Adaptive Virtual Enterprise Process Management

Perspective of Cloud-based Data Storage

Ahm Shamsuzzoha

, Sven Abels

and Petri Helo

Department of Production, University of Vaasa, PO Box 700, Vaasa, Finland

Ascora GmbH, Innovation & Product Development, Birkenallee 43, 27777 Ganderkesee, Germany

Keywords: Virtual Enterprise, Cloud-based Data Storage, Business Processes, Collaborative Network.

Abstract: This paper presents research outcomes on formation and operation management of virtual enterprises (VE).

A VE is considered as a temporary alliance of manufacturing companies with the objective to exploit fast-

changing business opportunities and to meet demands of globalized markets. In the operational phase of the

VE, its collaborative member companies - which are geographically distributed and organizationally

independent, cooperate with each other to execute different essential business processes. Keeping this

business objective in mind, this paper proposes an integration framework of VE, where necessary data

exchange and management among the partner companies are monitored and controlled by the help of cloud-

based data repository system. The cloud-based data storage system which provides the necessary data

storage and retrieval facilities to execute the VE processes is elaborated within this research scope. A case

example is also presented that highlights required interfacing and adaptation of the essential VE business

processes with the help from cloud-based data storage and retrieval system.

1 INTRODUCTION

In order to compete with today’s turbulent business

environment manufacturing companies, especially

small and medium enterprises (SMEs) are looking

forward to be collaborative, where the possibility of

sharing resources and expertise’s are quiet high.

This business requirement motivates companies to

form and operate a Virtual Enterprise (VE), which

enables them to fast and reliable adaptations of

business processes. Collaborative business

environment offers companies to overcome their

limited flexibility, real-time monitoring and control

over the complete supply chain in terms of buffer

level and delivery status, faster partner finding,

advanced forecasting of demand level, etc.,

(Kankaanpää et al. 2010; Carneiro et al., 2010).

VE is the concept where multiple factories form

a virtual factory and are integrated by a holistic ICT

platform, leveraging with required data transfer and

information flow among them. This collaboration

offers interoperability among the partner factories at

a deeper technical level and ensures that the

factories can be technically connected with each

other (Park and Favrel, 1999). The VE works on the

principle of plug and play, where on-the-fly

collaborative environment is established. In the plug

phase of the VE, factories provide information

which is semantically enriched descriptions of

manufacturing capabilities offered by them, which

are exposed as services (Shamsuzzoha and Helo,

2012). On the other hand, in the play phase, factories

are provided with semantically enriched descriptions

of required manufacturing capabilities and process

models as composition of services. The VE

framework provides methodologies to register new

factories in a joint registry and allows manufacturers

to find factories which meet the demands of a given

set of capabilities. This matchmaking process is

performed by comparing the semantic descriptions

and by taking into account various selection criteria

such as price, quality or speed. This approach allows

manufacturers to define a distributed manufacturing

process and to distribute the different steps of the

process to different factories.

In order to establish a successful VE,

collaborative factories need to develop a data storage

and retrieval system that control and monitor the

overall process execution. This data storage and

retrieval system needs to be efficient so that offers

enhanced agility. Today’s widely accepted cloud

services could be an answer for such purpose, where

Shamsuzzoha A., Abels S. and Helo P..

Adaptive Virtual Enterprise Process Management - Perspective of Cloud-based Data Storage .

DOI: 10.5220/0004394900880094

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 88-94

ISBN: 978-989-8565-59-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

factories could reduce their reliance on an internal

IT function and to make sure they get a satisfactory

level of service from cloud providers. Cloud-based

data storage refers as central data storage that allows

storing different types of data (binary, semi-

structured, semantic) and ensures high scalability for

data storage processes. It essentially provides

functionality to store different predefined and not

predefined semi-structured data like company

profiles, service descriptions, relationships,

monitoring data, processes etc., semantic data like

service descriptions and binary data like documents

or images.

2 THEORETICAL FRAMEWORK

Nowadays, growing trends within business supply

chains for the production of complex products in

collaboration with a number of autonomous

organizations. This collaboration can be in the form

of VE that provides functionality which goes

significantly beyond traditional approaches for inter-

organizational workflow management (Grefen et al.,

2009). Such collaboration contributes to added

agility, visibility and effectiveness among partner

organizations by creating and operating an

automated cooperative support for design, set up and

enactment of required business processes within the

VE (Camarinha-Matos et al., 2008; Shamsuzzoha et

al., 2010). The concept of VE is not only to

cooperate within the distributed and heterogeneous

business environment only but to ensure a proper

management of dependencies between activities

within the partners is in place (Kaihara and Fujii,

2008). Within this business environment, there

needs to store and retrieve huge amount of data

which demands for reliable and secure data storage

facility. The advancement of cloud technology can

provide such facility within the reduced budget and

expected security protocol.

