The techniques for decision making are used frequently in project management for problem solving. An assessment of decision tree vs.
grid analysis will reveal the most suitable technique that may be applied for project decisions. During this process, various options are analyzed, and the best is selected. Decision making is a difficult process since all the decisions involve some element of uncertainty. Decision making involves identification of the issues, and reasons why these should be settled. Relevant information is obtained by brainstorming techniques, interviewing techniques, decision trees, grid analysis, and other methods. Finally, the option that has been assessed to be the best is planned to be executed. Decision tree, or grid analysis, is used at several occasions, like during the vendor selection process, definition of scope, preparation of objectives, etc.
Decision trees are basically diagrams of several options that can be considered. This technique facilitates identification of the best alternative, in a particular condition. For example, a business needs to choose between three options, namely develop a gas field A, develop a gas field B, or deposit in bank. Investment in gas field A may yield US$ 200 million or a loss of US$ 50, with a success and loss probability of 50% each. Investment in Project B may generate US$ 300 million or a loss of US$ 20 million, with a success and loss possibility of 60% and 40% respectively. If this amount is deposited in a bank, it will deliver a yield of US$ 150 million in the same period. The likely results are as follows:
Returns in Project A = US$ (0.5 x 200) – (0.5 x 50) = US$ 75 million
Returns in Project B = US$ (0.6 x 300) – (0.4 x 20) = US$ 172 million
Returns on investment in a bank = US$ 150 million
It is revealed that the maximum yield is produced by Project B.
Grid analysis is a valuable method for decision making, and is particularly useful when there are a large number of options, with several issues that should be considered. This process can be applied for all vital results, where there is no evident best choice. Grid analysis is used by inserting the likely choices in columns, and the factors that should be deliberated are inserted in rows, in a table. The factors are allocated weights according to our best judgment. All the relevant choice and factor combinations in the table are marked according to our assessment. Adding the score will indicate the decision priority. In the example grid analysis, a car is required to be purchased. Grid analysis is applied to facilitate the decision. The factors that affect the decision are listed in rows, like appearance, price, luxury, and size. The likely options, like Toyota, Suzuki, Ford, and Honda are inserted in columns. The factors are allocated weights according to their relative importance, like appearance is allocated a weight of 3. The options are quantified into respective totals. For example, marks obtained by Toyota in appearance are 4. Appearance has been allocated a weight of 3. Thus, the net marks obtained by Toyota in appearance, after applying the weight factor, are 4 x 3 = 12. This process is repeated for all the factors, and all the choices. In this example, Honda has a net score of 48, and this option is determined to be the best option.
Comparison Decision Tree vs. Grid Analysis
Both decision tree and grid analysis are important techniques for decision making, and can be used alone, or in combination with other decision making techniques. Decision trees are relatively easy to comprehend, and can be used with little clarification. The main benefit of the decision tree method is that it allocates precise values to the results of various events, thus reducing the uncertainty of complex decisions. Decision trees are valuable in choosing between strategies, or prospects related to investments, with limited resources. Grid analysis is especially useful when the options are large in number, and several aspects need to be considered in the decision making process. Grid analysis concentrates on the larger aspects of decision making process. It allocates numbers to the several options, and the sum of scores assist in choosing the best option.