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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3, pp. 519-525 MARCH 2014 / 519
© KSPE and Springer 2014
Stochastic Model-based Framework for Assessment ofSustainable Manufacturing Technology
Yooeui Jin1 and Sang Do Noh1,#
1 Department of Systems Management Engineering, Sungkyunkwan University, 300, Cheoncheon-dong, Jangan-gu, Suwon, South Korea, 440-746# Corresponding Author / E-mail: [email protected], TEL: +82-31-290-7603, FAX: +82-31-290-7610
KEYWORDS: Sustainable manufacturing, Stochastic model, Technology assessment, Uncertainty
In recent years, manufacturing companies have been continually required to minimize energy usage, pollutant emissions,
manufacturing costs and lead-time. Therefore most companies have realized the necessity of investing in green manufacturing
technology. However, it is difficult to evaluate the benefit of a green technology investment. Although several technology evaluation
and assessment methods have been developed, these methods are unsuitable for evaluating alternative green technologies and their
long-term benefits. Moreover, estimating the future cash flow of a type of technology, which is one of the most important factors, is
a difficult task since energy prices and environmental regulations have major uncertainties. Unfortunately, most technology evaluation
methods do not currently consider future cash flows. In this research, we designed a new framework that can be used to evaluate green
technology investment based on stochastic model; two investment cases were then evaluated using the framework to verify the
reliability of the evaluation result and the framework usability.
Manuscript received: June 17, 2013 / Revised: January 4, 2014 / Accepted: January 12, 2014
1. Introduction
1.1 Investing in green manufacturing technologies
In recent years, company managers have been continually required
to minimize environmental impacts such as waste, pollutants, or CO2.
Many nations have been preparing and introducing environmental
regulations. These regulations place a great deal of pressure on
manufacturing companies because, as is well known, manufacturing
industries are mainly responsible for environmental impacts.26 In the
U.S., the manufacturing sector accounted for about 84% of energy-
related carbon dioxide emissions and 90% of energy usage.2 However,
it is believed that industry's costs of pollution prevention and cleanup
lead to higher product prices and reduced competitiveness. In this
situation, companies have not been proactive in reducing their
environmental impact. Although for many years researchers have
attempted to empirically prove Porter’s Innovation Effect (an
environmental regulation to trigger innovations that help companies to
become more competitive), these attempts have not been successful.3,4
As a result, companies are unwilling to voluntarily invest in green
technologies.
Nevertheless, as more environmental regulations are put in place,
companies need to comply with them by reducing energy usage and
pollutants through implementing green technologies. Therefore, it is
imperative that the value of a green technology is systematically and
monetarily measured. However, considering its importance, the issue of
how to measure the effect of applying a new technology, including a
green manufacturing technology, has not attracted significant attention.5
1.2 Measuring the benefits of a green technology application
To evaluate a technology investment, the Return of Investment (ROI),
or break-even-point, is typically used. However, the estimated benefits
may not be reliable because the return figure may vary depending on
who carried out the estimation.6 And the estimated benefits also depend
on the method carried out for the estimation.
Meanwhile, since the economy paradigm has changed and intangible
NOMENCLATURE
ROI=Return of Investment
NPV=Net Present Value
VDM = Value Decision Model
DOI: 10.1007/s12541-014-0366-1
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520 / MARCH 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3
assets such as intellectual property are much more valuable than tangible
assets, several methods to measure a technology value have been
developed. Technology valuation involves determining the maximum
economic gains through using information effectively and with a series
of more reasonable methods.7 These methods can be very useful in
measuring the benefit of applying a technology.8 They can help in
impersonally estimating future cash flows and determining ROI or
break-even-point.
However, there are still many difficulties in the use of these
methods as a decision-making tool for a green technology investment.
Firstly, many of these methods focus only on assessment, and therefore
do not consider the facility of the company applying the technology.
Secondly, in estimating future cash flow, these methods do not consider
future uncertainties such as environmental regulations or energy unit
prices which have considerably large uncertainties, resulting in the
potentially unreliable estimated benefit of the technology applied.
In this research, we propose a new framework to evaluate green
manufacturing technology investment based on a stochastic model; two
investment cases are then evaluated using the framework to verify the
reliability of the evaluation result and the framework usability.