The concept of cloud computing refers to the

delivery of both computing and storage capacity to a

heterogeneous community of end users. It resembles

services with a user’s data, software and

computation over a network (Marston et al., 2011).

There are three types of cloud computing are

generally available such as Infrastructure as a

Service (IaaS), Platform as a Service (PaaS) and

Software as a Service (SaaS). In the IaaS, users rent

use of servers according to needs as provided by the

cloud providers, whereas, in the PaaS, users rent use

of servers over which the software systems are

implemented. In the SaaS, the users also rent

different application software and databases

according to their needs. The cloud provider

maintains and manages the infrastructure and

platforms over which the specified applications are

run (Furht, 2010; Rimal et al., 2011; Sakr et al.,

2011).

In a collaborative business environment, cloud

services provide ample opportunity to the business

partners through managing the valuable real time

information exchange (Wang et al., 2009; Grossman

et al., 2009; Narasimhamurthy et al., 2012).

Different cloud services such as cloud computing

and data storage facility provide partners to know

the status update through process monitoring and

risk and event management. It allows partners to

concentrate on the core business aspects without

having to care about the technical management of

the cloud infrastructure and with benefiting from

elastic cloud facilities and flexible pricing models.

It also facilitates to message transfer among

various VE processes such as forecasting and

simulation, adaptation and execution, and designing

a process from a scratch by providing pre-stored

template. Information exchange among the

collaborative partners through the cloud storage

provides various supports to the VE networks such

as discovery of potential new partners, react quickly

to late changes, manage rush orders, real-time buffer

status, improve business interactions with the

customers, etc.

3 VE PROCESS MANAGEMENT

THROUGH CLOUD-BASED

DATA STORAGE

The process management is the prime task to control

and maintain the VE with its target goals. Member

companies within the VE are increasingly interested

in better organized their business processes and

optimizing them. The fundamental concern for the

partner companies is to find a way to deploy new

process management platform that lower costs with

enhanced overall process visibility. Every

collaboration network operates through the effective

implementation of a wide range of processes that

enable its members to perform their roles within the

business. The volume and complexity of these

processes grows exponentially as the business

grows. The objective to process management is to

design and efficiently organize the companies

various processes by promoting effectiveness,

improve management, reduce costs, improve quality

AdaptiveVirtualEnterpriseProcessManagement-PerspectiveofCloud-basedDataStorage

and customer service.

Enabling VE process management over the cloud

is a concept that organizations are embracing and

this service is growing in popularity among different

manufacturing companies that have yet to transition

IT operations over to the cloud (Mvelase et al.,

2011). In the recent years, there has been an

extended interest about cloud computing, which is

on the top of Gartner’s list of the ten most disruptive

technologies of the next years (Gartner, 2008).

Cloud computing offers the new paradigm for the

provision of computing infrastructure that reduce the

costs associated with the management of hardware

and software resources (Sakr et al., 2011). Different

cloud service providers such as Amazon, Google,

Face book and Salesforce.com, IBM, Microsoft and

Sun Microsystems have embrace this type of

infrastructure and make their applications

availability through web browser in various

locations around the world to provide redundancy

and ensure reliability in case of site failures.

Due to the requirement for providing scalable

database services many existing applications are

extended towards the cloud platform. This platform

uses cloud computing as one of the primary sources

for data storage. The data stored within the cloud in

a particular place with a specific name although that

place does not exist in reality. It is just a pseudonym

used to reference virtual space carved out of the

cloud (Wu et al., 2010).

The processes as related with the VE operation

are defined and stored within the cloud storage. All

the relevant information associated with a process

such as process template, process model, process

editor, process design, etc., is stored and retrieved

from the cloud storage as necessary. This

functionality from the cloud storage provides extra

benefits towards managing the processes from the

user side to the data management side. Typical

virtual enterprise interface between the client side

and the cloud storage side can be presented as

displayed in Fig. 1. From Fig. 1, it is seen that level

of VE interfaces starts from the user interface layer

followed by data exchange layer and process

management layer, which all access and exchange

data by using the cloud storage layer.

