Jim Sellers is thinking about producing a new type of ...



MSTC ProgramFinal ExaminationManaging Technology and Business RisksSpring 2017I agree that I will not use any materials other than a one page summary of notes, and that I will not use the internet in any way to contact others or to browse for information. Name: _Jon Kaminski________________________________________This examination contains 3 questions worth a total of 100 points. Note that the point totals are indicated for each part of the question. Please allocate your time appropriately.Good luck!Problem 1 (35 points)A professor you know has agreed to organize a conference in the field of simulation one year from now. Within the next two weeks, the professor must decide how many rooms to reserve with the hotel where the conference will be located. On the one hand, the professor does not want to reserve too few rooms because any potential conference participant who cannot get a hotel room will choose not to register for the conference since he/she will have no place to stay. On the other hand, the professor does not want to reserve too many rooms because he must pay a penalty of $500 for any unused room out of the conference revenue. Assume that the potential number of conference participants has a normal distribution with mean 600 and standard deviation 100. Each potential conference participant who obtains a hotel room and registers for the conference pays a conference registration fee of $395. The professor decides to use the following spreadsheet and @RISK model to help him decide how many room he must reserve with the hotel. In it he considers five different room quantities: 400, 500, 600, 700, 800. a) (3 points): What Excel formula goes in cell B8? b) (3 points): What Excel formula goes in cell B12? c) (3 points): What Excel formula goes in cell C12? d) (3 points): What Excel formula goes in cell D12? e) (3 points): What Excel formula goes in cell E12? After running @RISK for the five possible hotel room quantities simulations (Simulation 1 = 400 rooms, Simulation 2 = 500 rooms, Simulation 3 = 600 rooms, Simulation 4 = 700 rooms, and Simulation 5 = 800 rooms), the professor obtains the following results in the table below. Each simulation was run for 10,000 iterations.f) (3 points): Which simulation yields the largest median (50th percentile) revenue? g) (3 points): Which simulation yields the largest average (mean) revenue? h) (3 points): Which simulation has the most risk as measured by spread or dispersion in the data? Please state clearly what statistic you used to answer this question. i) (3 points): Are there any simulations in which there is at least than a 1 in 20 (i.e., 5%) chance of getting a negative revenue? Briefly explain in one sentence. j) (4 points): For each simulation what is the probability of exceeding $175,000 in revenue (approximate these numbers as closely as possible from the data given in the above table). Please put your answer in the following table:Simulation 1Simulation 2Simulation 3Simulation 4Simulation 5k) (4 points): In one sentence, please state how many hotel rooms you think that the professor should reserve in advance and explain why. Problem 2 (40 points)POCO is a large, well-diversified company with a broad mix of oil, gas and petrochemical products and with interests in biofuels, wind, solar power and hydrogen. The company has a history of taking new products and new processes for existing products from the drawing board or laboratory stage to the commercial process stage. Joost Steenbeeke, the director of operations at one of POCO’s divisions, was interested in increasing POCO’s process development effectiveness. He studied a number of the company’s case histories of successful process and product development as well as some failures. He noted that some of the processes had been scaled up directly from laboratory to commercial-scale facilities and some involved the intermediate step of building and operating a pilot plant. Joost realized that the decision of whether or not to build a pilot plant is greatly dependent on the value of the information that the pilot plant is expected to provide because in nearly all cases the only valuable product of a pilot plant is information (whether direct or indirect). Direct information may relate to technical factors such as chemical reaction rates, heat and mass transfer rates, yields, operating conditions, possible corrosion problems, power requirements, and chemical processing-stage efficiencies. In some cases the actual products of a pilot plant may be used as a test product or as a raw material in the next processing stage, and in this case the objective is still to obtain information—indirect information on factors such as quality and suitability.Joost decided that the focus on information and the inherent uncertainty in the process made it suitable for modeling using decision trees. As a result he chose to analyze the decision for a pilot plant that was shortly going to be made by his division. He wanted to see what insights he could generate by using this framework.Joost decided that the basic decision could be simplified into three choices: build