Introduction
The problem of choice in the decision-making process is present in all the spheres of life. The desire to improve the optimality of the choices made has led to the emergence of specialized automated support systems. Their essence is to help the participants of some processes to make the right decisions based on existing conditions and peculiarities. Decision support systems (DSS) enable help to cope with the sorting of various alternatives and choosing the most appropriate and available solutions. Such systems are especially relevant in the business sphere where much depends on this or that choice. In case, for example, an interested person faces with the task of finding and implementing an optimal and sometimes the only correct way to develop a particular company, DSS will be very useful in this work and will allow assessing potential risks. Therefore, the role of such systems in the business sphere is significant, and the enhancement and improvement of DSS can allow various business projects to reach a new level of development and achieve maximum recognition.
Literature Review
According to the review of the literature used, it can be assumed that all the authors hold the same opinion on the effectiveness of DSS and their benefits for the business sector. For example, Sauter notes that such systems allow providing business intelligence and help to improve the quality of analysis in this or that field (27). According to Stoltzfus, business planners can use DSS in order to evaluate the sales process, the order of storage and allocation of resources, and some other spheres. As Pannu remarks, artificial intelligence, which is one of the components of such systems, allows solving any problems when it is necessary to competently assess different potential risks and ways of development (79). All the authors agree on the fact that using DSS can significantly improve certain work in the business sphere. Their application can help to make sure that all the procedures are reconciled and justified from the point of view of relevance in current market conditions.
None of the authors of the articles used revealed any negative consequences of implementing DSS. The methodology used by the researchers differs. For instance, Sauter provides statistical analysis in order to reflect the benefits of implementing support systems and real financial benefits (59). Pannu in her scientific article argues in support of artificial intelligence in the form of tables and formulas, using variables for calculations (81). Stoltzfus’s article is rather recommendatory than a scientific one and also describes the benefits of using DSS, despite the fact that no statistical data are provided. All the three works are relevant and can be used to further study the advantages and impact of automated support systems on business development.
Decision Support in Business
The decision-making process in business is directly connected with the processing and structuring of information on which sales volume, potential profit, etc. depends. Modern decision support systems based on various mathematical methods allow replacing human resources at these stages (Stoltzfus). With the help of DSS, the choice in favor of certain business processes or solutions is made from alternatives among some unstructured and semi-structured tasks, including multi-criteria ones (Sauter 54). Thus, the activity of such systems aims at the fact that a certain interested person in business has specific knowledge and can intuitively operate with particular processes. It is essential to understand that the way of making business decisions is subjective and depends on the degree of person’s preparedness. DSS help to find those solutions that seem to be the best on the basis of some data. Nevertheless, without the help of these systems, this information would be very difficult or impossible to find because of the high complexity of the problem being solved. Therefore, the more advanced the DSS is, the more chances that a certain business project will be able to work successfully and compete in the modern market.
Classification of DSS for Business
Based on the data with which these systems operate in the business sphere, DSS can be divided into operational and strategic (Sauter 85). Operational DSS are designed to immediately respond to changes in the current situation in the management of financial and business processes of any company. Strategic systems aim at analyzing significant amounts of heterogeneous information collected from various sources (Sauter 86). The most important goal of these DSS is to find the most reasonable options for the development of the company’s business. The influence of various factors is taken into account: the conjuncture of target markets for the company, changes in financial and capital markets, changes in legislation, etc.
When it comes to operational DSS in the business sphere, they are a finite set of reports built on the basis of data from the enterprise’s transactional information system (Sauter 88). If this system adequately reflects the main aspects of production and financial activity in real time, the efficiency of work will be higher. DSS of a strategic type presuppose a sufficiently in-depth study of specific data that are specially transformed so that it would be convenient to use them during the decision-making process. An integral part of the DSS of this level is business decision rules. Based on aggregated data, they enable companies’ managers to justify their decisions, use the factors of sustainable growth of their enterprises’ business and reduce risks. According to Sauter, DSS of the second type have recently been actively developed (92). Technologies of this variant are built on the principles of multidimensional representation and data analysis. Using Internet resources in many companies for better understanding the scope of tasks and having a more advanced information base is quite popular.
Artificial Intelligence Technologies in Business
From the point of view of business, artificial intelligence is a broad concept: it includes machine learning, forecasting, and some other aspects. In order to find the most suitable approach for realizing specific business goals, it is necessary to study the types of modern artificial intelligence systems and the opportunities that they have. Due to the contemporary development of technologies and the constant appearance of updates in the field of digital resources, it is not only realistic but even necessary to introduce modern developments into the working environment. As Pannu notes, artificial intelligence is an integral component of most modern decision support systems (83). In this regard, there is a need not only to find the best methods for assessing the effectiveness of these technologies but also to analyze their diversity. Different systems can be used for certain purposes, and sometimes this or that final result of a business idea directly depends on the type of artificial intelligence technology used. The multifunctionality of this method of work is proved in practice, and the possibility of artificial intelligence systems to cope with various useful procedures can greatly simplify the process of decision making and achieve significant results.
Types of Modern Artificial Intelligence
Monitoring the infrastructure of a particular company is one of the opportunities to introduce artificial intelligence into the business sphere. Special digital technologies allow controlling the state of the enterprise and give a chance to prevent potential errors, timely responding to changes (Pannu 80). The possibilities of such systems are widely used in many advanced corporations where the leadership, for example, calculates an optimal development strategy and trust electronic networks to carry out the calculation of possible profits.
Another type of equipment that can improve the performance of a certain company is so-called predictive systems that are designed to analyze some types of work and issue a special verdict on the basis of calculations. This variant will be useful for companies of all sizes as both large and small enterprises need careful planning to avoid bankruptcy. Artificial intelligence shows good results in building predictions through the skill to learn. Unlike traditional approaches to forecasting, predictive analytics can be easily adapted to any possible changes.
Conclusion
Thus, the role of decision support systems and artificial intelligence that is usually included in these systems is significant, and their enhancement can help different business companies to reach a new level of development and be recognized in the modern market. Various types of DSS have different effects on the work process of enterprises and can be used to achieve distinctive goals. The opportunity to use artificial intelligence makes it possible to significantly simplify the whole workflow, create conditions for automation of all the activities, and achieve accuracy in forecasting and calculating potential profit. Achievements in this area can be useful for business development and provide favorable conditions for companies’ management to assess all the perspectives.
Works Cited
Pannu, Avneet. “Artificial Intelligence and Its Application in Different Areas.” Artificial Intelligence, vol. 4, no. 10, 2015, pp. 79-84.
Sauter, Vicki L. Decision Support Systems for Business Intelligence. 2nd ed., John Wiley & Sons, 2014.
Stoltzfus, Jonathan. “Decision Support Systems (DSS) Applications and Uses”. Business. Web.