The user interface layer usually contains

elements such as dashboards, process designers and

management/admin components. The data exchange

layer usually contains components for message

transfer but also for message transformation, e.g.

syntactically mapping XML to JSON formats or

even by semantically mapping one XML standard to

another. Based on this, the process management

layer contains components of the VE for executing

processes and managing the information flow of the

VE. This contains components for monitoring,

forecasting and process adaptation.

Figure 1: Cloud Storage structure within VE environment.

Finally, the cloud storage layer, as displayed in

Fig. 1, may consist of different storage back ends

such as SQL for structured data or Binary back ends

for e.g. images or enterprise specific files. This

aspect will be further discussed in the following

section, introducing the concept of so called “data

buckets”.

4 CLOUD-BASED DATA

REPOSITORY

As mentioned earlier cloud-based data repository

implement as an intrinsic part of the cloud

computing, where data is stored on multiple third-

party servers, rather than traditional networked

database. The architecture of the cloud storage is

built in a way that it allows the usage of different

storage types. The reason is that VEs may need to

store different data in the cloud reaching from binary

information via structured information to semantic

information. Each of those data types is usually

stored in different systems. For example, there are

many traditional hosted or managed service

providers (MSP) offering block or file storage,

usually alongside traditional remote access protocols

or virtual or physical server hosting (Wu et al.,

2010). Additional storage technologies exist to

manage the data types such as structured data (e.g.

offered by the Google Base storage).

Since the Cloud Storage has to store data from

several components, all data will be stored in so

called “data buckets”. A bucket is a data storage

space which is fully isolated from other spaces and

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

may be used by a component to store its data. One

component may use several different data buckets

and may specify their access level (private,

publically writable, publically readable) for sharing

buckets with other components. Each bucket has a

“bucket type”, which specifies the nature of the

storage space, e.g. “binary data storage” or

“semantic data storage”.

The cloud storage is maintained dynamically,

where its storage location can be varied from time to

time. The services as cloud storage offer are

typically cheaper than dedicated physical resources

connected to a personal computer or network. It

provides data security from accidental erasure or

hardware crashes, due to it is duplicated across

multiple machines and even if one machine crashes,

the data is copied on other machines in the cloud.

Generic cloud storage repository follows the

methodology as presented in Fig. 2.

Figure 2: Methodology for a typical cloud-based data

repository.

From Fig. 2, it is noticed that cloud storage

follows the methodology data integration, data

transformation, data security and data management.

The data integration stage offers the ability to

integrate data across and outside of the enterprise, as

well as electronically connect with business partners,

whereas, in the data transformation level, the data as

exchanged between the partners are made

compatible even it was not initially. This data

transformation and data mapping solutions helps

companies to coordinate and streaming the

translation of information between systems. In the

data security stage, cloud-based data repository

offers solutions to handle business data encryption,

tokenization and key management and secure file

transfer needs. At the final stage, based on a scalable

platform data management solutions help companies

to transform master data into accurate, consistent

and relevant information.

A typical architecture for cloud-based data

repository consists of a cloud provider that works as

a master control and several implementation servers

as shown in Fig. 3. Potential customers directly

Figure 3: Typical cloud-based data repository system

architecture.

interact with the third-party cloud providers and

indirectly interact with the implementation services

as displayed in Fig. 3. Customers access to cloud to

store and retrieve required data or information

through the cloud provider instead of storing data or

information to their own hard drives or other local

storage devices. Instead of local device the data is

saved to a remote database which is connected by

Internet between the client computer and the cloud

databases through cloud provider. This approach

advocates collaborative effort to get the data from

any location that has the Internet access.

5 CASE EXAMPLE FOR A VE

NETWORK

The prime task of process management layer is to

control and maintain the activities as needed to

execute the virtual enterprise successfully. This is

done by integrating various business processes

within the VE and is applied over a web-based

platform named as ‘Liferay’ (www.liferay.com).

This platform works as a dashboard user interface,

from where users (broker and partners) can monitor

and manage the entire VE network successfully. It

provides real-time control over the collaborative

network. This communication framework is

proposed within the ADVENTURE European

Commission project (Ref: 285220) (ADVENTURE,

2011), which will be updated eventually. This

framework consists of four layers such as Process

Design, Process Management, Partner Management

and Application Management as depicted in Fig. 4.

Each of the layers of this example VE contains

different sub layers. For instance, Process Design

layer consists of Process Model Template, Process

Designer, Simulation and Optimization sub-layers,

whereas, in the Process Management layer contains

Instance Management and Alerts. The Partner

Data

Integration

Data

Transformation

DataSecurity

Data

Management

AdaptiveVirtualEnterpriseProcessManagement-PerspectiveofCloud-basedDataStorage

Figure 4: VE processes monitoring and management

framework.

