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A Monte Carlo Factorial Design Approach for Assessing Environmentally Responsible Manufacturing
Cost-Benefit Tradeoffs
A Thesis Presented
By
Amin Torabkhani
to
Department of Mechanical and Industrial Engineering
in partial fulfillment of the requirements for the degree of
Master of Science
in the field of
Industrial Engineering
Northeastern University
Boston, Massachusetts
March 2008
1
ABSTRACT
The increased degradation rate of natural resources and heightened interest in sustainable
resources have led to a variety of practices that companies can implement to reduce their
environmental impact. To create a culture for change in industry, both engineering and business
students should understand how to assess the tradeoffs among economic, technical and
environmental factors if they are to become socially, as well as fiscally responsible designers,
manufacturers or leaders. Therefore, students must be imparted with knowledge regarding
interactions among multiple disciplines of economics, technology, environment, as well as social
and cultural values.
One challenge of teaching sustainability involves creation of an interdisciplinary
educational environment. Educational gaming may be an effective and efficient tool for teaching
the multi-faceted nature of sustainability. The use of educational games enhances learning by
providing a more realistic model of the environment, deeper learning by experience, along with
knowledge sharing and decision making among the team members. With these perspectives, a
board game was designed to help students learn about environmentally benign technologies in
the context of the automobile supply chain to explore trade-offs among technology, profitability
and environmental impacts.
Due to the variety of approaches toward sustainability, there are often ambiguities
associated with defining, implementing and measuring sustainability; there is no indicator set of
“universally accepted” metrics that are derived based on compelling theory and data analysis or
have become influential in policy-making processes. This has become an important challenge for
comparing alternatives. While encouraged to adopt environmentally friendly practices,
2
companies are frequently concerned that environmental compliance costs will reduce their
profitability. Despite a massive body of literature and studies aimed at providing insights, there
are no conclusive studies that address the dynamic interactions of the system or sources of
uncertainty. Therefore, an exploration of the relationship between economic and environmental
performance of business systems and the factors that impact the system can provide important
insights for decision makers. To address these issues, a review of different environmental
assessment methods and their limitations was undertaken, where; some of the more important
factors that impact the economic and environmental performance of firms are identified.
Given significant uncertainties about future environmentally responsible manufacturing,
regulations, incentives, ‘green’ technologies, and overall economic benefits, a probabilistic
approach is illustrated using computer simulation and factorial experiments for assessing
economic-environmental tradeoffs. For a simplified automobile supply chain that was based on a
board game, Monte Carlo models were developed to simulate the uncertainties and
corresponding corporate decisions as chance events that occur over time. The model tracks the
total costs for production, compliance, and implementation as well as an arbitrary green score for
different corporate decision strategies. Three types of decision makers were investigated to
reflect decisions that are considered more or less environmentally benign. Factorial experimental
designs were conducted on the simulation models to develop predictive equations for cost and
green score and to identify those model parameters (e.g., incentives, compliance rates, fine
amounts, technology development rate, and others) that would have the greatest influence the
results. Results also illustrate the impact of uncertainty on the competing cost and
environmentally responsible manufacturing objectives.
3
Table of Contents
ABSTRACT............................................................................................1
Table of Contents ...................................................................................3
List of Tables ..........................................................................................6
List of Figures.........................................................................................7
ACKNOWLEDGEMENTS......................................................................... .9
1. INTRODUCTION.......................................................................10
1.1. Sustainability ..................................................................................................................... 10
1.2. Teaching Sustainability...................................................................................................... 12
1 .2 .1. Learning through Simulation and Gaming ............................................................... 15
1 .2 .2. Tools to Teach System Thinking .............................................................................. 17
1 .2 .3. Review of Educational Games.................................................................................. 18
1.3. Automobile Supply Chain ................................................................................................. 23
1.4. Motivation.......................................................................................................................... 25
2. SHORTFALL: GAME OF SUSTAINABILITY...........................28
2.1. Early Board Game Version................................................................................................ 29
2.2. Shortfall: The Enhanced Board Game ............................................................................... 29
2 .2 .1. Shortfall Rules .......................................................................................................... 30
2 .2 .2. Description of Cards ................................................................................................. 34
2.3. Board Game Results .......................................................................................................... 36
4
2.4. Summary............................................................................................................................ 37
3. MEASURING ENVIRONMENTAL PERFORMANCE .............38
3.1. Environmental Accounting ................................................................................................ 39
3.2. Monetization Methods ....................................................................................................... 40
3 .2 .1. Willingness-to-Pay.................................................................................................... 41
3 .2 .2. Methods Not Based on Willingness-to-Pay.............................................................. 46
3.3. Panel Methods.................................................................................................................... 47
3.4. Qualitative Methods........................................................................................................... 48
3.5. Summary............................................................................................................................ 49
4. SIMULATION ANALYSIS OF ENVIRONMENTAL AND
ECONOMIC PERFORMANCE ........................................................51
4.1. ERM ASSESSMENT AND UNCERTAINTIES.............................................................. 53
4.2. SIMULATION MODEL ................................................................................................... 59
4 .2 .1. General Modeling Approach and Logic ................................................................... 59
4 .2 .2. Automotive Example ................................................................................................ 62
4 .2 .3. Model Logic.............................................................................................................. 67
4 .2 .4. Economic and Environmental Performance Measures ............................................. 69
4.3. RESULTS .......................................................................................................................... 70
4 .3 .1. Final Performance Measures..................................................................................... 70
4 .3 .2. Short versus Long Term Results............................................................................... 72
4 .3 .3. Experimental Design................................................................................................. 74
5
5. CONCLUSIONS AND FUTURE WORK .....................................89
6. REFERENCES.................................................................................94
APPENDIX A……………………..…………………………………101
APPENDIX B………………………..………………………………116
6
List of Tables
Table 4-1: Factors of uncertainty determined in the literature and incorporated in the simulation assumption .........59
Table 4-2: Automotive example chance events and parameter values........................................................................64
Table 4-3: General MC model events, parameters, and performance measures .........................................................65
Table 4-4: Five factors studied via 2k and 3k experimental design; where -1, 0, 1 indicate the low, medium and high
settings, respectively..................................................................................................................................75 Table 4-5: Design of experiment table for total cost. Labels P, C, F and A are defined in Table 4, where -1 and 1
indices correspond to low and high values for the factor...........................................................................76 Table 4-6: Design of experiment table for green score. Labels P, G, F and A are defined in Table 4, where -1 and 1
indices correspond to low and high values for the factor...........................................................................76 Table 4-7: Regression coefficients for 2k and 3k experiments (only the significant factors coefficients (p < 0.05) are
shown). Values used for comparison with the regression fits in Figures 7 and 8. .....................................78
7
List of Figures
Figure 1-1: The goal of sustainability is often described as maintaining the human, economical and ecological
growth at a long term process- or in summary three Ps (people, planet, profit) .................................12 Figure 1-2: System thinking enhances problem solving that assesses the short term and long term impacts of the
problem (Arnie Levin, Cartoon ID: 39203, Published in /The New Yorker/ December 27, 1976)...13 Figure 2-1: The simplified supply chain................................................................................................................31
Figure 2-2: Shortfall game board for car manufacturer processes and storage facilities.......................................36
Figure 4-1: Flow of simulation logic.....................................................................................................................61
Figure 4-2: Average green score and total cost for each type of decision maker, after 50 replications for 10 year
period; vertical bars indicate 95% confidence intervals for the mean. ...............................................70 Figure 4-3: (a) Total cost and (b) green score distributions after 10 years (40 quarters) for each type of DM. ....71
Figure 4-4: (a) Average cost per quarter considering event elimination. (b) Average cost per quarter (with no
event elimination, convergence occurs more quickly.).......................................................................73 Figure 4-5: (a) Average total cost values for each 2k experiment and (b) 2k experimental design responses for
average green score. Labels P, G, F and A are defined in Table 4, where -1 and 1 indices correspond
to low and high values for the factor. The dashed line in (a) divides the experiments into high (up)
and low (bottom) event cost regions and shows the largest impact on the results while shaded region
represents the green DM (P1) total costs. In (b) the dashed line divides the green scores to tow
regions based on green value level as the most effective factor. ........................................................79 Figure 4-6: (a) 3k experimental design response for total cost and (b) 3k experimental design response for green
score. Labels P, G, F and A are defined in Table 4 where -1, 0 and 1 indices correspond to low
medium and high values for the factor. ..............................................................................................82 Figure 4-7: Sensitivity analysis of total cost in response to changes in the event cost for the three DM types.
Regression equation represents the total cost for moderate DM and agrees with results in Table 7.
Whole numbers for event cost correspond to coded factor settings in Table 4; values in parentheses
indicate percent change from base values...........................................................................................84 Figure 4-8: Sensitivity analysis for the three DM types; (a) total cost in response to changes in the fine amount
(b) green score in response to changes in green value (c) total cost in response to changes in the fine
probability (d) green score in response to changes in the fine probability. Regression equation
represents the total cost for moderate DM and agrees with results in Table 7. Whole numbers for
event cost correspond to coded factor settings in Table 4; values in parentheses indicate percent
change from base values.....................................................................................................................85
8
Figure 4-9: (a) Contour plot for variation in fine probability and fine amount for the non-green DM; the lines
represent total cost curves with labeled non-green DM’s total cost constant along the line. (b)
Contour plot of the difference between the total cost of the green and non-green DM; lines represent
the differences in total cost between green and non-green DM with shaded area showing the higher
cost for non-green DM. ......................................................................................................................86 Figure 4-10: Sensitivity of green score to changes in fine probability and green value for (a) green DM and (b)
non-green DM. Green values indicated above each line remain constant along the line....................87
9
ACKNOWLEDGEMENTS
I would like to thank a number of people who have made my experience at Northeastern
productive, fun and stimulating.
First, I would like to thank Prof. Jacqueline Isaacs for first providing me with an
opportunity to experience academic research as a research assistant in my second month of study
abroad. She then supported me through the entire master program by her guidance and directions
on the research whenever I thought I am not making progress, and also by patiently teaching me
the high standards of academic research and writing and publishing. I would also like to thank
Prof. James Benneyan for his novel and stimulating ideas especially over the second year when
the core ideas of my thesis started to form. His high academic standards and high expectations in
publishing helped me prioritize and focus on the transparency of my results.
I would also like to thank Prof. Thomas Cullinane for his knowledge and experience in
providing inputs on multiple events used in the model of automotive industry. I also enjoyed
working with Donna Qualters, Ann McDonald and Jay Laird in the early design sessions of
board game. The collaborative work of the team was a source of motivation for me to remain
excited about my research.
I am deeply thankful to Prof. Mohammad Taslim for initially accepting me to the
graduate program at Northeastern and then kindly allowing me to find my interests and pursue
just those. Also, many thanks to Prof. Hameed Metghalchi as department chair for his
approachable personality that helped me better communicate my concerns or thoughts.
Finally, I am thankful to my parents for their unconditional love and support whenever I
needed them.
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1. INTRODUCTION
Increased rate of natural resource degradation and the need for sustainable use of
resources are challenges that some organizations are beginning to address. With increasing costs
of pollution remediation, environmentally benign manufacturing initiatives are becoming more
common across industries. The need for change resource consumption is pronounced by rising
social awareness, and has resulted in recent government environmental regulations by
environmental protection organizations around the world. To create a culture for change in
industry, both engineering and business students should understand how to assess the tradeoffs
among economic, technical and environmental factors if they are to become socially as well as
fiscally responsible designers, manufacturers or leaders.
This chapter addresses the interdisciplinary nature of the concept of sustainability,
emphasizes the importance of teaching sustainability and use of educational gaming as an
effective and efficient tool for teaching sustainability. The automotive industry is introduced as a
suitable case study to explore the challenges of sustainability and environmental protection, due
to its long history of technological advancement and how the industry has evolved to meet and
improve environmental standards. The motivation for this work is also discussed, and an outline
for the following chapters is provided.
1.1. Sustainability
During the past decade, the concept of sustainable development has been used to address
a broad range of issues regarding environment, society and economic growth (Parris & Kates,
2003). The challenge to the idea of how eco-systems can support an ever-increasing economic
growth was introduced in 1970s, creating the basis of what now referred to as sustainable
11
development (Ding, 2005). Schwartz (1998) has defined sustainability as a global phenomena
that
“…implies a global economic and social system that both satisfies human needs and does
not despoil the earth” (Schwartz, 1999).
Young (1997) describes sustainability as a 3-legged stool with the three legs of ecosystem,
economy and society representing three component of sustainability (Young, 1997).
Figure 1-1 illustrates the domain of sustainability. Development of methods and
techniques to measure sustainability has received enormous attention from numerous
governments and international organizations including UN Commission on Sustainable
Development, OECD as well as universities and research institutes (Tong, Ye, & Hou, 2006).
These efforts have been aimed at providing information and facilitating communication
regarding sustainable development and supporting the process of decision-making and strategy
setting (Young, 1997; Parris & Kates, 2003; Tong et al., 2006). Due to the broad appeal and
variety of approaches toward sustainability, there are often ambiguities associated with defining,
implementing and measuring sustainability such that few sustainability indicators are
“universally accepted”. Among the existing indexes, none is derived based on a compelling
theory with data analysis or has become influential in policy-making processes (Parris & Kates,
2003).
12
Figure 1-1: The goal of sustainability is often described as maintaining the human, economical and ecological
growth at a long term process- or in summary three Ps (people, planet, profit)
1.2. Teaching Sustainability
Sustainability is a subject of study among several disciplines from engineering,
economics, political science, etc. One of the challenges considered in sustainable development
(SD) education is its goal to integrate relevant disciplines from social science, technology and
environmental science into one framework, where a holistic view can be taught by applying
system thinking principles. System thinking approach enhances the solutions that assess all the
spatial and temporal impacts of the problem in hand to answer whether the net impact of the
problem (e.g., new technology, investment, legislation, etc.) will maintain sustainable growth.
13
Along with the three dimensions of sustainability (people, planet, profit) that should be
incorporated in sustainability education, time as the fourth component often plays a key role
(Dieleman & Huisingh, 2006) and should be considered in teaching sustainability. Short term
results of a decision, based on the current condition of the system, may be different from the long
term impacts that the decision would have throughout the time, as depicted in Figure 1-2. Several
empirical results suggest that human decision makers lack the appreciation for time delays and
the long term impacts of actions. Also, because of the time delay between the time that a
decision is made and the actual time perceived for the impact of the decision on the system,
“learning” is confounded and therefore limits proper long term decision making (Sterman, 1989;
Diehl & Sterman, 1995; Gibson, Fichman, & Plaut, 1997; Gibson, 2000).
Figure 1-2: System thinking enhances problem solving that assesses the short term and long term impacts of
the problem (Arnie Levin, Cartoon ID: 39203, Published in /The New Yorker/ December
27, 1976).
The use of game and simulations that involve system thinking requires a holistic view to
enable understanding of the dynamics of complex systems. It also provides insights about how
14
our decision making will impact the system in the long run (Gibson et al., 1997; Sterman, 2001;
Dahm, 2002; Gonzalez, Vanyukov, & Martin, 2005; Sterman, 2006).
Research has also shown that teaching sustainability is more efficient when the learning
environment provides more interaction among the students and between them and the teacher
comparing to traditional lecture-based classes (Philpot, Hubing, Hall, Flori, Oglesby, &
Yellamraju, 2003; Qualters, Isaacs, Cullinane, McDonald, & Laird, 2006; Feller, 2007).
New methods of teaching can promote learners to change their paradigms. They go
beyond the one-way lecture format and provide a frame-work for learners to learn while
practicing reality-based decision making. The most common approach to study the real world in
education for sustainability starts with a presentation of a case study (Dieleman & Huisingh,
2006). Case studies provide a context in which one can understand certain behaviors of decision
makers in the real-world environment, as well as provide an environment where students better
understand how science and technology concepts are applied to provide solution for the
particular problem. Although helpful, presenting case studies is limited in the sense that students
may become tempted to look for solutions within the boundaries of the prescribed case
(Dieleman & Huisingh, 2006). Thorndike recognized the limitation of the lecture model as he
states
“the commonest error of the gifted scholar, inexperienced in teaching, is to expect pupils
to know what they have been told……telling is not teaching” (Ledlow, White-Taylor, &
Evans, 2002).
“Academic pedagogy suggests that the development of critical thinking skills, creative
problem-solving abilities, reflective and experience-based learning as well as
15
interdisciplinary learning experiences offer new ways to teach and learn” (Wronecki,
2004).
One method for fostering such skills is through interactive simulation and games.
1 .2 .1. Learning through Simulation and Gaming
Lectures, standardized test methods and case studies have been dominantly utilized in
each field (discipline) as educational tools, despite their limitations and inefficiencies. Although
there is no perfect alternative to such methods, there is a growing trend to bring change in
science and engineering education to better prepare students to challenge the complex and multi-
disciplinary problems of sustainable development in future. Games and interactive simulations
offer a unique learning environment that satisfies many of the learning concepts and objectives
that are crucial for teaching sustainability. Use of educational games enhances the learning by
providing a more real model of the environment and problems within, deeper learning by
experience, facilitating group decision making and knowledge sharing among the team members.
These objectives are particularly important in teaching sustainability and are discussed in more
detail in the following sections.
Representation of Reality
Games are developed based on rules and conditions with certain assumptions to try and
capture aspect of real-world problems. The scope of the game can cover either a phenomenon in
the real world or, in a more complex design, include interaction of two or more key phenomena
in a dynamic system. By making appropriate simplifying assumptions in the design phase, the
game designer can draw the attention of the players to the underlying causal dynamics that are
usually ignored due to complexity of the real-world environment.
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Experiential Learning
The idea of experiential learning implies the concept of “learning by doing”. Researchers
have contended that the act of playing a game propels learners through three critical phases: (a)
experience, (b) reflection, and ultimately and (c) learning (Kharma, Caro, & Venkatesh, 2002).
Decision makers may assess outcomes to use as feedback to learn to improve their performance
(Gibson et al., 1997). However, in a more complex and dynamic environment, the outcomes are
not usually the result of one task but depend on a sequence of decisions that are inter-related.
Although this interaction confounds the process of learning, experimental investigations of
decision making in a complex environment with sequential task dependencies indicate that
decision makers show performance improvement (Diehl & Sterman, 1995; Gibson, 2000).
For someone who is playing an educational game, the game learning environment is a
sequence of “experiential modes”. The opportunity for game designer lies in understanding the
nature of the game and defining specific “experiential modes” that provides a fun and
educational environment (Appelman, 2005).
Sharing the Experiences through Group Interaction
Today’s complex problems, in particular, challenges of sustainable development require
utilization of knowledge from different fields and disciplines. Participating in team work
activities and sharing knowledge can be achieved through group game play, while, traditional
educational methods are focused on individual training. Team learning has been considered in
sustainability education as a process through which a group creates knowledge for its members ,
for itself and others (Bergea, Karlsson, Hedlund-Astrom, Jacobsson, & Luttropp, 2006). Group
members as different stakeholders learn to cooperate to find their way to solve different
17
problems. Furthermore, diversity among the group members with different educational
backgrounds, cultures and problem solving approaches will enrich students’ awareness about the
challenges of sustainable growth.
Next Generation Learning Styles
Learning style refers to the way students receive process and express information. There
are significant differences in learning style of new generation student versus the old generation
(Golden, 2003). The new students favor trial-and-error approaches versus the previous students,
who prefer to use logic-based algorithms. Educational games offer students trial-and-error
opportunities and provide instant feedback for their decisions. Scientists call educational video
games the next great discovery,
“a way to captivate students so much they will spend hours learning on their own” (Facer,
Joiner, Stanton, Reid, Hull, & Kirk, 2004; Feller, 2007).
1 .2 .2. Tools to Teach System Thinking
A game environment is a suitable framework to help players understand a complex
system. Dynamic games integrate different causes that can endogenously change the outcome of
the game and create a non-linear system. Most of the systems are non-linear; they involve time
delays between cause and effects. Dynamic games are an excellent platform for teaching
sustainability, because they involve a systems approach and can integrate complicated cause and
effect structure (Bergea et al., 2006). By using computer technology, games provide instant
feedback (outcome) to the decision maker. While the complexity in the system confounds
decision making, games can efficiently helps students, first to understand how the system
18
functions, and second to find ways to make necessary interventions to the true causes (Holweg &
Bicheno, 2002).
