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: sdnoh@skku.edu, 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

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

522 / MARCH 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3

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

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3 MARCH 2014 / 523

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

524 / MARCH 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3

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

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 3 MARCH 2014 / 525

career Researcher Program through NRF (National Research Foundation

of Korea) grant funded by the Ministry of Education, Korea.

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