It is an inventory strategy in the companies employ in order to increase the efficiency and decrease wastes by receiving goods only, as they are need in the process of the production, which results in reducing the inventory costs.
Electronic Commerce (E-Commerce) is a process by which the customers and the businesses involve in buying and selling products or services through electronic medium or internet (Huang & Benyoucef, 2013).
Software-as-a-service (SaaS) is a model of software delivery and licensing for the organizations, which have implemented cloud computing in their system. In this service, software is licensed based on subscription.
It is a process of the organization in order to define the direction or the strategy for the organization including the making decisions on the allocation of resources to pursue the strategy. It can also be applicable in controlling the mechanisms for guiding on implementing the strategy or strategies.
A supply chain system in the organization means the involvement of the organizations, activities, people, resources, information, and other objectives during the process of products or services delivery to the customers (Chen & Fen, 2012).
Decision Support System (DSS) is a computerized information system that is used to support the decision-making in the business or an organization. A DSS enables users sift through, analyze massive reams of data, and lets an organization to compile the information, which can be used to solve raised problems and make the better decisions for those problems (Ernvall et al., 2013).
It refers to the components of software and hardware like storage, servers, virtualization and network software that can be helpful in supporting the requirements in computing of Cloud Computing model. An abstraction layer, which virtualizes the resources, and logically help in presenting the users through API-enabled graphical interface or command lines are also included in the cloud infrastructure.
Tim O’Reilly and Dale Dougherty at the O’Reilly Media Web 2.0 Conference popularized the term in late 2004, though Darcy DiNucci coined it in 1999. It is a current state of online technology that compares the early days of the Web, which was characterized by the greater user collaboration and interactivity with enhanced communication channels and pervasive network connectivity.
Extranet is a private network, which uses public telecommunication system and internet technology to securely-share the part of information of the business or operations that are related with the vendors, suppliers, customers, partners, or other than these businesses (Barnier et al., 2013). It can be viewed as part of the intranet of the company, which is extended to users of outside the company. It can also be described as a “state of mind” in which Internet is perceived as a path to do businesses with different and other companies as well as to sell the products to various customers.
It is a process of examining the varied and large data sets of Big Data in order to uncover the hidden patterns, market trends, unknown correlations, customer preferences and several other useful information that could help the organizations in enhancing the business and making more-informed business decisions.
3: Because understanding the “reserve stock level” in ERP system can be more beneficial in understanding the inventory management of the organization, which is a challenging topic for the data mining. There are many objectives, where the errors in labeling stock levels can leave customers unhappy and employees frustrated (Lebedeva & Poluektova, 2013). The ERP could help in providing real data, which includes stock levels, expiry dates, location and status of the report. It can be helpful on managing the cost by identifying potential problems and inconsistencies related to the stock charges that are not triggering the re-order alerts. It also helps in understanding the supply chain management that includes triggering promotion and purchase on shifting stock to different location or on over stock (Palawatta & Perrera, 2016).
4: With the increase in the business, the IT facility of the Wines was not able to handle the increased amount of data that were being gathered due to the increase in the operational activities of the Wines. The systems that were being used earlier were slow as they had not been maintained properly or not formatted from a long time, which implies to the maintenance of these systems. These drawbacks result in the loss of the employee productivity, which resulted in the loss to the processes of the core business (Gheysari et al., 2012). The processes of the core business of the Wines includes inventory management and order processing, which were degraded because of the increase in the business and updated technology according to the needs of the employees.
There was not sufficient amount of the IT infrastructure for this new adoption, thus it resulted in negative impact to the competitive advantage of the Wine’s industry. The IT capacity in the organization was much less than needed which was not sufficient as per increase in the business. This affected in meeting the demands and needs of the customers. The orders of the customers were not processed in time due to this IT infrastructure.
Server virtualization benefit ‘Liberty Wines’ and the environment in many ways like it helped in reducing the quantity of physical servers from ten quantities to four quantities. Because of the reduced number of physical servers, Liberty Wines’ improved its customer services and inventory management as, this reduction resulted in the applications running faster than earlier through better utilization of the servers (Soomro & Hesson, 2012). Also its reduced the power consumption that the organization was baring due to extra number of servers by making savings in the replacement of the hardware that are needed for the servers.
5: The Financial Crimes Enforcement Network (FinCEN) is the financial intelligence unit and a bureau of the Treasury Department of the United States. Data and IT problems that were limiting the ability of FinCEN to financial crime can be stated as the main aim and objective of FinCEN is to safeguard the financial systems that are being operated in the city or country from the abuses of financial crimes that may harm the finance of any organization. Another objective is to institute the anti-money laundering programs. Last aim can also clear the asked question that is to promote national security through the process of analysis, collection, and the dissemination of intelligence in finance.
Data and IT problems that were limiting the ability of FinCEN in order to fight financial crime can be stated as there has been lack of standardization and validation including the inconsistent quality that were resulting FinCEN to suffer problems while reporting the data by the financial institution. FinCen was getting limited to simple routines and the small datasets, while making attempt to analyze the data that were being reported (Peirce, 2012). This resulted in the inability for the FinCEN to conduct analysis on the data that were saved in massive datasets and lacked in capabilities for the trend prediction and proactive analysis of the data. Numerous offline systems were performing in order to report the data to the agencies. The above circumstances resulted to face difficulties for the FinCEN to detect emerging and new threat earlier in order to aid in disrupting criminal companies.
Analytical capabilities of FinCEN have been upgraded, which includes database and IT infrastructure. This upgrade was made in order to enhance the performance of data analysis and collection of those data and send them to the local law enforcement, federal states and the regulatory authorities. FinCEN analysts have also improved the capabilities of examination and the analytic processes. The Databases were upgraded too, that transferred the data of 11 years from the legacy system of FinCEN to the new and upgraded system of record. This up gradation in IT infrastructure of FinCEN provides ability to receive, store and process the data electronically. A new search tool was introduced by the FinCEN, which was based on the web application, including the intelligence, law enforcement and the regulatory users in order to provide real-time access to the data of 11 years of Bank Secrecy Act. (BSA).
The financial intelligence depends on the effective data analytics in order to identify the patterns and the relationships, which could reveal the potential illicit related activities.
The ability to identify patterns and relationships is critical to national security as; this intelligence has resulted in the increment in ability and speed in manner to detect the terrorist financiers and the money launderers, which could be helpful in disrupting the criminal activities.
References:
Barnier, B. G., Mueller, R. S., Dean, T. T., & Madrid, R. T. (2013). U.S. Patent No. 8,566,398. Washington, DC: U.S. Patent and Trademark Office.
Chen, C. W., & Fan, Y. (2012). Bioethanol supply chain system planning under supply and demand uncertainties. Transportation Research Part E: Logistics and Transportation Review, 48(1), 150-164.
Ernvall, T., El Rouayheb, S., Hollanti, C., & Poor, H. V. (2013). Capacity and security of heterogeneous distributed storage systems. IEEE Journal on Selected Areas in Communications, 31(12), 2701-2709.
Fonté, E. F. (2017). Mobile Payments in the United States: How Disintermediation May Affect Delivery of Payment Functions, Financial Inclusion and Anti-Money Laundering Issues. Wash. JL Tech. & Arts, 8, 419.
Gheysari, H., Rasli, A., Roghanian, P., & Jebur, H. (2012). The role of information technology infrastructure capability (ITIC) in management. International Journal of Fundamental Psychology & Social Sciences, 2(2), 36-40.
Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd..
Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259.
Lebedeva, L. M., & Poluektova, N. R. (2013). Application of system dynamics for assessment of changes of the level of stock after introduction of ERP systems. ???????? ????????? (???????), (3), 321-329.
Palawatta, I., & Perrera, H. S. C. (2016). Inventory Optimization using ERP to reduce final product lead time, Inventory value an inbound logistics cost for MTO, FMCG Company. International Journal of multidisciplinary Studies, 3(2).
Peirce, H. (2012). Economic analysis by federal financial regulators. JL Econ. & Pol’y, 9, 569.
Soomro, T. R., & Hesson, M. (2012). Supporting best practices and standards for information technology Infrastructure Library. Journal of Computer Science, 8(2), 272.
Torgo, L. (2016). Data mining with R: learning with case studies. CRC press.
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