1 .2 .3. Review of Educational Games
As described in the introduction section, the study of sustainability brings together
different fields of science and technology and requires systems approach. The use of games and
interactive simulations is an effective and efficient method in teaching sustainability. In this
section, educational games that have been designed and developed to enhance science,
engineering and management knowledge and decision making skills are reviewed.
Engineering Games
Many engineering subjects have been characterized as theoretical, thereby lending
themselves to didactic lecture-based instructions followed by rigorous and sometimes tiring
problem-solving assignments and exercises. Although there is no perfect alternative to such
methods, new and more creative methods of teaching besides traditional lecture-based learning,
standardized testing, need to be developed to address the following findings: (Kharma et al.,
2002)
• The growing knowledge of engineering and science can not be covered in lectures;
• Engineering is now connected to other disciplines such as business and environment;
• The level of skills required by a practicing engineer is so high, that universities find it difficult
to effectively deliver a comprehensive curriculum, in about four years of undergraduate study;
• Accreditation requires universities to produce creative thinkers among their graduates
19
Simple presentation of information does not guarantee that the ideas and concepts transmitted
can be meaningfully integrated into students’ existing knowledge (Ledlow et al., 2002).
Many efforts have been aimed to the introduction of the World Wide Web (WWW) in the
engineering curriculum (Zheng & Keith, 2003). Some of the games that have been designed and
used for science and engineering teaching are discussed in this chapter.
1. Supported by NSF grant (DUE-0127426), several simple, computer-based games have been
developed at the University of Missouri-Rolla to help engineering mechanics students
develop their proficiency in static and mechanics of materials problem solving with focus on
hard-to-repeat calculations in problems (Philpot et al., 2003).
“There was a significant difference in performance in the classes using the software
relative to the others…. We've shown that the students who have used it have benefited
from it, and moreover, the software seems to help students enjoy the course
more…Hundreds and hundreds of people all over the place use it. It is cool to sit at my
computer and get email from South Africa, New Zealand or Brazil - from professors and
students who find the software to be a really helpful tool in their classes”(Philpot et al.,
2003).
2. With the support of an NSF grant (CCLI- 0126697), a web-based game simulates realistic
economic decision-making in running a company. This game has been developed and
integrated as a semester-long project into a Rowan University course on engineering
economics in 2001 and 2002 and at the University of Kentucky in the fall of 2003. The game
thus filled the roles of the traditional homework problems and created classroom
environment that was fun, relaxed and informal while still being instructive. It helped
students to not only learn various economic analysis techniques but also to determine which
20
ones were most applicable to the case at hand (Dahm, 2002; Dahm, Ramachandran, &
Silverstein, 2004).
3. Supported by NSF Research, Evaluation and Communication (EHR/REC) and Engineering
Education Center (ENG/EEC- 0238269) a web-based problem-solving environment was
designed to teach line balancing in automated manufacturing systems, based on analytical
and simulation models of an assembly line. Future directions include using the environment
for research on system integration skill development, field testing with engineers from
industry, and design of a “construction set” for robotic work-cell line balancing (Hsieh &
Kim, 2005).
4. Other computer games have been created with other funding to enhance student’s
understanding. These include a game kit for Digital-Logic (Kharma et al., 2002), a
simulation game to teach thermodynamic courses funded by Michigan Space Grant
Consortium (Zheng & Keith, 2003) and a board game about engineering ethics designed in
Lockheed Martin and then is used at the University of South Florida (Carpenter, 2005).
Business Games
The idea of educational gaming has been also applied in business and management. The
focus of business games is mostly on individual and team decision making within a more
realistic environment with more realistic assumptions than engineering games. While introducing
the different decision making challenges to the participants and providing them with team
working skills, business games often consider the long term business consequences by capturing
a bigger picture of the problem environment as well as various dynamics that may affect the
problem.
21
1. Perhaps the most used and oldest game played in business and MBA schools is the Beer
Distribution Game originally developed by Jay Forrester in the late 1950s to introduce
students of management to the concepts of delay and oscillations in the supply chain. The
game has been played on a board portraying a beer distribution supply chain from brewery to
distributor, wholesaler and the retailer, and each person manages one sector. Orders move
from retailer to the brewery and supply is shipped to retailer based on orders received. The
time delay and lack of appreciation of time delays creates oscillation in orders and supplies
and the inventory levels (Sterman, 2000). The game is now available online and can be
accessed through web.
2. The “Lean Leap Logistics Game” was developed primarily to foster collaboration in the
automotive supply network to help fill the lack of understanding of the core processes
throughout the supply chain. This lack of understanding is due to little collaboration and
information sharing between different sectors of the supply chain and causes distortion and
amplification of both demand and supply patterns. As a consequence, this deficit of
information is often replaced with inventory, resulting in increased lead times and pipeline
cost. To achieve higher collaboration, the game had to model reality, and was built on a
series of mapping activities (Holweg & Bicheno, 2002). Holweg (2002) reports successful
results and states:
“…Unexpectedly, it turned out that developing and running the game led to insights
into scheduler behavior, scheduling decision making, prioritizing improvement
activities and into supply chain dynamics, especially the Forrester or Bullwhip effect”
(Holweg & Bicheno, 2002).
22
Using supply chain simulations, the game facilitates discussion and group thinking about
ways to demonstrate and discuss supply chain improvements by simulating their individual
characteristics in order to deploy holistic improvements, rather than partial solutions.
3. The B&B game, developed by Prof. John Sterman at MIT Sloan School of Management, is
an interactive simulation of a firm launching a new product in a competitive market. Players
are responsible for marketing expenditure, pricing, and capacity expansion decisions to
maximize their cumulative profit over the next 40 quarters. However, the market in which
they are launching has unknown attributes, including its size and price elasticity, consumer
responsiveness to word of mouth, repurchase behavior as well as competitor’s decisions and
behavior. The game illustrates fundamental principles of corporate strategy including the
learning curve, time delays in capacity expansion, competitive dynamics, and market
saturation (Paich & Sterman, 1993). The online simulation is hosted along with other
business simulations by Forio Business Simulation, a San Francisco based company that
builds management and business educational simulations (Bean, 2007).
4. FISH BANKS, LTD. simulation is a group process involving analytic reasoning, negotiation,
and collective decision making. It creates profound insights into how depletion of natural
resources can result from the interaction of ecological, economic, corporate, and
psychological forces. It conveys factual knowledge about a major environmental issue and
motivates students to be informed and effective citizens. As an interdisciplinary model, it
provides linkages to environmental science, biology, economics, social studies, and
mathematics. One teacher can run the program with 5-50 students. There are many teacher-
selected extensions of this engaging exercise. A computer is used by the instructor to analyze
the effects of student decisions and produce yearly reports on corporate profitability and
23
productivity of the fishery. Typically, role playing extends over six to ten cycles (years) and
debriefing begins when teams realize the long-term consequences of short-term strategies.
Although mostly used in high school and colleges, it is also used in business schools to
introduce systems thinking and systems dynamics modeling (Meadows, 1991).
1.3. Automobile Supply Chain
One of the industries that was the center of attention in environmental debates from early
1960s, is the automotive industry. Automobile production and use around the world have a
significant impact on our ecosystem, economy and social life (Gutowski, Murphy, Allen, Bauer,
Bras, Piwonka, Sheng, Sutherland, Thurston, & Wolff, 2001). Automobiles have been a major
source of pollution in urban areas burning massive amounts of finite resources of fossil fuel.
Automobile production processes also consume raw materials such as steel, copper, aluminum,
etc. It is an industry that utilizes production and environmental technologies for which a study of
sustainability concepts can be explored.
An average car consists of 15,000 different parts, and requires a supply chain of material
and part suppliers. Supplies provide raw materials, as bottom Tiers, and sell it to the higher Tiers
such as part manufacturers. Tier 1 suppliers make products specifically for one of the original
equipment manufacturers (OEMs) and OEMs assemble the parts to form the automobile. The
size of the automobile supply chain is growing as the need for more automobiles is growing
(Gutowski et al., 2001). The U.S. automotive industry, as an example, relies on hundreds of
suppliers for components and materials to manufacture vehicles, with emissions released to air,
water, and soil throughout the entire supply chain.
More research is needed, in multiple dimensions, to support the growth of the automotive
industry in an environmentally sustainable way. The different dimensions of research include,
24
but not are limited to, the development of new sources of energy, more efficient energy storage
systems and advanced materials to improve energy intensity and rate of resource consumption
(Gutowski et al., 2001).
Recent environmental legislations, such as the European Union Directive on End-of-Life
Vehicles and the Japanese Home Electric Appliances Recycling law, has had a major influence
on product design from both an engineering and an economic perspective (Michalek,
Papalambros, & Skerlos, 2004). With the concept of extended producer responsibility taking
hold in the European Union, original equipment manufacturers (OEMs) are becoming more
concerned about environmental repercussions of material and manufacturing choices that affect
the use and disposal phases of their products. With concerns for their own accountability, OEMs
are beginning to require that their suppliers meet specific standards regarding their
manufacturing and material choices. Gutowski et al. (2001) visited and researched automaker
companies across US, EU and Japan, which all had or were pursuing ISO 14000, and reported
that all automakers are asking their suppliers to become ISO 14000 certified, although there was
variability in where the suppliers stand in this certification process (Gutowski et al., 2001). In the
U.S., the Tier 1 suppliers are also literate with regard to environmental issues, although there was
some variability evident in the commitment level of the suppliers to environmental issues. The
expectation is that this ISO certification requirement will be passed through the supply chain.
GM and Ford suppliers have notified their Tier 1 suppliers that they needed to be ISO 14001
certified by the end of 2002. Also, although the United States did not sign the Kyoto Protocol to
reduce greenhouse gases, US companies feel pressure to reduce gas emissions to be able to
comfortably maintain their business in countries that have signed the treaty (Deutsch, 2005).
25
These requirements, on top of the ubiquitous demand for minimum cost, place new
burdens on the various tiers in the supply chain. The communication between the OEM and its
suppliers to achieve these goals can be collaborative, but in many instances, there is a level of
competitiveness that prevents full disclosure of information between parties. Therefore OEMs
should have an environmental strategy and roadmap to meet the environmental requirements.
1.4. Motivation
Optimal designs commonly consider tradeoffs among engineering performance metrics,
combining multiple- often conflicting- objectives within a Pareto-optimal approach. However,
the challenge is how to weigh the importance of objectives, and typically the problem must be
reformulated iteratively (HAKIM, 2005). This conflict between business and engineering design
choices and the complexity of the supply chain infrastructure can be difficult for new engineers
to grasp. For those in business, it is equally difficult to understand how design tradeoffs are not
so straightforward. Engineering and business students alike should be made aware of
environmentally benign design and manufacturing methods.
Today’s students are tomorrow’s decision makers in academic and industrial
environments. Considering the importance of teaching the concepts of sustainable growth and
benefits of educational gaming to enhance their performance and learning, a board game was
designed to help students learn about environmentally benign technologies in the context of the
automotive supply chain. Automotive supply chain offers various instances of case studies for
trade-offs among technology, profitability and environmental impacts, which can be investigated
in sustainable development education.
In a broader context, the trade-offs among economic and environmental consequences of
technologies and sustainable growth is one of the most important challenges of governments and
26
decision makers. They encourage businesses to adopt environmentally friendly practices;
however, companies are concerned that environmental compliance costs and activities reduce
their profitability. The profitability or the economic success of environmental performance
depends on many factors and characteristics of the firms and governments. Despite a massive
body of literature and research studies aimed at providing answer to this concern, there is no
conclusive study that addresses the dynamic interaction among these factors as the source of
uncertainty and provides a general solution for determining this relationship. Therefore, an
understanding of the relationship between economic and environmental performance and the
factors that impact this relationship is important for both government and industry decision
makers. To address this issue, a review of different environmental assessment methods and their
limitations is provided. Also, some of the most important factors which impact the economic and
environmental performance of firms are identified.
In this thesis, a modeling approach was proposed to investigate the economic and
environmental performance of three environmentally-different decision making philosophies
given variant environments with these factors as the dimensions of uncertainty across the
different environments. The proposed approach helps policy and decision makers to better
understand the effect of environmental factors on firms’ performance, and provides analytical
techniques to quantify those factors.
The next chapters of the thesis are as follows; Chapter 2 discusses the designed board
game, Shortfall, and provides a review of the game rules, game results and discussion. Chapter 3
includes a review of valuation methods and techniques for measuring environmental
performance of firms discussing the advantage and limitations of the major methods. The
simulation approach to study of the economic and environmental performance of firms under the
27
uncertainty factors that create uncertainty in these studies is proposed in Chapter 4, followed by
final conclusions and ideas for future research in Chapter 5.
28
2. SHORTFALL: GAME OF SUSTAINABILITY
In Chapter 1, the concept of sustainability, its challenges and the importance of teaching
sustainability were discussed, and games as an effective and efficient educational tool were
introduced in this context. Due to the unique learning environments that educational games
provide, the idea of using educational games to teach concepts of sustainability was emphasized.
Also, automotive supply chain, due to its significant impact on our environment, economy and
social life, was suggested as a suitable case study to address important issues in sustainable
development and to be considered in designing educational games.
In this chapter, a board game that was developed to assess learning style of new
generation students within the context of interdisciplinary teaching of sustainability is
introduced. The board game entitled Shortfall addresses environmental and economic tradeoffs
within supply chain of automotive industry. The game, Shortfall, will help students meet learning
objectives in the following areas:
• History of environmentally benign technologies within the past decades;
• Environmental policies such as “technology-forcing” that influence manufacturing:
“Technology forcing is a strategy where a regulator sets a standard that is
unattainable with existing technology, at least at an acceptable cost”(Gerard &
Lave, 2005);
• Better understanding of how economics and environmental policies interact with
technology in an automotive supply chain;
• Current strategies used in industry to address environmental issues;
29
• Future environmental approach of auto industry and promising environmentally-
friendly technologies (green technologies);
• Economic and business issues associated with decision making;
• Effects of global events and unplanned crisis on automobile supply chain (Isaacs,
Cullinane, Qualters, McDonald, & Laird, 2006b).
2.1. Early Board Game Version
Entitled Shortfall, the board game was originally developed as part of an M.S. thesis
(Corriere, 2002), supported by NSF Career Grant (DMI-9734054), and was played several times
with students in engineering classes and once with students in a business class on supply chain
management. The game simulates the supply chain for an automobile manufacturing operation.
The goal of game play was to maximize profit while minimizing environmental impact, and
further, to foster better understanding and dialogue of these issues for future industry leaders. An
early prototype of the board game was formally assessed, and both engineering and business
students indicated that they enjoyed playing it and they thought that the game was informative.
The board game was reworked in collaboration with an educational game design company, with
several expectation: 1) to extend the learning objectives and their impact to greater numbers of
students, and 2) to create a platform which will allow dissemination of an educational game that
initiates and promotes real student learning, and finally 3) to reinforce environmentally
conscious decision making (Corriere, 2002; Isaacs, Cullinane, & Qualters, 2006a).
2.2. Shortfall: The Enhanced Board Game
Shortfall was more extensively developed with increased attention to game play logistics,
better developed scenarios and graphic organization. The revised board game prototype of
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Shortfall was developed in collaboration with Metaversal Studios and faculties from
Northeastern University’s multi-media studies and education departments.
The board game was piloted in Fall 2005. The prototype was utilized to test the
hypothesis around learning styles of the new generation of students. The objective was to teach
students that the decisions that are made in design and manufacturing can have a significant
impact on the environment. It was also the intent of the game to impress upon students that
decisions with respect to the environment are not always “cut and dry”. The game aimed to
impress upon the players that tradeoffs are involved for most decisions in the products, design of
equipment to produce products and the disposal of manufacturing waste. The game promotes
cooperation, strategy building for the greater good and increased knowledge of responsibilities
beyond traditional roles.
2 .2 .1. Shortfall Rules
In the board game, players each take on one of four roles in a company: the CEO, the
Environmental Manager, the Research & Development Manager, or the Production Manager.
Each four-player company assumes a position in an automobile manufacturing supply chain: the
OEM who produces the cars, the first-tier supplier who produces parts, and the second-tier
supplier who produces the useable materials from raw materials that create parts as shown in
Figure 2-1.
Number of Players:
ShortFall is a game for 12 players (3 teams of 4 players each), plus a moderator.
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Figure 2-1: The simplified supply chain
Objectives:
There are two winners: the winning team is the company with the most money at the end of the
game; the individual winner is the player with the most money at the end of the game. It is
possible for the individual winner to be from a different company than the winning team.
Setup:
With the moderator’s assistance, divide the 12 players into 3 teams of 4 players. Each team
selects a player to be the CEO by voting, or with the help of the moderator. All other players on
the team select one of the other three roles, either by agreement or with the help of the
moderator. No two players on a team may play the same role.
Board Game Parts List:
50 Waste tokens (blue) 30 Parts tokens (yellow) 30 Cars tokens (black) 30 Materials tokens (orange) Balance sheets (10 per team) Car Sales Results Table
30 Innovation cards 50 Environmental report cards 50 Market forces cards Game Boards (1 per team) Score Pad (for moderator)
Car ManufacturerOrig. Equipment Mfgr
Part Manufacturer
1st Tier Supplier
Materials
Manufacturer
Raw Materials
DEMAND
32
The Player Roles within Each Team:
CEO: The Chief Executive Officer is primarily concerned with the welfare and total cash supply
of the company. The CEO makes the ultimate decision on how to allot company resources and
earns points based on the company’s total income.
Production Manager: The Production Manager is primarily concerned with the production of
the materials, parts, or cars. The Production Manager earns points by persuading the CEO to
dedicate resources to the manufacturing within that company.
Environmental Manager: The Environmental Manager is primarily concerned with meeting the
environmental regulations, especially regarding waste disposal for the production processes. This
member earns points by persuading the CEO to dedicate resources to waste disposal.
R & D Manager: The Research and Development Manager is primarily concerned with the new
product development for the company. This member earns points by persuading the CEO to
dedicate resources to R & D.
The Supply Chain:
Each team contributes to manufacturing in the supply chain. For the game, only three stakeholder
are included: The OEM, a 1st tier supplier of parts and a 2nd tier supplier of materials:
OEM: The OEM is the manufacturer of the automobiles that are brought to market. The car
manufacturer relies on the parts manufacturer for supplies. The marketplace dictates demand for
cars.
33
1st tier supplier: The 1st tier supplier manufactures the components that are used to build the
cars. The parts manufacturer relies on the materials manufacturer for supplies. The car
manufacturer dictates the demand for parts.
2nd tier supplier: The 2nd tier suppliers manufacturers the materials that are used to build parts.
The materials manufacturer usually has a steady source of materials, but the cost and availability
of the materials can fluctuate depending on market effects.
The game moderator decides which team plays which role in the supply chain. No two
teams may play the same role in the standard three-team game. The game is played in a series of
rounds, each of which represented a fiscal quarter. In the final round, teams “sell off” their
companies, and the team with the highest profit wins. The game was tested with 12 engineering
sophomores (three teams in one supply chain), who self selected to participate from the class
MIMU310: Introduction to Industrial Engineering. The test play lasted five rounds including the
“sell off” round. The first round took 30 minutes, while subsequent rounds took 5-20 minutes.
Each round began with teams conferring privately to decide their budget allocations for the
round. The CEOs made the final decision on how much money each of their three managers
received for their departments, and then the players came together to negotiate sales and begin
the quarter’s production. The challenge in sales and production is that companies may only sell
product that is ready to ship. Therefore, teams must plan production at least one round in
advance, hoping that their predictions about the other teams’ needs (and random market
fluctuations) will be correct. The production of new product is limited by: each company’s
production budget, the number of parts/materials that each company currently has available, and
the amount of product and waste storage that the company currently has available.
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After sales and production, the Environmental Managers must handle waste disposal,
recovery, and recycling. The company is assessed a fee for disposal, but may be recompensed for
responsible disposal or recycling. Finally, the R&D Managers spend any part of their budgets on
factory improvements, which may reduce waste, lower direct costs, or take steps towards future
innovations through the use of “Innovation Cards”. At the end of the round, any unsold supplies,
product or waste are assessed a storage fee, and a “Current Event” card is drawn. These cards
describe real-world situations ranging from air pollution regulations to landfill seepage.