Management layer consists of Profile Editor,

Partner Search and Partner Analysis sub-layers. Fig.

5 displays an example screen shot of Partner

Management layer and its associated sub-layers

within the VE network.

Figure 5: VE partner finding and profile management

framework.

The Application Management layer of the VE as

shown in Fig. 6 is responsible for User Management,

Dashboard Configuration, Gateway Configuration

and Alarms Configuration. All the valuable

information as necessary to execute a successful VE

framework is stored within the cloud-based data

storage system, from where; VE users/partners

would be able to retrieve necessary data/information.

The cloud-based data storage for the ADVENTURE

platform is based on highly virtualized, distributed

infrastructure and has the characteristics such as

agility, scalability, elasticity and multi-tenancy.

Figure 6: VE configuration management framework.

6 DISCUSSIONS

AND CONCLUSIONS

Collaborative operational processes are the prime

concerns in today’s business arena, where

manufacturing companies are motivated to share

resources, competences and processes in order to be

benefited mutually (Chen et al., 2010; Lee et al.,

2012). This motivation influences them to form and

operate a VE immediately after identifying any

particular business opportunity. VE achieves its

objective by defining and implementing processes

that are distributed across several organizations. This

means that the processes must be coordinated and

synchronized at a global level, where VE

implementation needs to integrate resources,

organizational models and process models of the

participating companies.

To enable partners to adapt to this new business

environment, extensive communication framework

is needed which provides necessary data exchange

and flexible integration between partners in the

value chain. Data exchange platform is considered

as the foundation for collaborative business network.

The essential data exchange and data storage facility

can be provided through the cloud infrastructure,

where the main goal is for deploying data-intensive

computing application in cloud environments. The

cloud environment provides availability, scalability,

elasticity, performance, multi-tenancy, load and

tenant balancing, fault tolerance, ability to run in a

heterogeneous environment and flexible query

interface (Sakr et al., 2011).

This cloud-based data storage system within the

cloud environment provides necessary support for

all message formats and standards, transfer protocols

and processes as needed to execute a virtual

enterprise. Various hands on knowledge and

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems

experience as achieved by running a VE can be

safely stored and retrieved based on their availability

and implementation perspectives. The execution of a

VE fundamentally depends on the secured database

system which can be run within the cloud

environment. This research contributed towards the

basic integration principle between different VE

processes and with the cloud-based data storage

system. This implementation process is also

highlighted by presenting a case business network,

where different collaborative VE processes are

created and managed by the cloud-based data

repository system.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the co-

funding of the European Commission in NMP

priority of the Seventh RTD Framework Programme

(2007-13) for the ADVENTURE project (ADaptive

Virtual ENterprise ManufacTURing Environment),

Ref. 285220. The authors also acknowledge the

valuable collaboration provided by the project team

during the research work.

REFERENCES

ADVENTURE., 2012. ADaptive Virtual ENterprise

ManufacTURing Environment. FP7-2011-NMP-ICT-

FoF, Ref. 285220, European Commission Project,

Duration 01.09.2011 – 31.08.2014.

Camarinha-Matos, L. M., Afsarmanesh, H. and Ollus, M.,

2008. Ecolead and CNO base concepts. In Methods

and Tools for Collaborative Networked Organizations

(Camarinha-Matos, L.M., Afsarmanesh, H. and Ollus,

M, Eds.), Springer Science+Business Media, LLC.

Carneiro, L., Almeida, R., Azevedo, A. L., Kankaanpää,

T. and Shamsuzzoha, A., 2010. An innovative

framework supporting SME networks for complex

product manufacturing. In Proceedings of 11

IFIP

Working Conference on Virtual Enterprises (PRO-

VE’10), 11-13 October, 2010, Saint-Etienne, France.

Furht, B., 2010. Cloud computing fundamentals. In Furht,

B. and Escalante, A. (Eds.), Handbook of Cloud

Computing, part 1, Springer Science+Business Media,

LLC, pp. 3-19.

Gartner Inc., 2012. Gartner top ten disruptive technologies

for 2008 to 2012. Emerging trends and technologies

roadshow. http://www.nevillehobson.com/2008/05/31

/top-10-disruptive-technologies-according-to-gartner/,

accessed on 31

December 2012.

