Many enterprises have adopted information systems and business modelling tools to manage their operations. For instance, the Enterprise Resource Planning System (ERP) is currently used to address business challenges such as linking strategic objectives of the organization and performance (Serova, 2009, p. 269). The Enterprise Resource Planning consists of unique applications, languages and software which are used to generate diagrams and models.
The resultant diagrams and models are very useful in a number of ways. For example, they depict staff organization and business processes. In addition, they recommend critical modifications that must be implemented to improve business performance (Setlak & Markov, 2010, p.149).
Therefore, many enterprises have adopted computer modelling system and modern information technologies as part of their decision-making processes. This is based on the fact that these tools enable managers to make strategic decisions.
What is more, computer modelling systems and IT allow managers to utilize several tools such as synchronized video animation and object-based programming in their strategic decision-making process (Setlak & Markov, 2010, p. 149). Business organizations also use computer modelling to understand intricate nonlinear business relations (i.e. Describing behaviours of economic agents in different periods and predicting future flows of events).
Thus, computer modelling and IT help managers to derive qualitative and quantitative data from current models. Managers use qualitative data to understand the unique characteristics of a multifaceted system, integrity, sustainability, and development patterns within their organizations and targeted markets (Setlak & Markov, 2010, p. 150).
Previously, business organizations used IT applications to reduce cost of production (i.e. Labour costs). For instance, IT was used to enhance organizational communication, computerize business transactions (i.e. Payments) and automate information recovery and storage.
Thus, IT enabled companies to reduce the number of accountants and clerks hired to carry out these functions. Currently, many organizations are using IT to pursue other strategic objectives, such as improving customer services and enhancing marketing strategies (Hitt, 1999, p. 6; Setlak & Markov, 2010, p. 150).
For example, some organizations use information management systems to screen and improve the production process. These systems can automatically detect and solve problems that occur during the production process.
Business Intelligence (BI) applications are also regularly used by organizations to improve efficiency and augment the decision making process. In other words, Business Intelligence systems enable the management to have a comprehensive picture of the organization.
In addition, BI applications address the following questions: What is the current situation of the organization? What factors contributed to the current situation? What are the potential solutions to the current situation? Thus, BI systems can help managers to find practical solutions to these questions and improve performance (Stasienko, 2010, p. 143).
In addition, Business Intelligence applications are useful tools because they provide crucial and steadfast data required for decision making process. What is more, strategic and operational decisions are easier to make when an organization adopts Business Intelligence systems.
This is due to the fact that such systems allow the manager to access vital data on time. What is more, BI technologies facilitate information sharing among staff, boost collaboration within the organization and augment profit margins. Organizational success is subject to a number of factors. These include: forecasting, budgeting, and planning.
It has been established that planning is the most complex process in any business organization because it entails all components that are being transformed. What is more, business processes require considerable time to develop a business model and prepare budgets. Thus, Business Intelligence technology has a number of applications that can automatically carry out these functions (Stasienko, 2010, p. 145).
Business Intelligence systems also improve business processes especially during critical periods. They also help the organization to reduce strategic and operational risks at the management level. The implementation of BI applications can therefore enable an organization to set up additional automated systems in those areas where the employees’ input is considered significant.
It deserves merit to note that strategic and operational risks are mainly attributed to the human factor. Therefore, eradicating the human factor in the production process can dramatically reduce the operational risks and the resultant costs. It is against this backdrop that many enterprises have implemented Business Intelligence systems to automate the decision making process within the organization (Stasienko, 2010, p. 146).
BI applications also enable an enterprise to gain a competitive edge in the market since managers can use these applications to make accurate and prompt decisions. In addition, some of the solutions stemming from BI systems can also augment the functionality of the Enterprise Resource Planning System (ERP) within the organization.
What is more, Business Intelligence technologies employ various tools that an enterprise can use for data mining. These tools include neural networks, decision tree analysis and regression analysis (Serova, 2009, p. 269).
In conclusion, many business organizations are now using IT and computer applications to accomplish their strategic goals. Therefore, it seems that the successful implementation of computer applications and information systems will define organizational performance and success in the near future.
Hitt, LM. (1999). The Role of Information Technology in Modern Production: Complement or Substitute to other Input? Philadelphia, PA: University of Pennsylvania.
Serova, E. (2009). Modern Business Modeling Approaches and Tools for Management. International Journal of Information Technology & Knowledge, 3, 269-275.
Setlak, G., & Markov, K. (2010). Methods and Instruments of Artificial Intelligence (2nd ed.). Sofia: ITHEA.
Stasienko, J. (2010). Business Intelligence as a Decision Support System. ACM, 141 148.