Sometimes there was an immediate penalty or reward to one or all teams; sometimes the card
affected the play of the entire next round by imposing a fine for some action that could have a
negative impact on the environment or some other issue related to waste disposal. Both
“Innovation” and “Current Event” cards are discussed in greater detail in the next section.
In the final 5th round of the game, players did not produce further products, but instead
sold off remaining product and overstock supplies, and disposed of remaining waste. After all
products were sold and wastes disposed, the team with the most profit was declared winner of
the game.
2 .2 .2. Description of Cards
The following is the list of the “Current Event” and “Innovation” cards that were utilized
in the enhanced board game. The subtitles (Parts, Car, Material and All) refer to the part of the
supply chain to which the cards applied.
Current Event Cards
Current event cards were designed to capture the dynamic impact of the unplanned real-
world events or government interventions on the spply chain of automobiles. These events are
35
chosen based on an extensive literature search in the newspapers, government agencies websites
such as US Environmental Protection Agency (EPA) and academic and industrial publications.
The most relevant and updated events and legislation were selected and tailored to the context of
Shortfall. The values assigned to describe the costs or fines for each card were assumed by the
game designers in accordance with the magnitude of the impact of the event in the real-world.
These events may only apply to one part of the supply chain (e.g., part manufacturer) or it may
have an impact on the entire supply chain and all three teams would be affected. The actual
current events cards that were used in the board game are shown in Appendix A.
Innovation Cards
Innovation cards include some of the most important technology advancements that are
evaluated by the “R&D Manager” of each team in the game to help meet company’s financial
goals and environmental needs. The R&D managers may choose to buy an “Innovation” card;
whether to improve production demand, to reduce the unit cost of production or to comply with
environmental regulation that they face after a current event card is drawn. The cards capture the
most successful technological breakthroughs in automobile production history. The different
color codes and subtitles identify the tier of the supply chain and the specific sector within each
tier for which technologies can be applied. These areas of the game board (shown in Figure 2-2),
include storage for raw materials purchase, a production facility, a warehouse for storage of
products as well as several regions for disposal, including scrapped product recycling, onsite
waste storage and offsite waste disposal.
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Figure 2-2: Shortfall game board for car manufacturer processes and storage facilities
Innovations can apply to manufacturing, as well as any area that precedes or follows
manufacturing. The “Level” numbering in the “Innovation” cards was utilized to regulate the
order of use of different technologies. Lower “Level” technologies are prerequisites to
investment in higher “Level” technologies. The actual Innovation cards used in the board game
are presented in Appendix A.
2.3. Board Game Results
The assessment of the game has been fully documented in Isaacs et al (Isaacs et al.,
2006b)and Qualters et al (Qualters et al., 2006). Excerpts are provided below.
“The goal and challenge of Shortfall is for students to learn to minimize environmental
impact while maximizing profit. The auto industry manufacturing supply chain allows
exploration of relationships among design considerations, supply chain management,
environmental issues, research and development, and profitability. Although the supply
chain in the game is simplified, students can experience the ramification of materials
selection and processing decisions, i.e., technological solutions on the triple bottom line
37
through a unique educational format. Issues involved with game development are
reported along with results and reactions to the game play” (Isaacs et al., 2006b).
“Significant disparities in the learning styles of millennial students (students born after
1980) and those of their instructors have been documented. Shortfall is a board game
designed to raise awareness of the concept of environmental decisionmaking in the
supply chain. The project explores the hypothesis that millennial engineering students
approach learning in a communal, active manner using trial and error approaches.
Results of this pilot exploratory project suggest that engineering students are able to
learn new information in a collaborative game approach, which impacts their confidence
and self-awareness of their knowledge base” (Qualters et al., 2006).
2.4. Summary
The final measure (metric), used to decide the winning team was their profit at game end.
After all products are sold and waste disposed, the team with the most money was deemed the
winner. One of the disadvantages of choosing total profit as a single metric to rank the
performance of the teams is that it encourages the teams to focus on short term profitable
strategies and to overlook their environmental performance and use of environmentally friendly
technologies Because one of the learning objectives was to teach students the concept of
sustainability where a multi-criteria approach is favored (Parris & Kates, 2003; Beloff, Tanzil, &
Lines, 2004; Ding, 2005), the game has been modified to provide additional incentives for
environmental consideration. Chapter 3 brings into attention the importance of considering
environmental performance assessment and different environmental valuation methods.
38
3. MEASURING ENVIRONMENTAL PERFORMANCE
To better evaluate the performance towards sustainable development, both the economic
and environmental performance of the firms need to be measured. Economic performance
usually is measured in stock market performance, financial ratios or costs (Wagner, 2000;
Théophile, Phu, & Marcus, 2001). Environmental performance measures are less tangible
compared to economic or cost performance. Methods such as environmental indexing or
measurement of the emission levels have been used. Several studies have tried to assign values
for environmental performance (Ford, 1992; Nieuwlaar, Warringa, Brink, & Vermeulen, 2005).
Environmental indexing methods are used to quantify the environmental component of
sustainable development. They are fundamental in the process of monitoring and quantifying the
state and trends of environmental parameters and important for deriving a basis for ranking and
measuring sustainable development.
As businesses provide products and services to their customers, environmental costs are
one of the many different types of costs they face. Environmental costs can include the cost of
compliance with environmental regulations, the cost of investing in green technologies, the
overhead cost of environmental protection and environmental management systems (1995). With
the growing degradation of the natural resources, there is increased importance in the level and
scope of environmental performance in most of today’s competitive markets; companies are
challenged to decide about their environmental strategies (Sarkis, 1999; Turner, 1999; Ding,
2005; Gonzalez-Benito & Gonzalez-Benito, 2005).
This chapter reviews different environmental valuation methods and discusses their
advantages and disadvantages.
39
3.1. Environmental Accounting
Firm-level decision making is supported by reports and information that is provided
internally. The general terminology used to address this process in the literature is “management
accounting”, which is defined by Institute of Management Accountants as:
“…the process of identification, measurement, accumulation, analysis, preparation,
interpretation and communication of information used by management to plan, evaluate
and control within an entity and to assure appropriate use of and accountability for its
resources…” (Siegel & Shim, 2006).
Environmental accounting is a decision support process that provides a detailed list of all the
potential sources of environmental costs that should be taken into account while deriving the
environmental cost of a product or process or plant.
To support management’s environmental decision-making, “environmental accounting”
is used to measure environmental performance and to estimate environmental costs of
companies’ decisions and operations. Also, environmental accounting determines which types of
costs should be considered as environmental costs and suggests ways to calculate them as they
are associated with the decisions or operations.
However, there are challenges in how to truly measure the environmental cost of
business. The deployment of scarce resources highlights a fundamental valuation question: what
is nature’s value? There are various techniques developed to value nature and its resources. The
approaches toward environmental valuation can be classified into quantitative and qualitative
methods. Quantitative methods, mostly based on economics axioms and theories, can further be
categorized based on whether or not they assign a monetary value to environmental activities.
40
3.2. Monetization Methods
There is a wide range of methods proposed for monetizing environmental impact. Within
this range there is a significant body of literature that has focused on individuals “willingness-to-
pay” as the basis for deriving environmental valuation (Den Butter & Verbruggen, 1994; Weiher,
1997; Leggett, 2002; Finnveden, Nilsson, Johansson, Persson, Moberg, & Carlsson, 2003;
Daniel, Tsoulfas, Pappis, & Rachaniotis, 2004; Venkatachalam, 2004; Pouta, 2005; Bruce, 2006;
Wang, Zhang, Li, Yang, & Bai, 2006).
Furthermore, monetization methods have been developed in two ways based on economic
approaches taken toward monetization. Conventional economists approach this issue based on
the axiomatic assumptions that human individuals have different preferences for acquiring
particular utilities (including environment), and they almost always express their preferences in a
way that maximizes their utility (i.e., that is is beneficial for them and their welfare). Therefore,
conventional economists argue that the value of taking in to account the environment is limited
to the extent of money that individuals are willing to pay for satisfying a preference (Turner,
1999). In other words, they assume that each individual is a perfectly rational decision maker
with complete information- an assumption which has been criticized in different literatures
(Morecroft, 1983; Riddalls & Bennett, 2003; Grossler, 2004; Lane, Grossler, & Milling, 2004).
Using a cost-benefit analysis approach, economists conclude that environmental protection
should only be required if it can be demonstrated that it is justifiable based on people economic
interests (willingness to pay) (Turner, 1999).
The second monetization approach is taken by a group of environmentalists and is
supported by research in economics. Turner (1999) describes this approach as:
41
“Some environmentalists (including a minority of economists), on the other hand, either
claim that nature has non-anthropocentric intrinsic value and non-human species possess
moral interests or rights, or that while all values are anthropocentric and usually (but
not always) instrumental the economic approach to environmental valuation is only a
partial approach … (Turner, 1999)”
The latter approach leads to a level of environmental advocacy that considers
environment and natural resources as infinitely valuable resources; thus proactively tries to
conserve the environment, regardless of the cost of the environmental protection activities. The
following discussion reviews the monetization and non-monetization methods and discusses
some of the limitations of these methods. The amount of discussion devoted to each
methods/approach is proportional to the importance of the method and frequency of use
encountered in the literature.
3 .2 .1. Willingness-to-Pay
Willingness-to-pay is the amount an individual is willing to pay to acquire some good or
service (UNEP., 1995) and in the environmental protection context it refers to the avoidance or
willingness to remove some environmental damage (Ford, 1992). The amount of money that
individuals or societies are willing to pay can be derived from three different methods
(Finneveden, 1999):
1. Individuals revealed preferences
2. Individuals expressed preferences
3. Society’s willingness-to pay
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The following discussion is a review of the three different methods, followed by a more detailed
discussion around their underlying assumptions.
Revealed Preferences
Revealed preferences are determined by the amount of money people actually spend in
the market to receive any good or service. Essentially, this means that the preference of
consumers can be revealed by their purchasing habits. The methods that are based on the
revealed preferences are more suitable for valuating environmental damages rather than potential
environmental threats, since it is usually damages that are valued in the market. For example, the
value of damage to agricultural commodities or timber can be calculated based on their market
price in commodity markets (Finneveden, 1999). An environmental scientist can derive the
willingness-to-pay to reduce emissions that will damage the forests. However, the value of a
forested area or a national park is not only limited to the value of its timber or biomass; there are
also less tangible values (utilities) associated with having a green and healthier environment
ranging from health and flood safety advantages to recreational values. Using a river as an
example, it is not only the value of water, fishing industry and potential water energy that should
be taken in to account, but also the attraction value for people to fish, to sail or to walk near the
river should be taken into account. Moreover, if the river is polluted with waste stream of nearby
factories, besides destroying the recreational attractions it may be unhealthy for humans to live
close to the river.
Since the recreational, safety and health issues are not directly priced in markets, to
determine individual’s revealed preferences to avoid such environmental damages (i.e., to
maintain these less tangible goods and benefits), these methods look at human behavior in
related markets. The travel costs of people to go camping at recreational sites near lakes and
43
forests provide an estimate of their revealed preference. Another way of deriving a revealed
preference is to consider the difference in payments and salaries for similar jobs and services in
two environments with different pollution/emission level, or variation of wages due to the
environmental and health risk associated with the work environment.
Expressed Preferences
There are myriad of issues that are not directly priced or marketed in our lives. The rights
of the future generations to inhale clean air and inherit a clean environment, the right of fish to
swim in the clean water, the right of birds to fly and live in clean eco-systems are not directly
priced nor can be efficiently considered by revealed preference approach (Finneveden, 1999).
These values are referred to as “non-use values” (Turner, 1999) and are used to distinguish
between “use values” which was discussed in the revealed preferences. “Use values” can be
derived from market prices in a direct (e.g., commodity price) or indirect way (e.g., traveling
cost). The estimation of non-use values are, however, more complex than use values (Turner,
1999).
One of the methods used for estimating the individual’s preference for non-use values is
the contingent valuation method (CVM) which is discussed in detail in the literature
(Venkatachalam, 2004; Pouta, 2005; Wang et al., 2006), although there is still debate and
controversy in its scope and results (Turner, 1999). Contingent valuation is a survey-based
economic technique for the valuation of non-market resources, typically in environmental areas.
This technique is particularly used to the values (utilities) of certain aspects of the environmental
resources that do not have a market value (price) since they are not directly sold; for example,
people receive benefit from scenic landscapes in the mountains, but it is difficult to assign a
value for that value. The technique has been widely used by governmental departments in the
44
United States while doing cost-benefit analysis on the positive or negative impacts of the
different environmental projects. Examples include a valuation of water quality and recreational
opportunities in the river downstream from the Glen Canyon dam, biodiversity restoration in
Mono Lake and restoration of salmon spawning grounds in certain rivers (Wikipedia).
Unless the valuation of damage is for “use values” with a determined market value, there
are limitations in underlying assumptions for valuating the environmental damages and threats.
The preference method assumes that the decision maker is always making decisions that
maximize his/her welfare and is considered as rational (or perfectly rational). In addition to the
assumption of rational behavior, this technique and other forthcoming techniques require that the
decision maker preference satisfies the condition of “complete preorder” (Newell, 1998).
Complete preorder requires that for each pair of options (choices), the decision maker will either
prefer one or find the two to be equally preferable.
In the real world, the complete preorder condition is unlikely to be satisfied. Most
decision makers do not have enough knowledge about implications of different types of
emissions to be able to rank their choices definitively (Newell, 1998). Consider having to choose
or value two different types of emissions; NOx emissions and hydrocarbons. Both are causing
urban smog and both are regulated by the government, however, neither of them has a direct or
an indirect market price. Their effect on ozone and smog creation is very different; hydrocarbons
have an important impact on initial rate of ozone creation, but as the air mass ages and after all
the precursors have had their effect, ozone creation is sensitive to the NOx concentration. They
play very different roles in the production of smog and their different effects can not be easily
compared. Therefore, there is no definitive weighting or preference between these two
emissions. In the urban context where smog is a city wide problem, it is likely that control
45
strategies will overestimate the importance of hydrocarbon control and underestimate the
importance of NOx control. On the other hand, if the US Environmental Protection Agency
broadens its scope over greater distances and greater time periods, or in a global scale, the
importance of NOx control becomes more apparent (Sillman, 1993; Newell, 1998).
Also, there are other methods that can be used to valuate the damage of emissions that
neither are priced in market nor can their impacts be compared to other non use values. For
example, the potential threats from emission of 1Kg of NOx into air is usually translated to a
more sensible environmental damage such as the increased risk of skin cancer due to ozone
depletion or death of fish due to river water pollution (Newell, 1998; Finneveden, 1999). The
impacts are estimated from information about the amount and concentration of emission species
in the environment. After the information about environmental changes is gathered, the
relationship between environmental changes (concentration of pollutants, emissions, etc.) and
impacts on human health, ecosystem, etc. can be explored. Then, the decision maker can think of
1 kg of NOx in terms of lives of people at risk or the increase in risk of heart attack, etc.
However, those impacts can not be accurately predicted, because the temporal
information about where and when the emission has been released is not usually available.
Therefore an average environmental change is assumed, which is an approximation of emission
release and concentration in the environment. The approximation error can be modified to some
extent by characterizing the emission by a distribution. Even if a detailed data about emission
release were available, there is often debatable scientific research that supports opposing sides of
the issue. For example, epidemiological results depend on the scientist’s choice of model and
methodology and are also highly dependent on the area and population of study. After all, for
46
some species there is no concise description of how the world will change due to the emission of
that type unless simplifying assumptions and judgments are made (Newell, 1998).
Society’s Willingness-to-Pay
The main source to derive a society’s willingness-to-pay is to look at the political
decisions and governmental expenditures on the issues related to environmental protection.
Analysts take into account the value of a human life in a society by studying how much profit
society gains from the outcome of people’s work (Newell, 1998). This value varies for different
countries; there is a significant difference between the value of human life in a developed,
industrialized society and life value in a poor country (Newell, 1998). Therefore, the amount of
budget and research expenses that is contributed to improving the statistical life and life quality
can be perceived as society’s willingness-to-pay.
Alternatively, environmental tax is another important indicator to derive society’s
willingness to pay (Finneveden, 1999). Although the tax amount is a function of the political
situation in a country, the amount of tax applied to certain emissions can be translated to the
society’s willingness to pay for emission prevention.
3 .2 .2. Methods Not Based on Willingness-to-Pay
Monetization methods that are not based on willingness-to-pay are mostly related to the
amount of money needed for damage remediation. This cost includes technology cost,
environmental management and implementation cost and etc., and is different from individual’s
willingness-to-pay as there may be no one willing to spend the estimated amount.
There are, however, uncertainties in valuating the damage with these methods. Firstly, the
valuation of damage is possible if appropriate technologies and infrastructures are available to
47
make damage remediation possible. Secondly, the cost of damage remediation is driven by the
marginal cost of removing the damage such that the environmental measures are at goal levels or
below critical values. Therefore, the amount of reduction needed is an important determinant of
the cost. This introduces the challenges of how much is reduction enough or in another words,
what the environmental goal should be. Also, the scope of remediation plays an important role in
the valuation process since there may be priorities in what types of pollution should be reduced
first if not all, and by how much. Thirdly, in valuating the damage, certain level of discounting of
future impacts is usually assumed since the effect of the damage is prolonged infinitely to the
future. Unlike the methods using society’s willingness-to-pay that implicitly consider
discounting future, in damage valuation either a certain level of discounting for the future
impacts or a cut-off time to ignore any damage afterwards should be assumed to avoid incurring
infinite costs.
3.3. Panel Methods
As the name suggests panel methods are used when there is a need to capture the opinion
of a group of peers, usually experts in the field. In these methods, people are asked to give
weights to several issues related to a problem. Interviewees may be asked to rank among
different criteria such as ozone depletion, bio-diversity, human health and other categories for a
hypothetical product. Outcomes of these methods would provide a basis to create an indicator of
environmental performance that companies could use to measure their aggregated environmental
improvement (Bengtsson, 2000; Nieuwlaar et al., 2005). Several successful case studies have
been cited in a review by Finneveden (1999) (Finneveden, 1999; Finnveden et al., 2003).
Weighing methods are problem specific and are developed on an ad-hoc basis regarding
the case study. One of the limitations of this method is that the results (i.e., the weights of
48
factors) are sensitive to how the questions are being asked. Also, as reported in the literature
(Finneveden, 1999), there is a tendency among panels to assign more or less equal weights to all
criteria. Other limitations of panel methods can be due to: (Finneveden, 1999) 1) the way the
problem is presented, 2) the differences in information and knowledge of the panelists about the
problem 3) the different criteria applied by each panelist and 4) the values inside the company or
among the panelist may not represent the scientific values (Bengtsson, 2000).
3.4. Qualitative Methods
Qualitative methods are usually used to structure the information provided by
quantitative analysis and are often framed to facilitate discussions that lead to a conclusion or
priority ranking of the research results. Matrices are often used to help structure scoring or
weighing based on two different categories; 1) aspects or impacts of a product or process 2) the
environmental criteria. They are also used to determine the relative environmental importance of
an environmental impact such as global warming or toxicological impacts. In a study done by
Schmitz et al (1994), five criteria have been identified to determine the importance of different
environmental impacts. These criteria include:
• Ecological threat potential
• Reversibility- irreversibility
• Domain of impact: global, regional, local
• Environmental preferences of the population
• Relationship of actual and/or previous pollution to quality goals
To help determine the relative importance of environmental impacts based on these
criteria, a matrix was used with the environmental impacts (problems) on one axis and the
49
chosen criteria on the other axis. Based on these criteria different environmental impacts can be
ranked from “less important” to “very important” and the cumulative ranking for each impact
will determine its relative importance.
Qualitative methods are usually limited to be used to identify and prioritize major
environmental issues and risks and to suggest mitigation strategies. To further explore the
environmental impacts so that decision makers can compare and choose between different
alternative actions quantitative methods and rigorous analysis should be applied to the
problem(Finnveden et al., 2003).
The results from a qualitative valuation will often not be reproducible, because others
performing the valuation could possibly reach another conclusion. This means that the choice of
the individual or group that performs the valuation will be important for the overall result.
