DSS applications development

During the past few decades, many types of decision support systems have been developed to meet the different requirements of decision makers. The following are the common types of DSS.

Model-driven DSSs

Model-driven DSSs are designed to allow a user to access and manipulate model parameters in order to examine the sensitivity of outputs or to conduct a more ad hoc ‘what if’ analysis (Power 2008). Two main characteristics differentiate a model-driven DSS: (a) through an easy to use interface, it can be accessed by non-technical specialists such as managers; (b) it is envisioned that it will be used repeatedly in the same or similar decision situations. The types of quantitative model used in model- driven DSSs include: algebraic and differential equation models; analytical hierarchy processes; decision matrix and decision tree, multi-attribute and multi-criteria models;

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forecasting models; network and optimisation models; and quantitative behavioural models for multi-agent simulations. They should provide decision makers with a simplified and understandable representation of a decision. Generally, large databases are not required in model-driven DSSs (Power 2002).

Data-driven DSSs

The purpose of a data-driven DSS is to enable access and manipulation of internal and external real-time data (Power 2008). This kind of DSS provides a wide range of functionality, ranging from simple query to data manipulation through the use of computerised tools that are tailored to a specific task. It provides decision support that is important for the analysis of large collections of historical data.

Knowledge-driven DSSs

Knowledge-based Decision Support Systems (KBDSS) can be defined as computer information systems that support the making of effective decisions in complex and ill structured problem domains, by assisting with knowledge storage and retrieval, the interpretation of various alternatives, and providing methodological knowledge by using analytical decision models (Klein and Methlie 1995). Knowledge-driven DSSs can suggest actions to managers. These DSSs include human-computer interactions to provide specialised problem-solving expertise. The ‘expertise’ consists of knowledge about a particular domain, understanding of problems within that domain, and ‘skill’ at solving some of these problems (Power 2002).

Managerial productivity is considered to be a function of the time spent retrieving information, generating value added information and finding problems in the intelligence phase, and developing alternative solutions in the design phase of decision making (Raman and Phoon 1990). This can be addressed through the development of an effective KBDSS, because it reduces the time required for this process. Nonetheless, it is necessary to understand that the KBDSS is not designed to make decisions on behalf of users; rather it provides relevant information in an

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efficient and easy-to-access format that allows users to make more informed decisions (Supriadi 2014).

The success of knowledge-based systems usually depends upon their ability to represent the knowledge they possess for a particular subject (Fischer and Kunz 1995). This requires mechanisms that provide information and experiences. It requires construction of an explicit information repository, codification of information, and the selective routing of that codified information to decision makers (Parlby 1998).

Knowledge-based systems may be the solutions to: whether an organisation can obtain the right information for the right people in the right form at the right time (Sauter 2005). The likely advantages of knowledge-based systems include: improve the quickness and quality of responses to events, improved acquisition of resources, and enhanced control of strategic planning. On the other hand, the implementation of knowledge management systems are difficult and also only very few guidance exist (Sauter 2005). This is mainly because of the ambiguity associated with the implementation technique and the fact that knowledge systems are more of processes to follow rather than systems of specific procedures (Oxbrow 1998). Further, the cost of developing knowledge systems is usually high. Organisations may avoid the high cost by leveraging existing technologies and mechanisms for the collection of data and information (Sauter 2005).

Collaborative DSS

Recently, there is an increasing interest in the design of collaborative and intelligent society of agents that are capable of addressing complex problems and vast amounts of information (Adla et al. 2012). The increasing growth of DSSs, tools, and information can be exploited by forming a collective decision-making in which decision makers share the context and make decisions based on the opinions of other members within a global network of brains (Brandas and Didraga 2014).

Group Decision Support Systems (GDSS) is an effort to facilitate an environment for collaborative decisions (Turban et al. 2011). DeSanctis and Gallupe (1987) define GDSS as “an interactive, computer-based system that facilitates unstructured

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problem solving by a set of decision factors working together as a group”. The collaboration in GDSS in which a larger number of stakeholders can efficiently and effectively participate in the decision making process is likely to lead to improved decisions (Brandas and Didraga 2014). GDSS focuses on the use of meeting systems in order to support the generation of ideas and decision making in small group settings (Turban et al. 2011). It aims to remove communication barriers; provides decision modelling and group decision techniques to reduce uncertainty in the group decision process; as well as improving group decision patterns through expert advice (DeSanctis and Gallupe 1987).

The collaboration functionality of GDSS can be enhanced through the advances of databases, artificial intelligence, operational research, and particularly the development of web technologies. They facilitate the introduction of ‘web-based collaborative decision’ (Antunes and Costa 2012). They are commonly known as Web 2.0 and Semantic Web (Web 3.0) which introduces an improved ability to connect and organise the content of information distributed across multiple pages or sites (Zaraté et al. 2015). This includes the application of social networks that can be used for decision-making sharing and consensus or voting process within specific contexts (Brandas and Didraga 2014) In ‘web-based collaborative decision’ several entities (humans and machines) liaise to reach an acceptable decision. The entities are distributed and possibly mobile along networks (Adla et al. 2012). Ensuring a collaboration of the entities requires: removing communication difficulties, and providing techniques for structuring the decision analysis and systematically directing the pattern, timing, or content of the related decisions (Karacapilidis and Papadias 2001).

The advances in these technologies can be exploited in a way that allows decision makers to address the increasingly dynamic and complex process of migrating to the cloud. Particularly, the support required at the intelligence phase of migration decisions. The intelligence phase consists of finding, sharing, and analysing information. Application of web-based collaboration tools and GDSS is to search as well as aid in sharing information among participating group members. They can increase the efficiency of gathering information and its distribution (Turban et al. 2011).

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