Focusing only on Option #2 & #3, what is the best to work them out? Specifically how to work out the statistics portions of both options. Thank you!

Near Net Casting Solutions, Inc. (N NCS) produces castings for the automotive industry, specically engine blocks and transmission housings. The parts are not necessarily complex but NNCS' process yields a 70% loss from parts that contain unacceptable \"voids\" in the raw castings. To resolve this issue, NNCS produces more parts than what are necessary and absorbs the labor costs into their pricing. Defective materials are melted back down and included in the next batches worth of raw materials. Through studies, NNCS has found that the void issues are resulting in triple the costs for labor and energy. Because they reuse the rejected parts as a remelt in their batches, they save materials costs but believe this is a leading cause of contamination in their process. They do inspect the casted parts prior to machining and believe that their costs would be much higher than the projected $300,000 the defective casting are currently costing them. The costs are high enough that NNCS management is looking at a series of proposed solutions. 1. The next redesign for casting forms is expected in ve years. Therefore, the company could try to ride out the costs until the redesign is complete (t = 5). X! 2. Engineering could look at the current design and validate that material ows in the molds are correct. The anticipated costs are $150,000 in research hours, $50,000 in testing, and $25,000 in materials. Engineering anticipates that they would complete their work and have a solution at the end of the next year (t = 1). This would reduce the projected costs from the defective casting to $15,000. Engineering is also working on other projects and believe that there is a 90% likelihood of the costs holding true while there is a 10% chance that their total costs for the project will reach $275,000. They are also predicting that there is a 50% likelihood of nishing in 9 months, a 40% likelihood of nishing in 1 year, and a 10% likelihood of nishing in 15 months. X 3. Engineering could outsource the work to a thirdparty vendor. The quote was for $350,000 with deliverables at the end of the next year (t = 1) and would reduce the projected costs from the defective casting to $15,000. The company has worked with this vendor and knows that there is a possibility for scope creep that will affect the costs and the time until delivery. They rate the quoted costs at 80% likelihood of happening with a 20% chance that the costs inate to $380,000. They predict that the delivery in one year has a 50% likelihood of occurring, that 18 months has a 40% likelihood, and that 24 months has a 10% likelihood of occurring. 4. Procurement has found a lower cost subcontractor that they could sub the castings out to in Mexico. The defect rate for voids will be higher at 80% but the subcontractor can deliver the units and still save the company money on parts immediately (t = 0). The savings in material costs will be $200,000 though the increase in defective parts is projected to cause $100,000 in (205115. What are the present and annualized costs for each option? Which option do you think the company should pursue and why