3.5. Summary
Environmental valuation methods are used to measure or assess a change (impact) on
environment. These methods are different on their scope of assessment or the range they
consider consequences of an environmental change and level of quantification.
Using environmental valuation methods is problem specific and none of the mentioned
approaches can be used in all of the valuation stages. Key factors that influence choice of a
valuation method are the scope of problem (definition of system boundaries), the amount and
type of environmental changes included in the assessment, the degree of quantification desired
and the degree of aggregation of results desired.
Different case-specific weighting methods used for different products or processes inside
a company creates problem in internal communication. Use of one weighting method provides a
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single measure of environmental performance which helps the company to more easily follow a
path for environmental improvement (Bengtsson, 2000). Alternatively, by using more than one
weighting method, companies are more likely to consider the environmental priorities across
different markets. In a broader context, such as national or global environmental priorities, using
more than one method implies considering more “ways of thinking” especially when company is
active in different markets around the world (Bengtsson, 2000). Furthermore, there is a level
uncertainty and risk involved in the valuation process which may lead to prioritizing errors and
choosing the wrong alternative over the other.
Automotive Industry as an industry with the most commonly cited impact on
environment involves multiple methods of environmental valuation an assessments in variety of
processes and products. Also, as mentioned in Chapters 1 and 2; the automobile industry is
selected as the case study for teaching concepts of sustainability with focus of environmental and
economic performance using educational gaming. Finally, the economic and environmental
performance of firms and the trade-offs between these two is discussed in a broader context in
Chapter 4. Given the mentioned limitations in using and comparing the outcomes of multiple
methods, a unitless measure of environmental impact, referred as green value, is introduced to
allow comparison between environmental impacts of a plethora of events relating to different
areas of automotive industry from both legislation and technologies. Green value is event
specific and changes based on the importance or the order of environmental impact of each
event. Also, the sensitivity of economic and environmental performance for three different
decision makers representing different environmental policies is studied.
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4. SIMULATION ANALYSIS OF ENVIRONMENTAL AND
ECONOMIC PERFORMANCE
The importance of environmental performance in many organizations is growing along
with the growing degradation of the natural environment (Sarkis, 1999; Turner, 1999; Ding,
2005; Gonzalez-Benito & Gonzalez-Benito, 2005). This introduces the questions of how
implementation of environmental practices contributes to economical performance and whether
competitive opportunities exist for voluntary adoption of environmental technologies. Several
authors have explored the economic benefits of adopting environmental technologies and
regulations in corporate decisions ((Porter & van der Linde, 1995; Gonzalez-Benito & Gonzalez-
Benito, 2005; Clemens, 2006).
In competitive markets where environmental protection is important, the means for
implementation of environmental decisions and practices are likely to affect the economical
benefits of those activities. In other words, whether an environmental practice will be beneficial
to the company depends also on how efficiently the environmental management system within
the company manages the implementation (Schaltegger & Synnestvedt, 2002). The choice of
effective and economically efficient environmental practice, given the market and organization
profile, may be key for determination of successful environmental management systems.
Although the relationship between these two important dimensions of environmental
practices is important for environmental policy making and has been studied for more than two
decades, no conclusive results have been reached (Wagner, 2000). Although the results have
been “contradictory” (Sarkis & Cordeiro, 2001), several studies have highlighted factors that
create uncertainty in determining the profitability of environmental practices and therefore limit
52
the findings to be a more generalized solution instead of a case-study under certain assumptions.
Factors such as the type of environmental regulations, type of environmental technologies
(abatement vs. source reduction), the industry of study, corporate environmental profile and firm
size are some of the highlighted factors (Sarkis & Cordeiro, 2001; Théophile et al., 2001) These
factors are cited as sources of uncertainty in determining the relationship between economical
and environmental performance and, therefore, decision making about the extent of
environmental practices. This paper:
• reviews approaches toward the environmental and economic relationship assessment,
• identifies factors as the sources of uncertainty cited in the research findings,
• proposes an approach to explore environmental and economic tradeoffs given the
described uncertainty factors,
• compares the results for short term and long term time frames, and
• explores the relationship under different settings using factorial analysis.
A Monte Carlo simulation model therefore was developed that provides results to help explore
economic and environmental tradeoffs and the longer term implications of company decision
making. The intent is to gain an understanding of how decisions that are consistent with “green”
or “non-green” management styles over a longer period of time will impact a company’s
economic competitiveness and environmental performance and to gain further insight into these
dynamics.
The results are provided based on two performance metrics over the 10 year period (40
quarters) for three different management philosophies. Economic and environmental
performance of the three decision makers (DMs) are reported as their total cost and a unitless
53
environmental index referred as green score. The long term versus short term comparison of their
performance is provided followed by an extensive factorial analysis that is focused on factors
that impact economic and environmental performance of firms and their impact on three
different DMs in the model.
Section 4.1 summarizes the literature on ERM assessment and the primary types of
uncertainty facing regulators and manufacturers. Sections 4.2 and 4.3 illustrate the Monte Carlo
simulation and experimental design approaches, respectively, using a simplified automotive
supply chain example.
4.1. ERM ASSESSMENT AND UNCERTAINTIES
In general, three different research methodologies historically have been undertaken to
study relationships between economic and environmental performance (Day, 1998; Curkovic,
2003):
(i) Studies that examine environmental events or innovations and subsequent market
responses or consequences;
(ii) Studies that consider portfolios of firms and compare the performance of
environmentally proactive and reactive companies, and
(iii) Studies that use statistical methods such as regression to determine relationships
between environmental and economic performance.
In general, environmental strategies employed by companies occur across a spectrum of
beliefs, ranging from the two extremes being that environmental improvement: 1) reduces
competitiveness and profitability or 2) increases competitive opportunities and profitability.
Having recognized the early industry mindset of “help the environment, harm your business”
(Walley & Whitehead, 1994), the traditional view of the corporations considers environmental
54
costs as limit to profitability (Walley & Whitehead, 1994; Pava & Krausz, 1996; Taylor &
Friedman, 2001). However, many recent studies have often led to conflicting results and have
argued that efficient environmental performance creates economic opportunities (Porter & van
der Linde, 1995; Klassen & McLaughlin, 1996; Wagner, 2000; Konar & Cohen, 2001; Théophile
et al., 2001; Schaltegger & Synnestvedt, 2002; Clemens, 2006).
Schaltegger and Synnestvedt (2002) described two general views about the relationship
between environmental practice and economic success. The first view is that investing in
environmental protection activities puts an extra cost burden on the company which will divert
the company from being economical and profitable (Walley & Whitehead, 1994). They believe
profit margins are reduced if companies increase investments in environmental protection. The
second view is that an optimal point exists where companies can maximize their economic
success by investing in some level of environmental technologies and practices, although
improving environmental performance beyond this point will decrease profits (Schaltegger &
Synnestvedt, 2002). Supply curves have been used to demonstrate thresholds beyond which
further investment in ERM technologies reduces overall profit (Nieuwlaar, Warringa, Brink, &
Vermeulen, 2005). This analysis helps prioritize environmental measures and actions so that they
are reconciled with a company’s economic goals (Nieuwlaar et al., 2005).
Since environmental practices are partially driven by economic incentives, a company
can either choose to stand in the optimal point or decide to maximize their environmental
performance at the lowest possible cost. Porter and van der Linde (1995) emphasize the
importance of “resource productivity” driven by innovations in processes and product design as a
source of compensation for environmental expenditures. Considering pollution as a source of
inefficiency they consider environmental technologies or innovations driven by environmental
55
regulations as a win-win strategy. The main reason is the innovation offset that will follow the
efficient use and process of resources (Porter & van der Linde, 1995). Many successful
innovations have resulted in companies such as Dow Chemical, 3M, or Texano. However, this
notion is based on assumptions that regulations are “innovation friendly”.
From a different perspective, the relationship between environmental proactivity and
business performance has been analyzed by studying the different dimensions or aspects of
environmental proactivity, as well as different definitions of business performance measures
(Gonzalez-Benito & Gonzalez-Benito, 2005).
Gonzalez-Benito (2004) identified two main groups of work based on how they have
addressed proactivity: 1) one-dimensional and 2) multi-dimensional. The classification is based
on whether higher levels of environmental proactivity were achieved through implementation of
1) one or 2) more sets of environmental practice. The study concluded that there is no single
response for the question of whether environmental proactivity has positive effects on business
performance and that this relationship must be disaggregated into more specific and concrete
relationships (Gonzalez-Benito & Gonzalez-Benito, 2005).
The literature is not all anecdotal, but there are studies that approach this problem by
proposing generic or case-specific models. Klassen and McLaughlin (Klassen & McLaughlin,
1996) proposed a theoretical model suggesting a relationship between environmental and
economical performance. In this study, a company’ environmental awards or crises were used to
indicate the collective experience through the environmental management activities where the
“Efficient Market Theory” was used to measure the economic performance of the company.
56
The relationship between economic and environmental performance depends on several
factors, including the (i) type of environmental regulations, (ii) type of technology innovation,
(iii) type of industry, (iv) company environmental profile, and (v) company size. The uncertainty
of each of these factors makes ERM decision-making difficult. Early studies were conducted on
small number of firms (Wagner, 2000). Some studies are limited to one industry or one country
or location such that the country-specific characteristics bias the outcomes (Buysse & Verbeke,
2003). Five common important factors that emerge from these studies as impacting the
relationship between economic and environmental performance are summarized in Figure 4-1,
described below, and represented in the simulation model.
(1) Type of environmental regulation
Regulations can promote or prevent innovation leading to different levels of
competitiveness and profitability. Porter and van der Linde (1995) suggest the term “good
regulation”, as a regulation that encourages innovation and gives credit to improvements in the
product design and process line that decreases pollution emission and reduces the waste rather
than end of line emission reduction or waste disposal technologies. In contrary, “bad regulation”
discourages risk taking and experimentation. “Bad regulation” does not provide competitive
opportunities or offsetting periods for companies to gain value from partial improvement in their
process. Therefore, they tend to adopt safe, but highly likely “more expensive” end-of-pipe
treatments (Porter & van der Linde, 1995).
Studies have examined the nature of regulations in America, Europe and Japan, and
revealed that national goals as well as cultural and social values play significant roles in driving
the environmental practices. Several authors (Gutowski, Murphy, Allen, Bauer, Bras, Piwonka,
57
Sheng, Sutherland, Thurston, & Wolff, 2005) on their visit from Northern EU countries stated
that there are “protectionist” views among EU industries to set goals and regulations that
contribute to the well-being of those countries. In Japan, however, industries believe in taking
advantage of environmental technologies and process improvement innovation to add to their
competitiveness in the market.
(2) Type of technology innovation
Different approaches toward implementation of environmental practices have different
results and efficiencies. The literature suggests that proactive approaches that lead to source
reduction technologies, in the long term, may prove more efficient than end-of-pipe technologies
(Porter & van der Linde, 1995). In the short term, Sarkis and Cordeiro (2001) showed that
although both strategies are negatively correlated with financial performance, the end-of-pipe
technologies have higher negative impact on short run financial performance than source
reduction (Sarkis & Cordeiro, 2001).
(3) Type of industry
Depending on the industry of study, the cost to make a unit of environmental
improvement may vary (Sarkis & Cordeiro, 2001). In this case, environmental regulations are
often found to be a disadvantage for profitability. However, for many other cases it may be
beneficial to improve environmental performance. Therefore, in a larger sample of data from
firms across several industries, it is less likely that a significant relationship between
environmental and economic performance can be identified (Sarkis & Cordeiro, 2001).
(4) Compay environmental profile
58
Firms with low levels of environmental performance are more likely to incur higher costs
through non-compliance fines that reduce profits. Also, the non-green firms’ market share may
be affected, because “greener” competitors may have a better corporate image to attract more
customers. Although environmentally proactiveness may lead to innovation and competitive
edge, it also incurs the cost of environmentally responsible technologies and environmental
management systems. Therefore, corporate profile is an important factor in studying the
relationship between economic and environmental performances of firms.
(5) Company size
The traditional notion that environmental costs have a negative impact on firm
performance do not necessarily hold for the large firms (Aragon-Correa, 1998; Aragon-Correa &
Sharma, 2003). Henriques and Sadorsky (1996) argue that larger firms are more likely to have
environmental plans, because they are more prone to public scrutiny. Clemens (2006) suggests
that small firms that show a better environmental performance are also the most successful
financially (Clemens, 2006), though, other studies found no significant firm size effect
(Théophile et al., 2001). In the proposed framework, the firm size is not taken into account and is
assumed constant.
As discussed, these factors are some of the important underlying assumptions of studying
the relationship between economic and environmental performance. Variations in these factors
can impact the study results; therefore, a model that simulates a simplified decision making
process in the automotive industry is used to calculate performance of companies under these
factors and to analyze the sensitivity around each factor to help understand the effect of these
59
uncertainty factors and their order of magnitude. Section 4.2 discusses our model framework and
the underlying logic in detail.
4.2. SIMULATION MODEL
4 .2 .1. General Modeling Approach and Logic
Given the above motivation, computer simulation can be used to help explore various
management styles and policies in the presence of uncertainty factors. Five factors are described
in Table 4-1, and how (or whether) the effects are implemented in the model. With periodic
decisions made based on a company’s ERM policies, external regulations, emerging
technologies, and economic considerations, the model is then capable of performing sensitivity
analysis and factor analysis over the economical and environmental performance of a firm.
Table 4-1: Factors of uncertainty determined in the literature and incorporated in the simulation assumption
Factor Description Model ImplementationType of Regulation The design and regulation of environmental
standardRandom events of different types occur in the simulation, ranging from mandatory regulations (push) to incentives and innovation-friendly legislations (pull). Also, “green score” and “fine amount” represent government leverages to control the industry.
Type of Technology Innovation
The characteristic of the environmental practice (source reduction or end-of-pipe)
Random events in the simulation capture technologies and innovations.
Type of Industry Each industry has different environmental challenges and priorities.
The focus is automotive industry with ability to generalize to wider range of businesses
Corporate Environmental Profile
The profile of company in regard to approach toward environmental issues
Three different scenarios are compared. Environmental profile is modeled by a beta distribution over a range of likelihood to take action.
Firm Size Size of the company (small business, medium or large corporation)
The model does not capture the effect of the firm size.
60
Figure 4-1 illustrates the general logic of one such simulation model, based loosely on a
simplified automobile manufacturing example (Isaacs et al., 2006b; Qualters et al., 2006;
Torabkhani, Isaacs, & Benneyan, 2007). This model simulates three different types of decision
makers (DM) along an environmentally responsible continuum, herein referred to as “green”,
“moderate”, and “non-green”. In each of several consecutive time periods, a chance “event” is
randomly selected from a list of possible regulations or technology innovations and the DM
decides how to respond, i.e. whether to comply with the regulation or invest in the technology,
based on a set of defined probabilities for each type of event and type of DM, and with
probabilistic compliance or investment costs. Non-compliance with regulations can result in
fines, again based on defined detection probabilities and with the fine amounts being drawn from
user-specified probability distributions.
61
Fine1. Add fine amount of event to DM’s total cost2. Subtract green value from DM’s total green score
NOU(0,1) random number compared to the fine probability.
Get Fined?
Gain Event Value1. Add cost of event to DM’s total cost2. Add possible green value of event to
DM’s total green score
End of Quarter1. Check the occurrence times2. Update time period and input table3. Compute results (Avg., $/ quarter and green score)
NO
YES
NO
FINISH
YES
End of the Simulation?
Compute Results1. Compute Avg. and Std Dev
DM Responds to Event
Event Occurs1. A uniform random number is generated2. P(legislation) = 0.4, P(technology) = 0.63. Event type determined
Simulation Setup1. Input decision maker (DM) type2. Set number of quarters and replications3. Initialize between replication variables
START
YES
Conduct One Replication1. Initialize within replication variables2. Reset event list3. Increment replication counter
Evaluate One Time Period1. Increment time period2. Reset DM response variables
YES
End of the Replication?
NO
Fine1. Add fine amount of event to DM’s total cost2. Subtract green value from DM’s total green score
NOU(0,1) random number compared to the fine probability.
Get Fined?
Gain Event Value1. Add cost of event to DM’s total cost2. Add possible green value of event to
DM’s total green score
End of Quarter1. Check the occurrence times2. Update time period and input table3. Compute results (Avg., $/ quarter and green score)
NO
YES
NO
FINISH
YES
End of the Simulation?
Compute Results1. Compute Avg. and Std Dev
DM Responds to Event
Event Occurs1. A uniform random number is generated2. P(legislation) = 0.4, P(technology) = 0.63. Event type determined
Simulation Setup1. Input decision maker (DM) type2. Set number of quarters and replications3. Initialize between replication variables
START
YES
Conduct One Replication1. Initialize within replication variables2. Reset event list3. Increment replication counter
Evaluate One Time Period1. Increment time period2. Reset DM response variables
YES
End of the Replication?
NO
Figure 4-1: Flow of simulation logic
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Economic and environmental measures are updated at the end of each time period based
on the DM’s actions and their impact on an overall green score, associated costs, and any fines.
This simulation logic repeats for a user-defined number of time periods (50 replications for the
reported analysis in this work) and then is replicated for a user-specified number of replications,
after which summary measures are computed and outputted by the program for analysis. User-
defined information are associated with each type of simulation event and include technology
implementation costs, environmental impact expressed as green value, fine probabilities for non-
compliance, fine amounts, and event occurrence frequency. These model parameters are
described in greater detail below in Section 4.2.2. To illustrate this approach, the model logic,
events, performance measures, and input values for the automotive example then are described in
Section 4.2.3.
4 .2 .2. Automotive Example
Chance events
Table 4-2 lists all the possible chance events for the automotive example, separated into
regulation and technology categories, and their associated probabilities, parameters, and other
values. The US Automotive industry, because of its competitive market and wide utilization of
technologies, provides sufficient variability and scope of ERM technologies and related
regulations. Also, given the regulations and ERM technologies available, there are numerous
opportunities for firms to pursue different environmental strategies ranging from “environmental
proactivism” to “wait and see” approach (Curkovic, 2003).
For this case, ten regulatory events represent manufacturing requirements relevant to this
industry, such as the Clean Air Act of 1970, and future possible regulations. Thirteen technology
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events represent past breakthroughs or potential technologies that might impact some areas of the
automotive supply chain such as raw materials, part production, original equipment
manufacturer, etc. Examples include the invention of catalytic converters, alternate fuel vehicles
or hybrid technology, and material technologies leading to improved or lighter parts.
Manufacturing technologies that can benefit any stage of the supply chain, such as just-in-time
production and automated guided vehicles also are included.
64
Table 4-2: Automotive example chance events and parameter values
Event Event Description Cost Green Value
Fine Prob.
Fine Amount
# Occurrences
1The 1970 Clean Air Act mandates 90% reductions in tailpipe emissions. Noncompliance results in heavy fines or vehicle recall.
1,000/car 200
1- 0.3 2- 0.6 3- 0.9 4- 1.0
1- 100/car 2- 500/car
3- 5,000/car 4-10,000/car
4
2By 2030, EPA plans to require cuts of 90% of the harmful emissions of nitrogen oxides and diesel particulate from 2006 levels.
