cloud computing study

Green Supply Chain Management: Final Paper

Professor: Dr. Roland Geyer

Authors: Christian Del Maestro, Adam Rohloff

Servicizing the Computer Supply Chain: Potential Economic and Environmental

Benefits of Cloud Computing

April 8, 2023

GSCM: Thin Client/Cloud ComputingDel Maestro, Rohloff

The IT industry has recently been included as a significant actor in global greenhouse gas (GHG)

emissions, at approximately 2-3% of emissions, roughly equivalent to the international airline

industry [IUSE, 2009]. An ever increasing innovation cycle with shorter product lifecycles,

exponentially increasing demand for data and processing power, and more energy intensive

processing have fueled this emissions increase. We suggest a strategy that can substantially

dematerialize the industry by increasing computing utilization rates through centralization of

computing power, and distribution of computing as a service. This concept, cloud computing, has

been emerging for the past decade, but only to a certain degree, and without any evidence of

dematerialization. As an academic demonstration, we seek to show the potential that cloud

computing has to fully servicize the computing industry, significantly reducing material and

energy consumption while enhancing performance and productivity.

1 Cloud Computing

The concept of cloud computing can be implemented to varying degrees. In its most simple and

existing form, it offers online data storage on a remote server that can be accessed via the

internet. The next level of the cloud is where not only data but applications are accessed via the

cloud, known as software as a service (SAAS) or platform as a service (PAAS) deployment

[Right Scale]. This eliminates the need to install and manage the hardware-software interface

internally and can ease the burden of providing sufficient computing power and IT management

for a firm. Companies such as Google, Amazon, and Microsoft have begun investing in this type

of infrastructure, and Google Docs is a simplified application of this concept. The ultimate level

of cloud computing, known as infrastructure as a service (IAAS) is where software , operating

systems, and server hardware and infrastructure are all managed as a service within the cloud.

Computing resources can be distributed amongst one or many remote servers and computers, and

delivered via the internet to the end user.

The potential energy, material, time and cost savings for this ultimate form of cloud computing

are vast. The utilization rates of a common desktop in a business environment are between 10-

20% [Zhou, 2009]. By centralizing the computing power in the cloud, the need for end-user

processor power is minimized, and utilization rates can be vastly increased to as high as 80-90%

[Zhou, 2009]. Not only is this far more energy efficient, but reduces the material needs for

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procuring local machines. Instead of high performance desktops, thin- client machines can be

used that are vastly simpler, lighter smaller, and less energy intensive. Programs like Windows

Remote Desktop and GoToMyPC.com are the closest thing to true cloud computing that exists

today, however these services our not intended to necessarily replace your desktop, but to

complement it. Coordination between machine makers, software designers, and internet

infrastructure and bandwidth providers will have to continue to develop, so that a seamless IASS

service can eliminate the need for desktops.

2 Goal and Scope

The goal of our analysis is to show that even on a small scale, implementing the principles and

infrastructure of cloud computing to the degree of IAAS can reduce both environmental and

economic costs of computing. Additionally, because the benefits of cloud computing come

primarily from dematerialization and productivity increase, environmental and economic costs

are highly correlated.

The economies of scale of a national scale cloud computing infrastructure are a complex system

to simulate. For the purposes of our analysis, we chose to compare the life cycle of a collection of

100 traditional desktops vs. a network of 100 thin-client machines (w/server allocation) to

simulate a micro-scale version of a cloud. This 100 client cloud system is comparable to the

computing needs of a small business. Small businesses often do not have the capital to invest in

internal computing infrastructure and IT, and cloud computing would perfectly suit their needs.

Companies such as Right Scale are targeting these kinds of customers for their cloud services.

Full cloud computing for larger firms is less realistic, as they have their own IT capabilities,

higher utilization rates, and security concerns that would discourage cloud use, at least in its

infancy stages before security and lock-in issues are resolved [Right Scale]. The scope of our

analysis is small, and it should be recognized that economies of scale in a true cloud

infrastructure would further enhance the potential economic and environmental savings.

Our analysis looks at the life cycle of a thin-client + server, desktop, from material procurement,

pre-component and component production, final product assembly, use, and disposal. Our

quantitative analysis focuses on manufacturing and use phase environmental and economic costs.

Quantitatively we only consider global warming potential (GWP) as an environmental indicator

while waste, water, and toxicity indicators are described qualitatively. Our economic scaling

factor could be used for a quantitative comparison for the remaining environmental indicators.

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This is defensible as the major difference between the two supply chains is the use of less

material, which reduces costs over almost every link of the supply chain.

