MITS5502: Research Report on Developing Enterprise Systems
VerifiedAdded on 2022/11/17
|11
|2388
|3
Report
AI Summary
This report reviews the article "Challenges and current developments for sensing, smart and sustainable enterprise systems" by Weichhart et al. (2016), focusing on the S3 (Smart, Sustainable, and Sensing) enterprise framework. It discusses the complexity of enterprise systems, emphasizing the importance of sustainability, smart decision-making, and the integration of technological and human systems. The report explores S3 enterprise languages, architectures, and evolution, highlighting the need for proper models and frameworks for intelligent systems. It also covers education and management model methods, referencing ISO standards and the evolution of enterprise modelling. The conclusion emphasizes the significance of adapting to environmental dynamics and managing enterprise models for reuse and collaboration, aiming to turn the S3 enterprise concept into reality through learning, sensing, and adaptation.
Contribute Materials
Your contribution can guide someone’s learning journey. Share your
documents today.

Running head: DEVELOPING ENTERPRISE SYSTEMS
Developing Enterprise Systems
Name of the Student
Name of the University
Author Note
Developing Enterprise Systems
Name of the Student
Name of the University
Author Note
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.

1DEVELOPING ENTERPRISE SYSTEMS
Table of Contents
Introduction......................................................................................................................................2
Discussion........................................................................................................................................2
Complex system of the S3 (Smart, Sustainable and Sensing) Enterprise....................................2
Enterprise Systems Complexity...................................................................................................3
S3 Enterprise: Languages and Architectures...............................................................................3
Education and management model methods...............................................................................5
S3 Enterprise: Evolution..............................................................................................................6
Conclusion.......................................................................................................................................6
References........................................................................................................................................7
Table of Contents
Introduction......................................................................................................................................2
Discussion........................................................................................................................................2
Complex system of the S3 (Smart, Sustainable and Sensing) Enterprise....................................2
Enterprise Systems Complexity...................................................................................................3
S3 Enterprise: Languages and Architectures...............................................................................3
Education and management model methods...............................................................................5
S3 Enterprise: Evolution..............................................................................................................6
Conclusion.......................................................................................................................................6
References........................................................................................................................................7

2DEVELOPING ENTERPRISE SYSTEMS
Introduction
The purpose of this report is to review the popular article: “Challenges and current
developments for sensing, smart and sustainable enterprise systems", that has been
collaboratively written by G. Weichhart, A. Molina, D. Chen, L. E. Whitman, and F. Vernadat
(Weichhart et al. 2016). The article was published in the year 2016 and established firm policies
for enhancing various companies by making them sustainable in terms of environmental, social
and economic senses. In order for an enterprise to become sustainable, smart systems
(comprising of technological and human systems) must be built within the organizational
framework. This approach, as elaborated within the article will help the organizations or
enterprises to react to the growing changes in a flexible and rapid manner. In addition to that the
article also justifies the increasing importance of smart decisions for dealing with a large amount
of data. Moreover, the article comprises of specific ICT models, modelling techniques and
architectures for dealing with the evolving challenges in accordance with the regulatory
compliances.
Discussion
Complex system of the S3 (Smart, Sustainable and Sensing) Enterprise
For dealing with all the potential challenges associated with the digital enterprise, the
authors of this article have proposed an appropriate framework for developing a smart,
sustainable and sensing enterprise (Chavarria-Barrientos et al. 2017). The concept of
sustainability involves social, economic, ethical and environmental concerns in an organizational
framework. Although sustainability plays a vital role in an enterprise, the implementation phase
is quite complex as it comprises a completely new approach in terms of process definitions,
Introduction
The purpose of this report is to review the popular article: “Challenges and current
developments for sensing, smart and sustainable enterprise systems", that has been
collaboratively written by G. Weichhart, A. Molina, D. Chen, L. E. Whitman, and F. Vernadat
(Weichhart et al. 2016). The article was published in the year 2016 and established firm policies
for enhancing various companies by making them sustainable in terms of environmental, social
and economic senses. In order for an enterprise to become sustainable, smart systems
(comprising of technological and human systems) must be built within the organizational
framework. This approach, as elaborated within the article will help the organizations or
enterprises to react to the growing changes in a flexible and rapid manner. In addition to that the
article also justifies the increasing importance of smart decisions for dealing with a large amount
of data. Moreover, the article comprises of specific ICT models, modelling techniques and
architectures for dealing with the evolving challenges in accordance with the regulatory
compliances.
Discussion
Complex system of the S3 (Smart, Sustainable and Sensing) Enterprise
For dealing with all the potential challenges associated with the digital enterprise, the
authors of this article have proposed an appropriate framework for developing a smart,
sustainable and sensing enterprise (Chavarria-Barrientos et al. 2017). The concept of
sustainability involves social, economic, ethical and environmental concerns in an organizational
framework. Although sustainability plays a vital role in an enterprise, the implementation phase
is quite complex as it comprises a completely new approach in terms of process definitions,