1M 500 0 0 3
3 Legislation requiring recyclability. 10M 3001- 0.3 2- 0.6 3- 1.0
1- 50/car 2- 200/car 3- 500/car
3
4 Government tax break to companies with good recycling practices. (-) 1M 100 0 0 M
5 Regulation bans the use of lead and other toxic materials in vehicles. 60/car 80
1- 0.3 2- 0.6 3- 1.0
1- 30/car 2- 60/car
3- 500/car3
6 Inflation in healthcare requires payments for worker health care .
3,800/ worker 500 0.9 0 M
7 The Energy Policy Act (EPAct) of 1992 requires development of alternative fuel vehicles. 1,500/car 30 0 0 1
8 The Energy Policy Act (EPAct) of 2005 provides a base tax credit for the purchase of LDV fuel cell. 40M 70 0 0 1
9 DOT may raise CAFE from current 27.5 to 40 MPG in the future. 1,500/car 30 0 0 1
10 All LDVs require dual airbags for passengers safety. 1,000/car 50 0.9 0 1
11 Water-Based paint coating reduces emission of environmentally harmful VOCs, but increases costs. 30/car 300
1- 0.3 2- 0.6 3- 1.0
1- 30/car 2- 300/car 3- 700/car
3
12 Use of aluminum engines cuts vehicle weight, improves fuel economy. 150/car 200 0.9 0 1
13 Advanced catalytic convertors with improved insulation reduce total emissions by 10%. 860/car 100 0.6 2,000/car 1
14 Automatic Guided Vehicles (AGV) 50/car 0 0 0 M
15 Just-In-Time Manufacturing (JIT) 50/car 0 0 0 M
16 Automated machining leads to more flexible mfg less lead time, and greater variety of product. 100/car 0 0 0 M
17 Tailor-welded blanks reduce weight by reducing the part count and assembly. 50/car 50 0 0 1
18 Plasma Heating makes magnesium production faster, less energy and and less wasteful. 50/car 0 0 0 1
19 Recover scrap aluminum from production that can be sold as a recycled material. (-) 40/car 100 0 0 2
20 Advanced recycling (scrap segregation technologies). (-) 30/car 100 0 0 2
21 Froth flotation plastic recycling (-) 10/car 50 0 0 1
22 Optimized glazing systems for cars reduce vehicle vision and side panels weight by 30%. 400/car 20 0 0 1
23 Decreasing fuel prices and good economy, lead to market for more powerful SUVs (20% of LDV sales). 0 (-) 150 0 0 1
TECHNOLOGY
LEGISLATION
65
Implementation probabilities
The implementation probabilities represent the likelihood that each type of decision
maker adopts a new technology or complies with an environmental regulation. On average, a
green DM is more likely to comply with an event than a moderate DM, who in turn is more
likely to comply with an event than a non-green DM. For a DM of a given type, inherent
variation between events and replications is simulated via beta probability distributions, with
parameters estimated by the minimum, most likely, and maximum probabilities, shown in Table
4-3, in the usual manner such as used in project management (Law & Kelton, 1995). For each
event that occurs within the model, a beta random variate is generated with these parameters and
used as the probability that the DM makes the particular decision. In the automotive example,
these probabilities are generated using the values given in Table 4-3 (converted to beta
parameters α1 and α2), which in turn are used to draw a probability via acceptance-rejection that
the DM complies with this particular event. Thus event-to-event, these probabilities will have a
mean of (a+4b+c)/6 and variance ((b-a)/6)2. Once determined, these probabilities are used to
determine compliance or non-compliance using a Uniform (0, 1) pseudo-random number in the
standard manner.
Table 4-3: General MC model events, parameters, and performance measures
Type of Decision Maker Minimum Most Likely MaximumGreen 0.8 0.85 0.9
Moderate 0.625 0.775 0.825Non-green 0.45 0.5 0.55
Likelihood of Acceptance
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Event costs
The event cost is the dollar amount a company pays either to invest in the technology or
comply with the regulation. These costs depend only on the type of event (and not the type of
DM) (Table 4-2) and were estimated from the literature and historical data on regulations and
their compliance costs (Michalek et al., 2004; Gerard & Lave, 2005). Each cost occurs either as a
one time fixed cost or a cost per vehicle, based on what information was available through the
above sources.
Green values
In each time period, a ‘green value’ (a unitless value ranging from 0 to 500 based loosely
on the idea of an environmental Dow Jones type of index) is assigned to the DM’s response to an
event, based on its environmental value and representing the overall impact of investing in or
complying with the new regulation or technology.
Higher values are given to events, technologies, and regulations with larger
environmental benefits. For example, the green values for each automotive event are given in
Table 4-2, with the Clean Air Act of 1970 having a higher green value than a technology that
produces lighter weight windshield glass. Typically, compliance with mandatory regulations has
lower green value than proactively addressing potential environmental concerns.
Fine probabilities and amounts
If a company chooses not to comply with a randomly generated requirement event, fines
are levied with the probabilities and amounts listed in Table 4-2. For any given regulation that
67
may occur more than once, these fine probabilities and amounts increase for the next occurrence
of the same event if a company remains non-compliant. As above, each fine is expressed either
as a one time fixed cost or cost per car.
Event frequency
Event frequency is used to limit the occurrence of events over time. Each type of event
can occur in one of the following three ways; (i) once (such as using aluminum for engine
blocks), (ii) repeatedly over time (such as machining technologies, healthcare costs, and
incentives for recycling), or (iii) a limited number of times. An example of the third case might
be clean air regulations that might be revised, tightened, or reinforced over time. The analysis
was done under the event elimination without replacement assumption under which once an
event reaches its maximum occurrence limit, or it is complied by DM before reaching the limit,
the event is eliminated from the table and does not happen in the later quarters. However, the
effect of no elimination of events is shown in Section 4.2.
4 .2 .3. Model Logic
Event occurrence
The simulation model can be run for any number of time periods, with the duration
chosen at 10 years or 40 quarters. In each time period, one event is randomly selected from the
candidate event list as follows. The event type is first determined, currently with a 40% chance
of being a regulation and a 60% chance of being a technology event, and then a specific event is
selected. For simplicity, in the automotive example once the category is determined all
68
regulations or technologies within that category are assumed to have equal occurrence
probabilities, although this is not necessary.
Company response
A company’s response to an event is determined by random beta distribution acceptance
probabilities with parameters estimated based on minimum, most likely and maximum likelihood
values in Table 3. Based on the acceptance probabilities, three types of DMs are represented in
the model; environmentally proactive (green), moderate, and less environmentally friendly (non-
green). Note that a green DM may “choose” to not comply with a regulatory event or invest in a
technology event, although with lower probabilities than a non-green DM.
Computing costs and green values
If a DM complies with or invests in an event, the company pays the cost associated with
the event while gaining any associated green value assigned in Table 2. Technology investments
are assumed not to impact sales revenue (such as from environmentally responsible purchasers or
due to increased sales price), with the only advantage being to improve the green score. Not
accepting the technology events produces no new costs nor green value for the company,
although non-acceptance of some technologies results in their green value being subtracted in the
same manner as for non-compliance with regulations. If the DM does not comply with a
regulatory event, they also may be fined and penalized with the tabulated fine probabilities. If
penalized, the company’s total cost is incremented by the associated fine amount and the green
score is decremented by the associated green value, following the integration logic described
previously. Additionally, for regulatory events that can occur more than once, the fine
probability and fine amount increase after each non-compliance.
69
For example, events 11-13 in the automotive example were assigned a fine probability,
putting the non-investing companies at risk of losing the event’s green value, because these
technologies have direct impact on the emission from either the plant (e.g., emission of harmful
volatile organic compounds from paint shops) or automobiles (e.g., catalytic converter
technology that reduces the tail-pope emission). This technology forcing bias reflects actual
requirements in emission reduction by the Clean Air Act. Although currently the green score is
reduced if a penalty is enforced, somewhat representing public perception, this logic can be
modified or omitted.
Updating the variables and inputs
At the end of each simulation time step considered as a quarter year in the model, the
company’s total cost and green score are updated based on the tabulated costs and green values
for the event. Number of occurrences of the event is updated to make sure that the event does not
occur more than its maximum number.
4 .2 .4. Economic and Environmental Performance Measures
The two primary performance measures computed during the simulation are the total cost
of all compliance and technology investments over all time periods (including any non-
compliance fines) and a total green score that is calculated by integrating over time the total of
all green values to-date, in the same manner that simulation time-based measure are computed
(e.g., mean queue length) (Law & Kelton, 1995). Note that this calculation (rather than simply
the total of each period’s green value) appropriately results in a DM with a consistent
compliance record having a higher score than a non-compliant DM who only in the latter time
periods implements all the same technologies as the first DM.
70
4.3. RESULTS
4 .3 .1. Final Performance Measures
As an illustration, the automotive model was run separately for the three types of decision
makers for 50 replications of 40 quarters (i.e., 10 years), using partially common random
numbers for variance reduction. Figure 4-2 summarizes the estimated expected total costs and
green scores for three DM types and their respective 95% confidence intervals. Not surprisingly,
the green DM has both the highest estimated total cost and the highest estimated total green
score, whereas the non-green DM has the lowest cost and green score.
54
43
23
$247
$191
$232
0
10
20
30
40
50
60
70
Green Moderate Non-Green
Gre
en S
core
(X10
3 )
$0
$50
$100
$150
$200
$250
Tota
l Cos
t (X1
06 )
Green Score
Total Cost
Figure 4-2: Average green score and total cost for each type of decision maker, after 50 replications for 10 year
period; vertical bars indicate 95% confidence intervals for the mean.
The between-replication standard deviation of each performance measure also provides
insight into the large amount of uncertainly and unpredictability of these results. For example,
71
Figure 4-3 illustrates the distributions of total costs (Figure 4-3a) and green scores (Figure 4-3)
for each type of DM, with their modes corresponding to the expectations shown in Figure 4-2.
These distributions are very well approximated by normal distribution, due to the large central
limit effects from the many additive actions inherent within the model logic.
$0 $50 $100 $150 $200 $250 $300 $350 $400Total Cost (X106)
GreenModerate
Non-Green
-5 15 35 55 75 95
Green Score (X103)
Green
ModerateNon-Green
(a)
(b)
$0 $50 $100 $150 $200 $250 $300 $350 $400Total Cost (X106)
GreenModerate
Non-Green
-5 15 35 55 75 95
Green Score (X103)
Green
ModerateNon-Green
$0 $50 $100 $150 $200 $250 $300 $350 $400Total Cost (X106)
GreenModerate
Non-Green
-5 15 35 55 75 95
Green Score (X103)
Green
ModerateNon-Green
(a)
(b)
Figure 4-3: (a) Total cost and (b) green score distributions after 10 years (40 quarters) for each type of DM.
72
As shown, the total cost for the non-green DM exhibits higher uncertainty (i.e., larger
variability) than the other DM types, whereas the green scores appear fairly homoskedastic for
all three DM types. This difference also is reflected by the larger confidence intervals in Figure
4-2 for non-green DMs and is due to smaller compliance and implementation probabilities (i.e.,
closer to 0.50 than to 1.0) producing greater variability in costs and fines. For example, the total
10 year cost for a non-green DM could be anywhere between roughly $31 and $351 (95%
probability interval: $191 ± $160), making it difficult to make effective decisions and plan
accordingly. This variability also suggests that only increasing the fine probability or fine
amounts might cause a non-green DM to have the same expected costs as the other types, but due
to lower compliance probabilities the total cost uncertainty still may be greater than for the other
DM types.
4 .3 .2. Short versus Long Term Results
Figure 4-4a and 4b summarize the expected cost per time period (quarter) extended over
80 quarters for each type of DM and averaged across 50 replications under two different
scenarios of event elimination without replacement and no elimination of events, respectively.
The logic of event elimination has been explained in Section 3.2.5. For green DM, the cost per
quarter is initially remarkably higher than the other DMs, however as more event occurs higher
compliance of green DM lowers its cost per quarter by preventing from fines in later quarters.
The mean cost-per-quarter for a non-green DM initially is almost half of the green DM’s due to
non-investment and non-compliance but also stagnates over time because of the higher fine
amounts incurred in later quarters. Therefore, as shown in Figure 4 with moderate DM incurring
costs somewhere between these two extremes, the cost per round for all DMs converges to a
73
same level in both scenarios with higher rate of convergence under no event elimination
assumption.
$0
$2
$4
$6
$8
$10
$12
$14
$16
0 10 20 30 40 50 60 70 80
Time (Quarter)
Cost
per
Qua
rter (
X106 )
$0
$2
$4
$6
$8
$10
$12
$14
$16
0 10 20 30 40 50 60 70 80
Time (Quarter)
Cost
per
Qua
rter
(X10
6 ) GreenModerate
Non-Green
(b)
(a)
Green
Moderate
Non-Green
Figure 4-4: (a) Average cost per quarter considering event elimination. (b) Average cost per quarter (with no
event elimination, convergence occurs more quickly.)
In Figure 4a assuming event elimination, the convergence occurs with a slower rate since
most of the listed events can occur only one time or up to their occurrence limit before they get
eliminated from the event table. As a result, in the final quarters few events are left to occur.
Since each event has a constant chance of happening and only 5 events in Table 2 can occur
74
multiple times, there are only few events left in the final quarters that may occur and change the
performance measures, otherwise, with majority of events eliminated nothing happens that can
change the total cost and green score and therefore, the rate of change in performance measures
is slower and convergence happens later.
As shown in Figure 4-4b the cost per quarter for different DM types converges earlier
when no events are eliminated after they occur (i.e., no maximum limit for event occurrence),
such as if assuming comparable new technologies and regulations could be introduced in the
future.
As shown, the green DM can reduce the cost of business to the same level as that of non-
green DM while maintaining superior environmental performance and potentially higher gains
from its environmental activities.
Scenarios (a) and (b) represent two extreme conditions. In comparing the short term and
long term performance of firms, the combination of these two scenarios is more realistic where a
larger number of events are considered for a 40-quarter simulation and events have limited
number of occurrence before they are eliminated.
4 .3 .3. Experimental Design
To better understand which variables have the biggest impact on total cost and green
score, 2k and 3k factorial experimental designs were conducted on the simulation model. Factorial
designs allow the analyst to efficiently model how results could change due to changes in the
model variables separately or in combination factors, representing different market, legislative,
and decision making environments. Different policy and market environments can be simulated
by using the high (+1) and low (-1) settings for each variable shown in Table 4-4. For example,
75
for a green management strategy (P1) in a non-competitive market the effect of changing the
level of government environmental scrutiny (F-1 vs. F1) on firm performance and on creating and
targeting incentives for companies to adopt green strategies can be explored. To explore this
policy using the simulation model, the levels of fine probability and green values were changed
simultaneously to explore their combined effect on final green score and its sensitivity to change
in these factors as different government policies. DOE can also be used in other policy testings
for different types of decision makers, such as:
• Exploring the effect of increasing non-compliance fines as a government policy on an
environmentally proactive firm and comparing it with the effect on reactive firms.
• Testing for different levels of government scrutiny on non-compliant firms by
changing fine probability.
• Testing for different government incentives by changing the green values or financial
bonuses
• Testing the effect of companies’ environmental profile on its environmental
performance by changing the probability of complying with regulations or investing
in an ERM technology.
Table 4-4: Five factors studied via 2k and 3k experimental design; where -1, 0, 1 indicate the low, medium and
high settings, respectively.
Factor Low Value Medium Value High Value
Probability of Acceptance Non-Green (P-1) Moderate (P0) Green (P1)
Cost 75% Cost (C-1 ) Nominal Cost (C0 ) 125% Cost (C1 )
Green Value 75% Green Value (G-1) Nominal Green Value (G0) 125% Green Value (G1)
Fine Probability 75% Fine Probability (F-1) Nominal Fine Probability (F0) 125% Fine Probability (F1)
Fine Amount 50% Fine Amount (A-1) Nominal Fine Amount (A0) 150% Fine Amount (A1)
76
For the automotive example, Table 4-5 and Table 4-6 summarize the design run matrices
and resultant total cost and green score means and standard deviations over 50 replications, with
plus and minus signs indicating factors set to their high and low values, respectively. Only the 2-
level factorial analysis results are shown for brevity. The green value of each legislation or
innovation was not included in the cost analysis since it has no impact on cost (Table 4-5).
Similarly, event costs and fine amounts were not included in the green score factor analysis
(Table 4-6) since they have no effect on the total cost.
Table 4-5: Design of experiment table for total cost. Labels P, C, F and A are defined in Table 4, where -1 and
1 indices correspond to low and high values for the factor.
P C F A PC PF PA CF CA FA PCF PCA PFA CFA PCFA Mean StDev1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 313 722 1 1 1 -1 1 1 -1 1 -1 -1 1 -1 -1 -1 -1 311 723 1 1 -1 1 1 -1 1 -1 1 -1 -1 1 -1 -1 -1 308 714 1 1 -1 -1 1 -1 -1 -1 -1 1 -1 -1 1 1 1 307 715 1 -1 1 1 -1 1 1 -1 -1 1 -1 -1 1 -1 -1 189 436 1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 1 1 187 437 1 -1 -1 1 -1 -1 1 1 -1 -1 1 -1 -1 1 1 185 438 1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 -1 -1 184 429 -1 1 1 1 -1 -1 -1 1 1 1 -1 -1 -1 1 -1 250 105
10 -1 1 1 -1 -1 -1 1 1 -1 -1 -1 1 1 -1 1 233 7511 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 225 9912 -1 1 -1 -1 -1 1 1 -1 -1 1 1 1 -1 1 -1 214 7913 -1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 -1 1 160 8814 -1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 1 -1 143 4915 -1 -1 -1 1 1 1 -1 1 -1 -1 -1 1 1 1 -1 142 7716 -1 -1 -1 -1 1 1 1 1 1 1 -1 -1 -1 -1 1 131 50
Run Total Cost ($)Factors and Interactions
Table 4-6: Design of experiment table for green score. Labels P, G, F and A are defined in Table 4, where -1
and 1 indices correspond to low and high values for the factor.
P G F PG PF GF PGF Mean StDev1 1 1 1 1 1 1 1 66,241 27,4562 1 1 -1 1 -1 -1 -1 68,227 26,4633 -1 1 1 -1 -1 1 -1 26,503 22,6914 -1 1 -1 -1 1 -1 1 32,740 19,4395 1 -1 1 -1 1 -1 -1 39,744 16,4746 1 -1 -1 -1 -1 1 1 40,936 15,8787 -1 -1 1 1 -1 -1 1 15,902 13,6148 -1 -1 -1 1 1 1 -1 19,644 11,664
Green ScoreRun Factors and Interactions
77
The 2-level and 3-level factorial analyses conducted on the factors were used to
determine the significance of each factor and interaction on the performance measures and to
develop predictive regression equations. The non-linearity test reveals lower confidence in using
the 2-level analysis comparing with the 3-level analysis since the regression estimates at the
center points (β0 intercept) are small compared with the actual middle point scores generated
from 50 replications of the simulation which is equal to the center point in the regression model
from 3-level analysis. The following shows the significance of the non-linearity for total cost and
green score results:
5062
)217232()( Z1
01
−=−= σβModerateCT , and
( )50
21890)3874243118(G Z
2
0'
2−=−= σ
βModerateS ,
where ModerateCT is the average total cost for the moderate DM and ModerateSG is the average
green score for the moderate DM.
Although both the intercept values of cost and green score function fall in the 95%
confidence interval of the 50 data points obtained from the simulation, the p-values for the total
cost and green score intercepts are only slightly above 0.025 (two-tail test) with p-values of 0.03
and 0.078 respectively. Despite the lack of strong reason to reject the validity of the linear
regression, the p-value for the total cost shows low confidence in the non-linearity test and
suggests more validity in using 3-level analysis to further explore the relationship between
performance measures and the factors.
78
The regression equation coefficients for the results of both the 2k and 3kexperiments are
shown in Table 4-7 with coefficients corresponding to the coded values. Only the coefficients for
statistically significant factors (p < 0.05) are shown for brevity with all main effects (factors) are
significant. Note that green value was not included in the total cost factorial and regression
analysis as well as fine amount and event cost that were not included in the green score analysis.
Table 4-7: Regression coefficients for 2k and 3k experiments (only the significant factors coefficients (p < 0.05)
are shown). Values used for comparison with the regression fits in Figures 7 and 8.
2-level 2-level
Intercept 217.64 232.65 <0.0001 Intercept 38,742 43,118 <0.0001
C 52.50 57.50 <0.0001 P 15,045 15,452 <0.0001
P 30.31 28.25 <0.0001 G 9,685 10,778 <0.0001
P*C 9.13 8.93 <0.0001 P*G 3,761 3,863 0.0002
P*F -3.70 -3.70 <0.0001 F -1,645 -1,000 0.0012
P*A -3.10 -3.31 <0.0001 P*F 850 850 0.001
F 5.58 3.15 <0.0001 G * F -411 -250 0.0033
A 3.83 1.48 0.0020 P*G*F 213 213 0.0028
P*C*F -0.60 -0.60 0.0310 ----- ----- ----- -----
P2 N/A -13.17 <0.0001 P2 N/A -5,063 0.0004
P2*C N/A -4.81 <0.0001 P2*G N/A -1,264 0.0007
P2*A N/A 2.79 <0.0001 F2 N/A 675 0.003
P2*F N/A 2.43 0.0002 P2*F N/A -644 0.0022
P*F2 N/A 2.06 0.0018 P*F2 N/A -407 0.0035
P*C2*F*A N/A -0.65 0.0032 G*F2 N/A 171 0.0049
----- ----- ----- ----- P2*G*F N/A -161 0.0063
----- ----- ----- ----- P*G*F2 N/A -102 0.01
Cost Function Results Green Score Function Results
Variable 3-level
Variable 3-level
Coefficient Estimate
Coefficient Estimate P-Value Coefficient
EstimateCoefficient Estimate P-Value
Regression results shown in Table 4-7 for the 2-level and 3-level runs calculate the
magnitude of the effect for each factor; although some much less so than others. Both
performance measures also have significant non-linear terms in the 3k models.
