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C

Abstract.

GDP Indo SMEs is v themselves.

wisely. They the phenom construct th impact imp modified th The result s Keyword

1. Intr 1.1. Back Small m role for made Indones The am

% from that dat market i the follo SMEs w

1. S t a

*Correspon

Received: 2 Accepted:6 DOI: http:

Print ISSN Copyright@

School of

Th

Cloud Co

1Schoo

t.Small medium e onesia. The amo

very big market The limitation o ey have to think menon why SME

he variables. Aft plementation of i he variables in th shows that cloud ds : Cloud compu

roduction kground medium ente

r GDP in I contribution sia [Kement mount of SM

m total com ta, we know

in Indonesi owing years we can define SMEs creat to reduce th and poverty

nding author. E

27th October 20 6thDecember 20 ://dx.doi.org/1 N: 1978-6956; O

@2014. Publish Business and M

he Asian Journ

omputin A S

ol of Business enterprises (SME ount of SMEs i t in Indonesia. H of capital is one

very carefully how Es are hard to ter that, we use t information techn he model and sim d computing is sui uting, SME, Sys

erprises (SM ndonesia. In n 59,08 % trian Kopera MEs in Indo mpany in Ind w that SME a. And it w s. In genera

e as the follo te many job he level of u y in Indonesi

Email: arief.abdil

14;Revised:18th 014

0.12695/ajtm.2 nline ISSN: 208 ed by Unit Rese Management-Inst

nal of Technol

ng as an A System D

Mocham s and Manage Es) play a big ro in Indonesia is 9 However, despite

of challenges th w to expand thei implement inform the variables to b nology to flow bus mulated it. We co itable to be imple stem Dynamic, I

MEs) play a n 2012, SM

% for GD asi dan UKM onesia is 99

donesia. Fro Es is very will be raised al the role owing : b opportunit

unemploym ia

[email protected]

November 201

014.7.2.2 89-791X.

earch and Know titutTeknologi B

logy Managem

Alternati Dynamic

mad Arief A ement, Institu

ole for GDP in 99,99 % from t e many big contr at SMEs have t ir business. Is it rmation and syst build the model u siness of SMEs ompared the resu emented in SME Information Tech

big MEs DP M].

9,99 om big d in of ties ent

Ho SM the of lim cap car pro

c.id

4;

wledge Bandung.

ment Vol. 7 No

ive IT So c Approa

Abdillah1*

ut Teknologi Indonesia. In 20 total company in tributions that S to face. The limi profitable or not tem technology. W using system dyna s. To propose new ult between own Es.

hnology, Model

2. Have c GDP Indon 3. Have of exp owever, desp MEs gave, S emselves. T f challenges mit of cap

pital wisely refully how ofitable or n

o. 2 (2014): 65-

olution f ach

Bandung, In 012, SMEs ma n Indonesia. Fro SMEs gave, SM it of capital mak t. In this paper w We used observa

amics. And then w alternative tech information tech

contribution and growt esia

contribution port from In

pite many bi SMEs also The limitatio that SMEs ital make y. They ha

to expand not.

-74

for SMEs

ndonesia de contribution 5 om that data, we MEs also have ch ke SMEs spend we want to under ation and second n we simulate it hnology (cloud com hnology and cloud

n for the inc th of econ n for the in ndonesia

ig contribut have challe on of capita

s have to fa SMEs spen ave to thi

their busin

s:

59,08 % for e know that hallenges for their capital rstand about dary data to to know the mputing), we d computing.

crease of nomy in ncreasing

tions that nges for al is one face. The nd their

nk very ness. Is it

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own IT infrastructure.

In some organizations, the investments of IT may exceed 50 % of annual capital investment and it has been suggested that, by 2010, the average IT expenditure will be 5 % of revenue [Graeser, Willcocks, Pisanias, 2010]. That is why few SMEs implement IT infrastructure. So we want to propose alternative information technology that can implement by SMEs but can answer the limitation of capital that SMEs face.

Most of papers discussed about cloud computing side more than SMEs side.

