Major Decision Support Systems
Computerized information systems that are used to support decision making process are known as Decision Support Systems (DSS). These are computer-based information systems and their associated subsystems that aim at helping decision makers in an organization execute the decision making process effectively (Lambin et al., 2017). They utilize data, documents, communication technologies and knowledge incorporated with models so as to aid in decision making. Information is represented graphically by the decision support and is might require an expert system or artificial intelligence. The system may be intended for business executive or a group of knowledge workers. A decision support system may be used to collect and gather information such as asset information, comparative data figures and projected figure depending on new assumptions and data acquired (Arnott and Pervan, 2016).
Several decision support systems have been implemented by Granby. An analysis of different supports systems implementation is done below.
- Communication-driven DSS
This kind of decision support system put emphasis on communication, collaboration and decision-making support. Communication driven decision support systems have one of the following characteristics, it enables communication between different groups of people, it enhances information sharing, supports decision making is groups and also help in coordination and collaboration of people. These kind of decision support systems are mostly meant for internal teams and partners. They are mainly used to conduct meetings or to facilitate collaboration of users. A web or client server is used to deploy this decision support system. Examples of such systems include, instant messaging apps, chats, net-meeting systems and online collaboration.
- Data-driven DSS
Data-driven decision support systems target managers, product suppliers or service provider as well as staff. The system is used to query a data warehouse of a database so as to get specific answers for specified uses. These decisions systems are deployed using a mainframe system, client server system or a web system. Examples include, computer oriented database that is incorporated with a query system. These kind of DSS emphasizes on access and manipulation of a time series of the company’s internal data and at time the external data as well. Its functionality is defined by use of query and tools for data retrieval.
- Document-driven DSS
This type of decision support systems is very common. They aim at a broad base group of users. This can be considered a new field in decision support. It is focused on retrieval and management of unstructured documents. The documents involved can take many forms but can be generally categorized into; video, written and oral. Oral documents may include transcribed audio while videos include clips or commercials and written documents can be in form of reports, letters, catalogues, memos or even emails. They use specific keywords or search items to search web pages and documents. Document driven decision support system is set up using client/server systems or web service.
- Knowledge driven DSS
An example on How a Decision-Problem is Supported by a Decision Support System
They are also referred to as knowledge based decision support systems. They cover a wide range of system in the organization and requires commitment from organizational members to set it up. This type of decision support system covers a wide range of the users within an organization and may also involve other people who interact with the organization. knowledge driven decision support systems is meant to help the organization makes product or services decision or advice the management on various issues. Mostly it is set up using the client server system, web or a standalone software installed in personal computers.
- Model-driven DSS
These are very complex decision systems that are used to analyze decision or select one between several options. They are used by the organization’s management and staff members to handle different issues like scheduling and decision analysis. They are deployed using hardware and software installed in personal computers, web or client/server systems. Model-driven decision support system put emphasis on access and manipulation of models. These models include, statistical models, financial and optimization or simulation models. Model-driven DSS combine the use of complex simulation, financial and optimization models to support decision making at Granby. The data used by the system is provided by the users who are in need of decision making.
In Granby, decision support systems are employed by the operation manager and other departments involved in planning so as to help in compilation of information and data and then produce intelligence that is actually actionable. The decision systems can be used to predict the revenue of the Telemarketing company, Granby, which is based in UK. The system will make this projection by using assumptions about the sake of services and products. The decision system will be very helpful in performing the calculation which cannot be manually done due to the many factors that have to be considered. The system therefore integrates all these variables and then produce an outcome and its associated alternatives. These results are based on the company’s history of product sale as well as current data variables.
It is true that decision making is one of the core areas of any organization. The decision making process is made of many steps that are greatly affected by new technologies. Information technology offers two vital systems for the business; a decision support system and an artificial intelligence system (Lu et al., 2018). A combination of these two technologies is used to gather information through online analytical process (OLAP) that facilitate s the process of making decisions. Technology basically simplifies the way business decisions are handled. The major roles played by technology in the business decision making include; data processing capabilities and increasing speed. Information technology helps the business make quick and accurate decisions within the business. This is because it provides a way to simplify massive amounts of data. It also offers data mining techniques for data warehouse to aid in computerizing the decision making process. Huge amounts of data in collaboration with effective data processing capabilities help acquire information that is necessary for decision making. New technologies give great power but it is also important that the decision maker have the correct information. Information technology also supports groups decisions. The management or employee can use a group of decision support systems to make decisions. A group decision support system (GDSS) is one of the DSS systems that combines multiple decision support system in an attempt to make a team decision. It does so by combining groupware, telecommunications and its capabilities. Group decision making is made up of the following stages; brainstorming, issue categorization and analysis, ranking and voting. Technology helps the organization makes decisions based on the intelligence, design, choice and implementation of the choice.
Professional Skills and Ability
Data is key towards the success of a decision support system. This therefore calls for integration of data mining algorithms in the implementation of a decision support systems. The major data mining algorithms applied include, C4.5. C4.5 creates a classifier in the form of a decision tree. For the algorithm to perform this action, it is provided with a set of already classified data. Another algorithm is the k-means which develops k-groups from a set of objects such a way that group members look more similar. It is popularly used to explore datasets. Support vector machines (SVM) is yet another algorithm used for data mining. It classifies data into two classes. It works similarly to C4.5 at higher levels except it does not deploy the use of decision trees. SVM projects data provided into a higher dimension and then figure out the best hyperplane to separate the data into two classes. Finally, Apriori is also used to mine data. This algorithm learns rules for association and is applied to huge databases that contain a large number of transactions. The Granby telemarketing company uses this to analyze financial transactions for the business.
