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Develop a decision tree for problem

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Michael Finkster, chief data engineer of Bullseye Electronics, has to decide whether to invest in a new state-of-the-art analytics software. If the new software meets the promised potential, the company can realize an increase in profits of $200,000. If it fails to meet the potential, Bullseye Electronics could lose $180,000. At this time, being conservative, Finkster estimates a 60% chance that the new process will fail.

Michael Finkster has an option to make a smaller investment to implement a partial software solution and then decide whether to invest in the full state-of-the-art analytics software. The partial solution would cost $15,000 to implement. Finkster estimates a 50-50 chance that the partial solution will work, and not fail.

If the partial solution works, that would be an optimistic sign for the success of the full analytics software -- there would be a 75% chance that (if purchased) the analytics software will be successful and meet the promised potential.

If the partial solution is a failure, there is only a 15% chance that (if purchased) the analytics software will be successful and meet the promised potential.

Finkster faces a dilemma. Should he invest in a new state-of-the-art analytics software?

Should he first invest in the partial software solution and then depending on outcome make a decision?

Develop a decision tree for this problem and determine the optimal decision strategy.

Reference no: EM132474472

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