Several researchers that discussed about cloud computing and SMEs are from information technology background like Kourik, 2011; Briscoe and Marinos, 2009;

Weindhardt, 2009. Kourik, 2011 discussed about implementation cloud computing in SMEs from risk assessment and security side.

Briscoe and Marinos, 2009 discussed about cloud computing in community. How cloud computing used in community service.

Although some of researchers have discussed about cloud computing and SMEs like Sharma, et al. 2010.

The originality of this paper is the author wants to propose cloud computing as an alternative IT solution for SMEs especially in Indonesia. Sharma, et al. 2010 already discussed about implementation cloud computing for SMEs in India. They used qualitative and quantitative as their research methodology. In Indonesia, there is no paper that discuss about propose implementation of cloud computing in SMEs. The author used different research methodology and

SMEs’s capital.

1.2. Research objectives

The research objectives of this paper are : 1. To understand why information

technology is needed especially for SMEs

2. To propose alternative technology that suitable for SMEs condition 3. Compare between own information

technology and alternative technology (cloud computing) based on affect for SMEs’s capital

2. Literature Review 2.1. Cloud Computing

Many definitions related to cloud computing.

Basically the concept of cloud computing is access the source of information through internet, anywhere and anytime. Vaquero ,Rodero-Merino, Caceres, and Lindner defined cloud computing could be defined as the integration of virtual resources according to user requirements, flexibly combining resources including hardware, development platforms and various application to create services [Vaquero, Rodero-Merino, Caceres, Lindner, 2009]. U.S. NIST (National Institute of Standards and Technology) defined cloud computing is a model for enabling convenient, on demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.

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Figure 1 In figure connect or custo anywher consider server. B based on The be [Saugatu

• S c a m o

• L b e p

• L i S b e

• C o w a

• I o S S e

• R r r o

1. Cloud Com e 1, we see t tion to use c omer can a re and anyti r about th Because clo n the needs enefits of uck Technol

Short impl computing e as a servic minimal tim of a button Low entry based prici entirely line planned Low risk of interest of t SaaS offer benefit fro extended fu Customizati offer a wide well adapte and medium Integration, offers web Service-orie SaaS solutio existing soft Reduced d resources, c reduce the organization

mputing Co that user jus cloud compu access their ime. They d he spesifica oud comput

of custome the clou logy, 2008], i ementation especially a ce) can be me, often ju

costs, due ing approa ear to usag f obsolescen

the provider rings up-to om new unctionality e

ion, usually e range of d the requi m enterprises

if the S b service ented Archit ons can be i tware enviro demand f cloud servi requiremen n of an

oncept Map t need inter uting. The u sources fro do not have

ation of ting will ad er.

ud comput include :

cycles, clo SaaS (softw

employed st with a cl to the usa ch, costs ge and can

nce, it is in r to keep th o-date. Us releases w easily

cloud servi configuratio irements sm s

SaaS provi –APIs a tectures (So integrated in onments for own

ices drastica nt on the

enterprise net user om e to the dapt

ting oud ware in lick age-

are be the heir sers with ices ons mall der and oA), nto IT ally IT in

Cl org res op [V 20 20 20 foc ch op ma cap pro Th co Wa

terms base

• Focus small from related

• Collab service enterp anytim interne especi and/o oud compu ganizations;

sources and pportunities Vouk, 2008; B

009; Schub 010; Chang 010;Chang,

cus of clo hange from c perational ex any SMEs p pital expen oposed to ch here are thr

mputing pro alters, 2011]

• Infrast divided Resou provid resour

of required on the core and medium the reductio d overhead boration an

es offer ins prise applic me and from

et access i ally benef or distributed uting provid

; saving c d staff-as w for servic Briscoe, Ma ert, Jeffere g, Bacigalu

Wills, De oud compu capital expen xpenditure (

perceive tha nditure). Cl

hange that p ree kind of opose [Chan

:

tructure as d into Com rce Clouds de users acce

rces such as

d personnel e business, e m enterprise on of “tedi nd mobility stant access cation envi

m anywher s available.

ficial for d workforce des added v osts in op well as new

ce-oriented arinos, 2009;

ey, Neideck upo, Wills,

Roure, 201 ting is in nditure (CA (OPEX). No

at IT resou loud comp problem.

services th ng, De Rou

a Service mpute Clou s. Compute

ess to comp s CPUs, hyp

and skill especially e benefit ious” IT y, cloud s to the ironment re where

This is mobile es.

value for peration,

business models

; Haynie, ket-Lutz, Roure, 10]. The

how to APEX) to

owadays, urces as puting is

hat cloud re, Wills, (IaaS) is uds and Clouds utational pervisors

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execution according to user requests (e.g. access rate).