We purposely omitted the impacts of keyboards, monitors, and mice in our analysis, as these

would be necessary in both cases. We also do not calculate office cooling savings from using thin

clients. Thin client machines use considerably less power, and therefore distribute less heat than

traditional desktops into the office environment. This was difficult to quantify and left out of our

scope.

Baseline numbers for the environmental impact of the computer components and processing

(packaging, transport, manufacturing, etc) were derived from Williams (2004), and scaling

factors were used to compare our thin-client machine. Scaling factors included the: economic

factor, based on cost difference and used when no other data was applicable; transport factor,

based on volume (since density of thin client was roughly equal to desktop, volume was assumed

to be the constraining factor for transport costs); and packaging factor, based on surface area

difference. Power consumption requirements, used for use-stage calculations were based on

manufacturer specifications.

3 Description of the Supply Chain

Rather than individual supply chains descriptions, a simultaneous supply chain comparison is

more useful in this case because the actual stages of the supply chain are nearly identical. The

critical differences along most stages are less material and energy inputs, and so in most cases a

simple scaling factor can be implemented. The only supply chain additionality is the cloud

distributor stage, interjected between computer hardware distributor and end-user. Figure 1

describes the traditional supply chain of the desktop and identifies changes and reductions in

inputs when comparing the thin client cloud system. In most cases environmental and economic

consideration are linked to dematerialization and lower energy requirements.

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Figure 1 -Supply Chain of the Desktop vs. Thin–Client (w/ server share)

Supply ChainStages

Components, processes, inputs Environmental Considerations

Economic Considerations

Desktop (100) Thin Client (100 + Server)

Pre-component manufacturing(raw materials, production)

semi conductors, circuit boards, silicon wafers, other

Less input (economic scaling factor)

Less material input, less environmental impact

Less material and energy input, lower costs

Component manu-facturing(production)

Main board (fans, CPU) Drivers (HDD, optical drives, power supply) Cards (memory, graphic, sound, modem), Casings ( Fan, Wire, tower, other)Packaging (box, cushion)

No fans, smaller CPU, no HDD, smaller power supply, simpler graphics card, less casings, less packaging, less cushioning b/c no fragile optical and HDD drives

Fewer components, simpler components, less environmental impact.

Same as above

Assembly Primarily energy requirements

Less energy requirements

Less energy, smaller GWP

Same as above

Transport Mass, Volume, packaging requirements,

Smaller mass and volume, less cushioning because no fragile optical drives

Less fuel, smaller GWP

Same as above

Thin-Client Cloud Provider

Not part of traditional supply chain

Server, IT management, (server and infrastructure)

Server impacts are allocated amongst thin clients.

Added link to the supply chain. Distributed server costs and labor costs (IT)

End Use Desktop power requirements (~130 Whr per user), and maintenance

Thin client power requirements (~12 Whr per user). Simpler machine, less maintenance

GWP directly related to power requirements

Costs related to power requirements and maintenance.

Disposal (recycling, reuse, landfill)

Volume, Mass, materials toxicity. Product life cycle, ~ 3 years

Less material, less disposal. Product Life Cycle, 3-5 years, as performance is at server, not at thin-client.

Less material input and longer lifecycle, less environmental impact.

Similar to environmental costs

Total Economic Cost

$500 $350

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3.1 Additional Value Considerations of Cloud Computing

The cloud distributor supply link indicates potential for innovation and business growth for this

sector, stemming from added value for the consumer as the computer is servicized. Besides

reducing economic and environmental costs, cloud computing provides additional performance

benefits, inherent to the cloud. In fact it is these performance benefits that are most likely driving

the current growth of the cloud computing industry, and not necessarily the environmental, or

even economic benefits. Some of these noted benefits include:

Significantly greater computing power by tapping into powerful supercomputer [Business Week, 2007] capabilities;

1. Lower cooling costs because less heat loss; less security (theft) concerns because thin client machines have no value without the cloud;

2. Reduced labor costs because less requirements of on site IT department;

3. Greater mobility because of enhanced remote desktop capabilities;

4. Lower switching costs as upgrading computing performance does not require purchasing a new machine.

5. Reduced office noise pollution w/o fans

It is important to recognize the possibility that cloud computing will not replace but supplement

(rebound effects) desktops because of the added performance benefits of cloud computing.

Consumers may retain their desktops, and simply add cloud computing capabilities to it.

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6. Environmental Valuation

3.2 Methodology and Scaling Factors

Inventory values for modeling in this analysis were used from Eric Williams’ study titled ‘Energy

Intensity of Computer Manufacturing: Hybrid Assessment Combining Processes and Economic

Input-Output Methods’ published in the Environmental Science and Technology Journal (2004).

