IT Infrastructure Management PG Report

This report delves into various aspects of IT infrastructure management, including definitions of key concepts such as Just-in-Time delivery, E-commerce, SaaS, and strategic planning. It also examines the challenges faced by organizations like Liberty Wines and FinCEN in managing data and IT systems, highlighting the importance of data analytics in crime detection and the role of ERP systems in inventory management. The report is well-researched, featuring references from various academic sources, making it a valuable resource for students in the field.

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Contents
Question 1: Definition.....................................................................................................................4
Just in-time delivery.....................................................................................................................4
E-commerce.................................................................................................................................4
SaaS..............................................................................................................................................4
Strategic Planning........................................................................................................................4
Supply Chain systems..................................................................................................................5
DSS..............................................................................................................................................5
Cloud infrastructure.....................................................................................................................5
Web 2...........................................................................................................................................6
Extranet........................................................................................................................................6
Big Data analysis.........................................................................................................................6
Question 2........................................................................................................................................6
Key elements of Data mining with examples..............................................................................6
Question 3........................................................................................................................................7
Understanding the importance of reserve stock level function of ERP for system performance 7
Question 4........................................................................................................................................7
Kind of business risks faced by Liberty Wines............................................................................7
Methods that impact of competitive advantage of Liberty Wines’ IT infrastructure..................7

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Question 5........................................................................................................................................8
Limitation of Data and IT issues in FinCEN’s ability to fight financial crime...........................8
IT upgrades and capabilities required by FinCEN.......................................................................8
Factors on financial intelligence depend......................................................................................8
Ability to recognize patterns and relationship critical to national security..................................8
Financial crimes detected by FinCEN.........................................................................................9
Role of the data analytics in crime detection...............................................................................9
Reference.......................................................................................................................................10

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Question 1: Definition
Just in-time delivery
The concept of Just in Time is an inventory based strategy where organizations adopt it to raise
the efficient and reduce the waste by receiving products only as they are required in the process
of production and in the end it further reduced the cost of inventory. It is important for the
producers to forecast the demand in right manner (Filippini & Forza, 2016).
E-commerce
E-commerce also called as Electronic commerce. It is important to note that it is a kind of a
business model or in other cases it is called as a segment of large business model which make
sure that a company can conduct a business with the help of an electronic network specifically
the internet (Laudon & Traver, 2013). E-commerce functions in all kinds of four important
market segments like business to business, consumer to consumer, business to consumer and
consumer to business.
SaaS
This can be defined as Software as a service since software owned, managed and delivered by
one or sometimes more providers. The company or the software provider provide the software
based on common type of codes along with definitions of data which is consumed in one out of
many models by all customers at any point of time (Janikashvili et al, 2015).
Strategic Planning
It is a form of systemic process that is able to envision for a specific kind of future and further
translates the present vision into detailed discussed objectives and a series of steps to accomplish

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it. On the opposite side there is side term planning along with strategic planning initiated with
desired level end with work going backward to the present status (Cassidy, 2016).
Supply Chain systems
Supply chain management also called as SCM can be defined as an oversight of materials, with
information and finances since it moves towards the process from manufacturer to suppliers to
retailers to wholesalers to customers. Supply chain systems consist of coordination along with
integration of the flows that goes within and among the organization (Azadegan & Jayaram,
2018).
DSS
DSS is also called as decision support system and can be defined as a computerized based
information system used to help decision making in a company or a business. A DSS based
system helps the users to move towards and evaluate huge reams of information as well as data
which can be complied with data that can be utilized to solve the issues and make more informed
decisions (Petry et al, 2014).
Cloud infrastructure
The concept of cloud infrastructure is based on the hardware along with software elements like
storage, servers, network and virtual based software that are required to help the computing
based needs for the cloud computing kind of model. This concept also consists of different layers
that properly virtualize different kind of resources and present logically to the users with the help
of application based interface or application program interfaces (Kumar et al, 2014).

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Web 2
It is also based on the collective term for specific based application of the Internet and the web
also called as World Wide Web and it consist of blogs, videos, and social media web pages like
Facebook that aims on communicative sharing along with collaboration rather them simple kind
of delivery of content (Mamaqi, 2015).
Extranet
The concept of extranet is called as a private network that utilizes the internet based technology
and the telecommunication in public system to save and share parts of information of the
business and also operated with vendors, customers, partners and other kind of businesses. It
further can be viewed as a part of internet of the company that is further extended to users that
outside the company (Rekhter et al, 2016).
Big Data analysis
The concept of big data analytics is based on the process of evaluating large and many kinds of
data sets which are called as big data. In order to completely uncover the hidden form of
patterns with many unknown correlation along with market trends and preferences of customer it
is important help the company make much more informed decisions (Chen et al, 2014).
Question 2
Key elements of Data mining with examples
Following are the key elements of data mining: -
1. Application on statistics and the concept of data mining which is completely against the
complete database. For example, it consist of many BI based software and users of