• Software as a Service (SaaS), referred to as Service or Application Clouds, offer implementations of specific business functions and business process that are provided with cloud capabilities. Therefore, they provide applications and/or services using a cloud infrastructure or platform, rather than providing cloud feature themselves.

Cloud computing also have some characterization. Necessity, reliability, usability, and scalability are the characters of cloud computing. Necessty related to utility to satisfy everyday needs of the users.

Reliability related to expectation about the utility when the user requiree it. Usability related to the convenient and easy to use, despite cloud computing is quite complex architecture. Scalability related to capacity that fit with the needs of users and can be expand according to the needs of the customer.

2.2. System Dynamic

The fundamental idea of System Dynamics is that socioeconomic and business systems can be regarded as continous feedback control systems that have self-regulating properties by virtue of the technological and accounting relationships between system variables and the policies that are used to manage system [Sharp, 1977]. System dynamic is used to change or modify the structure of a problem.

It is different with statistical approach.

Statistical approach just focus to adapt the

• Defining problems dynamically, in terms of graphs overtime

• Striving for an endogenous, behavioral view of the significant dynamics of a system, a focus inward on the characteristics of a system that themselves generate or exacerbate the perceived problem

• Thinking of all concepts in the real system as continous quantities interconnected in loops of information feedback and circular causality

• Identifying independent stocks or accumulations (levels) in the system and their inflows and outflows (rates)

• Formulating a behavioral model capable of reproducing, by itself, the dynamic problem of concern. The model is usually a computer simulation model expressed in nonlinear equations, but is occasionally left unquantified as a diagram capturing the stock-and- flow/causal feedback structure of the system

• Deriving understanding and applicable policy insights from resulting model.

• Implementing changes resulting from model-based understandings and insights

3. Model Building 3.1 . Scope of the model

In this section, we do some steps to build the model. Figure 2, explain the steps that we follow to build the model.

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Figure In step business capital i informa informa amount analyzed capital o is affect Because the level we defin some as variable.

that has analyzed step 4,

Figure 3

2. System D 1, we desc s on SMEs.

is influence ation techno ation techno

of server d how the of SMEs. Th ted by level e the functio l of materia ned the leve ssumptions t . In step 3 s been buil d the structu

we design

3. Causal Lo

Dynamic Ste ribed the sy

We focus o ed by imple

ology infras ology that w r that SME

amount of he amount o

of inventor on of server al for produc el in each v to define th 3, we simula

lt. After sim ure of the m

alternative p

op Diagram

eps From Pr ystem of fl on how SM ementation structure. T we observed Es need. W f server aff

of server its ry of mater r is to moni ction. In ste variable. We

e level of ea ate the mo mluated it, model. And policies in

m for Own IT

roblem Sym low MEs of The d is We fect self rial.

itor p2, do ach odel we d in the

mo ne an co the po foc ad cap exp 3.2In thaexp thahav

T Infrastruc

ptomps to I odel. We m ew model a nalyzed the b mpared it w en in step olicies. As w cus on capit vantages of pital exp penditure.

2. Causal Loo this paper, at explain plained the at build the ave to buy se

cture

Improvemen modified som

and simulate behavioral o

with the pr p 6, we im we stated ear

tal and expe f cloud com penditure

ops/ Causal R we built 2 c of each c causal loop eir own IT erver by them

nt [Forrester me variable ed it.In ste of new struc revious mod mplement t rlier, that th ense. Becaus puting is ch to op

Relationship causal loop d condition. F p diagram o

infrastructu mselves.

r, 1994]

es in the ep 5, we cture and

del. And the new his model

e one of hange the perational

diagrams Figure 3 of SMEs ure. They

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Figure 4 Because change expendi affect th need co manpow cost. An is the decrease example so the a But wh data so decrease provider amount