79
Figure 4-5a and 4-5b summarize the cost and green score results for the 2k experiments,
respectively. The coded labels near each data point with 1 or -1 indices indicate the settings of
the factors in Table 4-4.
P1G-1F-1
P-1G1F-1P-1G1F1
P1G1F-1
P-1G-1F1
P-1G-1F-1
P1G-1F1
P1G1F1
15
25
35
45
55
65
0 1 2 3 4 5 6 7 8
Experiment Condition
Gre
en S
core
(X10
3 )
P-1
P1
G-1G1
P1C1F-1A1
P1C1F1A-1
P1C1F1A1
P1C1F1A-1
P-1C1F-1A-1
P-1C1F1A1
P-1C1F1A-1
P1C-1F-1A-1
P1C-1F1A1
P1C-1F1A-1
P1C-1F-11A1
P-1C-1F1A-1
P-1C-1F1A1
P-1C-1F-1A1
P-1C-1F-1A-1
P-1C1F-1A1
$90
$140
$190
$240
$290
0 4 8 12 16
Experiment Condition
Tota
l Cos
t (X1
06 )
C-1
C1
P1 P-1(a)
(b)
P1G-1F-1
P-1G1F-1P-1G1F1
P1G1F-1
P-1G-1F1
P-1G-1F-1
P1G-1F1
P1G1F1
15
25
35
45
55
65
0 1 2 3 4 5 6 7 8
Experiment Condition
Gre
en S
core
(X10
3 )
P-1
P1
G-1G1
P1C1F-1A1
P1C1F1A-1
P1C1F1A1
P1C1F1A-1
P-1C1F-1A-1
P-1C1F1A1
P-1C1F1A-1
P1C-1F-1A-1
P1C-1F1A1
P1C-1F1A-1
P1C-1F-11A1
P-1C-1F1A-1
P-1C-1F1A1
P-1C-1F-1A1
P-1C-1F-1A-1
P-1C1F-1A1
$90
$140
$190
$240
$290
0 4 8 12 16
Experiment Condition
Tota
l Cos
t (X1
06 )
C-1
C1
P1 P-1(a)
(b)
Figure 4-5: (a) Average total cost values for each 2k experiment and (b) 2k experimental design responses for
average green score. Labels P, G, F and A are defined in Table 4, where -1 and 1 indices correspond to low and
high values for the factor. The dashed line in (a) divides the experiments into high (up) and low (bottom) event
cost regions and shows the largest impact on the results while shaded region represents the green DM (P1) total
costs. In (b) the dashed line divides the green scores to tow regions based on green value level as the most
effective factor.
80
The groupings of factor settings in Figure 4-5a lead to several conclusions for total cost.
The acceptance probability P and cost factor C appear to have the largest impact, with a probable
PC interaction suggested in the right-hand 2 groups. The large impact of P and C is illustrated by
the two lines dividing the data points into distinct categories. As seen, the cost factor (C) has a
dominant impact on the total cost as all the data points with high cost are clustered above the
dashed line and data points with low cost levels fall below the dashed line. The next dominant
factor P divides the data points into the white and shaded sides. Although the data points in either
side are not consistently higher or lower in total cost, but the average total cost in data points
with high probability level (P1) is higher than the average of the points in the shaded area (P-1).
The combination of P and C settings changes at every fourth experiment, resulting in the
observed clusters and trends. Changes in the other factors have a greater impact under low
compliance probability (corresponding to a non-green DM in shaded area), which makes
intuitive sense since higher non-compliance probability implies higher probabilities to receive
fines whereas the fine amount in the first 8 experiments is not that important since they are rarely
levied.
The green score results in Figure 4-5b reveal similar patterns and interpretations. The
acceptance probabilities and green values appear to have the highest impact since all the points
with high green value level G 1 are higher than the points with low green value level G -1 and the
average of points with high acceptance probability P1 is larger than the average of the points with
low acceptance probability P -1 shown in the shaded area. Also, there is a slight interaction
between these two factors and a small effect due to the size of the fine probability, more
pronounced for the non-compliance DM indicated below the dashed line. That is, the slope for
each PG combination is greater for lower values of F, the fine probability. Since non-compliers
81
are less likely to comply or accept events, they are more prone to variations in fine level,
therefore; increase in the fine probability reduces their green score by a larger amount comparing
to the effect on green compliers.
Figure 4-6a and 6b shows the responses to the 3k factorial analysis for the total cost and
green score, respectively. The indices 1, 0, -1 correspond to high, medium, and low values of
each factor. For the total cost (Figure 4-6a), the data points are clustered according to the order
of P and C variation in Table 6 which reveals the acceptance probability (P) and cost (C) are the
dominant factors as similarly discussed in Figure 4-5. For the green score results (Figure 4-6b),
data points are clustered according to the variation in the values of the acceptance probability (P)
and the green value (G), as two most important factors. In each green score cluster, the 3 data
points vary by the level of fine probability from high to low, reading from left to right.
82
$100
$150
$200
$250
$300
$350
0 10 20 30 40 50 60 70 80
Experiment Condition
Tota
l Cos
t (X1
06 )
P1C1
P1C0
P1C-1
P0C1
P0C0
P0C-1
P-1C1
P-1C0
P-1C-1
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25
Experiment Condition
Gre
en S
core
(X10
3 )
P1G1
P0G1
P1G0
P0G0
P0G-1
P1G-1
P-1G1
P-1G0
P-1G-1
Green Moderate Non- Green
Green Moderate Non- Green
(a)
(b)
$100
$150
$200
$250
$300
$350
0 10 20 30 40 50 60 70 80
Experiment Condition
Tota
l Cos
t (X1
06 )
P1C1
P1C0
P1C-1
P0C1
P0C0
P0C-1
P-1C1
P-1C0
P-1C-1
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25
Experiment Condition
Gre
en S
core
(X10
3 )
P1G1
P0G1
P1G0
P0G0
P0G-1
P1G-1
P-1G1
P-1G0
P-1G-1
Green Moderate Non- Green
Green Moderate Non- Green
(a)
(b)
Figure 4-6: (a) 3k experimental design response for total cost and (b) 3k experimental design response for green
score. Labels P, G, F and A are defined in Table 4 where -1, 0 and 1 indices correspond to low medium and
high values for the factor.
83
The experiments that occur under the non-green DM condition (P -1) show the greatest
impact on performance measures due to variation in fine probability level (F). Since a non-green
decision maker is less likely to accept events (i.e., there are more opportunities to incur fines)
this probability has a larger effect on non-green DMs total costs and green scores, whereas
changes in fine probabilities from low to high settings significantly decrease a company’s green
score.
For the total cost, the most significant factor is the cost of regulation compliance or
technology implementation C, as is also illustrated in Figure 4-5a, followed by the compliance or
implementation probability P and the CP interaction. The compliance probability P also has the
only significant quadratic effect and is involved in all non-linear interactions. For green score,
the most significant factor is the compliance probability P, followed by green value G and the
PG interaction. This suggests that environmental rewards and bonuses are second to firm’s
internal strategy to be environmentally responsible. Similar to as for total cost, compliance
probability P is the most significant quadratic term and appears in other non-linear interactions,
although the fine probability F now also has significant (of smaller magnitudes) quadratic and
non-linear effects.
Figure 4-7 illustrates the sensitivity of total cost to changes in compliance and
implementation costs (holding all other factors constant), ranging from 0 to 2 times the base
value of event cost listed in Table 4-2. The unit on the X-axis is equal to the delta between high
values and medium values in the factorial design (Table 4-4). Since the low and high values in
the DOE analysis are mapped to -1 and +1 in the regression model, the same numbering was
applied to the sensitivity analysis graphs so that the linear regression fit shown in Figure 4-7
have the same scale on the coefficients.
84
y = 55.104x + 232.65
$0
$100
$200
$300
$400
$500
-4 -3 -2 -1 0 1 2 3 4
Event Cost
Tota
l Cos
t (X1
06 ) Green
Non-Green
Moderate
(-100%) (-50%) (+50%) (-100%)
Figure 4-7: Sensitivity analysis of total cost in response to changes in the event cost for the three DM types.
Regression equation represents the total cost for moderate DM and agrees with results in Table 7. Whole
numbers for event cost correspond to coded factor settings in Table 4; values in parentheses indicate percent
change from base values.
The linear regression line shown on top of the data points was computed only from
values in the -1 to +1 range of the factorial analysis. The regression model derived from the
factorial design can also be used to estimate this linear fit regression if all the non-variant factors
in Table 7 set equal to zero. Therefore, the linear regression fit for the moderate DM shown in
Figure 4-7 is comparable with the regression coefficients in Table 4-7 since they are the values in
the case of moderate DM (P = 0). The linear regression for the Moderate DM in the graphs
agrees with the results from the 3-level non-linear regression model listed in the Table 4-7.
Figure 4-8 illustrates the sensitivity of both performance measures (total cost and green
score) to the other factors (fine amount, green value, and fine probability), again holding all other
factors constant. The units on the abscissa correspond to the low and high values fr each factor in
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the factorial design; therefore the range of the variation is still from 0 to 2 times of the base value
in Table 4-4.
y = 6.116x + 232.65
$0
$100
$200
$300
$400
$500
-2 -1 0 1 2
Fine Amount
Tota
l Cos
t (X1
06 )
GreenModerate
Non-Green
y = -980.64x + 43461
0
20
40
60
80
100
-4 -3 -2 -1 0 1 2 3 4Fine Probability
Gre
en S
core
(X10
3 )Green
Moderate
Non-Green
y = 10780x + 43118
0
20
40
60
80
100
-4 -3 -2 -1 0 1 2 3 4
Green Value
Gre
en S
core
(X10
3 )
Non-Green
Moderate
Green
y = 3.2593x + 232.09
$0
$100
$200
$300
$400
$500
-4 -3 -2 -1 0 1 2 3 4
Fine Probability
Tota
l Cos
t (X1
06 )
GreenModerate
Non-Green
(-100%) (+100%)(+50%)(-50%)
(a)
(c)
(b)
(d)
(-100%) (+100%)(+50%)(-50%) (-100%) (+100%)(+50%)(-50%)
(-100%) (+100%)(+50%)(-50%)
y = 6.116x + 232.65
$0
$100
$200
$300
$400
$500
-2 -1 0 1 2
Fine Amount
Tota
l Cos
t (X1
06 )
GreenModerate
Non-Green
y = -980.64x + 43461
0
20
40
60
80
100
-4 -3 -2 -1 0 1 2 3 4Fine Probability
Gre
en S
core
(X10
3 )Green
Moderate
Non-Green
y = 10780x + 43118
0
20
40
60
80
100
-4 -3 -2 -1 0 1 2 3 4
Green Value
Gre
en S
core
(X10
3 )
Non-Green
Moderate
Green
y = 3.2593x + 232.09
$0
$100
$200
$300
$400
$500
-4 -3 -2 -1 0 1 2 3 4
Fine Probability
Tota
l Cos
t (X1
06 )
GreenModerate
Non-Green
y = 3.2593x + 232.09
$0
$100
$200
$300
$400
$500
-4 -3 -2 -1 0 1 2 3 4
Fine Probability
Tota
l Cos
t (X1
06 )
GreenModerate
Non-Green
(-100%) (+100%)(+50%)(-50%)
(a)
(c)
(b)
(d)
(-100%) (+100%)(+50%)(-50%) (-100%) (+100%)(+50%)(-50%)
(-100%) (+100%)(+50%)(-50%)
Figure 4-8: Sensitivity analysis for the three DM types; (a) total cost in response to changes in the fine amount
(b) green score in response to changes in green value (c) total cost in response to changes in the fine probability
(d) green score in response to changes in the fine probability. Regression equation represents the total cost for
moderate DM and agrees with results in Table 7. Whole numbers for event cost correspond to coded factor
settings in Table 4; values in parentheses indicate percent change from base values.
Figure 4-8a and 4-8b show a linear response for three DMs across the range … (-100% to
+100%). Figure 4-8c and 4-8d illustrate the sensitivity of the total cost and the green score to fine
probability. The coefficients for the linear regression line passing through the data points
between -1 and +1 for the moderate DM fall in the 95% confidence interval of the regression
coefficients from the 3-level experiment (2.53, 3.76). In Figure 4-8c and 8d, both green score and
total cost response to fine probability show greater deviation from the linear regression in
86
comparison with other factors, especially for the non-green DM. This indicates a higher level of
non-linearity as it is also reflected in the regression coefficients for the factor F2 in Table 4-7.
The contour plots in Figure 4-9 illustrate the combined effect of fine probability F and
fine amount A on total cost, with each contour line in Figure 9a representing all combinations of
F and A that produce the same total cost for a non-green DM. As expected, for higher fine
amounts the total cost is more sensitive to variations in fine probability as illustrated the contour
lines appear to be closer and denser on the high end of fine amount axis. This suggests that
higher scrutiny can be more effective in bringing change in companies environmental strategies
if the amount of fine and enforcements are proportionally high.
(a) (b)(a) (b)
Figure 4-9: (a) Contour plot for variation in fine probability and fine amount for the non-green DM; the lines
represent total cost curves with labeled non-green DM’s total cost constant along the line. (b) Contour plot of
the difference between the total cost of the green and non-green DM; lines represent the differences in total cost
between green and non-green DM with shaded area showing the higher cost for non-green DM.
Total Cost for Non-green DM Difference Cost, Green and Non-green
87
The contour plot in Figure 4-9b represents the difference total cost between green and
non-green decision makers, with the contour line shown in bold indicating the combination of all
breakeven points beyond which the cost for a non-green DM strategy exceeds that of a green
DM. These results suggest that a non-green DM will incur higher expected costs after 100%
increase in fine probability if the fine amounts are at highest as illustrated by the shaded area in
Figure 9b. However, other combinations of fine probability and amount can make the cross-over
happen for the non-green DM. The trend in the total cost differences suggests that by increasing
the fine probability and amount, non-green DM incurs higher costs than green DM.
Figure 4-10 shows the values of the resulting green score in response to increase in the
amount of fine probability (F) and green value (G) with each contour line representing the green
score for green (Figure 10a) and non-green (Figure 10b) DMs.
(a) (b)(a) (b)
Figure 4-10: Sensitivity of green score to changes in fine probability and green value for (a) green DM and (b)
non-green DM. Green values indicated above each line remain constant along the line.
Green Score for green DM Green Score for Non-green DM
88
Other factors kept constant in their base value while F and G were varied from zero to
two times of their base value tabulated in Table 4-2. For green DM, the green score lines slightly
increase in response to change in the fine probability (Figure 10a) showing low uncertainty in
environmental performance due to change in government scrutiny. This outcome agrees with the
regression equation quantified in Table 7 where green value found to be 10 times more effective
than fine probability on green score of firms. However, the effect of fine probability is more
pronounced on green score, since the acceptance of events is less likely and more fine chances
occur therefore higher fine probabilities impact the green scores with more certainty.
Figure 10 also shows that reducing the fine probability will increase the green score
because non-compliant companies are more likely to evade the possible fines. However, same
green score can be achieved by increasing the green values gained by accepting the regulations
or technologies.
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5. CONCLUSIONS AND FUTURE WORK
The simulation modeling approach was undertaken to investigate the economic and
environmental performance of three different environmental decision making philosophies given
various uncertainty factors. Two factors were explored in particular: regulation type and
technology specifications. Results from the model provide information on the effect of these
factors on firms’ performance, and quantify the degree of dependence of the final performance
measures on the factors and level of uncertainty caused in the measures due to variation of these
factors.
Unlike studies where the effect of one technology or legislation is analyzed or where
environmental valuation using valuation techniques is done for a specific case (Newell, 1998;
Gerard & Lave, 2005), this work captures a variety of events relating to the automotive industry
supply chain. Therefore, the concept of green score was introduced to compare companies’
environmental performance across the wide range of events they face.
As explained in Chapter 3, environmental valuation techniques that are suitable for cost
and benefit analysis are mostly monetized techniques such that the gains from environmental
practices are reported in terms of monetary values. However, green value was used instead in the
simulation model due to the followings limitations of using monetized environmental valuation
techniques:
• The valuation of environmental cost or benefit analysis of environmental practices are
case-specific and two different monetized valuation methods may not be compared to each
other although the are all reported in monetary values, unless they consider same values and
scope.
90
• Environmental costs are case-specific and depend on multiple uncertainty factors such as,
type of the technology or regulation, environmental practice implementation, and corporate
environmental profile.
• Static mindset in valuation of environmental costs has led to over estimation of the cost.
Porter and van der Linde (1995) state that business and environmental decision makers have
focused only on the impact of environmental regulation on the current business condition and
tend to ignore the significant offsetting savings from resource productivity. This has caused
companies to generate higher cost estimates for addressing environmental regulations than
what they actually are. Examples for this can be found in the paper and pulp industry and
auto industry when they were responding to the Clean Air Act of 1970 (Porter & van der
Linde, 1995).
The use of green value allows for comparison of events from different areas and with
different focus. Environmental legislation and technology events are the events DMs face in the
model and based on their compliance with regulations or investment in technologies they receive
the associated green value, but pay the implementation cost for the event as well. However, there
are limitations in using green value and cost as the single environmental value and cost of the
events. The model and the input data structure limit the green value or cost of an event to be one
number. However, in the real-world, the costs or fines associated with environmental regulations
are described in a more complex way; either in terms of annual expenditure or cost per car or
cost per emission tons and sometimes in non-monetary values such as tons of emission or lives
of people, considering the long term consequences. These numbers in different formats are
difficult to map into one number in the simulation structure. Also, companies usually face fixed
91
and/or variable investment expenditures and they gain return on investment in the future years
that off-sets the costs.
Future development of this methodology can address the limitations mentioned in
capturing the event cost and green value of events. Another level of complexity can be added in
the way the industry is modeled to consider potential feed back effects and interactions within
the model.
For example, the effect of increasing the green value or enforcing higher fines, on
compliance probabilities can be considered as a dynamic relationship, often referred to as a pull
and push system. In this case, increasing green values (environmental incentives) and enforcing
higher fines may increase a DM’s probability of compliance, which could be captured in the
model.
Another limitation of the model is that the green score and the total cost of firms are
modeled and tracked separate from each other throughout the entire simulation, and there is no
assumption about the feedback effect between these two. One suggestion is that a superior
environmental image for companies can lead to increase in their sales and that can reduce their
environmental costs, which then can improve environmental performance if the returns are spent
on research and green product development.
Furthermore, the final performance metrics can be combined using weighting techniques
to form a single performance indicator. This approach is particularly recommended for the board
game or a different version of simulation model where DMs are individuals making decision
every round. In this setting, individuals may choose how they want to be evaluated at the end of
the simulation (game) based on the weights they pick for environmental and economic
performance. Knowing of how their performance is evaluated at the end of the game, aligns
92
players’ interests and focuses their attention on the best way to score to minimize the effect of
uncertainty of the market environment.
The simulation model proposed in Chapter 4 is informative on two different levels: 1)
for comparison with outcomes of board game winners and 2) for effective implementation of
environmentally responsible manufacturing.
Inspired by the board game, simulation results can be used as a post game assessment
tool to highlight some of the key uncertainty factors and trade-offs in environmental and
economic performance of companies in automotive industry context. Due to the nature of games
and competition among teams to increase their revenue or final performance measures,
understanding of external factors that create uncertainty in their performance outcomes is
confounded. Therefore, the simulation results including short term and long term analysis and
DOE can be used as a post game assessment tool.
