An Overview Of Decision Support System

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Introduction

To succeed in today’s competitive business era, companies require an information system that helps them with diverse information and better decision making. Decision support systems are computer-based information system providing interactive information and decision-making capabilities to managers and business professional

Since companies are moving towards the e-business models, they are investing ample buck in a decision support system or DSS. The examples of DSS includes manual system, hybrid system, analytical models, specialized databases, an interactive computer-based modelling process, decision-maker own judgement in order to support semi structured business decisions.

Martins, 2019 opined, the core principle of DSS has evolved from a theoretical framework to producing concise executive information. For instance, sales managers typically rely upon a sales information system to generate sales reports. A DSS will show a sales manager the effects of the changes in the sales caused due to varied factors including promotion expense or sales compensation. Aftermath DSS further uses several criteria like expected gross margin or company’s market share to evaluate the alternative ranks and combining sales factors to produce well-interpreted sales reports. Thereby it can be said, a DSS is designed to solve a prompt business problem that can be both initiated and controlled by decision-makers. In this article, we will explore more details of DSS accompanied by several management information systems and discuss how DSS strengthen the role of IT in a business environment.

Components Of DSS

Unlike any other management information system. DSS is based on model bases, databases and vital system resources. A DSS model has software components consisting of the model including computation and analytical techniques mathematically express the relationship between the variables. For instance, a spreadsheet model containing information will simply show the accounting relationship among the variable that is profit, revenue, expenses etc(Tyler et.al, 2019). A DSS model also contains an analytical model that shows a much more complex relationship. For example, it may consists of the linear programming model, forecasting model, capital budgeting modelling, multiple regression etc. As per the assignment maker, such a model can be stored in the spreadsheet or the form of statistical modules.

Chart- Web Enabled Components Of Marketing Decision Support System

In addition to this DSS software typically built-in analytical modelling routines also enables them to develop their model. Since business is aware of the DSS, they are trying to use it in the large amount. And if you want to know how business is using DSS for your assignment, connect plagiarism-free essay help driven by OmanEssay

Decision Support System Usages

A DSS involves an interactive analytical modelling process for instance using DSS managers may come to know about series of display in response to what-if analysis changes. Managers use DSS to solve the queries finding the information that can help them with decision making

In DSS, there are four basic types of analytical activities-

What-if analysis

Sensitive analysis

Goal seeking analysis 

Optimization analysis

What-If Analysis-

Under the what of analysis- the user will make changes in the variables observe resulting changes in relation to the value of a variable. For instance, if a person is using a spreadsheet, he/she can change the revenue amount in a simple financial spreadsheet model. Then he/she would command the algorithm to do calculation based upon changes(Oger, et.al, 20190. This type of analysis will be repeated until managers can the expected results.

Sensitivity Analysis- 

It is a special case of what-if analysis. Typically one variable values will be changed repeatedly and the resulting change on the second variable will be examined. Therefore sensitivity analysis will be followed in that case where repeated changes assuming the maximum value of changes can occur in future. The widest example of this analysis can be explained via at what point a loan will be not feasible in the project. Using this managers can determine the range of acceptable interest under which the project can be further progressed(Yan, 2020). 

Goal Seeking Analysis-

Goal seeking analysis is opposite to sensitivity and what-if analysis. Instead to changes the variable, goal-seeking concerned with setting the target values and making repeatedly changes on the other variable until targets will be achieved, With this a person can negotiate the answer of the question, what happens if we increase expense or reduce, or how can achieve company produce revenue $2 million. For more details, contact OmanEssay online assignment help to get remarkable and error-free analytical reports for your tuition assignments.

Optimization Analysis-

It is a complex extension of goal-seeking analysis. This instead of setting target values, a user find optimal values for one or more target values, thereby one and more variable changes repeatedly. However, changing such variable may constrain to limited capacity. Optimization generally performs with the Solver tool in Microsoft Excel or linear programming to get the best possible values for making profits.

Analytical ModelingActivities and Examples
What if analysisSuggest how change must be selected for one variable influence other variables e.g. What will happen to sales if advertising is cut by 10%?
Sensitivity analysisObserving repeated changes to a single variable influencing other variable for instance advertisement cut by $100 everything what will be its impact on sales
Goal seeking analysisMaking changes frequently to the selected variable until set goal achieved.Assignment writer suggested the example how much advertisement cost would be increased to generate sales of $1 million
Optimization analysisFinding an optimum value for the selected variable under certain constraints. For instance,what would be biggest amount given to advertisement as per our choice of media(Polyakova, 2019).

Conclusion

DSS can support management on various decision-making level including predicted the changes drive-by involvement of end-users. DSS are an interactive computer-based system that can help managers to solve the problem of unstructured decision making, In this article, we had defined how DSS helps the business to generate revenue and analytical techniques a company can use to predict future outcomes. This will include what-if analysis, sensitivity analysis, goal-seeking analysis and optimization analyzation. The major domain of DSS analytical activities used in greater amount is what-if analysis however company uses other techniques to make more advanced decisions.

References

Martins, D., Assis, R., Coelho, R., & Almeida, F. (2019). Decision Support System for Business Ideas Competitions. Journal of Information Systems Engineering and Management, 4(3), em0093.

Oger, R., Lauras, M., Benaben, F., & Montreuil, B. (2019, September). Strategic Supply Chain Planning and Risk Management: Experiment of a Decision Support System Gathering Business Departments Around a Common Vision. In 2019 International Conference on Industrial Engineering and Systems Management (IESM) (pp. 1-6). IEEE.

Polyakova, A., Loginov, M., Strelnikov, E., & Usova, N. (2019). Managerial decision support algorithm based on network analysis and big data. International Journal of Civil Engineering and Technology, 10(2), 291-300.

Tyler, N. S., Mosquera-Lopez, C. M., Wilson, L. M., Dodier, R. H., Branigan, D. L., Gabo, V. B., … & Jacobs, P. G. (2020). An artificial intelligence decision support system for the management of type 1 diabetes. Nature metabolism, 2(7), 612-619.

Yan, M. R. (2018). Improving entrepreneurial knowledge and business innovations by simulation-based strategic decision support system. Knowledge Management Research & Practice, 16(2), 173-182.

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