4. Causal Lo e the basic

IT infras ture (OPEX he expense ost of ma wer because nother advan amount of e depend e, if monitor

amount of en the mon o the amo ed. SMEs ju r to decrea of server.

op Diagram of cloud structure a X), the cost of SMEs.

aintenance all costs in ntage of clo f server ca on SME ring system

server will nitoring jus ount of se

ust have to ase the spe

m of Cloud C computing as operatio of server o SMEs do n

and cost ncluded in r

oud comput an inrease s need. F needs big d l be increas t needs a f erver will ask the clo esification a

Computing g is onal only not of rent ting For or data sed.

few be oud and

3..In buof is the theexp infma Figif

Thinc andif imclo exp

3 SFD/ Ma SFD sectio uild SFD. W f SMEs. The fix. And the e value of o e effect to pense als frastructure.

aintenance, gure 5 descr

SMEs build he amount crease. It wi nd increase

fferent mplementatio

oud comput pense of SM

ain Formulatio on, we do We set that s e price of th e amount of order manua the capital so just . So the ex manpower o ribe the stoc d their own I of server i ill affect the

the expen with cl on. We can ting affect MEs.

on some limita sales is only he product o f order is fix ally in order l and expen

affected xpense just

of IT infras ck and flow IT infrastruc n figure 5 e amount o se of SME loud co

see in figur the capital

ations to y income of SMEs x. We set

to know nse. The

by IT cost of structure.

diagram cture.

is fix or of capital

Es. It is omputing

re 6 how and the

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Capital Profit

Expense

Income

Price

Order Inventory Production Sold Product

Production Capacity

Output each machine Amount of Machines

Production Materials

Level Inventory of Raw

Materials Monitoring System

Server Capacity

Cost of Server

Price per Server Output Machines

Server

The amount of server that have to buy

Server

Additional Server

Maintenance Server

Cost of Maintenance Server Server

Cost of Server

Man Hour

Capital Profit

Expense

Income

Price

Order Inventory Production Sold Product

Production Capacity

Output each machine Amount of Machines

Production Materials

Level Inventory of Raw

Materials Monitoring System

Server Capacity Output Machines

Server

The amount of server that have to buy

Server

Additional Server

Cost of Server

The Price Rent Server per month Server

The amount of server that have to

buy Reduction of Server

Figure 5. Stock and Flow Diagram for Own IT Infrastructure Capital is our parameter to differentiate

between owning IT infrastructure and implementation of cloud computing for SMEs.

The equation will be :

Capital = Profit – Expense, where Profit = income * price

Expense (maintenance server) = cost of maintenance server * man hour * amount of servers

Figure 6. Stock and Flow Diagram The Implementation of Cloud Computing

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Jan Feb Mar Apr Mei Jun Jul Agust Sep Okt Nop Des Rp60.000.000

Rp70.000.000 Rp80.000.000 Rp90.000.000

Capital

Jan Feb Mar Apr Mei Jun JulAgustSep OktNopDes (Rp3.000.000)

(Rp2.000.000) (Rp1.000.000) Rp0 Rp1.000.000 Rp2.000.000 Rp3.000.000 m o^-1

Profit (m o^-1) Expense (m o^-1) Incom e (m o^-1)

Jan Feb Mar Apr Mei Jun Jul Agust Sep Okt Nop Des (Rp50.000.000)

Rp0 Rp50.000.000

Capital

(Rp4.000.000) (Rp2.000.000) Rp0 Rp2.000.000 Rp4.000.000 Rp6.000.000 m o^-1

Profit (m o^-1) Expense (m o^-1) Income (m o^-1)

will impact to the cost of IT infrastructure (server). The cost of maintenance server and manpower of IT in figure 5 is fix and could be increased. In figure 6 the cost could be increased and decreased depend on the amount of server. It wil save the expense of SMEs.

