage and
requires further research. Parameter estimation will be focused in future investigation to
obtain robust generalized measures for risk probabilities, mitigation and consequences.
FALSE
Strategic
+
Risk Agent
-4,07
+
Risk Agent
-5,10
Planning Level
-2,05
FALSE
Tactical
20,2%
Risk Source
+-4,23
Pipeline
Condition
12,12%
Risk Source
+-1,50
Tankfarm
Condition
18,18%
Risk Source
+-1,10
Business
TRUE
Operational
Risk Agent
-2,05
97,5%
Pipeline
0
pure
products
6,34%
Propane
0
transfer
Risk Agent-2
-0,31
0,999%
4,23%
0,6665%
Butane
transfer
0
Gasoline 16,99%
0
transfer
Diesel
transfer
52,54%
0
0,00
0,00
2,6772%
0,00
49,0% Mitigation Strategy
Third
0
+-0,74
party
Risk Source
-0,60
Project
16,0%
0
Risk Source-1
+-0,50
FALSE
Product
-5
Buffer
Corrosion 14,0% Mitigation Strategy
-0,74
0
Risk Object
-15,00
+
Inventory 13,02%
TRUE
Risk
0
based
Inspection
Risk Event
-0,14
+
Risk Object
-0,74
Sales
Pump
Station
3,26%
Risk Event
-0,48
39,17%
Risk Object-1
+ -0,01
+
0
Rupture 1,0%
0
upto 16"
5,0%
Hole in
0
pipe
Risk Event-1
+ -132,78
Risk Event-1
-8,39
+
Detonation
Ignition
4,0%
Crack
0
Coating
damage
90,0%
0
3,2%
Risk Event-1
-0,73
No
ignition
96,8%
Reported
60,0%
BlockValve
Station
4,21%
Remote
Monitoring
Station
Intake
Station
7,89%
0
0
0
0,55%
0
-38,39
3,94E-06
-8,20
7,81E-07
-1,54
99,0%
Product
-0,52
Spill
Risk Event-2
-0,52
0,0142%
1,0%
Increase
-0,17
feed
pressure
Risk Event-2
-0,44
1,43E-06
Corrosion 32,5%
-1,2
leak
Risk Event-2
-0,50
0,0433%
67,5%
0,0898%
-0,16
-0,16
-0,52
-0,17
+
40,0%
No leak
31,9%
8,05E-09
-38,39
Risk Event-1
-0,47
Unreported
Transport
Duct
0,17%
Risk Event-2
-7,15
High
83,33%
thermal
-8,2
damage
16,5%
Torch
-1,54
fire
-1,20
Risk Event
-2,20
Risk Object-1
+ -0,01
Risk Object-1
+ -0,01
Risk Object-1
+ -0,01
7,0%
Risk Object
Ground
0
+-0,20
movement
Operator 14,0% Mitigation Strategy
0
+-0,28
error
JET
transfer
Pipeline 16,16%
Operation
Tankfarm 24,24%
Operation
9,0%
Finance
0,1%
Hazard
FALSE
Contingencial
+
19,89%
0
3,1342%
0,00
Risk Agent-1
-0,33
2,5%
Pipeline
Interfaces 0
Risk Agent-1
+-0,99
Risk Agent-2
-1,00
+
Risk Source
+-1,99
Risk Source
-340,00
+
Risk Agent
-2,67
Figure 4: Framework decision tree for the Petroleum Supply Chain
4. Modeling approaches
The framework presented in earlier sections assists us in structuring the risk sources and
mitigation information. Nevertheless the development of a risk management model is of
L.J.Fernandes et al.
prime importance in order to generate maximum potential from this information. The
framework may require enhancement to feed appropriate modeling techniques which in
turn should be selected taking the framework into consideration.
Various modeling approaches can be found in the literature that model supply chain and
risk management problems. Stochastic dynamic programming, two-stage multiobjective modeling, decomposition models, mixed integer linear and non-linear
programming, scenario simulation, genetic algorithms, decision tree analysis, and agentbased and artificial intelligence are seen to have potential for risk modeling. More
specifically the agent-based modeling used in Adhitya et al. (2007), the multiobjective
model used in You et al. (2009) and the model predictive control from Puigjaner (2008)
have special interest for the PSC risk management due to their proved applicability in
the process industry.
The presented work will evolve from the translation of the presented framework into a
quantitative model approach. Further investigation and testing is required to develop
this framework methodology into a functional model.
5. Conclusions and future work
The current investigation adds to earlier research on methodologies and models on
supply chain risk management which now results in the development of a new SCRM
framework. The framework is used to define a decision tree for the deployment of a risk
methodology. The framework is demonstrated using typical risk and mitigation
scenarios from the petroleum supply chain. Potential modeling directions are considered
for the quantitative risk management model.
Future research will focus on building an elaborate risk profile for the PSC. Major
impact agents such as business, operations and finance risk areas will be targeted. This
effort is directed to developing and case testing an integrated model for risk
management in the petroleum supply chain.
6. Acknowledgements
The authors thank Fundação para Ciência e Tecnologia (FCT) and Companhia Logística
de Combustíveis (CLC) for supporting this research.
References
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manage abnormal supply chain events, AIChE Journal, Vol. 53, No. 2, 397-422.
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