No data was available to assess the environmental impacts of the thin-client unit. A series of

scaling factors were calculated to adjust the desktop inventory values from Williams 2004 to

estimate the impact from the manufacturing of a thin-client.

3.3 Economic Scaling Factor

Because the energy inventory for manufacturing is only available for a desktop, an economic

scaling factor was used to project the environmental impact of thin-client manufacturing. The

thin-client has fewer printed circuit boards, no hard disk, no fan, slower processor speeds and less

overall components which result in an overall lower environmental impact from manufacturing.

The retail prices were used for the two machines to calculate the economic scaling factor. The

implied assumption is that if a good costs more, it will contain additional materials and embedded

energy. The prices and scaling factor are provided below:

Type Desktop Thin Client

Model Dell Optiplex 745 [Optiplex] HP T5145 [T5145]

Price ($) 482 200

Picture

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Equation 1 - Economic/Environmental Scaling Factor

i. Sample Calculation

Desktop computer assembly requires 35.5 MJ of direct fossil energy and 51.2kWh of electricity

[Williams 2004]. Using the economic scaling factor, the impact of the thin client’s assembly

process can be projected:

Full series of calculations found in Table 2.

3.4 Power Usage Calculation

The inventory values for the desktop are several years old (2004), so an adjustment was required

to account for the increased power efficiency of a newer machine. The electricity used in the use

phase was calculated with the specs for the machines in this analysis.

Type Desktop Thin Client Server

Model Dell Optiplex 745 HP T5145 2DLW Serv N20

Energy Demand – Active (watts) 123 11.4 865 (full utilization)

Energy Demand – Sleep (watts) 3 1

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Instantaneous Power Demand for Thin Clients and Desktops

0

20

40

60

80

100

120

140

Thin Client + Server Allocation Desktop

wat

ts

Server Allocation

Figure 2

Equation 2-Thin Client Power (Use Phase- 1 Year)

Equation 3 - Desktop Power (Use Phase - 1 Year)

Because the server powers multiple thin-clients simultaneously, an allocation was used for an

accurate comparison (Figure 2).

3.5 Transportation/Logistics Scaling Factor

There are considerable environmental gains that can be achieved with respect to transportation

logistics.


Model Dell Optiplex 745 HP T5145

Dimensions (inches) 4.5x15.7x13.9 7x7x2

Volume 982 98

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Packaging Surface Area 702.86 154

Mass (lbs) 23 3

The size of the thin-client is considerably smaller than the standard desktop used in this analysis.

In addition, the mass of a thin-client is only 3lbs compared to the desktop that weighs 23lbs.

More units can be packed into the same container volume and shipped from OEMs to distribution

outlets and consumers.

Equation 4 - Transportation Scaling Factor

For every desktop that is shipped, it is estimated that 10 thin-clients can fit in the same volume of

a container or package. To ensure that mass would not be an additional constraint while

shipping, the weights of each shipping box are compared:

Mass of 10 thin-clients Mass of 1 Desktop

3lbs*10=30lbs 23lb

The mass is not significantly more for the grouped package, so the likely shipping constraint is

the volume of the container.

3.6 Packaging Scaling Factor

The smaller packages that return transportation gains also require less packaging for shipping. To

calculate the packaging scaling factor, the surface area of material that is required to package the

container was used.


Model Dell Optiplex 745 HP T5145

Packaging Surface Area 703 inches 154 inches

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3.7

3.8 For each box shipped, it is estimated that 4.5 times less packaging will be

required due to the considerable size differential.

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3.9 Impact and Energy Requirements

Table 1

Color Data Source Scaling Factor

Data from Williams 2004 n/a

Calculated from Economic Scaling Factor 2.41

Calculated from Transport Scaling Factor 10

Calculated from Packaging Scaling Factor 4.56

Calculated from Manufacturer Power Specs n/a

Table 2

Desktop Thin Client Desktop Thin Client

Production

Direct Fossil

Use (MJ)

Direct Fossil

Use (MJ) Electricity (kWh)

Electricity

(kWh)

Production Analysis

Semi Conductors 298.00 123.65 170.00 70.54

Printed Circuit Boards 26.70 11.08 7.71 3.20

Bulk Materials: Control Unit

Silicon Wafers 38.10 15.81

Computer Assembly 35.30 14.65 51.20 21.24

IO Analysis

Electronic Chemicals 381.00 158.09 18.50 7.68

Semi Conductor Manufacturing

Equipment 392.00 162.66 29.40 12.20

Passive Components 109.00 45.23 10.30 4.27

Disk Drives and Parts 365.00 0.00 23.00 0.00

Transport 338.00 33.80 3.50 0.35

Packaging 120.00 26.32 4.80 1.05

Other Processes 973.00 403.73 61.00 25.31

Total Production 3038.00 979.20 417.51 161.66

Use Phase 275.88 111.36

Total Production + Use Phase 693.39 273.01

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The energy inventories from Table 2 are used to estimate the global warming potential (GWP) of

desktop and thin-client use and manufacturing.