3DEVELOPING ENTERPRISE SYSTEMS
business modelling, strategies and core competencies. Therefore a holistic view point is required
in order to achieve sustainability.
A smart enterprise can be stated as both knowledge driven as well as inter networked.
This is the reasons why they are able to deal with the evolving challenges and adapt to a new
working framework. Moreover, the concepts of smart, sustainable and sensing plays an
extremely vital role while digitalizing the operations, decisions and strategies (Chavarría-
Barrientos et al. 2017). According to the authors, proactive involvement of all the associated
stakeholders is what drives digital strategy.
Enterprise Systems Complexity
Understanding and properly sensing the business environment along with ethical,
environmental and economic dimensions is highly required for the purpose of maintaining a
sustainable organization. Resilience and agility within an organization require certain
organizational capabilities, such as organizational learning, continual improvement in the
organizational processes and sensing the potential opportunities associated with the business
(Miranda et al. 2019). According to the authors, one of the prime focus of the enterprises should
be to follow the customer trends that have been set up by the other competitor organizations.
Proper implementation of AI techniques and tools help in developing a smart and intelligent
enterprise system, thus leveraging the future of computing applications and theories. It is evident
from the thorough learning of the article that complex and adaptive systems of enterprise is the
key factor that constitutes towards understanding, modelling, designing and creating appropriate
and required future models, theories, applications and languages for an S3 enterprise (Guerrini,
de Sousa and Yamanari 2018). Some of the subtopics discussed by the authors involve complex
systems, complex systems of the S3 enterprise and the organizational viewpoints.
business modelling, strategies and core competencies. Therefore a holistic view point is required
in order to achieve sustainability.
A smart enterprise can be stated as both knowledge driven as well as inter networked.
This is the reasons why they are able to deal with the evolving challenges and adapt to a new
working framework. Moreover, the concepts of smart, sustainable and sensing plays an
extremely vital role while digitalizing the operations, decisions and strategies (Chavarría-
Barrientos et al. 2017). According to the authors, proactive involvement of all the associated
stakeholders is what drives digital strategy.
Enterprise Systems Complexity
Understanding and properly sensing the business environment along with ethical,
environmental and economic dimensions is highly required for the purpose of maintaining a
sustainable organization. Resilience and agility within an organization require certain
organizational capabilities, such as organizational learning, continual improvement in the
organizational processes and sensing the potential opportunities associated with the business
(Miranda et al. 2019). According to the authors, one of the prime focus of the enterprises should
be to follow the customer trends that have been set up by the other competitor organizations.
Proper implementation of AI techniques and tools help in developing a smart and intelligent
enterprise system, thus leveraging the future of computing applications and theories. It is evident
from the thorough learning of the article that complex and adaptive systems of enterprise is the
key factor that constitutes towards understanding, modelling, designing and creating appropriate
and required future models, theories, applications and languages for an S3 enterprise (Guerrini,
de Sousa and Yamanari 2018). Some of the subtopics discussed by the authors involve complex
systems, complex systems of the S3 enterprise and the organizational viewpoints.
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.

