Research Paper Literature Review 2022

RESEARCH PAPER 0
Efficiency improvement
in decision support
systems

RESEARCH PAPER 1
Literature review
It is identified that artificial intelligence is a common topic for the research
and there are various articles were published in the last five years. The
major significance of this literature is to explore the importance of artificial
intelligence for increasing the effectiveness of the decision support systems.
Aljaaf, et al., (2015) argued that artificial intelligence is an effective
technique that is able to enhance the efficiency of the decision support
systems and provide reliable services to the companies [1]. Using such kind
of technology the companies can reduce complexity from the systems and
enhance business processes appropriately. The AI-based decision support
systems are able to make extensive utilization of artificial intelligence
techniques and help the companies for developing effective decisions in the
workplace. From the previous study, it is found that AI is capable to control
and reduce errors from the systems and help the companies for improving
the business process by providing effective services to the consumers.
According to Barbieri, et al., (2016) utilization of artificial intelligence is the
decision support is the best strategy that can help organizations for
managing complex situations and enhancing the rate of customer
satisfaction [2]. It is true that AI systems are able to work continuously and
reduce the human errors that occurred in the systems and in the decision
support system it can be used because of their larger effectiveness. It is
argued that the use of artificial intelligence is increasing day by day and
many business communities are utilizing AI-based systems because of their
efficiency of enhancing decision support systems and reducing problems.
From recent literature conducted by Boonpeng and, Jeatrakul, (2016) it is
found that there are various components involved in the AI-based decision
support systems, for example, data management, model management,
consumer interface, and decision architecture.

RESEARCH PAPER 2
It is observed that artificial intelligence connects human things with
computer devices which can help the companies for solving problems of the
consumers and taking effective decisions in the context of business. In the
context of the business process, artificial intelligence enhances flexibility
and adaptability that effectively enhance decision support systems.
Castaneda, et al., (2015) supported this argument and provided depth
analysis in the area of decision support systems and artificial intelligence.
As compared with the previous studies the chosen paper provided effective
evidence and also highlighted how AI improve the effectiveness of the
decision support systems [4]. In the context of decision support systems, the
companies can use artificial intelligence techniques because of their
capability to handle complex situations and providing effective solutions to
the companies. Mainly, decision support systems drive faster, smarter
decisions based on numerical data rather than subjective criteria. Such kind
of systems can be managed with the help of artificial intelligence where
companies can develop effective systems in order to manage work
effectively. Using artificial intelligence decision support systems can
suggest numerous questions for troubleshooting the issues and solving
them through computing devices and human things.
Chiang, Enke, Wu and, Wang, (2016) provided their views on artificial
intelligence and identified the impact of AI-based systems on decision
support systems. It is argued that the advanced generation of artificial
intelligence into the decision support system has produced a more
sophisticated, issue-oriented intelligent system and effective decision
support system which can evaluate and understand the large range of
inputs and chose the next best course of action. Utilizing AI-based systems
and machine learning programs, decision support systems can enhance the
performance of the developed systems and help the companies for solving
complex tasks through computing devices and networks. Dilsizian and,
Siegel, (2014) observed that more than 56% of companies are using