Grefen, P., Mehandjiev, N., Kouvas, G., Weichhart, G.

and Eshuis, R., 2009. Dynamic business network

process management in instant virtual enterprises.

Journal of Computer in Industry, Vol. 60, No. 2, pp.

86-103.

Grossman, R. L., Gu, Y., Sabala, M. and Zhang, W., 2009.

Compute and storage clouds using wide area high

performance networks. Future Generation Computer

Systems, Vol. 25, No. 2, pp. 179-183.

Kaihara, T. and Fujii, S., 2008. Supply chain management

for virtual enterprises with adaptive multi-agent

mechanism. International Journal of Manufacturing

Technology and Management, Vol. 14, No. 3-4, pp.

299-310.

Kankaanpää, T., Shamsuzzoha, A., Carneiro, L., Almeida,

R., Helo, P., Fornasiero, R., Ferreira, P. S. and Chiodi,

A., 2010. Methodology for non-hierarchical

collaboration networks for complex products

manufacturing. In Proceedings of 16

International

Conference on Concurrent Enterprising, June 21-23,

2010, Lugano, Switzerland.

Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J. and

Ghalsasi, A., 2011. Cloud computing – the business

perspective. Decision Support Systems, Vol. 51, No. 1,

pp. 176-189.

Mvelase, P., Dlodlo, N., Williams, Q. and Adigun, M.,

2011. Virtual enterprise model for enabling cloud

computing for SMMEs. In Proceedings of the

International Conference on Intelligent Semantic Web-

Services and Applications, Article no: 13, New York,

USA.

Narasimhamurthy, S., Muggeridge, M., Waldschmidt, S.,

Checconi, F. and Cucinotta, T., 2012. Data storage in

cloud based real-time environments. In Kyriazis, D.,

Varvarigou, T. and Konstanteli, K.G. (Eds.),

Achieving Real-Time in Distributed Computing: From

Grids to Clouds, IGI Global Publishers, pp. 236-258.

Park, K. H. and Favrel, J., 1999. Virtual enterprise –

information system and networking solution.

Computers & Industrial Engineering, Vol. 37, No. 1-

2, pp. 441–444.

Rimal, B. P., Jukan, A., Katsaros, D. and Goeleven, Y.,

2011. Architectural requirements for cloud computing

systems: an enterprise cloud approach. Journal of Grid

Computing, Vol. 9, No. 1, pp. 3-26.

Sakr, S., Liu, A., Batista, D. M. and Alomari, M., 2011. A

survey of large scale data management approaches in

cloud environments. IEEE Communications Surveys &

Tutorials, Vol. 13, No. 3, pp. 311-336.

Shamsuzzoha, A. and Helo, P., 2012. Virtual enterprise

management to enhance the manufacturing process

collaboration. In Proceedings of 22nd International

Conference on Flexible Automation and Intelligent

Manufacturing (FAIM 2012), June 10

- 13

Helsinki, Finland.

Shamsuzzoha, A., Kankaanpää, T., Helo, P., Carneiro, L.,

Almeida, R. and Fornasiero, R., 2010. Non-

hierarchical collaboration in dynamic Business

Communities. In Collaborative Networks for a

Sustainable World (Proc. 11

IFIP WG 5.5 Working

Conference on Virtual Enterprises, PRO-VE 2010,

Saint-Etienne, France, October 2010), Camarinha-

AdaptiveVirtualEnterpriseProcessManagement-PerspectiveofCloud-basedDataStorage

Matos, L.M., Boucher, X. and Afsarmanesh, H.

Edition, Springer.

Wang, Q., Ren, K. and Lou, W., 2009. Ensuring data

storage security in Cloud Computing. In Proceedings

of the 17

International Workshop on Quality of

Service, Chicago, USA, 13-15 July, pp. 1-9.

Wu, J. Ping, L., Ge, X. and Fu, J., 2010. Cloud storage as

the infrastructure of cloud computing. In Proceedings

of the International Conference on Intelligent

Computing and Cognitive Informatics, pp. 380-383.

Chen, T-Y., Tsaih, D. and Chen, Y-M., 2010. A

knowledge-commercialised business model for

collaborative innovation environments. International

Journal of Computer Integrated Manufacturing, Vol.

23, No. 6, pp. 543-564.

Lee, S. M., Olson, D. L. and Trimi, S., 2012. Co-

innovation: convergenomics, collaboration, and co-

creation for organizational values. Management

Decision, Vol. 50, No. 5, pp.817 – 831.

ICEIS2013-15thInternationalConferenceonEnterpriseInformationSystems