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Goal-seeking analysis

What is Goal-Seeking analysis? 

    Goal seeking analysis searches what components are needed in order to meet specific goals. For example, say that I am the plant manager for GM. I could develop goals for how many cars I would like to sell. So, let's say that the goal is to earn $2 million in profit alone, with sales coming directly from  the new Chevy Tahoe. Say that there are three new models that retail for $45,000, $65,000 and $100,000.  With the help of data from a variety of spending patterns, trends of SUV sales, just to name a few, the input is compiled into the DSS, Decision support system, and managers are given a figure of how many units are needed to be sold, in order to reach a profit of $2 million. The data that is compiled to assist managers in making their decisions comes from, and can be compiled into data martsdata warehouses and databases. Goal-seeking analysis determines the least amount of units that are needed to be sold in order to earn $2 million in profit. It could state that the more inexpensive models will sell a larger number of units, however, this will take a lot longer than just selling several units of the more expensive models. Also, goal-seeking analysis looks for things other than profit alone. Goal-seeking analysis also looks for how many units will need to be sold in order to make breaking even points, or how many units need to be sold in order to generate enough money to cover overhead. There are variety of ways that this method can be applied. In addition, Goal seeking analysis is just a single element that takes part of a Decision support system.

What is a Decision support system, DSS, and what role does goal-seeking analysis play?
 In short, a Decision support system is a type of information system, that is run by computers, and arranges a wide variety of data that helps managers to make appropriate choices concerning organizational and business related activities. The data is compiled by computers and transformed into charts, scales, in addition to a wide variety of ways that allows random variables and statistics to be useful. Business models are also compiled from all of the given data which can not only be used to make decisions, but can be used to see what potential problems could develop. Some potential information that can be compiled consists of sales figures, customer relations or spending patterns and consumer demographics. Goal-Seeking analysis simply informs managers what, based on the arranged data, requirements needed to be met in order to reach their goals.

A Decision Support System example in Everyday life

     A real life example is Amazon.com. Amazon.com has a huge warehouse of products available online. When consumers make a purchase, like a blu-ray player, the data from the individuals purchase is stored and managers can see how well particular brands are selling. Say a sony blu-ray player was purchased, managers could take the data and compile it and could also pair the player with a new HD television, in order to help the blu-ray player to sell faster. On the other hand, since blu-ray players are so new, and rather expensive in comparision with traditional dvd players, managers could offer discounted blu-ray dvds with every purchase of a player. This was the case when blu-ray players and HD-players first came out. Managers had to determine which of these two brands would triumph, and which machine should be dropped from the companies website. Sales records and data from customer purchases were complied and managers took this information and determined what the best course of action would be. In the long run blu-ray players, in addition to blu-ray dvd's, outsold HD dvd's and HD players, becoming the standard for the ultimate movie experience. Eventually, due to slow sales, HD players and Dvd's were not longer sold through Amazon.com. The data compiled from consumers purchases allowed managers to make the decision on what format would sell better. However, a Decision Support System is not just a matter of determining what goals need to be met in order to reach maximum profitability, it is composed into three different parts, which are as follows:

The 3 Parts that make up DSS,

        1.) Sensitivity analysis: which is  the study of how different variables effect one and other, when change occurs. For example, sticking with the example from Amazon, when dvd players become cheaper due to a new technology such as a blu-ray player, dvd sales are effected. Money that could have been spent, by consumers on regular dvd's, is now directed twoards the sale of blu-ray players. A change in the sales of dvd's, will affect the sale of dvd players. Sales could decrease due to the new technology, or sales could skyrocket due to the falling price on dvd players. Change does not just effect one element, it also effects surrounding products.
     2.) What-If analysis : is used to determine what some of the possible changes could be on a theoretical solution. For example, say that I aim to sell 1,000 blu-ray players, within one month, over Amazon.com. In addition, say that I aim to lower, the prices of the players by 20% to increase the sales and reach my quota. What-if analysis comes into play, but showing what the possible outcomes could be. Blu-ray could become more popular or less popular and sales could remain constant reguardless of the given discount.
     3.) Goal-Seeking analysis: Is the most important portion of DSS, in my opinion, because it compiles all of the given data and determines what inputs are required to reach specific goals. Sensitivity analysis is great and can be used to determine what portions of DSS, effect one and other. However, it does not look at the bottom line. It just demonstrates how portions interact with one and other. In addition, What-If analysis just looks as the possibilites and given scenarios. It attempts to determine how well things could or could not go. These are both great however, they fail to look at the overall picture. In order to reach goals, these specific requirments need to be met. In addition, What-if analysis uses Goal-seeking analysis. It looks at what numbers and goals are required in order to well, just average, and to do poorly.  The whole purpose of the DSS is to compile raw data into useful information that managers can use effectively and apply to organizational and business decisions. 

Sensitivity Analysis:
Decision Support Systems:
Goal-Seeking Analysis
What-if Analysis: