It is the inventory strategy employed by the organizations for raising the efficiency and decreasing the waste through receiving goods.
It is the selling and buying of services and foods, transmission of data or funds primarily over internet.
It is the software delivery and licensing model on the basis of subscription and is hosted centrally.
It the organization’s process to define the direction or strategy and make decisions over allocating the resources for pursuing the strategy.
It is the system of the resources, information, activities, people and organizations that are included to move the service or product from the supplier to the consumer (Torgo, 2016).
DSS or the Decision Support System is the information system that is computer based. It has been supporting the organizational or business activities in decision making.
This is the virtual infrastructure. It is accessed pr delivered through the network or Internet.
It is the present state of the technology in online compared to the earlier times of the Web. This is characterized by the larger user collaboration and interactivity and more pervasive connectivity of network and developed communication channels.
It is the intranet partially accessed by the authorized external users. This has been enabling the business for exchanging data online in a secured manner.
It is the method to test the large and various data sets like the big data. This is done to disclose the patterns, market trends, unknown correlations, customer preferences and many more (Witten et al., 2016).
The key elements of the data mining are:
The data mining needs the inclusion of the customer and client. For example, currently it has been simple to have any amount of data using information technology without the client’s help.
People have been supplying the incomplete information regarding them. For example this might happen due to the fear of exchange of data during the surveys that are conducted by the system of data mining for their benefit (Witten et al., 2016).
In the data mining the data has been gathered utilizing the techniques of information collection. For example most of them are collected manually and the rest via technology. The determination and understanding of the mining could have complex data structure.
The “Reserve Stock Level” function of the ERP has been critical for the performance of the system for various reasons. They are explained below:
This has been the all-too-common issue as the inventory data has not been accurate. The spoiled or damaged inventory, outdated, wrong labels, delayed updates of stock, lost products and any other issues related to inventory has been happening at everyplace.
At many times the same products are available in spate types or styles. From the standpoint of ERP, they have been the distinct items (Larose, 2014). For the customers, every one of them has been the distinct versions of the similar product.
As the business of Liberty Win has been developing, the IT facility of then have been unable to handle the rise in data volume. The systems came out to be slow and needed more efforts to maintain (Wu et al., 2014). This indicated that the loss in the productivity of the employees. In this way they have been affecting the processes of core business like the inventory management and order processing.
The IT infrastructure has not been enough. They have impacted negatively to the competitive advantage. Thus the lacks of the capacity of IT have been resulting in the loss of the customers. This is orders could not have been processes in due time. The staffs have been unable to access the stock, order and customer information enough to deliver the service level expected there (Shmueli & Lichtendahl Jr, 2017). Moreover the maintenance of the old system was to demanding
The server virtualization has helped in reducing the amount of physical servers from ten to four in number. The applications were found to be running faster with the better utilization. This in turn has lead to more effective customer service and the inventory management (Fan & Bifet, 2013). The decreases in the number of physicals servers have lead to the savings in replacement of hardware and the decrease in the power consumption. The benefits of server virtualization have been manifold.
With the tem virtual servers at place, the organization could change or update the applications pr software without any disrupting to the users. The dynamic balancing of load has been enabling the staffs of IT in shifting the virtual machines to the underutilized servers as they get surpassed the ability of the servers over which they have been hosted (Braha, 2013).
The new applications could be deployed in the controller scenario. Moreover the unforeseen issues like the multiple installing with various libraries are eliminated. As any server gets crashed, the virtual image of server affected could be copied to any other machine.
As Liberty develops the stability and efficiency of the systems, the overheads are also lowered. There have been also in the decreased floor space need to host four instead of the ten physical servers.
The costs of air condition and the power usage have been cut by the sixty percent because Liberty needed lesser physical machines. It has also decreased the carbon footprint of Liberty.
With the more effective elastic system deployment and provisioning, the capacity could be incorporated at the notice of moment (Torgo, 2016). This occurs as soon as the requests come in. This has been without any need to wait out the lengthy process of installation.
The data that are to be reported by the financial institutions to the FinCEN has been suffering from the inconsistent quality. This has been lacking standardization and validation. While the data is tries to analyzed, FinCEN has been restricting to the simple routines and small datasets. The bureau has been unable to conduct the analysis around the huge datasets and has been lacking the capabilities, regarding trend prediction and proactive analysis.
The FinCEN has been updating their capabilities of analytics, databases and IT infrastructure. The updated analytics have been required for better collection and analysis of data from various sources. These have been delivered to the local, state and federal enforcement and the regulatory authorities.
The financial intelligence has been depending on the efficient data analytics for identifying the relationship and patterns that has been revealing the effective illicit activity.
The intelligence has developed the ability and speed to identify the terrorist financiers and money launderers and disrupting the criminal activities.
Through the analysis of the huge amount of data relevant to events such as the customer transactions, FinCEN could establish the basis for the healthier activities. The data analytics over the fraudulent data have been helping to the time in the day that has been more likely to coincide with the fraud (Grossman et al., 2013).
References:
Amatriain, X., & Pujol, J. M. (2015). Data mining methods for recommender systems. In Recommender Systems Handbook (pp. 227-262). Springer US.
Braha, D. (Ed.). (2013). Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.
Fan, W., & Bifet, A. (2013). Mining big data: current status, and forecast to the future. ACM sIGKDD Explorations Newsletter, 14(2), 1-5.
Grossman, R. L., Kamath, C., Kegelmeyer, P., Kumar, V., & Namburu, R. (Eds.). (2013). Data mining for scientific and engineering applications (Vol. 2). Springer Science & Business Media.
Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd..
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
Shmueli, G., & Lichtendahl Jr, K. C. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Torgo, L. (2016). Data mining with R: learning with case studies. CRC press.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
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