Analyzing Technology for Decision Making Essay

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Technology for Decision Making

Information is of key importance in any company. For any organization to succeed, high quality information has to be produced. Decision Support Systems (DSS) are information frameworks that are based on computers. The manner in which they are designed helps administrators to choose one solution to a problem from a variety of solutions. A DSS is an interactive information system that is based on computers that has people, models, procedures, databases, software, devices and telecommunications. These help those involved in the decision making process to solve business problems that are semi-structured or unstructured. Sensitivity analysis is useful for analyzing information. This helps us to settle on the right decision. In this paper, two methods of decision making will be deeply discussed. These are the Sensitivity Analysis and the Decision Support System (DSS). Below is an approach of the research paper.

Thesis Statement

The methodology to use in order to assist in decision making in a business can be technology-based or one that follows a strong analytical approach. The research paper discusses the better system, be it the Decision Support System (DSS) or the Sensitivity Analysis.

The paper is structured such that both procedures are explained autonomously in detail; followed by a comparative analysis of the circumstances under which one would work better. Several uses of the strategies in business will also be summarized. An opinion on the way in which these strategies are of help in an organization has also been provided, followed by the conclusion.

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Decision Support System (DSS)

Essay on Analyzing Technology for Decision Making Assignment

A Decision Support System (DSS) is a flexible, adaptable and interactive information system that uses computer technology. It uses the guidelines of decisions, models and model base together with a database that is comprehensive and specific bits of knowledge from the decision makers. This prompts decisions that can be implemented to solve problems that would otherwise not be solved through science models. Subsequently, a DSS supports the making and effectiveness of complex decisions (TRIPATHI).

Characteristics of DSS

i. Handles a lot of data like searches in databases.

ii. Gathers data, including that which has been stored internally and externally in mainframe networks and computer systems and processes it.

iii. Gives report and presentation flexibility to suit the requirements of the decision maker.

iv. Includes graphical and textual orientation such as tables, charts, trend lines and more.

v. Uses software packages that are advanced to perform comparisons and analysis which are complex and sophisticated.

vi. Provides the decision maker with a lot of flexibility to solve problems that are both simple and complex through the support of optimization and methodologies that are heuristic and fulfilling.

vii. Performs "what if" objective-seeking analysis.

Components of DSS

Schematic View of DSS

DSS applications can be made of the following subsystems (TRIPATHI).

1. Data Management System: A database is the central component of the database management system . This contains important, situation relevant data. It is overseen by software known as the "database management system" (DBMS). An interconnection can be made between the database management system and the firm data warehouse, an archive for data that is important in making corporate decisions.

2. Model Management System: The model subsystem provides a wide range of models to decision makers and helps them in the process of making decisions. The model subsystem management software (MBMS) can be a part of the model subsystem. It coordinates model use in DSS. External data storage can also be linked to this component.

3. Knowledge-Centered Management System: This system can either perform as an independent component or support all additional systems. It increases the decision maker's intelligence through provision of its own. It can be interconnected with the organization knowledge base, the repository of information in the organization.

4. User Interface System: Also referred to as the management of dialog facility, the user interface permits users to get information through interaction with the DSS. Two capabilities are required by the user interface; the action dialect that.

Sensitivity Analysis (SA)

Sensitivity Analysis is the examination of how the variety in a model's output can be allocated to varying variation sources. Subsequently, the concepts of model and uncertainty are firmly connected to sensitivity analysis. Initially, SA was developed to handle uncertainties in the parameters of the model and input variables. The concepts have extended their boundaries to include model conceptual uncertainty. The study has included the expectations, ambiguity and specifications related to models over time. The Bayesian outline is used by arithmeticians to give a natural depiction of uncertainties on models and in variables and restrictions.

Sensitivity analysis is important in decision making. It can be used as an alternative of "What If?" analysis. Sensitivity analysis involves differentiating a model's input constraints within a stated area and evaluating the results. It also looks at methods that can be used to bring about a result from the various changes in the causes. Sensitivity Analysis works to find the level of change needed on the input data in order for the production recorded by linear programming to remain constant. From this, we can tell the level of sensitivity of the data given. If there is a big change in the prime solution for a model, caused by a little modification on the input; then the problem can be said to be less vigorous. However, if the little modification has not much effect on the prime solution, it can be said to be more vigorous. The latter has lower sensitivity to the modifications on input (L & Sawant, 2014).

Models are meant to mimic or estimate processes and systems which have fluctuating complexity and different forms; such as economic, social, physical or environmental. Those experiments that are deemed hard or unachievable can be mimicked through simulation modeling, which has been emphasized quite well in Rosen's ratification of modeling. (Rosen, 1991) talks about Aristotle's classification of connectedness. She states that the world is motivated by efficient and measureable cause whereas the model is motivated by formal connection. The two are connected through "decoding" (from model to world) and "encoding" (from world to model). The inside "model" and inside "world" causativeness is dominant, whereas decoding and encoding have no demand. They are simply used as objects of the art of the model designer. Decoding and encoding are the basis and principles of modeling. Models are designed with anticipation that the decoding process will create awareness for the world. This cannot be achieved unless the doubtfulness in the facts from the model (the object for decoding) is keenly assigned to the uncertainty from encoding (Center, 1999).

Applications for Sensitivity Analysis

Model designers can use SA to find out

i. the similarity of the model with the subject being studied,

ii. the value of the description of the model, iii. major aspects that lead to the output fluctuation

iv. the area in the space of aspects of input which lead to maximum model variation

v. optimum areas within the range of features used in a successive standardization study

vi. Relations between aspects.

Making Use of Decision Support System -- Business Case 1

A system was established for a car maker in the U.S., to which cars exceeding 1 million are returned from rentals or leases annually. The cars belong to the manufacture, and the issue is through which means these cars can be most successfully given out to hundreds of auction places all over the U.S. They are of different mileage, make, choices and damage, among other factors. These factors are among the determinants of the selling price of the cars in every auction. Our main problem was to give out the most possible number of cars to the auction sites. This would help maximize the sales profits. The procedure of coming up with prime approvals entails numerous contemplations, which range from estimation of price for different makes of cars in different places, to reduction in price and bulk effects, to issues of shipping. A million cars in a year equal around 4,000 cars in 1 working day. Therefore, a remarketing department has to make 4,000 resolutions, daily, on the auction site expected to maximize the selling price per car. Moreover, as a result of bulk effects, allocation of cars to the auction sites is greatly interconnected, making it impossible to make these cars consecutively (Michalewicz, Schmidt, Michalewicz, & Chiriac, 2005).

So, the problem can in one word be defined as volume. It is more economical to transport many cars at a go than it is to ship one or a few. Other than shipping expenses, other factors must be considered, such as shipping and auction schedule, the bulk effect, risk and insurance (due to theft or damage while transporting), and devaluation. The bulk effect comes in with increase in the number of alike cars on sale. If many similar cars are sent to one auction site (which may be appropriate if it has the most desirable price) the bulk will bring about less money for each car. Scheduling, too, is a big issue. There is… [END OF PREVIEW] . . . READ MORE

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