Business Intelligence

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Introduction

Business intelligence (BI) includes sets of methodologies, theories, technologies, architecture, and processes, which convert raw data into useful, meaningful and refined pieces of information. On the other hand, electronic business commonly referred to as or an internet business involves ICT applications, which support different business activities. Commerce constitutes the exchange of products and services between businesses, groups and individuals and can be seen as one of the essential activities of any business (Current & Marish 1993, p.18). Electronic commerce focuses on the use of ICT to enable the external activities and relationships of the business with individuals, groups and other businesses. These methods enable companies to link their internal and external data processing systems more efficiently and flexibly, to work more closely with suppliers and partners, and to better satisfy the needs and expectations of their customers. The internet is a public through way. Firms use more private and hence more secure networks for more effective and efficient management of their internal functions (Rodriguez, Daniel, Florian, Fabio 2010, p.32).

Applications of Business Intelligence

Business intelligence can be applied to the following business purposes, in order to drive business value. Measurements include programs, which create a hierarchy of benchmarking and performance metrics that informs business leaders about progress towards business goals (Aneja & Nair 1979, p.76).

Analytics are programs, which create quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery (Verma, Biswal, Biswas 1997, p.39).

Enterprise reporting are programs, which design infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequency includes executive information system as well as data-visualization. In addition, collaboration platforms include programs, which find different areas (both internal and external) to work together through electronic data-interchange and data sharing (Rodriguez, Daniel, Casati & Cappiello 2009, p.118).

Knowledge management makes the company data driven through strategies and practices to identify, present, create, distribute items created, and facilitate adoption of insights and experiences, which are real business knowledge. Knowledge management results in regulatory compliance as well as learning-management (Zadeh & Bellman 1970, p.56).

In order to provide a pro-active approach, such as an ALARM function to alert the end-users. There are many types of alerts, for example, if some business value exceeds the threshold value the color of that amount in the report will turn RED and the business analyst is alerted (Ringuest & Rinks 1987, p.106). Sometimes an alert mail will be sent to the user as well (Power 2007, p.64). This end to end process requires data governance, which should be handled by the expert.  For instance, in a transportation problem generally a single criterion of minimizing the total transportation cost is considered but in certain practical situations two or more objectives are relevant (Luhn, 1958, p.312). For example, the objectives may be minimization of total cost, consumption of certain scarce resources such as energy, total deterioration of goods during transportation (Power 2008, p.2).

The problem can be solved using any of the multi-objectives linear programming techniques, but the computational efforts needed would be prohibitive in many cases. Therefore, the Bi-objective transportation problem, where only objectives are considered as fuzzy, experts apply the fuzzy programming technique with hyperbolic membership function to solve a bi-objective transportation problem as vector minimum problem (‘SaaS BI growth will soar in 2010’, p.1).

The transportation problem (TP) can be formulated as a linear programming problem, where the constraints have a special structure. However, in most real world cases transportation problems can be formulated as multi-objective problems. In certain situations two objective problems are relevant in transportation problems (Labeling 1981, p.112). For example, two linear objectives may be minimization of the cost and minimization of the total deterioration. Aneja and Nair developed a criteria space approach for bicriteria TP [1].

Leberling [5] used a special- type non-linear (hyperbolic) membership function for the vector maximum linear programming problem. He showed that solutions obtained by fuzzy linear programming with this type of non-linear membership function are always efficient (Dhingra & Moskowitz 1991, p.355). Dhingra and Moskowitz [4] defined other types of the nonlinear (exponential, quadratic and logarithmic) membership functions and applied them to an optimal design problem. Verma, Biswal and Biswas [7] used the fuzzy programming technique with some non-linear (hyperbolic and exponential) membership functions to solve a multi-objective transportation problem (Waiel & Abd 2001, p.32).

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