2. Literature Review and Previous Works
2.1 Stochastic model
2.1.1 Uncertainty
A stochastic model is a model which includes uncertainties. Here,
uncertainty refers to a state of having limited knowledge where it is
impossible to exactly describe the existing state, a future outcome, or
more than one possible outcome. Knights defined such uncertainty as
the lack of an additive objective probability distribution over outcomes,
compared with a risk which has a probability for each outcome.9
Environmental uncertainties that influence the manufacturing strategy
include manufacturing flexibility, market competition, inflation, demand,
and so on. Especially, environmental regulations and energy usage have
considerably large uncertainties in terms of green technology
investment.
2.1.2 Uncertainty forecasting: Scenario analysis
To estimate uncertain future status, Stirling suggested that a scenario
analysis could be useful.9 A scenario analysis, which is widely used by
leaders ranging from corporate managers to military leaders, is a process
of analyzing possible future events by considering alternative possible
outcomes.24,25 Thus, the scenario analysis presents several alternative
future developments instead of trying to show one exact picture of the
future. Consequently, a range of possible future outcomes is able to be
observed and the scenario analysis can help establish the best-case and
worst-case scenarios.
Scenario planning is used to define possible events and their
probability. That should be performed first and the mathematical function
should then be derived from the scenarios. The proposed framework in
this study includes this scenario planning process.
2.2 Previous work
This section discusses the many valuation methods for frameworks
that have been developed to evaluate a technology value and sustainable
production technologies. Li proposed a new scheme for clarifying the
content and scope of a technology valuation and a technology evaluation
framework using four stages: assessment, valuation, pricing, and price.10
Li also suggested a hybrid valuation formula for valuing different
contexts.
Park developed a new single technology valuation method which
generates monetary value, rather than score or index. The method is
based on the structural relationship between technology factors and
market factors.8 Jung proposed a new model for evaluating the economic
value of technologies by a modified process which adopted a Markov
Chain and a Risk-averse Utility Function. Jung designed a model to
estimate the lifespan of a technology based on probability data. The new
technology valuation model is designed to reflect the characteristics of
defense industries.11 Diederen developed a valuation model which
calculates ROI considering the stochastic process of energy price
following Brownian motion and energy tax following the Poisson jump
process to evaluate energy saving technology investment.12 Radulescu
mathematically formulated an optimization problem using multi-
objective programming approach. And he derived an optimal production
plan problem to maximize return and minimize pollutant and solved it
on the subject of textile plant.29
To evaluate green technologies, most of the evaluation models
reflect the characteristics of energy generating industries, and few green
manufacturing technology investment valuation methods have been
developed. There was a research to mathematically and exactly expect
return of sustainable production technology. However, the formulation
is so complex that it may be hard for company managers to use it.
Thus, in this research we propose a simple framework which reflects
manufacturing industry characteristics to evaluate green manufacturing
technology.
3. Framework Design
3.1 Value of green manufacturing technology investment
In this research, the value of a green manufacturing technology
investment is defined as the sum of future cash flows over the total
period. We assumed that years are discrete times and there are no
uncertainties during the investment period. In each year, the cash flows
are calculated as Net Present Value (NPV). The value can be calculated
as follows:
(1)
where, CostSavingt is the total cost saving amount and Investmentt
is the investment cost during the tth year.
The investment value varies as time passes. Fig. 1 shows the
accumulated value change of a technology investment proposed in this
research. X represents the time it takes to invest in the technology, LTdev
refers to the lead time for technology development, and LTapp refers to
the lead time for applying the technology. After technology
implementation, the benefit can vary due to uncertainties as shown in
Fig. 1. The ultimate value we attempt to determine is the range of
technology investment benefit (the range of end points of all lines).
ValueCostSavingt Investmentt–
1 r+( )t
----------------------------------------------------------------t all period∈
∑=
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3.2 Value decision factors
In the previous chapter we explained that the value of a green
manufacturing technology investment is defined as the sum of future
cash flows, which means Cost saving minus investment. Here Cost
saving means production cost reduction by implementing the technology.
To calculate the total cost saving, we arranged cost saving factors as a
hierarchy. By calculating each item, we can estimate the amount of
total cost saving-plus cash flows.
There are three major factors included in cost saving: one is input
resource reduction. Another is discharge cost savings which are reduced
waste disposal cost and pollutant emission cost. The other factor is
facilities maintenance cost reduction. While, investment costs include
the technology development cost (which is the technology purchasing
cost in some cases), and technology implementation cost. We categorized
all factors in detail as shown in Figs. 2 and 3.
As expected, most of the child elements in the cost saving tree have
uncertainties. Moreover, each child element includes several items.