4DEVELOPING ENTERPRISE SYSTEMS
S3 Enterprise: Languages and Architectures
Proper models and frameworks are extremely important for intelligent systems as well as
agents. This helps them in framing proper decision making policies. Model based operation and
engineering are the prime aspects that are needed by S3 enterprise. However the current
languages and architectures do not have certain aspects that are required to build the S3
enterprise. To begin with, proper inclusion of the service concepts is required for the purpose of
covering almost all of the enterprise aspects (Mauricio-Moreno et al. 2015). After that, a mutual
core ontology is required within a heterogeneous supply network, comprising of suppliers having
expertise in various domains. The basic purpose of the core ontology is to facilitate the data
exchange along with interoperation in between the information systems. Intelligent information
systems play a vital role in framing proper decision by the managers. Lastly, the discussed
systems of intelligent information have the ability to utilise intelligent agents that are
autonomous in nature. A set of executable models is highly required by these models. In this
section of the article, the authors have thoroughly reviewed the modelling languages and
architectures under the five basic architecture viewpoints, namely enterprise viewpoint,
information viewpoint, computational viewpoint, engineering viewpoint and technology
viewpoint.
The authors have clearly discussed about the associated challenges in developing a
mutual core ontology with the purpose of enterprise modelling in accordance with the S3
enterprise. According to the authors, this is a complex project as it comprises of various
knowledge and domain corpus (Hinkelmann et al. 2016). However, for the purpose of dealing
with these complexities, adopting a combination of bottom down and top down approach can be
S3 Enterprise: Languages and Architectures
Proper models and frameworks are extremely important for intelligent systems as well as
agents. This helps them in framing proper decision making policies. Model based operation and
engineering are the prime aspects that are needed by S3 enterprise. However the current
languages and architectures do not have certain aspects that are required to build the S3
enterprise. To begin with, proper inclusion of the service concepts is required for the purpose of
covering almost all of the enterprise aspects (Mauricio-Moreno et al. 2015). After that, a mutual
core ontology is required within a heterogeneous supply network, comprising of suppliers having
expertise in various domains. The basic purpose of the core ontology is to facilitate the data
exchange along with interoperation in between the information systems. Intelligent information
systems play a vital role in framing proper decision by the managers. Lastly, the discussed
systems of intelligent information have the ability to utilise intelligent agents that are
autonomous in nature. A set of executable models is highly required by these models. In this
section of the article, the authors have thoroughly reviewed the modelling languages and
architectures under the five basic architecture viewpoints, namely enterprise viewpoint,
information viewpoint, computational viewpoint, engineering viewpoint and technology
viewpoint.
The authors have clearly discussed about the associated challenges in developing a
mutual core ontology with the purpose of enterprise modelling in accordance with the S3
enterprise. According to the authors, this is a complex project as it comprises of various
knowledge and domain corpus (Hinkelmann et al. 2016). However, for the purpose of dealing
with these complexities, adopting a combination of bottom down and top down approach can be

5DEVELOPING ENTERPRISE SYSTEMS
immensely effective. Moreover, the authors have also jotted down some steps that can be
followed in order to successfully design the ontology. These steps are as follows:
Setting up a proper framework for defining the key dimensions that are needed to be
addressed by the extensions as well as the core ontology.
Choosing an acceptable and satisfactory representation language of the ontology: Easy to use
as well as formal enough for ensuring logical and lexical semantics of the potential terms or
concepts.
Adopting to a participative and evolutionary approach for making the ontology able to be
enriched by various other users within the due time scale.
According to the authors, the operating system of an enterprise for the purpose of
manufacturing an organization does not only involve linking the ERP and the MES (where ERP
stands for Enterprise Resources Planning and MES stands for Manufacturing Execution
Systems) (Moones et al. 2015). Rather, it involves far more than that. Although the combined or
linked system of ERP and MES is one of the prime components of the operating system, the core
EOS involves much more than this.
Education and management model methods
This part of the article discusses the methods and enterprise architectures that were
documented in the ISO standards 19439, 15704 and 19440. This establishes a strong foundation
in terms of enabling the enterprises to document the enterprise reality (Shaytura et al. 2016).
Although the diverse nature of the enterprise models provides a set of viewpoints in terms of
enterprise understanding and communication, there are certain associated challenges that might
obstruct the smoothness of enterprise integration. As a result of this, a potential evolution in the
enterprise modelling system is taking place in order to properly deal with the associated
immensely effective. Moreover, the authors have also jotted down some steps that can be
followed in order to successfully design the ontology. These steps are as follows:
Setting up a proper framework for defining the key dimensions that are needed to be
addressed by the extensions as well as the core ontology.
Choosing an acceptable and satisfactory representation language of the ontology: Easy to use
as well as formal enough for ensuring logical and lexical semantics of the potential terms or
concepts.
Adopting to a participative and evolutionary approach for making the ontology able to be
enriched by various other users within the due time scale.
According to the authors, the operating system of an enterprise for the purpose of
manufacturing an organization does not only involve linking the ERP and the MES (where ERP
stands for Enterprise Resources Planning and MES stands for Manufacturing Execution
Systems) (Moones et al. 2015). Rather, it involves far more than that. Although the combined or
linked system of ERP and MES is one of the prime components of the operating system, the core
EOS involves much more than this.
Education and management model methods
This part of the article discusses the methods and enterprise architectures that were
documented in the ISO standards 19439, 15704 and 19440. This establishes a strong foundation
in terms of enabling the enterprises to document the enterprise reality (Shaytura et al. 2016).
Although the diverse nature of the enterprise models provides a set of viewpoints in terms of
enterprise understanding and communication, there are certain associated challenges that might
obstruct the smoothness of enterprise integration. As a result of this, a potential evolution in the
enterprise modelling system is taking place in order to properly deal with the associated