Table 3 – Coefficients provided by PE International GaBi4.3

Type Coefficient Unit

Electricity Generation (US Average) 0.804 Kg/kWh

Fossil Fuel Use 0.223 Kg/MJ

Greenhouse Gas Equivalent for Production and Use Phases for Computers

0

200

400

600

800

1000

1200

1400

1600

1800

Desktop Thin Client/Server

kg C

O2e

qu

ival

ent

Server ProductionAllocation

Use Electricity

Production Electricity

Direct Fossil use

Figure 3 – Use phase is estimated to be three years.

Total Impact for Small Business with 100 Machines

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

kg C

O2

Eq

uiv

.

Desktop

Thin-Client

Figure 4

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7. Economic Evaluation of Thin-Clients

The following scenario assumes that a business purchases the server for the internal computation

and storage power to run a small company.

Type Desktop Thin Client Server

Model Dell Optiplex 745 HP T5145 DLW Serv N20

Price ($) 482 200 4000

The server can handle the computational power of approximately 20 machines performing

common business tasks and applications (Linux Terminal Service Project 2009). The cost of the

server is allocated at a ratio of 1/20.

Equation 5

Life Cycle Cost of Desktop and Thin Client/Server Allocation

$-

$100

$200

$300

$400

$500

$600

$700

Thin Client + ServerAllocation

Desktop

Co

st (

$) Electricity Cost

Unit Cost

Figure 5 - Cost for three years of operation

A rate of 10% was used to discount the electrical cost cash flows on an annual basis. If a

company purchased the equipment internally (server + thin-clients) the cost savings would be

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significant over the three years. For a typical small business with 100 machines, the savings

returned would be approximately $20,000 over three years.

Disposal

The economic costs of disposal were not quantitatively evaluated in this analysis. Because the

thin-clients are smaller and have less components, disposal costs will likely be lower, and could

be estimated using our economic scaling factor.

Computing as a Service:

If the computational power and data storage services were provided by an external agent even

greater efficiency gains would be obtained through increased optimization and economies of scale

for cooling. In addition, a small company would need less internal information technology staff

to run their systems if the servers were offsite.

The utilization rate for the server’s computational power would increase because the cloud

service company can take advantage of national economies of scale. The peak computational

demand required by businesses would be more evenly distributed because of the time zones in the

country. A three year time span was used for this analysis, but there are no technical constraints

limiting the thin-client to such a short-lifespan. It is feasible that the hardware could last much

longer (5+ years) because they are not as performance-dependent as a desktop machine. The

servers can be upgraded centrally rather than the individual desktop machines, but with the thin-

client setup, users still receive the same unit of computing at their desks.

If the internet can reliably and quickly route business machine computational power, the market

for traditional desktop machines could be affected. Companies that provide desktop business

machines (Dell) would need to shift their business models. A greater reliance on high-

performance centralized servers will likely drive the market for computing in coming years.

Innovation in computational service contracts will emerge and small companies will have less

risk investing in IT equipment. Cloud providers could provide the thin-clients for free or a

monthly rental rate similar to the approach currently utilized by cable companies. Because the

thin-clients do not need to be upgraded as frequently because computation occurs offsite, cloud

providers could re-use thin-clients further decreasing the environmental burdens.

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8. Economic and Environmental Evaluation

This case study represents the coveted win-win outcome of strategic supply chain management.

The switch from traditional desktops to thin-clients returns both environmental and economic

savings. The strong linear correlation between economic and environmental benefits from thin-

client implementation verifies the pollution prevention benefits of dematerialization through

decreased resource use.

Economic and Environmental Evaluation of Thin-Clients Compared to Desktops

31%

60%

0%

20%

40%

60%

80%

100%

Economic Savings Environmental Savings (GWP)

Figure 6

In an alternative study, the cost savings with 175 thin-clients returned an economic savings of

€660 or 28% (IUSE 2009). The IUSE study included disposal costs that were omitted from this

study.