4. Experiments

b. Scenario 2, the amount of order is higher than server capacity 2. Condition 2 (SMEs implement cloud

computing)

a. Scenatio 3, the amount of order is lower than server capacity

b. Scenario 4, the amount of order is higher than server capacity

Figure 7. Scenario 1

Figure 8. Scenario 2

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Jan Feb Mar Apr Mei Jun Jul Agust Sep Okt Nop Des Rp100.000.000

Rp105.000.000 Rp110.000.000 Rp115.000.000 Rp120.000.000

Capital

Jan Feb Mar Apr Mei Jun Jul AgustSep O kt Nop Des Rp0

Rp500.000 Rp1.000.000 Rp1.500.000 Rp2.000.000 m o^-1

Incom e Expense Profit

Jan Feb Mar Apr Me i Jun Jul Agust Se p O kt Nop De s R p93.000.000

R p96.000.000 R p99.000.000

Capital

Jan Fe b Ma r Apr Mei Jun Jul AgustSep O k t Nop Des R p0

R p500.000 R p1.000.000 R p1.500.000 R p2.000.000 m o^-1

Incom e Expense Profit

Figure 9. Scenario 3

Figure 10. Scenario 4

In scenario 1 and 2 the modal will increase because the amount of cost by server (maintenance, manpower, and server) is fix. It is different with scenario 3 and 4.

5. Conclusions

From the experiment, we can conclude that : 1. Information technology is very useful

for SMEs to increase their income and

make their business effective and efficient.

2. Cloud computing is one of promising alternative IT solution for SMEs and is useful to be implemented

3. The implementation of cloud computing did not affect capital expenditure of SMEs directly. So compare with build own IT infrastructure, cloud computing is very affordable.

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al., 2010 a).

Chang, V, Wills, G, and De Roure D .(2010).

A Review of Cloud Business Models and Sustainability. IEEE Cloud, the third International Conference on Cloud Computing, 5-10 July, Miami, Florida, USA (Chang, Wills and De Roure, 2010 b).

Chang, V, De Roure D, Wills G, Walters R J.

(2011). Case Studies and Organizational Sustainability Modelling Presented by Cloud Computing Business Framework. International Journal of Web Servies Research, 8 (3).

Haynie, M. (2009). Enterprise cloud services:

Deriving business value from Cloud Computing. Micro Focus, Tech. Rep.

Kourik, J.L. (2011). For Small and Medium Size Enterprises (SME) Deliberating Cloud Computing: A Proposed Approach”. ECC’11 Proceedings of The 5th European Conference on European Computing Conference, 216-221. 2011.

L. M. Vaquero,L. Rodero-Merino,J. Caceres, and M. Lindner. (2009). A break in the clouds: towards a cloud definition,”

ACM SIGCOMM Computer Communication Review, 39(1): 50-55, January.

Lusch, R. F., and S. L. Vargo. (2006).

Conceptual Foundations for a Science of Service. Presentation for the Conference on the Art and Science of Services, May 24, Madrid, Spain.

Marinos, A, G . Biscoe . (2009). Community Cloud Computing. First International Conference on Cloud Computing, 1-4 December, Beijing, China.

Kementrian Koperasi dan Usaha Kecil dan

Anand Kumar, Madhvendra Misra, and Vijayshri Tiwari. (2010). Scope of Cloud Computing for SMEs in India”.

Journal of Computing, 2, May 2010.

Sharp, J .(1977). System Dynamics Applications to Industrial and Other Systems”, Opl. Res Q., Pergamon Press, 28 (3-i): 489 to 504, Great Britain.

Schubert L, Jeffery K and Neidecker-Lutz B . (2010). The Future for Cloud Computing: Opportunities for European Cloud Computing Beyond 2010. Expert Group report, public version 1.0, January.

V. Graeser, L. Willcocks, and N. Pisanias .(1998). Developing the IT Scorecard: A Study of Evaluation Practice and Integrated Performance Measurement, Business Intelligence, London.

Vargo, L. S .(2009). Alternative Logics for Service Science and Service Systems. Service Systems Workshop University of Cambridge, March 20.

Vouk M A .(2008). Cloud Computing – Issues, Research and Implementations.

Journal of Computing and Information Technology-CIT 16 (4): 235–246.

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M. T., Michalk, D. I. W. W., and Stößer, J. (2009). Cloud computing–a classification, business models, and research directions. Business &

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