6DEVELOPING ENTERPRISE SYSTEMS
interoperability issues and enterprise collaboration. This is mainly gained from the process based
approaches.
The approach of collaboration as discussed in this article is contextual in nature. This also
means that changing the nature or scope of the collaboration will in turn change the overall
collaboration mechanics and the associated rules (Qian e al. 2016). The federated approaches of
the enterprise collaboration completely rely on the ontological implementations. This is one of
the prime reasons why more complex ontological mechanics are required to be incorporated
within the federated approaches. In fact, most of the domain ontologies that have been reported
within the literature are generally stated as static sematic and single minded networks. Context
aware ontologies are the prime requirements of enterprise collaboration (Ma et al. 2018). These
ontologies can be successfully used in various contexts without any associated complexities.
Several different perspectives and contexts, having the same core concept can be coped up with
these ontologies. This can be possibly done over the course of time.
S3 Enterprise: Evolution
A subsequent change in the behavioural aspect is evident as positive result. The key
purpose of a sustainable enterprise is to deal with the current complexities by regularly adapting
its behaviour according to the environmental dynamics. Since human agents are the main factors
behind driving an enterprise, following or driving these changes is immensely important
(Aleatrati Khosroshahi, Hauder and Matthes 2016). However, the embodiment aspect of
enterprise management and modelling to a comparatively larger environment, supporting the
evolution is still lacking a firm base. As this kind of environment enables the evolution of
intelligent agents within the organizational and enterprise system, it is vital for the enterprise
enhancement.
interoperability issues and enterprise collaboration. This is mainly gained from the process based
approaches.
The approach of collaboration as discussed in this article is contextual in nature. This also
means that changing the nature or scope of the collaboration will in turn change the overall
collaboration mechanics and the associated rules (Qian e al. 2016). The federated approaches of
the enterprise collaboration completely rely on the ontological implementations. This is one of
the prime reasons why more complex ontological mechanics are required to be incorporated
within the federated approaches. In fact, most of the domain ontologies that have been reported
within the literature are generally stated as static sematic and single minded networks. Context
aware ontologies are the prime requirements of enterprise collaboration (Ma et al. 2018). These
ontologies can be successfully used in various contexts without any associated complexities.
Several different perspectives and contexts, having the same core concept can be coped up with
these ontologies. This can be possibly done over the course of time.
S3 Enterprise: Evolution
A subsequent change in the behavioural aspect is evident as positive result. The key
purpose of a sustainable enterprise is to deal with the current complexities by regularly adapting
its behaviour according to the environmental dynamics. Since human agents are the main factors
behind driving an enterprise, following or driving these changes is immensely important
(Aleatrati Khosroshahi, Hauder and Matthes 2016). However, the embodiment aspect of
enterprise management and modelling to a comparatively larger environment, supporting the
evolution is still lacking a firm base. As this kind of environment enables the evolution of
intelligent agents within the organizational and enterprise system, it is vital for the enterprise
enhancement.
Paraphrase This Document
Need a fresh take? Get an instant paraphrase of this document with our AI Paraphraser