In a broader context of environmentally responsible manufacturing, the simulation results
can inform decision makers about uncertainty factors that they should consider to assess the
environmental and economic relation ship of their actions. The factorial analysis provides greater
insight on the nature of dependence of performance measures on variation in factors. Based on
the results, non-green DM performance has the highest sensitivity to change in fine probability
and fine amounts. Non-green companies are instead not as sensitive as green companies to
change in green value or environmental incentives unless the fine probabilities are at high levels.
The findings suggest that to encourage companies to adopt green strategies, increasing green
value is a better policy for green companies, and while increases in fine probability and fine
amount are more effective in pushing non-green companies to adopt green manufacturing. Also,
93
cost per quarter for green companies will decease as they maintain their level of compliance with
environmental legislation and invest in green technologies.
The results for performance measures depend highly on the values assumed for each
event and therefore the validation of the final performance of firms is not the main focus of this
work. However, the focus is on the range of uncertainty and the nature of change in the
performance metrics caused by variation in the factors. As suggested, further research on finding
validated cost and fine values for events as well as their environmental impact would help
broaden the use of the simulation model as a predictive tool to assess the future performance of
companies, as well as validating the sensitivity and risk analysis of the future performance.
94
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Appendix A
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Appendix B
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Appendix B- Table of Contents
B1- List of Constants……………………………………………………….118
B2- List of Functions……………………………………………………….119
B3- List of Arrays………………………………………………………….120
B4- C Code………………………………………………………………….121
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B1- List of Constants
List of Constants Current Values Description
NumOfEvents 25
number of active events that are happening in the simulation, the value should be equal to the number of legislation plus the number of technologies used in the event table which is called by the function pdf.
MAX_NumOfEvents 40
Maximum number of events which determines the size of the arrays used in the simulation to store all the event related information. If more events want to be included in the simulation, this is the only value that has to be changed for the arrays variables to be able to store the event information.
NumOfLegs 11 number of active legislation active in the model.
MAX_NumOfLegs 20
Maximum number of legislation in the event table, and should be greater or equal to the number of Legs. This value is set to fix the table slots for legislation so that addition of extra legislation is possible without re-numbering all the events
NumOfTechs 14 number of active technology events used in the model.
Column 11 Number of culomns in the event table. The maximum number of rows of the event tables is equal to MAX_NumOfEvents.
CarPrice 20000
The price of each car in dollars for the case of automotive industry. This value is used to calculate the revenue of each company (DM). The revenue (total revenue) was never used in the final results and discussion.
Multiple 10 Occurrence time for the event that can happen multiple times.
SEED 2 Initial seed to start the random generation process
LegsProb 0.4 probability that an occurred event is a legislation
TechProb 0.6 probability that an occurred event is a technology
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B2- List of Functions
List of Functions (number of inputs) Inputs Output
uniform () generates uniform random number
gamma (2) alpha, bta generates gamma random number which gets used to generate a beta random number
beta (3) psmst, ave, optmst
generates a random number based on beta distribution. The inputs are the minimum (pessimistic), most likely (Average) and maximum (optimistic) probabilities that a DM accepts the event.
assign ()
once called, populates the event tables and assigns the values for cost, green value, fine probability, fine amount and etc for each event.
initialize () initializes the necessary variables and arrays such as counter at the start of each replication.
Poisson (1) mean_events
Creates a Poisson random number that was originally used to create more than one event in each simulation time step but this feature is not currently used.
RepetitionIndex (2) Repeat, TheEvent
Returns the number of times that an event has happened. The input variables to this function are the event number and the number of times it has occurred. The functions compares the number with the maximum allowed number of occurrence for that event, defined in the table and will return the number the event has happened if it is less than the maximum allowed.
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B3- List of Arrays
Array Name (#Dimensions) Size Initial Value Description
pdf (3) MAX_NumOfEvents, Column, 5 Initialized
pdf is the event table. Number of rows and columns are set as constants and are explained in the list of constants. The third dimension is for the 3 different decision makers and it is reserved as 1=Green, 2=Moderate and 3=Non-Green therefore the first element in the 3rd dimension (labeled 0) is blank and the fifth element is for future expansions and is also currently blank.
happened (1) MAX_NumOfEvents 0
Keeps track of how many times each event has happened. The number of occurrence of each event is counted in the row number equal to the event number.
ComplianceRecord(1) MAX_NumOfEvents+1 0 Compliance record shows if an happened event has been complied by the player or not
Repetition(1) MAX_NumOfEvents+1 0 Tracks number of times each event has occurred
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B4- C Code
/*
Replicates a 40-round scenario 50 times. writes scores and detail event history in separate files.
The current version has a loop to iterate for the DMType from 1 to 3 (Green, Moderate, Non-Green) so
the user is not prompted to enter DMType. For each DMType iteration the seed will be changed.
Legislation Probability=0.4;
Technologies Probability=0.6;
Other Probability=0.0;
*/
#include <iostream.h>
#include <math.h>
#include <fstream.h>
#include <iomanip.h>
const int SEED=2; //SEED is used to initialize the seed for random number generator.
const int NumOfEvents=25; //Current number of active events
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const int MAX_NumOfEvents=40;//if you want to expand more than 40 events you have to change the code,
see the "Array PDF"
const int NumOfLegs=11; //The current number of legislation events
const int MAX_NumOfLegs=20;
const int NumOfTechs=14; //The current number of technology events
//const int NumOfOthers=2; //Other type of events can be included such as hurricanes, strikes, ...
const int column=11;
const int CarPrice=20000; //Assumption about price of the car. used to calculate total revenue but not
included in outputs or any end analysis
const int Multiple=10; //For the events that have multiple occurring allowance.
const float LegsProb=0.4; //probability that an occurred event is a legislation
const float TechProb=0.6; //probability that an occurred event is a technology
const float OtherProb=0.0; //probability that an occurred event is of other type
long seed=SEED; //This is the seed used in the random number generator algorithm
float pdf[MAX_NumOfEvents][column][1+3+1],happened[MAX_NumOfEvents]; // event table - array to
record if an event has occurred (1= occurred, 0 otherwise
float probability,Cost=0, // input variables used in the simulation flow
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PresentTotCost=0,TrueGreenScore=0, PublicGreenScore=0, CumPublicGreenScore, SumGreenScore=0,
SumTrueGreenScore=0, SumCumGreenScore,
SumTotCost=0, SumTotRevenue=0, TotRevenue=0.00,
capacity, InterestRate=0.00, //inputs and assumption- capacity and interest rate are considered as
placeholders but are not included in the simulation flow
AveTotCost, AveTotRevenue, AveGreenScore, AveTrueGreenScore, AveCumGreenScore,
//Average of all the replications
CostPerRound, GSPerRound; //Aditional metrics
int Accept=0, // 1 if DM accepts the event that has occurred, 0 otherwise
row=MAX_NumOfEvents, //Number of rows in the event table. This is always bigger than or equal
NumOfEvents
ComplianceRecord[MAX_NumOfEvents+1],//Compliance record shows if an happened event has been
complied by the player or not.
event, //event number, in each round a number will be generated and event is looked up from the event
table (pdf) based on the generated number
Repetition[MAX_NumOfEvents+1]; //this array keeps track of the number of occurrence of each event
/******************************* List of Functions (explained in the attachments)
**********************/
float uniform(void);
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float gamma(float alpha, float bta);
float beta(float psmst, float ave, float optmst);
void assign(void);
void initialize(void);
void update_pdf(int event);
void cancel_event(int event);
int Poisson(int mean_events);
int RepetitionIndex(int Repeat,int TheEvent);
/**************************************************************************/
void main(void)
{
const int ReplicationTimes=50,RoundTimes=40; //For experiment purpose (Each replication is n
rounds)
int i,round, replication, DMtype, EventType,Accept=0,HowManyEvents;
float p, response;
/******* The files that will contain the output results are created here *****/
ofstream fout, Dout, Eout, Gout, COSTout, GSout;
fout.open ("Score413SN3.txt");
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Dout.open ("Detail_event_history413SN3.txt");
Eout.open ("CostPerRound413SN3.txt");
Gout.open ("codecheck413SN3.txt");
COSTout.open ("costtrack413SN3.txt");
GSout.open ("GSperRound.txt"); //If you do not need to record all this information every step,
you may de-activate
//this line or any output to
file line so that the code runs faster
//Promt user to select decision making type (G,M,N)--- Not Active
// cout<<"Please select the type of decision that you want to make about events: \n (Green=1, Moderate=2,
Non-Green=3) : ";cin>>DMtype;
for (DMtype=1;DMtype<=3;DMtype++) // Once run, the code is run for all the three DM type
{
seed=SEED; // Initializes the seed at the start of
the code for each DM type
//Creating heading for the score txt file
fout<<"Rep#\t"<<"TotCost(Billion $)\t"<<"TotRevenue(Billion
$)\t"<<"PGS\t"<<"TGS\t"<<"CumPGS"<<"\n";
fout<<DMtype<<"\n";
126
//Creating heading for the Detail-Event-History output file
Dout<<"\nDecision maker type in this game is= "<<DMtype<<"\n"; //If you do not need to
record all this information every step, you may de-activate
//this line or any output to file line so that the code runs faster
Eout<<"\n\nDMtype="<<DMtype<<"\n"; //File Output
GSout<<"\n\nDMtype="<<DMtype<<"\n"; //File Output
Eout<<"\t"; //File Output
GSout<<"\t"; //File Output
for (i=1;i<=RoundTimes;i++) //File Output
{
Eout<<i<<"\t"; //File Output
GSout<<i<<"\t"; //File Output
}
Eout<<"\n"; //File Output
GSout<<"\n"; //File Output
127
//Game starts NOW,...
for (replication=1;replication<=ReplicationTimes;replication++) //The code is replicated to
the number of times set as "ReplicationTimes" for each DM
{
Accept=0; //Initialization at the start of each rep
initialize(); //Once this function is called, a set of variables, defined globally above, such as counters
get initialized
assign (); //Resets the event table for the next replication.
/**/ Dout<<"Replication "<<replication<<" of "<<ReplicationTimes<<"\n"; //File Output
Eout<<"\n"<<"Rep# "<<replication<<"\t"; //File Output
GSout<<"\n"<<"Rep# "<<replication<<"\t"; //File Output
for (round=1;round<=RoundTimes;round++) //Total number of runs is equal to
"RoundTimes" set defined in variable definition
{
128
HowManyEvents=1+floor(2*uniform()); // Currently not in use, the number generated here is
the number events that are going to happen in this round
// Dout<<"\nHowManyEvents"<<HowManyEvents<<"\n";
// while (HowManyEvents>=1) //While loop for including multiple event
occurrence in each round, currently not in use.
{
/******************************** Occurrence of an Event
**********************************/
//Probability of occurring an event
p=uniform();
/***** The following if then else structure will determine which event number happens on the round, by
comparing the generated random number (p) to
legislation probability and technology probability. E.g., If p higher than LegsProb(legislation probability) then
the event will be a technology event.
once the category is determined, the event number will be determined by assuming equal chance of happening
any event within a category.
The algorithm is exactly similar to Monte Carlo method used in picking a card from a deck of cards.*******/
if (p<=LegsProb)
{
EventType=1; //Legislature
event=0*20+floor(p/(LegsProb/NumOfLegs));
}
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else if (p<=LegsProb+TechProb)
{
EventType=2; //New Technology
event=MAX_NumOfLegs+floor((p-LegsProb)/(TechProb/NumOfTechs));
}
/*
else //Other Events 'Inactivated Now'
{
EventType=3;
event=NumOfLegs+NumOfTechs+floor((p-(LegsProb+TechProb))/(OtherProb/NumOfOthers));
}
*/
//Counts that this event is occurred and tracks how many times it occurs if the happened status is still 0
if (happened[event]==0)
Repetition[event]++;
/*File Output*/ Dout<<setw(7)<<"round="<<round<<setw(12)<<"Event="<<event<<"\t";
/******************************* Player's Response *******************************/
//PLAYERS RESPOND in this round is a uniform random number.
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response=uniform();
/*DMType's probability of accepting an event is a beta distribution assigned in a input table (Array named
PDF)
In the line above the first dimension of the pdf array is the event number, so it can be "event" variable
instead of zero if you decide to go with
different acceptance probabilities for each event. However, in my thesis, and paper I have used the
probability acceptance of the DMs for the first
event "event 0" in all of the events. In other words, the probability of acceptance is constant for each DM
and does not change based on different events*/
probability=beta(pdf[0/*event*/][DMtype][1],pdf[0/*event*/][DMtype][2],pdf[0/*event*/][DMtype][3]);
/*File Output*/ Dout<<"\tPcompy="<<setw(6)<<probability;
/*File Output*/ Dout<<"\tRespond= "<<setw(7)<<response<<setw(10);
//If Player complies
/*Monte Carlo method; if uniform random number generated above (response) is less than probability of
acceptance (probability),
that means DM accpets the event*/
if (response<=probability)
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{
Accept=1;
ComplianceRecord[event]+=1; //Record that DM has complied with this event (keep
track of the number of compliance with this event too)
/*The following condition controls that the DM that complied with the event gains the cost, Green Scores,
capacity ONLY IF THE COMPLIANCE IS HAPPENING
FOT THE FIRST TIME OR this is a type of event that happens multiple time and requires compliance
every occurring time*/
if ( (ComplianceRecord[event]==1)|| (pdf[event][8][1]==Multiple))
{
if (happened[event]==0)
/*File Output*/ Dout<<"Comply-First Time/Multiple"; //File output
showing the compliance was for the first time or this event is a multiple occurrence type event
else
/*File Output*/ Dout<<"CANCELLED EVENT-comply";
Cost = pdf[event][4][1]; //
PublicGreenScore += pdf[event][5][1];
//TRUE Green score is similar to green score except it is subtracted if event is not
complied regardless of the fine probability
TrueGreenScore += pdf[event][5][1];
// capacity = capacity*(1+pdf[event][9][1]); //NOT IN USE- Increse in capacity due to
complying with the event
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}
/*File Output*/ Dout<<"\tGreen Value Gained="<<pdf[event][5][1]<<"\t"; //File Output
}
else //Not Comply
{
/*Also for the non-compliant DMs, based on this condition if the DM had already complied with
an event that is happening again the DM won't
be charged again unless it is a multiple happening event*/
if (ComplianceRecord[event]==0 || (pdf[event][8][1]==Multiple))
{
Dout<<"\tNot Comply";
//True Green Score will be deducted anyway, and does not depend on the probability of
getting caught
TrueGreenScore -= (pdf[event][5][1]+abs(pdf[event][5][1]))/2;
/*File Output*/ Dout<<"\tTrue Green Value Lost="<<pdf[event][5][1];
/*File Output*/ Dout<<"\tfine
prob="<<pdf[event][6][RepetitionIndex(Repetition[event],event)]<<"\tFine="<<pdf[event][7][RepetitionIndex(
Repetition[event],event)];
if ((uniform()<=pdf[event][6][RepetitionIndex(Repetition[event],event)])) //Not
Comply and get fined
{
//Plyer doesn't get rewarded if not complies.