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business actually relies on the developers to implement the analytical based functions or
models which is against the certain set of data (Garofalakis et al, 2016).
2. Integrating the concept of plug and play based architecture for analysis of varied analysis
functions.
3. The integration is completely seamless with right kind of data tools for mining.
Question 3
Reserve stock level function of ERP for system performance
There is a possibility to work on the inventory records with the outdated and the damaged
inventory that is set for the delayed stock updates and the other inventory related issues. The
inaccuracy is based on handling the storage locations with the inaccurate pricing as well. From
the different ERP standards, the customers need to work on the different versions where the
administered group achievement comes through the base product and the other optional variants.
Question 4
Kind of business risks faced by Liberty Wines
The increased size of the company, IT facility with more data, not able to handle the data. the
system slows down and needed better maintenance. Less productivity of the employees which
affected the core growth. The order processing and the functions related to the inventory
management are also some issues.
Methods that impact of competitive advantage of Liberty Wines’ IT infrastructure
In order to reduce the cost that also improves the level of resilience and brings stability with the
help of backup system along with speeding up the processes of business by ensuring that the

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apps operate in a faster manner and with the bottom line improvement and workforce can further
help in providing better level of customer services with improved level of productivity (Wright,
2014). In addition, the future business development can further help in accommodating in easy
and rapid manner. Every such issue contributes to the level of gaining advantage over
competition by decreasing the cost and improving the level of service and also providing for the
future development.
Question 5
Limitation of Data and IT issues in FinCEN’s ability to fight financial crime
In this case, the data reported by the financial institution were actually suffering from
inconsistent level of quality and also lacks the process of standardization along with validation.
While trying to evaluate the data, FinCEN was restricted to small number of datasets with simple
and easy routines. Here the bureau was unable to conduct proper analysis across massive sets and
lacking the right ability for such kind of proactive level of analysis with prediction of trends
(ASSADI et al, 2015).
IT upgrades and capabilities required by FinCEN
The FinCEN further upgrades it capacity to analyze with right IT based infrastructure along with
database. Upgrading the analytics was also required for better collection and evaluating the data
for multiple sources and provides them further with state, federal and state laws by enforcing
different authorities. With right upgrading the IT based infrastructure and the ability to receive it
electronically with process and also stores all the reports from FinCEN (ASSADI et al, 2015).

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Factors on financial intelligence depend
Financial level intelligence relies on data analytics with effective actions to recognize different
patterns and association that come up with political and illicit level of functions (ASSADI et al,
2015).
Ability to recognize patterns and relationship critical to national security
The level of intelligence has also increased with high speed and ability to detect launderer based
money and terrorists financers and disruption with so many criminal based functions (ASSADI
et al, 2015).
Financial crimes detected by FinCEN
The detection of the money laundering, information sharing and the services for the assistance to
handle the building of investigations and preparation the prosecutions. The drug control activity
support with the domestic and international partners for handling money laundering.
Role of the data analytics in crime detection
For any tracking of the activity, there is a possibility to track the activity of the account of the
customer, with the monitoring of the transactions of the customer with the engagement tools. The
behavioral profiling with the setup of the detection scenarios helps in administering the
predictive analytics.

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Reference
Filippini, R., & Forza, C. (2016). The Impact of the Just-in-Time Approach on Production
System Performance: A Survey of Italian Industry. A Review and Outlook. In A Journey through
Manufacturing and Supply Chain Strategy Research (pp. 19-39). Springer International
Publishing.
Laudon, K. C., & Traver, C. G. (2013). E-commerce. Pearson.
Janikashvili, N., Trad, M., Gautheron, A., Samson, M., Lamarthée, B., Bonnefoy, F., ... &
Gaugler, B. (2015). Human monocyte-derived suppressor cells control graft-versus-host disease
by inducing regulatory forkhead box protein 3–positive CD8+ T lymphocytes. Journal of Allergy
and Clinical Immunology, 135(6), 1614-1624.
Cassidy, A. (2016). A practical guide to information systems strategic planning. CRC press.
Azadegan, A., & Jayaram, J. (2018). Resiliency in Supply Chain Systems: A Triadic Framework
Using Family Resilience Model. In Supply Chain Risk Management (pp. 269-288). Springer,
Singapore.
Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H. J., Mößle, T., ... &
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Kumar, R., Jain, K., Maharwal, H., Jain, N., & Dadhich, A. (2014). Apache cloudstack: Open
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Mamaqi, X. (2015). The efficiency of different ways of informal learning on firm performance:
A comparison between, classroom, web 2 and workplace training. Computers in Human
Behavior, 51, 812-820.
Rekhter, Y., Rosen, E., Aggarwal, R., Cai, Y., & Morin, T. (2016). Extranet Multicast in BGP/IP
MPLS VPNs (No. RFC 7900).
Chen, M., Mao, S., Zhang, Y., & Leung, V. C. (2014). Big data analysis. In Big Data (pp. 51-
58). Springer International Publishing.
Garofalakis, M., Gehrke, J., & Rastogi, R. (Eds.). (2016). Data Stream Management: Processing
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Wright, A. (2014). ICT540: Enterprise Architect Blog. Management.
ASSADI, D., ALIJANI, S., & SOGALA, S. (2015). CHAPTER FIVE AN EXPLORATORY
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