7DEVELOPING ENTERPRISE SYSTEMS
Conclusion
To conclude, the peer reviewed article that has been written by G. Weichhart, A. Molina,
D. Chen, L. E. Whitman, and F. Vernadat establishes a thorough understanding of enterprise
networking and integration. In addition to that, this paper also summarises the discussed
enterprise models that are required for a smart, sustainable and sensing enterprise. Envisioning a
new type of enterprise model is the prime goal of this paper. With the thorough expansion of
enterprise and technological complexities, the concept of smart, sustainable and sensing
enterprise is to be viewed as a complicated system in terms of adapting to it. Furthermore, it is
immensely important to appropriately manage the enterprise models for the purpose of enabling
‘reuse’ in a particular enterprise. In addition to that, it also helps in successfully collaborating
with various other enterprises that are dissimilar in nature along with their models. In order to
turn the concepts of S3 enterprise (smart, sustainable and sensing) into reality, the enterprises
along with their respective models need to learn, sense and adapt to their environment.
Conclusion
To conclude, the peer reviewed article that has been written by G. Weichhart, A. Molina,
D. Chen, L. E. Whitman, and F. Vernadat establishes a thorough understanding of enterprise
networking and integration. In addition to that, this paper also summarises the discussed
enterprise models that are required for a smart, sustainable and sensing enterprise. Envisioning a
new type of enterprise model is the prime goal of this paper. With the thorough expansion of
enterprise and technological complexities, the concept of smart, sustainable and sensing
enterprise is to be viewed as a complicated system in terms of adapting to it. Furthermore, it is
immensely important to appropriately manage the enterprise models for the purpose of enabling
‘reuse’ in a particular enterprise. In addition to that, it also helps in successfully collaborating
with various other enterprises that are dissimilar in nature along with their models. In order to
turn the concepts of S3 enterprise (smart, sustainable and sensing) into reality, the enterprises
along with their respective models need to learn, sense and adapt to their environment.

8DEVELOPING ENTERPRISE SYSTEMS
References
Aleatrati Khosroshahi, P., Hauder, M. and Matthes, F., 2016. Analyzing the evolution and usage
of enterprise architecture management patterns.
Chavarría-Barrientos, D., Camarinha-Matos, L.M. and Molina, A., 2017, September. Achieving
the sensing, smart and sustainable “everything”. In Working Conference on Virtual Enterprises
(pp. 575-588). Springer, Cham.
Chavarria-Barrientos, D., Chen, D., Funes, R., Molina, A. and Vernadat, F., 2017. An Enterprise
Operating System for the Sensing, Smart, and Sustainable Enterprise. IFAC-PapersOnLine,
50(1), pp.13052-13058.
Guerrini, F.M., de Sousa, T.B. and Yamanari, J.S., 2018, September. Sensing, Smart and
Sustainable S^ 3 Enterprises: Principles, Goals and Rules. In Working Conference on Virtual
Enterprises (pp. 147-155). Springer, Cham.
Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., Van der Merwe, A. and Woitsch,
R., 2016. A new paradigm for the continuous alignment of business and IT: Combining
enterprise architecture modelling and enterprise ontology. Computers in Industry, 79, pp.77-86.
Ma, X., Fu, L., West, P. and Fox, P., 2018. Ontology Usability Scale: Context-aware Metrics for
the Effectiveness, Efficiency and Satisfaction of Ontology Uses. Data Science Journal, 17.
Mauricio-Moreno, H., Miranda, J., Chavarría, D., Ramírez-Cadena, M. and Molina, A., 2015.
Design S3-RF (Sustainable x Smart x Sensing-Reference Framework) for the future
manufacturing enterprise. IFAC-PapersOnLine, 48(3), pp.58-63.
References
Aleatrati Khosroshahi, P., Hauder, M. and Matthes, F., 2016. Analyzing the evolution and usage
of enterprise architecture management patterns.
Chavarría-Barrientos, D., Camarinha-Matos, L.M. and Molina, A., 2017, September. Achieving
the sensing, smart and sustainable “everything”. In Working Conference on Virtual Enterprises
(pp. 575-588). Springer, Cham.
Chavarria-Barrientos, D., Chen, D., Funes, R., Molina, A. and Vernadat, F., 2017. An Enterprise
Operating System for the Sensing, Smart, and Sustainable Enterprise. IFAC-PapersOnLine,
50(1), pp.13052-13058.
Guerrini, F.M., de Sousa, T.B. and Yamanari, J.S., 2018, September. Sensing, Smart and
Sustainable S^ 3 Enterprises: Principles, Goals and Rules. In Working Conference on Virtual
Enterprises (pp. 147-155). Springer, Cham.
Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., Van der Merwe, A. and Woitsch,
R., 2016. A new paradigm for the continuous alignment of business and IT: Combining
enterprise architecture modelling and enterprise ontology. Computers in Industry, 79, pp.77-86.
Ma, X., Fu, L., West, P. and Fox, P., 2018. Ontology Usability Scale: Context-aware Metrics for
the Effectiveness, Efficiency and Satisfaction of Ontology Uses. Data Science Journal, 17.
Mauricio-Moreno, H., Miranda, J., Chavarría, D., Ramírez-Cadena, M. and Molina, A., 2015.
Design S3-RF (Sustainable x Smart x Sensing-Reference Framework) for the future
manufacturing enterprise. IFAC-PapersOnLine, 48(3), pp.58-63.