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// BUT, there is a cost incur to the player whichi is basically "fine"
if (pdf[event][8][1]>1 && pdf[event][8][1]!=Multiple)
Cost = pdf[event][7][RepetitionIndex(Repetition[event],event)];
else
Cost = pdf[event][7][1];
//DM will loose Green Score if not complies
PublicGreenScore -= (pdf[event][5][1]+abs(pdf[event][5][1]))/2;
/*File Output*/ Dout<<"\tPublic Green Value lost="<<pdf[event][5][1];
/*File Output*/ COSTout<<Cost<<"\n";
}
}
}
Accept=0;
/*********************************************************************************/
//UPDATE Variables
CumPublicGreenScore+=PublicGreenScore;
PresentTotCost += Cost;///pow(1+InterestRate,round-1);
Cost=0;
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/**************************************************************************************/
/* In this step, if the number of occurrences for the event is higher than the maximum occurrence times assigned
in the pdf table, then the event is
considered as happened.*/
if (Repetition[event]>=pdf[event][8][1])
{
happened[event]=1;
// update_pdf(event); //NOT IN USE - This function is expected to eliminate the event from
the list
cancel_event(event);//Temporarily used instead of update_event
}
HowManyEvents= HowManyEvents - 1;
/*File Output*/
Dout<<"\tTC="<<PresentTotCost<<"\tPGS="<<PublicGreenScore<<"\tTGS="<<TrueGreenScore<<"\n";
}//Close the "While (HowManyEvents)"
/*File Output*/ Dout<<"Total Present Cost= "<<PresentTotCost<<"\tTotal Money before sale=
"<<TotRevenue<<"\n";
TotRevenue-= PresentTotCost;
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TotRevenue +=CarPrice*capacity; //Company sells all the products
/*File Output*/ Dout<<"Total Money at the end of the round= "<<TotRevenue<<"\n\n";
CostPerRound=PresentTotCost/round;
GSPerRound=CumPublicGreenScore/round;
Eout<<CostPerRound<<"\t";
GSout<<GSPerRound<<"\t";
}//--> Close "round" loop
//Generate Output
PresentTotCost=PresentTotCost/1000000;
TotRevenue=TotRevenue/1000000;
/*File Output*/
fout<<replication<<"\t"<<PresentTotCost<<"\t"<<TotRevenue<<"\t"<<PublicGreenScore<<"\t"<<TrueGr
eenScore<<"\t"<<CumPublicGreenScore<<"\n";
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/*File Output*/
Dout<<replication<<"\tTC="<<PresentTotCost<<"\tTRev="<<TotRevenue<<"\tGS="<<PublicGreenScore
<<"\tTGS"<<TrueGreenScore<<"CumPGS="<<CumPublicGreenScore<<"\n";
SumTotCost+=PresentTotCost;
SumGreenScore+=PublicGreenScore;
SumTotRevenue+=TotRevenue;
SumTrueGreenScore+=TrueGreenScore;
SumCumGreenScore+=CumPublicGreenScore;
Gout<<"DM="<<DMtype<<"\tReplication= "<<replication<<"\n";
Gout<<"event"<<"\t"<<"Repetition No."<<"\t"<<"RepetitionIndex\n";
for(i=0;i<MAX_NumOfEvents;i++)
Gout<<i<<"\t"<<Repetition[i]<<"\t"<<RepetitionIndex(Repetition[i],i)<<"\n";
}//--> Close "replication" loop
/**** Calculate the average scores ****/
AveTotCost=SumTotCost/ReplicationTimes;
AveGreenScore=SumGreenScore/ReplicationTimes;
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AveTotRevenue=SumTotRevenue/ReplicationTimes;
AveTrueGreenScore=SumTrueGreenScore/ReplicationTimes;
AveCumGreenScore=SumCumGreenScore/ReplicationTimes;
/*** MAIN RESULTS; Avg Total Cost and Avg Green Score is outputted here ****/
fout<<"DMtype=\t"<<DMtype<<"\tAveTotCost="<<"\t"<<AveTotCost<<"\tAveTotRevenue="<<AveTot
Revenue<<"\tAveGreenScore="<<"\t"<<AveGreenScore<<"\tAveTrueGreenScore="<<AveTrueGreenScore<<
"\tAveCumGreenScore="<<AveCumGreenScore<<"\n";
/**** Zero the sum variable so that it can be used for the next DM iteration ****/
SumTotCost=0;
SumGreenScore=0;
SumTotRevenue=0;
SumTrueGreenScore=0;
SumCumGreenScore=0;
}//--> Close "DMtype" loop
//CLOSE OUT ALL THE OUTPUT FILES
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fout.close();
Dout.close();
Eout.close();
GSout.close();
Gout.close();
COSTout.close();
}//--> Close "main" loop
/**** Algorithm to generate uniform random number once the function is called; Ref: Law and Kelton ***/
/******************************* UNIFORM
**********************************************************/
float uniform(void)
{
int i;
long a=16807, m=2147483647, q=127773, r=2836;
long hi, low, test;
float uniform;
hi=seed/q;
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low=seed%q;
test=a*low - r*hi;
if (test>0)
seed=test;
else
seed=test+m;
uniform=(float)seed/m;
return uniform;
}
/**** Algorithm to generate Poisson random number once the function is called; Ref: Law and Kelton ***/
/*********************** Poisson RNG
*******************************************************/
int Poisson(int mean_events)
{
int i=-1;
float Total=0;
while (Total<=1.0)
{
Total+= -log(uniform())/float(mean_events)/*mean*/;
140
i++;
}
return i; //Shift the poisson RV 1 to the right
}
/**** Algorithm to generate GAMMA random number once the function is called; Ref: Law and Kelton ***/
/******************************** GAMMA
********************************************************/
float gamma(float alpha, float bta)
{
float u1,u2,p,b,Y,a,q,teta,d,V,Z,W;
if (alpha<1)
{
b=1+alpha/exp(1);
u1=uniform();
p=b*u1;
u2=uniform();
if (p<=1)
{
Y=pow(p,1/alpha);
while (u2>exp(-1*Y))
{
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u1=uniform();
p=b*u1;
Y=pow(p,1/alpha);
u2=uniform();
}
return Y;
}
else
{
Y=-log((b-p)/alpha);
while (u2>pow(Y,alpha-1))
{
u1=uniform();
p=b*u1;
Y=-log((b-p)/alpha);
u2=uniform();
}
return Y;
}
}
else if (alpha >1)
{
142
a=1/sqrt(2*alpha-1);
b=alpha-log(4);
q=alpha+1/a;
teta=4.5;
d=1+log(teta);
u1=uniform();
u2=uniform();
V=a*log(u1/(1-u1));
Y=alpha*exp(V);
Z=pow(u1,2)*u2;
W=b+q*V-Y;
while (!((W+d-teta*Z>=0)||(W>=log(Z))))
{
u1=uniform();
u2=uniform();
V=a*log(u1/(1-u1));
Y=alpha*exp(V);
143
Z=pow(u1,2)*u2;
W=b+q*V-Y;
}
return Y;
}
}
/**** Algorithm to BETA uniform random number once the function is called; Ref: Law and Kelton ***/
/******************************** BETA
***********************************************************/
float beta(float psmst, float ave, float optmst)
{
float Y1,Y2,mean,var,A,B, alpha1, alpha2;
mean=(psmst+4*ave+optmst)/6;
var=pow((optmst-psmst)/6,2);
A=(mean)/(1-mean);
B=(mean*(1-mean)/var)-1;
alpha1=A*B/(1+A);
alpha2=B/(1+A);
144
Y1=gamma(alpha1,1);
Y2=gamma(alpha2,1);
return Y1/(Y1+Y2);
}
/*** This function is called at the beginning of the simulation for each DM to initialize the score keepers and
counters, so that no residual value from the old runs can affect the new iteration ****/
/******************************** Initialize *******************************************/
void initialize(void)
{
int i;
TotRevenue=1000000000.00;
PresentTotCost=0;
PublicGreenScore=0;
TrueGreenScore=0;
CumPublicGreenScore=0;
Cost=0;
Accept=0;
capacity=40000;
for (i=0;i<MAX_NumOfEvents;i++)
{
Repetition[i]=0;
145
ComplianceRecord[i]=0;
happened[i]=0;
}
}
/********************************* Assign values to each event
***********************************/
void assign(void)
{
pdf[0][1][1]=0.80; pdf[0][1][2]=0.85 ; pdf[0][1][3]=0.90;
pdf[0][2][1]=0.625; pdf[0][2][2]=0.775 ; pdf[0][2][3]=0.825;
pdf[0][3][1]=0.45; pdf[0][3][2]=0.50; pdf[0][3][3]=0.55;
pdf[0][4][1]=1000*capacity;
pdf[0][5][1]=200; //Green Value
pdf[0][6][1]=0.3; pdf[0][6][2]=0.6; pdf[0][6][3]=0.9; pdf[0][6][4]=1; //Prob of getting fined
pdf[0][7][1]=100*capacity; pdf[0][7][2]=500*capacity;
pdf[0][7][3]=5000*capacity; pdf[0][7][4]=10000*capacity;//Fine Amount
pdf[0][8][1]=4; //Ocurrance type
pdf[0][9][1]=0; //Contribution to capacity improvememnt
146
pdf[1][1][1]=0.80; pdf[1][1][2]=0.85 ; pdf[1][1][3]=0.92;
pdf[1][2][1]=0.65; pdf[1][2][2]=0.7 ; pdf[1][2][3]=0.75;
pdf[1][3][1]=0.45; pdf[1][3][2]=0.50; pdf[1][3][3]=0.55;
pdf[1][4][1]=1000000;
pdf[1][5][1]=500;
pdf[1][6][1]=0;
pdf[1][7][1]=0;
pdf[1][8][1]=1;
pdf[1][9][1]=0;
pdf[2][1][1]=0.80; pdf[2][1][2]=0.85 ; pdf[2][1][3]=0.90;
pdf[2][2][1]=0.65; pdf[2][2][2]=0.70 ; pdf[2][2][3]=0.75;
pdf[2][3][1]=0.45; pdf[2][3][2]=0.50 ; pdf[2][3][3]=0.55;
pdf[2][4][1]=10000000;
pdf[2][5][1]=300;
pdf[2][6][1]=0.30; pdf[2][6][2]=0.6; pdf[2][6][3]=1;
pdf[2][7][1]=50*capacity; pdf[2][7][1]=200*capacity; pdf[2][7][1]=500*capacity;
pdf[2][8][1]=3;
pdf[2][9][1]=0;
147
pdf[3][1][1]=0.80; pdf[3][1][2]=0.85 ; pdf[3][1][3]=0.90;
pdf[3][2][1]=0.65; pdf[3][2][2]=0.7 ; pdf[3][2][3]=0.75;
pdf[3][3][1]=0.42; pdf[3][3][2]=0.45; pdf[3][3][3]=0.55;
pdf[3][4][1]=-1000000; // 1,000,000 Reward
pdf[3][5][1]=100;
pdf[3][6][1]=0;
pdf[3][7][1]=0;
pdf[3][8][1]=Multiple;
pdf[3][9][1]=
pdf[4][1][1]=0.80; pdf[4][1][2]=0.88 ; pdf[4][1][3]=0.92;
pdf[4][2][1]=0.68; pdf[4][2][2]=0.73 ; pdf[4][2][3]=0.78;
pdf[4][3][1]=0.55; pdf[4][3][2]=0.65; pdf[4][3][3]=0.68;
pdf[4][4][1]=60*capacity; //60$/car
pdf[4][5][1]=80;
pdf[4][6][1]=0.3; pdf[4][6][2]=0.6; pdf[4][6][3]=1;
pdf[4][7][1]=30*capacity; pdf[4][7][2]=60*capacity; pdf[4][7][3]=500*capacity;
pdf[4][8][1]=3;
pdf[4][9][1]=0;
148
pdf[5][1][1]=0.78; pdf[5][1][2]=0.82 ; pdf[5][1][3]=0.86;
pdf[5][2][1]=0.55; pdf[5][2][2]=0.60 ; pdf[5][2][3]=0.68;
pdf[5][3][1]=0.40; pdf[5][3][2]=0.45; pdf[5][3][3]=0.47;
pdf[5][4][1]=0;
pdf[5][5][1]=0;
pdf[5][6][1]=0;
pdf[5][7][1]=0;
pdf[5][8][1]=1;
pdf[5][9][1]=0; //0.05*0.5=0.025 50% increase in hybrid sale
pdf[6][1][1]=0.78; pdf[6][1][2]=0.82 ; pdf[6][1][3]=0.85;
pdf[6][2][1]=0.55; pdf[6][2][2]=0.60 ; pdf[6][2][3]=0.68;
pdf[6][3][1]=0.45; pdf[6][3][2]=0.48; pdf[6][3][3]=0.52;
pdf[6][4][1]=3800*5000; //$3800/worker For 5000 workers
pdf[6][5][1]=500;
pdf[6][6][1]=0.9;
pdf[6][7][1]=0; //Fine?
pdf[6][8][1]=Multiple;
pdf[6][9][1]=0;
149
pdf[7][1][1]=0.85; pdf[7][1][2]=0.87 ; pdf[7][1][3]=0.90;
pdf[7][2][1]=0.7; pdf[7][2][2]=0.8 ; pdf[7][2][3]=0.85;
pdf[7][3][1]=0.45; pdf[7][3][2]=0.55; pdf[7][3][3]=0.60;
pdf[7][4][1]=1500*capacity;
pdf[7][5][1]=30;
pdf[7][6][1]=0;
pdf[7][7][1]=0;
pdf[7][8][1]=1;
pdf[7][9][1]=0;
pdf[8][1][1]=0.7; pdf[8][1][2]=0.75 ; pdf[8][1][3]=0.90;
pdf[8][2][1]=0.5; pdf[8][2][2]=0.6 ; pdf[8][2][3]=0.7;
pdf[8][3][1]=0.3; pdf[8][3][2]=0.4; pdf[8][3][3]=0.5;
pdf[8][4][1]=40000000;
pdf[8][5][1]=70;
pdf[8][6][1]=0;
pdf[8][7][1]=0;
pdf[8][8][1]=1;
pdf[8][9][1]=0;
150
pdf[9][1][1]=0.6; pdf[9][1][2]=0.7 ; pdf[9][1][3]=0.8;
pdf[9][2][1]=0.5; pdf[9][2][2]=0.55 ; pdf[9][2][3]=0.6;
pdf[9][3][1]=0.35; pdf[9][3][2]=0.4; pdf[9][3][3]=0.5;
pdf[9][4][1]=1500*capacity;
pdf[9][5][1]=300;
pdf[9][6][1]=0;
pdf[9][7][1]=0;
pdf[9][8][1]=1;
pdf[9][9][1]=0;
pdf[10][1][1]=0.8; pdf[10][1][2]=0.9 ; pdf[10][1][3]=0.99;
pdf[10][2][1]=0.7; pdf[10][2][2]=0.8 ; pdf[10][2][3]=0.85;
pdf[10][3][1]=0.6; pdf[10][3][2]=0.65; pdf[10][3][3]=0.70;
pdf[10][4][1]=1000*capacity;
pdf[10][5][1]=50;
pdf[10][6][1]=0.9;
pdf[10][7][1]=0;
pdf[10][8][1]=1;
151
pdf[10][9][1]=0;
pdf[20][1][1]=0.80; pdf[20][1][2]=0.85 ; pdf[20][1][3]=0.90;
pdf[20][2][1]=0.65; pdf[20][2][2]=0.70 ; pdf[20][2][3]=0.75;
pdf[20][3][1]=0.65; pdf[20][3][2]=0.70; pdf[20][3][3]=0.75;
pdf[20][4][1]=50*capacity; //20% Reduction in Material Handling
pdf[20][5][1]=0;
pdf[20][6][1]=0;
pdf[20][7][1]=0;
pdf[20][8][1]=Multiple;
pdf[20][9][1]=0.1;
pdf[21][1][1]=0.80; pdf[21][1][2]=0.85 ; pdf[21][1][3]=0.90;
pdf[21][2][1]=0.65; pdf[21][2][2]=0.70 ; pdf[21][2][3]=0.75;
pdf[21][3][1]=0.60; pdf[21][3][2]=0.65; pdf[21][3][3]=0.70;
pdf[21][4][1]=50*capacity; //20% Reduction in Material Handling
pdf[21][5][1]=0;
pdf[21][6][1]=0;
pdf[21][7][1]=0;
152
pdf[21][8][1]=Multiple;
pdf[21][9][1]=0.05;
pdf[22][1][1]=0.80; pdf[22][1][2]=0.88 ; pdf[22][1][3]=0.92;
pdf[22][2][1]=0.68; pdf[22][2][2]=0.73 ; pdf[22][2][3]=0.78;
pdf[22][3][1]=0.45; pdf[22][3][2]=0.50; pdf[22][3][3]=0.55;
pdf[22][4][1]=30*capacity; //$30/Car
pdf[22][5][1]=300;
pdf[22][6][1]=0.3; pdf[22][6][1]=0.6; pdf[22][6][1]=1;
pdf[22][7][1]=30*capacity; pdf[22][7][2]=300*capacity; pdf[9][7][3]=700*capacity;
pdf[22][8][1]=3;
pdf[22][9][1]=0;
pdf[23][1][1]=0.80; pdf[23][1][2]=0.85 ; pdf[23][1][3]=0.90;
pdf[23][2][1]=0.65; pdf[23][2][2]=0.70 ; pdf[23][2][3]=0.75;
pdf[23][3][1]=0.65; pdf[23][3][2]=0.75; pdf[23][3][3]=0.75;
pdf[23][4][1]=100*capacity; //12% of
pdf[23][5][1]=0;
pdf[23][6][1]=0;
pdf[23][7][1]=0;
153
pdf[23][8][1]=Multiple;
pdf[23][9][1]=0.2;
pdf[24][1][1]=0.78; pdf[24][1][2]=0.82 ; pdf[24][1][3]=0.86;
pdf[24][2][1]=0.55; pdf[24][2][2]=0.60 ; pdf[24][2][3]=0.68;
pdf[24][3][1]=0.40; pdf[24][3][2]=0.45; pdf[24][3][3]=0.47;
pdf[24][4][1]=50*capacity; //$50/car (?)
pdf[24][5][1]=50;
pdf[24][6][1]=0;
pdf[24][7][1]=0;
pdf[24][8][1]=1;
pdf[24][9][1]=0.01;
pdf[25][1][1]=0.80; pdf[25][1][2]=0.85 ; pdf[25][1][3]=0.92;
pdf[25][2][1]=0.67; pdf[25][2][2]=0.72 ; pdf[25][2][3]=0.75;
pdf[25][3][1]=0.60; pdf[25][3][2]=0.65; pdf[25][3][3]=0.70;
pdf[25][4][1]=150*capacity; //$150/car
pdf[25][5][1]=200;
pdf[25][6][1]=0.9;
pdf[25][7][1]=0;
154
pdf[25][8][1]=1;
pdf[25][9][1]=0.05;
/*Deleted Event*/
pdf[26][1][1]=0.80; pdf[26][1][2]=0.85 ; pdf[26][1][3]=0.95;
pdf[26][2][1]=0.60; pdf[26][2][2]=0.63 ; pdf[26][2][3]=0.68;
pdf[26][3][1]=0.42; pdf[26][3][2]=0.45; pdf[26][3][3]=0.55;
pdf[26][4][1]=0; //$100/car
pdf[26][5][1]=0;
pdf[26][6][1]=0;
pdf[26][7][1]=0;
pdf[26][8][1]=1;
pdf[26][9][1]=0.0;
pdf[27][1][1]=0.78; pdf[27][1][2]=0.82 ; pdf[27][1][3]=0.86;
pdf[27][2][1]=0.55; pdf[27][2][2]=0.60 ; pdf[27][2][3]=0.68;
pdf[27][3][1]=0.40; pdf[27][3][2]=0.45; pdf[27][3][3]=0.47;
pdf[27][4][1]=50*capacity;
pdf[27][5][1]=0;
pdf[27][6][1]=0;
pdf[27][7][1]=0;
155
pdf[27][8][1]=1;
pdf[27][9][1]=0.01;
pdf[28][1][1]=0.80; pdf[28][1][2]=0.85 ; pdf[28][1][3]=0.95;
pdf[28][2][1]=0.65; pdf[28][2][2]=0.70 ; pdf[28][2][3]=0.75;
pdf[28][3][1]=0.60; pdf[28][3][2]=0.65; pdf[28][3][3]=0.70;
pdf[28][4][1]=-20*capacity; //$20/car
pdf[28][5][1]=100;
pdf[28][6][1]=0;
pdf[28][7][1]=0;
pdf[28][8][1]=2;
pdf[28][9][1]=0;
pdf[29][1][1]=0.78; pdf[29][1][2]=0.82 ; pdf[29][1][3]=0.86;
pdf[29][2][1]=0.55; pdf[29][2][2]=0.60 ; pdf[29][2][3]=0.68;
pdf[29][3][1]=0.78; pdf[29][3][2]=0.43; pdf[29][3][3]=0.46;
pdf[29][4][1]=-10*capacity; //$10/car
pdf[29][5][1]=100;
pdf[29][6][1]=0;
pdf[29][7][1]=0;
156
pdf[29][8][1]=2;
pdf[29][9][1]=0.005;
pdf[30][1][1]=0.78; pdf[30][1][2]=0.82 ; pdf[30][1][3]=0.86;
pdf[30][2][1]=0.55; pdf[30][2][2]=0.60 ; pdf[30][2][3]=0.68;
pdf[30][3][1]=0.40; pdf[30][3][2]=0.45; pdf[30][3][3]=0.47;
pdf[30][4][1]=-1*capacity; //$1/car
pdf[30][5][1]=50;
pdf[30][6][1]=0;
pdf[30][7][1]=0;
pdf[30][8][1]=1;
pdf[30][9][1]=0;
pdf[31][1][1]=0.85; pdf[31][1][2]=0.87 ; pdf[31][1][3]=0.90;
pdf[31][2][1]=0.7; pdf[31][2][2]=0.8 ; pdf[31][2][3]=0.85;
157
pdf[31][3][1]=0.45; pdf[31][3][2]=0.55; pdf[31][3][3]=0.60;
pdf[31][4][1]=400*capacity;
pdf[31][5][1]=20;
pdf[31][6][1]=0;
pdf[31][7][1]=0;
pdf[31][8][1]=1;
pdf[31][9][1]=0;
pdf[32][1][1]=0.85; pdf[32][1][2]=0.87 ; pdf[32][1][3]=0.90;
pdf[32][2][1]=0.7; pdf[32][2][2]=0.8 ; pdf[32][2][3]=0.85;
pdf[32][3][1]=0.6; pdf[32][3][2]=0.65; pdf[32][3][3]=0.7;
pdf[32][4][1]=860*capacity;
pdf[32][5][1]=100;
pdf[32][6][1]=0.6;
pdf[32][7][1]=2000*capacity;
pdf[32][8][1]=1;
pdf[32][9][1]=0;
158
pdf[33][1][1]=0.5; pdf[33][1][2]=0.55 ; pdf[33][1][3]=0.60;
pdf[33][2][1]=0.55; pdf[33][2][2]=0.65 ; pdf[33][2][3]=0.7;
pdf[33][3][1]=0.8; pdf[33][3][2]=0.9; pdf[33][3][3]=0.99;
pdf[33][4][1]=0;
pdf[33][5][1]=-150;
pdf[33][6][1]=0;
pdf[33][7][1]=0;
pdf[33][8][1]=1;
pdf[33][9][1]=0;
}
/******************************** UPDATE_PDF *********************************/
void update_pdf(int event)
{
int i,j;
for (i=event;i<=row-1;i++)
{
pdf[i][1][1]=pdf[i+1][1][1]; pdf[i][1][2]=pdf[i+1][1][2] ; pdf[i][1][3]=pdf[i+1][1][3];
pdf[i][2][1]=pdf[i+1][2][1]; pdf[i][2][2]=pdf[i+1][2][2] ; pdf[i][2][3]=pdf[i+1][2][3];
159
pdf[i][3][1]=pdf[i+1][3][1]; pdf[i][3][2]=pdf[i+1][3][2] ; pdf[i][3][3]=pdf[i+1][3][3];
pdf[i][4][1]=pdf[i+1][4][1]; pdf[i][4][2]=pdf[i+1][4][2] ; pdf[i][4][3]=pdf[i+1][4][3];
pdf[i][5][1]=pdf[i+1][5][1];
pdf[i][6][1]=pdf[i+1][6][1];
pdf[i][7][1]=pdf[i+1][7][1];
pdf[i][8][1]=pdf[i+1][8][1];
}
row=row-1;
}
/***************************** CANCEL_EVENT ************************************/
void cancel_event(int event)
{
pdf[event][1][1]=0.98; pdf[event][1][2]=0.99 ; pdf[event][1][3]=1;
pdf[event][2][1]=0.98; pdf[event][2][2]=0.99 ; pdf[event][2][3]=1;
pdf[event][3][1]=0.98; pdf[event][3][2]=0.99 ; pdf[event][3][3]=1;
pdf[event][4][1]=0 ; pdf[event][4][2]=0 ; pdf[event][4][3]=0;
pdf[event][5][1]=0;
pdf[event][6][1]=0;
pdf[event][7][1]=0;
pdf[event][8][1]=0;
160
}
/******************************* RepetitionIndex **************************************/
int RepetitionIndex(int RepetitionNumber,int TheEvent)
{
if (RepetitionNumber<=pdf[TheEvent][8][1])
return RepetitionNumber;
else
return pdf[TheEvent][8][1];
}