Thus, it is not easy to consider all uncertainties. We suggest that only
one uncertainty is considered at any one time.
3.3 Stochastic model-based green manufacturing technology
evaluation framework
3.3.1 Framework
We designed a framework to evaluate a green manufacturing
technology investment using the pre-described technology investment
value and decision factors. The framework consists of inputs, Value
Decision Model (VDM), and output. Fig. 4 shows the framework.
(1) Input of framework
Inputs consist of green technology characteristics, information of
target factory to be implemented the technology and other information
such as discount rate, yearly energy unit price, etc. Any input that has
uncertainties needs to be defined as a stochastic input and converted
into a scenario. For this process, the stochastic input for the framework
must include several projection data. We suggest using “Event tree” to
convert forecasts into scenarios. We can obtain scenarios by determining
what events influence uncertainties, assigning probabilities to events,
and drawing an event tree.
Fig. 5 shows an event tree and scenarios for greenhouse gas
regulations. Here, the uncertainties include the kind of regulations to be
introduced, the start time of the regulation, and the amount of the
greenhouse gas penalty. We assumed that the carbon emission trading
scheme and carbon tax are possible and are the probabilities. The
scenarios can be treated as deterministic inputs.
(2) VDM and output of framework
The VDM calculates the cost saving effects and investment using
the inputs. It finally calculates the best case, the worst case, and the
weighted average case of a technology investment. The calculated range
of the value and expected value are outputs of the framework.
3.3.2 VDM Value Decision Model (VDM)
The total value of a green manufacturing technology investment can
be calculated according to Eq. (2). The detail calculation equations for
cost saving effect are as follows:
(2)
where Qt is the production volume in the tth year.
Input cost saving during the tth year can be calculated as Eq. (3)~(5).
(3)
(4)
(5)
In the Eq. (3) Mit is the saving amount of material i when making
one production unit during the tth year by applying the alternative green
technology. Similarly, ∆Lit, ∆Eit, ∆Wit, ∆Pullit and ∆Mait, mean saving
amount of labor, energy, waste, pollution maintenance cost during tth
year by applying the alternative green. Pricemit(ωmaterial) is a scenario of
CastSavingt Qt InputSavingt Disch eSavingarg t+( )×=
+MaintenanceSavingt
InputSavingt Σi all materials∈M∆ it E Pricemit ωmaterials( )( )×=
+Σi all labors∈L∆ it E Pricelit ωlabor( )( )×
+Σi all energies∈E∆ it E Priceeit ωenergy( )( )×
Disch eSavingarg t Σi all wastes∈W∆ it E DisWit ξwaste( )( )×=
=Σi all pullutions∈Pull∆ it E PanPullit ξpullution( )( )×
MaintenanceSavingt Σi all facilities∈Ma∆ it=
Fig. 1 Accumulated value change of a technology investment
Fig. 2 Cost saving factors
Fig. 3 Investment factors
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the unit-cost of material i in the tth year. Similarly, Pricelit(ωlabor) is a
scenario of the cost of labor grade i and Priceeit(ωenergy) is a scenario of
i kind of energy. E(Pricemit(ωmaterial)) is the expectation value of all
scenarios and the other E()s are each expectations.
∆Wit is the reduced amount of waste i when making one production
unit during the tth year by applying the alternative green technology and
∆Pullit is the same as ∆Wit. E(DistWit(ξwaste)) is the expectation value of
all scenarios of the waste discharging cost.
The investment cost is the sum of the R&D cost, which means the
cost for developing or purchasing the green technology and then applying
the cost. The equations to calculate investment costs are as follows:
(6)
(7)
where Setupt is the set-up cost for R&D and R&DLi is the amount
of labor grade i required to develop the technology and Pricelit is the
cost of labor grade i during the tth year. Facilityt is the cost of purchasing
or upgrading facilities during the tth year and AppLi is the amount of
labor grade i required to apply the technology. TrainCostt is the training
cost after implementing the technology and OppCostt is the opportunity
cost during the tth year.
Using the model, we obtain the expected investment which is
calculated by the weighted average of all scenarios. To obtain the value
range, the expectations must be changed with the best scenario and
worst scenario.
4. Case Study
Using the proposed framework, we evaluated two investment cases
of a green manufacturing technology: a re-evaluation case previously
evaluated by Galitsky in 2001 and an assumed situation in which a
facility is changed.