a pilot plant, build the full-scale plant with no pilot plant, or abandon the project. For the purposes of the analysis, if the full-scale plant were built with no pilot plant, the simplification was made that the outcome would either be “Good performance” or “Bad performance”. In reality there were an infinite number of possible outcomes from the decision to build the full-scale plant, but these two outcomes were judged to be enough to represent the uncertainty in the initial analysis.The assessment of the uncertain probabilities was carried out by a team of engineers and marketing people who were able to make the overall assessment of the unconditional probability of good performance of the full-scale plant to be .9. In assessing the probability of the success of the pilot plant, the team realized that the information that would be obtained from the pilot plant was far from perfect. For example, the team recognized that favorable results from the pilot plant could still lead to poor performance in a full-scale plant. And, even if the pilot plant did not perform well, the full-scale plant might still work, since some of the “lessons learned” might be applied to improve the design of the full-scale plant. Further, it was easier to assess the probability that the pilot plant would have favorable results conditional on knowing whether or not the full-scale plant would have good performance than vice versa. The team reasoned as follows. If the full-scale plant would have good performance, then they estimated that the probability that the pilot plant would have a favorable performance level would be approximately .95, and otherwise the pilot plant would have a .05 chance of unfavorable performance. On the other hand, if the full-scale plant would have poor performance, then the probability that the pilot plant would have favorable performance would be only about .15, with a probability of .85 of unfavorable performance. These probability assessments were based on the assumption that the full-scale plant would be constructed based on the current design specifications. However, if the pilot plant results were unfavorable, the team recognized that the full-scale plant could be redesigned at some cost, and possibly retested. If this were to occur, then they estimated that the probability of good performance from the full-scale plant would be .8.Joost realized that each path on a decision tree must lead to an eventual outcome with a corresponding value. The monetary values of these outcomes were estimated by considering the present values of all profits from the full-scale plant assuming technical and marketing success, and also the present values of all profits from the full-scale plant assuming a technical failure and, therefore, some marketing difficulties. These estimates were made using a pro forma Excel spreadsheet with projected sales and cost estimates over a 10 year time horizon. There were some additional costs to consider, however, including the cost of constructing and operating the pilot plant, the present value of the delay in the profits that would result from the time required for the pilot study, and the outcomes if the full-scale plant were redesigned. On the positive side, the construction and operation of the pilot plant was expected to result in some savings in the construction costs for the full-sized plant as a result of the experience that would be gained. These monetary outcomes are summarized in Table 1.Table 1. Estimates of Monetary OutcomesDescriptionValue (millions)Net present value of all profits assuming technical and marketing success (Good performance). Note that the investment cost of the plant is included in the NPV calculation.$220 millionNet present value of all profits assuming technical failure and, hence, marketing difficulties (Bad performance)-$50 millionCurrent estimate of pilot plant construction and operating cost$10 millionPresent value of cost of delay to the NPV of the full scale plant caused by the pilot study$4 millionPresent value of benefits and savings in construction costs of full-scale plant as a result of pilot plant experience$2.5 millionPresent value of cost of redesigning plant if pilot plant operation is unsuccessful$3 millionNet present value of profits if pilot plant fails and the full-scale plant is redesigned and it achieves “good performance”$164.5 millionNet present value of profits if the redesigned plant is unsuccessful in technical or marketing aspect and it achieves “bad performance”-$53 millionNote: Figures shown are in present value terms and include all cash flows (present and future)(10 points) First, suppose Joost considers only the options of building the full-scale plant without building the pilot plant, or abandoning the project. What is the expected value of building the full-scale plant? Show the simple decision tree below.(5 points) What would be the value of controlling (or eliminating) the risk associated with the full-scale plant?(10 points) What would be the value of perfect information regarding the risk of the full-scale plant? Show the appropriate tree and your calculations below.