Redraw the flowchart with the following change.

The first check, after the patient presents, is not whether she's in labor, but whether she's been admitted. Present the new flowchart with a glossary: that is, label the various activities and decision A, B, C, etc. and provide a list explaining each label.

In part 2 (Decision Trees), one of the examples states, "If the IT system were upgraded, then there's a 40% chance (i.e., a 0.40 probability) that (some entity) would buy (the company)..." If the 0.40 figure happens to be wrong, then any decision based on it will also be wrong. So the obvious question is, where did that figure come from, and what gives us a warm feeling that it's right?

In part 3 (PERT-CPM), each Case problem begins with a list of project tasks, along with three estimated completion times for each; the optimistic (shortest) time, the pessimistic (longest) time, and the most likely (somewhere-in-between) time. These times are the necessary starting point for the problem; but where did they come from? In this instance, of course, the person who devised the problem simply made them up. But if you were using PERT-CPM to run a real-world project, such as building a factory, you'd need time estimates that had some basis in reality. Where would you get them?

In part 4 (Linear Programming), one of the examples begins with the statement, "An electronics firm produces a calculator. Customer demand is for 100 ... calculators per day." Oh, really? Is that the average demand over the past year, the demand yesterday, the seasonally-weighted daily demand, or something else entirely? Whatever it may be, it's only useful to the extent that it accurately reflects future customer demand. After all, production decisions must be made on the basis of what a company expects to sell, not what it's sold in the past. So that 100-per-day number needs to be seen as a forecast. Who made that forecast, and how reliable is it?

Attachment:- ASSIGMENT.docx