4.1 Case 1: Adopting adjustable speed drives
4.1.1 Description
Adjustable Speed Drives (ASDs) are very successfully match speed
to load requirements for motor operations, and therefore ensure that
motor energy use is optimized to a given application. Adjustable-speed
drive systems are offered by many suppliers and are available
worldwide.13 Worrell et al. (1997) provide an overview of savings
achieved with ASDs in a wide array of applications, where typical
energy savings are shown to vary between 7% and 60%.14
Galitsky, from the Ernest Orlando Lawrence Berkeley National
Laboratory, claimed the monetary benefit of ASDs was $68,600/year
with $99,400 investment cost when it was adopted by the General
Dynamics Company in 2001. He supposed a yearly saving of 443,332
Kwh of electricity and 17,480 MBtu of natural gas as well as a yearly
reduction of 213,000 kg of CO2 emission.13 Based on Galitsky’s report,
we arranged the deterministic inputs as shown in Table 1.
We set the natural gas price as a stochastic input and collected 7
projection data reported from 1990 to 2004 on the internet site of
Energy Information Administration, then generated 7 scenarios with the
same probabilities. Fig. 6 shows the price scenarios, where the dashed
R&DCostt Setupt Σi all labors ∈R&DLi Pricelit×+=
AppCostt Tech( ) Facilityt Σi all labors ∈AppLi+ Pricelit×=
+TrainCostt OppCostt+
Fig. 4 Evaluation framework for green manufacturing technology investment
Fig. 5 Event tree for greenhouse gas regulations
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line shows the actual history of the natural gas price from 2001~2010.
4.1.2 Evaluation result
From the results, the investment value of ASDs was determined as
$472,847 over 10 years. The result was obtained from the weighted
average of all scenarios. To accept this single result, the decision maker
must be risk-neutral. However, the framework provides the range of the
value to help decision making. Table 2 shows the result of evaluation.
We calculated the investment value of ASDs using the actual natural
gas price history for 2001~2010;15 the value is about $700,000.
Consequently, the investment value determined by the proposed
framework has 47% error, while the value reported by Galitsky has
119% error. This considerably large error may be because Galitsky
calculated the benefit based on the current natural gas price. He did not
consider the uncertainty of future natural gas price movement. Through
this case study, we partly confirmed that considering uncertainty may
derive a more exact value than when uncertainty is not considered.
It is necessary to make clear that using multiple projection data does
not always make more accurate evaluation result. However, with
multiple future scenarios, we can minimize the risk to make a wrong
decision with one projection data or without projection data. In other
words, using this framework, we can minimize the influence of projection
variation, resulting in more accurate result as case study 1 shows. Of
course, the more accurate projection data we use, the more accurate
result we can get. So it is true that the evaluator must take projection
cautiously.
4.2 Case 2: Changing pneumatic actuators with electric actuators
4.2.1 Description
An actuator is a type of motor for moving or controlling a mechanism
or system. In engineering, actuators are frequently used as mechanisms
to introduce motion, or to clamp an object to prevent motion.
Compressed air is used as the energy source of a pneumatic actuator.
However, generating the compressed air requires a lot of electricity.
While electric actuators are expensive, they consume even less electricity
than pneumatic actuators. In this case study, we assumed a situation
whereby a company considers replacing their pneumatic actuators with
electric actuators. Thus reducing electricity usage and CO2 emissions
to prepare for CO2 emission trading.16
We assumed there are 100 pneumatic actuators and a 50HP
compressor to generate compressed air. We also assumed the company
runs the actuators for 16 hours a day, 5 days a week, 52 weeks a year,
and the actuators move (go and come back) 6 times per minute. Under
this situation, the compressor consumes about 150,000 Kwh and emits
more than 100,000 kg CO2 yearly. A detailed calculation to estimate
the electricity consumption and CO2 emissions is as follows:
We also obtain the electricity used by electric actuators from “Electric
actuator cost evaluation sheet” developed by Bimba Solutions.21 The
Fig. 6 Natural gas projection (2001~2010)
Table 1 Input of framework (case 1)
Green
Technology
Name Adjustable Speed Drives
Cost to develop
and apply$99,400
Lead time to
develop and apply1 Year
Production resource
reduction level (Yearly)
Saving 443,332 KWh
electricity Saving
17,480 MBtu Natural gas
Waste and emission
reduction level
Reducing 213,000 Kg
CO2 yearly
Others
Investment time (X) 2001
Planning period 2001~2010 (10 Years)
Discount rate 0.096
Electricity unit priceSummary Electricity
Statistics 1999~2010 15
Table 2 Result of evaluation (case 1)
Item Value (NPV)
Cost $99,400.00
CostSaving
$572,247.85
Electricity Natural gas$417,183.06
$451,597.23
CO2 emission penalty $0
Technology Investment Value $472,847.85
Value Range
Table 3 Electricity consumption of a compressor
Compressor
Capacity
A 50HP compressor can generate 237 ft3/min
compressed air with 7.5 Bar pressure,
which means compressor capacity is 2010SCFM.
