A Reflection of the Scenario of Ventron Engineering Company
The aviation system of the U.S. army has offered the Ventron Engineering Company a lucrative order worth $ 2,000,000. Completing that order will require the company to choose from two options, which are improving its extrusion process or going for sectioning process. Deciding on the improving extrusion process will involve two steps, each requiring a span of six months. The steps will cost 300,000 dollars and 960,000 dollars respectively to be completed. Alternatively, the sectioning process will cost 1.8 million dollars, and will require twelve months to be completed.
Develop a decision tree to maximize Ventron’s EMV. This includes the revenue from this project, the side benefits (if applicable) from an improved extrusion process, and relevant costs. You don’t need to worry about the time value of money; that is, no discounting or net present values are required. Summarize your findings in words in the spreadsheet.
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Below is the summary of the costs and benefits involved in each scenario:
Summerized Costs and Benefits of the Scenarios | Cost and Benefits | |||||||||||
Case One | when both steps one and two are succesful comleted | $2,740,000 benefit | ||||||||||
Case Two | when step one has not been successfully completed | $100,000 loss | requires completion of sectioning in twelve months | |||||||||
Case Three | when step two has not been sucessfully completed | $1,660,000 loss | requires completion of sectioning in six months | |||||||||
Case Four | sectioning process | $200,000 benefit | no extrusion process is require | |||||||||
A precision tree, also called to a decision tree is a tool that makes use of a tree-like model to support decisions and their possible consequences (Coyle & Oakley, 2007). The consequences may include the chances events outcome, cost of resources, as well as the utility. A precision/decision tree is a way of displaying algorithms that only contain conditional control statements (Boncompte, 2018). Constructing the precision tree for the above given data will follow the procedure outlined below:
Selection of the precision tree tab from the tool bar, followed by selection of decision tree tab. The next step is creation of the node at the cell A17 in which the name ‘Ventron Engineering Company’ is appropriately keyed.
Creation of a decision node from the cell B17, a box of different color is convenient for this. The branches are then renamed as ‘Extrusion process’ and ‘sectioning process.’
Creation of the probability node from cell C20, an octagon of a different color is convenient here. A similar procedure is done to create and name the branches in cell C25 as ‘modifying extrusion successful’, and ‘modifying extrusion unsuccessful’.
The final step is to enter the monetary values and their corresponding probabilities in the already developed precision tree (Coyle & Oakley, 2007). From the information in the table above, 200,000 dollars is a benefit, and is keyed in the cell B16. The probability 0.1 is keyed in the cell C19, and the cost of 100,000 dollars is keyed in the cell C20. The probability 0.9 will be keyed in the cell C23. The probability of running a cost of 1,660,000 dollars is 0.25 and will be keyed in the cell D23. The probability of obtaining the benefit of 2,740,000 dollars is 0.75 and will be keyed in the cell C27.
Below is the image of the constructed precision/decision tree, which has been obtained by means of screenshot.
The precision tree for output will be as given below:
As it is evident from the precision tree displayed above, it will be of benefit for the Ventron Engineering Company to consider the option of extrusion process.
What value of side benefits would make Ventron indifferent between the two alternatives?
In the event that a variety of the provided data values are changed, the side benefit becomes two million dollars. The value can be decreased to test whether the expected monetary values (EMV) are similar for both of the processes. The expected monetary value is the amount of money that is likely to be ma de from a particular business decision (Boncompte, 2018) . For instance , in the event that someone bet one hundred dollars for choosing a heart from a pack of standard deck cards , his or she have only one out of the chances of winning one hundred dollars (getting a heart) and three out of the chance s of losing the one hundred dollars (getting any other) . As it can be noticed, the Ventron Engineering Company will make a benefit of 124,450 dollars regardless of the process decided upon. As such, the company may remain indifferent for either of the two decisions.
How much would Ventron be willing to pay, right now, for perfect information about both steps of the improved extrusion process? (This information would tell Ventron, right now, the ultimate success or failure outcomes of both steps.)
There will be a need for construction of another precision tree in order to calculate the expected value of perfection information (EVPI) (Coyle & Oakley, 2007). In the theory of decision making, EVPI is th at price that an individual would be comfortable to pay so as to get a perfect information relevant to the task in question . EVPI concept is common in the health economics discipline. The constructed precision tree will be labeled EVPI (Boncompte, 2018). The EVPI precision table will be constructed from node C22 and will be copied as a sub-tree in a different worksheet. Upon copying all the values, an expected monetary value (EMV) of 1,466,000 dollars is obtained. It can be noted that the EMV of the sectioning process in from the first tree is 200,000 dollars without information. Below is a diagram showing the developed EVPI precision tree:
The calculation of the EVPI is done as shown below;
EVPI = EMV with free PI-EMV without PI
= 1466000-200000
= 1266000
The above amount is what Ventron Engineering Company may be comfortable paying in order to get relevant information about the possibility of success or failure of extrusion process.
To entirely reference this problem, the information given in the excel worksheet below will be useful.
| Ventron Engineering Company | |||||
| Benefits Accrued from the Contract | 2,000,000 Dollars | ||||
| Cost of Sectioning for One Year | 1,800,000 Dollars | ||||
| Cost of Sectioning for Six Months | 2,400,000 Dollars | ||||
| Cost of Improving Material | 300,000 Dollars | ||||
| Probability | 0-90 | ||||
| Modifying Extrusion Process | 960,000 Dollars | ||||
| Probability | 0-75 | ||||
References
Boncompte, M. (2018). The expected value of perfect information in unrepeatable decision-making. Decision Support Systems, 110, 11-19. https://doi.org/10.1016/j.dss.2018.03.003
Coyle, D., & Oakley, J. (2007). Estimating the expected value of partial perfect information: a review of methods. The European Journal Of Health Economics, 9(3), 251-259. https://doi.org/10.1007/s10198-007-0069-y
Sabol, S. (2015). The Monetary Policy Risk Premium and Expected Bond Returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2708336