Type 100 Client Cloud Simulation IUSE Study(175 client)

Scope 3 years 5 years

GWP (kg) per machine 1679 1220

GWP (kg) per year 560 244

There are many factors that were outside the scope of this analysis, but it is likely that in most

cases the un-quantified aspects will provide additional environmental and cost savings for the

thin-client option.

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9. Constraints and Challenges

3.10 Reliability and Security

With any reliance on new technology, businesses may be hesitant to fully move to a cloud based

thin-client service. If all computational and data storage is provided live in the cloud, a business

is completely reliant on their internet connection. If for any reason the internet goes down, all the

workstations would be effectively useless and productivity would suffer.

Businesses may also be wary of outsourcing their sensitive data to external third party companies

that provide a cloud service. Privacy issues could hinder deployment of full cloud computing as

the importance of data security can trump potential economic and environmental gains.

3.11 Switching Costs

If computation and data storage is provided as a service, there could be considerable business

costs required to switch service providers. Businesses would essentially be locked into their

service provider and even if another company could offer cheaper rates, a considerable

transaction cost would have to be considered.

3.12 Scope of Analysis

The only indicator used in this analysis is global warming potential. There are other

environmental impacts in computer manufacturing such as human and aquatic toxicity that are

important to consider. As much as 70% of the human toxicity impact occurs during the disposal

stage of the extended supply chain [Byung-Chul et. al 2004]. However, because the switch to

thin-clients (and the cloud) is essentially a dematerialization strategy for pollution prevention, all

of these other environmental impacts associated with component manufacturing will be reduced.

When data storage and computation is provided as a service that is routed through the internet,

additional network components and electricity will be required. This study omits the impacts

associated with an increasing demand requirement for internet infrastructure.

3.13 Uncertainty

The raw values for energy requirements for PC manufacturing published by Williams 2004

already have embedded uncertainty and then they are scaled again in this study to account for

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thin-client manufacturing impacts. There is a considerable error margin with our projected GWP

reduction of %60.

10. Conclusions

The dematerialization strategy for altering the IT supply chain with thin-clients and computing

resources in the cloud can return both environmental and economic savings. The suggestion in

literature [Williams 2004, Byung-Chul et. al 2006] that increased PC reuse rates and

upgradability potential will lead to environmental savings is correct, however no trends in this

area are emerging. The usage of thin-clients and cloud computing may turn out to be the

preferable route to reduce the environmental impacts of computing. The issue of computer

obsolescence may become less of an issue because the thin-client’s performance can increase

with additional computational resources from remote servers. This effectively extends the life of

the equipment used in office computing as computational services in the cloud can always be

upgraded. Computing as a service is an exciting proposition and will potentially grow as internet

bandwidth becomes cheaper, faster, and more reliable. The switch to the cloud also has the

ability to promote innovation and entrepreneurship because small businesses and start-ups will

face much lower capital costs for IT and computing. A large focus with computing revolves

around the energy-efficiency of chips, however the cloud provides a path for dematerialization

that does not rely on re-use and upgradability.

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11. Sources

Choi, Byung-Chul, Hang-Sik Shin, Su-Yol Lee, and Tak Hur. "Life Cycle Assessment of a Personal Computer and its Effective Recycling Rate." Int. J LCA 11 (2006): 122-28.

Computing Heads for the Clouds 16 Nov. 2007. Business Week. 5 June 2009 <http://www.businessweek.com/technology/content/nov2007/tc20071116_379585.htm>.

Linux Terminal Server Project. 2009. LTSP.org

GaBi 4.3 Software. PE International. 2009.

Environmental Comparison of the Relevance of PC and Thin Client Desktop Equipment for the Climate, 2008. Fraunhofer Institute for Environmental, Safety and Energy Technology, UMSICHT.

Williams, Eric. "Energy Intensity of Computer Manufacturing: Hybrid Assessment Combining Process and Economic Input-Output Methods." Environ. Sci. Technol. (2004): 6166-174.

Why Right Scale: Cloud Portability, Right Scale. Accessed June 5th, 2009 <http://www.rightscale.com/products/advantages/cloud-portability.php>

Zhou, Ben. "Cloud Computing." Personal interview. USCB. 03 June 2009.

HP Compaq t5145 Thin Client. Brochure. 5 June 2009 <http://h20195.www2.hp.com/v2/GetPDF.aspx/c01555466.pdf>.

Dell Optiplex 745 Tech Specs. Brochure. 5 June 2009 <http://www.dell.com/downloads/global/products/optix/en/opti_745techspecs.pdf>.

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