Compressor spec. (kaeser.com)17
HP: Horse Power
SCFM = Standard Cubic Feet per Minute
Compressed Air
Consumption by
Pneumatic
Actuators
A Ø100 and 200 mm-stroke pneumatic actuator
consume 1.2ft3/min compressed air with 5 Bar,
which means 712SCFM compressed air is
required for 100 actuators.
V = nSAβ18
Electricity Usage
by Compressor
151,391.32 KWh/Year
Electricity usage = HP × 0.746 × 90% ×
(load time + unload time × 30%)
[Quick calculation of electricity costs
for a compressor operating19
CO2 Emissions100,100 Kg/Year
CO2 Conversion rate: 0.6612 Kgs/Kwh20
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framework inputs are as shown in Table 4.
We set the CO2 credit price as a stochastic input and collected 43
projection data,23 and then generated 42 scenarios of CO2 credit price
movement. Figs. 7~8 show the event tree and scenarios.
4.2.2 Evaluation result
Table 5 shows the result obtained using the proposed framework.
From the result, we expect the company could save $7,000 over 20
years. However, as can be seen in Table 5, for the case of more than
30% scenarios, the value of the investment is negative. We expect this
range of information will help decision makers to understand and manage
the risks.
5. Conclusion and Future Work
The society’s increased concerns about environmental issues forced
governments to improve the environmental regulation. As a result
companies have to make more efforts to improve their technologies and
management in order to reduce the impacts of their production on the
environment. Even though many green manufacturing technologies have
been developed, manufacturing companies are unwilling to invest in
green technologies and to reduce environmental impact. This is why it
is believed that pollution prevention and cleanup costs lead to lower
competitiveness.3 In addition, measuring the benefits of investing in
green technologies is difficult.
In this research, we proposed an evaluation framework and decision
model to determine the value of green manufacturing technology
investment. The framework is developed to measure green manufacturing
technology benefit under future uncertainties based on stochastic model.
The framework reflects the characteristics of manufacturing industries.
Subsequently, we evaluated two investment cases using the proposed
framework. The framework is expected to enable decision makers to
evaluate a green manufacturing technology under uncertainties as well
as provide them with risk information.
In order to improve this research, the framework needs to be extended
while considering the correlation between uncertainties. A Cross-Impact
Matrix can be useful to analyze the correlation. In addition, research
could determine how to derive the distribution of the technology value
rather than only the value range. Finally, it is essential to verify and
validate the framework by applying green manufacturing technology
investment evaluation based on actual cases.
ACKNOWLEDGEMENT
This work (NRF-2013R1A2A2A03068143) was supported by Mid-
Table 4 Input of framework (case 2)
Implementation
Target
Production volume
(running condition)
16 hours a day, 5 days a week,
52 weeks a year, and the
actuators move (go and come
back) 6 times per minute.
Yearly resource plan
712 ft3/min Compressed air
151,391.32 KWh/Year
Electricity
Yearly emission
after production100,100 Kg/Year CO2
Green
Technology
Name Electric Actuators
Purchasing cost $80,000($800 A? 100ea)
Technology
applying lead time1 Year
Resource
saving level
Saving 137,414 KWh
electricity yearly
Emission
reduction level
Reducing 90,858 Kg CO2
emission yearly
Others
Investment time (X) 2012
Planning period 2012~2031 (20 years)
Discount rate 0.096
Electricity unit price Electricity Price Projection22
Fig. 7 Event tree for CO2 trading regulation and CO2 credit price
movement
Fig. 8 CO2 credit price movement scenarios
Table 5 Result of evaluation (case 2)
Item Value (NPV)
Cost $80,000
CostSaving
$87,258
Electricity $94,166
CO2 Credit purchasing $30,835
Maintenance $-37,743
Technology Investment Value $7,258
Value Range
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career Researcher Program through NRF (National Research Foundation
of Korea) grant funded by the Ministry of Education, Korea.
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