9DEVELOPING ENTERPRISE SYSTEMS
Miranda, J., Pérez-Rodríguez, R., Borja, V., Wright, P.K. and Molina, A., 2019. Sensing, smart
and sustainable product development (S3 product) reference framework. International Journal
of Production Research, 57(14), pp.4391-4412.
Moones, E., Vosgien, T., Kermad, L., El Mhamedi, A. and Figay, N., 2015, May. Plm standards
modelling for enterprise interoperability: A manufacturing case study for erp and mes systems
integration based on isa-95. In International IFIP Working Conference on Enterprise
Interoperability (pp. 157-170). Springer, Berlin, Heidelberg.
Qian, Y., Gong, Y., Yuan, H. and Zhang, J., 2016, June. Extracting enterprises collaborative
network from massive online documents. In 2016 13th International Conference on Service
Systems and Service Management (ICSSSM) (pp. 1-4). IEEE.
Shaytura, S.V., Stepanova, M.G., Shaytura, A.S., Ordov, K.V. and Galkin, N.A., 2016.
Application of information-analytical systems in management. Journal of Theoretical & Applied
Information Technology, 90(2).
Weichhart, G., Molina, A., Chen, D., Whitman, L.E. and Vernadat, F., 2016. Challenges and
current developments for sensing, smart and sustainable enterprise systems. Computers in
Industry, 79, pp.34-46.
Miranda, J., Pérez-Rodríguez, R., Borja, V., Wright, P.K. and Molina, A., 2019. Sensing, smart
and sustainable product development (S3 product) reference framework. International Journal
of Production Research, 57(14), pp.4391-4412.
Moones, E., Vosgien, T., Kermad, L., El Mhamedi, A. and Figay, N., 2015, May. Plm standards
modelling for enterprise interoperability: A manufacturing case study for erp and mes systems
integration based on isa-95. In International IFIP Working Conference on Enterprise
Interoperability (pp. 157-170). Springer, Berlin, Heidelberg.
Qian, Y., Gong, Y., Yuan, H. and Zhang, J., 2016, June. Extracting enterprises collaborative
network from massive online documents. In 2016 13th International Conference on Service
Systems and Service Management (ICSSSM) (pp. 1-4). IEEE.
Shaytura, S.V., Stepanova, M.G., Shaytura, A.S., Ordov, K.V. and Galkin, N.A., 2016.
Application of information-analytical systems in management. Journal of Theoretical & Applied
Information Technology, 90(2).
Weichhart, G., Molina, A., Chen, D., Whitman, L.E. and Vernadat, F., 2016. Challenges and
current developments for sensing, smart and sustainable enterprise systems. Computers in
Industry, 79, pp.34-46.
Secure Best Marks with AI Grader
Need help grading? Try our AI Grader for instant feedback on your assignments.

10DEVELOPING ENTERPRISE SYSTEMS
1 out of 11
Related Documents

Your All-in-One AI-Powered Toolkit for Academic Success.
+13062052269
info@desklib.com
Available 24*7 on WhatsApp / Email
Unlock your academic potential
© 2024 | Zucol Services PVT LTD | All rights reserved.