The main goal of artificial intelligence is to create machines that are smart and knowledgeable. This technology involves teaching a program or hardware things rather than programming it to perform these actions. AI can therefore be implemented in industries to support decision making systems. These deployment helps the systems analyze data faster and accurately. This can be attributed to availability of a variety of huge data, cheap data storage facilities and smart computational and processing power. Considering this complexity, machine learning is used to unlock the value of this data in a way that no human can (Mohri et al., 2018). This results to artificial intelligence being able to provide better support for decision making and provide intelligent solutions without the need of human intervention. Artificial intelligence is used to speed up decision making process which can be slow even in the most established organizations. Decision support systems collaborated with artificial intelligence provide the business with an automated means of data analysis. It also performs detailed research on current trends that would be incorporated in solutions for decision making process. Once the system has made specific number of correct suggestions, it is considered perfect and does not need human intervention to make decision any more (Russell and Norvig, 2016).
The Major Algorithms Used for Decision Support Systems
Internet of things is a field of technology that is growing fast. On the other hand, support for business decision have broadened its boundaries and capabilities. Internet of things refers to smart devices that have sensing capabilities and can also collect, share and transfer data through the internet. Microcomputers embedded in objects, things and people that are able to send, process and receive data or instructions, connected through a wireless network is what makes up the internet of things (Wortmann and Flüchter, 2015). Internet of things can be used to support shared decision that entails a big range of decision support tools and analytical tools. Internet of things can be used for identification; radio frequency identification (RIFD) uses electromagnetic fields to automatically identify and track tags that are attached to objects. This helps to monitor the inventory and also prevent loss. IoT also provide tools for monitoring. This includes devices that transmit information using wireless connections like video cameras, motion sensors and temperature or moisture sensors. With internet of things it is also possible to access location services using Global Positioning Satellite commonly referred to as GPS. It is used to find and track people or mobile devices. Finally, it is possible to control distant objects. This make possible telecommunication, teleoperations, tele monitoring and telepresence. All these capabilities incorporated in a decision making system enhance the capability an accuracy of the systems at Granby.
Big data on the other hand has influenced the requirements of executives. There is a lot of data available in various sources. The data can be structure, unstructured or semi structured. Granby can tap information and value from data sets to make strategic, tactical and operational decisions. The business transaction data when focused on will provide insight for the decision support system when it comes to products and demand prediction. Big data can be used to make decision that improve customer engagement as well as their retention. Considering that the customers carry the most weight when it comes to survival of the organization, companies have been using real-time data to provide personalized solution and customer service (Janssen et al., 2017). Big data has also been used to offer custom loyalty programs for customers. Data from all the customers is obtained and it is then analyzed to generate actionable insights that help enhance the customer loyalty and organization’s profitability. Big data has also been attributed to enhanced operation efficiency. Data is collected to automate processes, optimize selling strategies and the overall efficiency of the business. For instance, Granby may collect data from consumers and clients that will help them improve the services they offer. The more data that is collected the easier it becomes to use this data to enhance the operation of the organization. finally, big data increases organization decision making capacity without having to incur extra expenses. Granby company employs big data to minimize errors, optimize resources, improve customer care. This is all achieved by providing the decision support system with information that is fundamental towards its accurate decision making. Big data plays a big role in improving decision support systems as it provides the needed information which in turn contribute towards accurate decisions and solutions being realized (Schrage, 2016).
Conclusion
To summarize, the major decision support systems used by the Granby company include, communication-drive, data-drive, knowledge-based, document-based and model based decision support systems. Additionally, the new technologies can be used to greatly improve the efficiency of the Granby decision support systems. These technologies include artificial intelligence, machine learning, big data and artificial intelligence.
References
Arnott, D. and Pervan, G., 2016. A critical analysis of decision support systems research revisited: the rise of design science. In Enacting Research Methods in Information Systems (pp. 43-103). Palgrave Macmillan, Cham.
Janssen, M., van der Voort, H. and Wahyudi, A., 2017. Factors influencing big data decision-making quality. Journal of Business Research, 70, pp.338-345.
Lambin, P., Zindler, J., Vanneste, B.G., Van De Voorde, L., Eekers, D., Compter, I., Panth, K.M., Peerlings, J., Larue, R.T., Deist, T.M. and Jochems, A., 2017. Decision support systems for personalized and participative radiation oncology. Advanced drug delivery reviews, 109, pp.131-153.
Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018. Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), pp.368-375. Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018. Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), pp.368-375.
Mohri, M., Rostamizadeh, A. and Talwalkar, A., 2018. Foundations of machine learning. MIT press.
Russell, S.J. and Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,.
Schrage, M., 2016. How the big data explosion has changed decision making. Harvard Business Review, 25.
Wortmann, F. and Flüchter, K., 2015. Internet of things. Business & Information Systems Engineering, 57(3), pp.221-224.
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