(15 points) Next, suppose Joost adds the alternative to build the pilot plant. What is the best decision? Should the pilot plant be built, or should the full-scale plant be built with no pilot plant? Show the part of the decision tree to estimate the present value of the alternative of building the pilot plant on the following page and calculate its expected value. Compare the results with the alternative of not building this pilot plant. Which alternative would you recommend? Use the information below and on the next page to answer these questions.Problem 3 (25 points)Eric Clark was in the throes of settling terms and deciding whether and how to do the OS-7 project, a major implementation of Oracle software in all seven international locations of a large multinational company (the “client”). The client had told Clark that they would like Appshop to perform all of the consulting for the OS-7 project. Clark and a team of consultants spent two weeks working on the strategy, scope, and timeline for this rollout. Based on that analysis, Appshop had proposed to spend 1000 hours of work per month from a variety of professionals. This would result in a total cost to Appshop (for the time of professionals, their support personnel and related apportioned operating costs) of $140/hour. Clark’s team had projected that they would bill the client and receive at the end of each month $175,000 for 24 months. This would provide a contribution of $35,000 per month. This amounted to a present value contribution of $790 thousand for the OS-7 project using the Appshop discount rate of ?% per month (which compounds to 6.2% per year). After significant discussions, Appshop was told that they would be awarded the work but not for the $175,000 monthly payment. The client offered two alternatives: equal payments of $155,000 per month over 24 months or $125,000 per month plus a $1.5 million bonus paid at the end of month 24 if the work is completed ahead of schedule with commendable performance, using standard measurements against stated benchmarks. Even though a system might work satisfactorily, and be tuned to meet a specific benchmark, the multiple benchmarks were much harder to meet simultaneously. Based on previous experience with other implementations, and the complexity and uniqueness of this international project, Clark’s team gave a consensus probability of 0.7 of achieving the bonus. Appshop would earn a positive contribution under the client’s equal payments offer. With the bonus payment schedule, though, it all depended on receiving that bonus. If Appshop does not accept one of the two pricing options currently offered, then the officers of the client company have said that they will generate a Request for Proposal (RFP) and distribute it to the “Big 4” Appshop competitors. If the RFP were issued, Appshop would bid their normal rate of $175,000 per month. At that bid, the consensus estimate from the team was a 55 percent chance of winning, inasmuch as they typically priced projects just below the “Big 4.” The terms of the RFP set by the client were to pay 80% of the revenue amount bid directly each month to the winning bidder plus a gain share at the end of the 24th month to make up for the 20% of the bid revenue not paid out. The client would base the gain share on the documented savings they would realize due to the new Oracle applications. This was a common approach in the software consulting industry used successfully on some Appshop contracts in the past. Appshop would receive a share of the savings according to the following schedule:SavingsAppshop Share of Savings< $4.0 million0$4 million up to $6 million20 percent of excess above $4 million$6 million up to $8 million$400,000 plus 40 percent of excess above $6 million> $8 million $1.2 million plus 60 percent of excess above $8 millionClark’s team had used prior experience and judgmental assessment of the OS-7 implementation to forecast the savings to be realized by the client. The result of their discussions was that savings would have a triangular distribution with a low of $3.2 million, a high of $12.8 million and a most likely value of $5.6 million. (5 points) What would be the return associated with the equal payments alternative? Hint: Consider using the Excel function PV.(5 points) What would be the expected return associated with the bonus payment alternative? What would be the standard deviation associated with the bonus payment alternative? Hint: Consider solving this using problem using @Risk so that you can obtain the expected value and the standard deviation. Copy your calculations below (show the formulas), along with the solution.(5 points) Now, consider the return from the RFP alternative. Develop a simple @Risk model to estimate this return, and copy the model below (show the formulas). Hint: You may want to use a “nested IF statement”, such as IF(a>100,1000,IF(a>50,500),100).(5 points) Report the expected value associated with this RFP alternative. Also, what is the probability that Appshop will experience a loss if they chose this strategy?e. (5 points) Which of these alternatives would you recommend, and why? ................
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