Decision Support Tools Conditional Profits

Question: Discuss about theDecision Support Tools forConditional Profits. Answer: The conditional profits as calculated by the manufacturer can be shown as: p(s1) = 0.3 p(s2) = 0.7 s1 s2 a1 30,000 16,000 a2 10,000 24,000 Table 1: The Conditional Profits (source created by author) Part a From the decision support tools we can use the method of Expected value with perfect information criteria (EVPI). By using the method of EVPI For the method For the method In comparing the EVPI for the production methods a1 and a2 we find that the EVPI of a1 is 20,200 and a2 is 19,800. The EVPI of a1 is greater than the EVPI of a2. Hence the manufacturer should choose a1. Part b According to the marketing consultant considering prior probabilities the demand being good s1 is 0.35 and the demand being poor s2 = 0.65 p(s1) = 0.35 p(s2) = 0.65 s1 s2 a1 30,000 16,000 a2 10,000 24,000 Table 2: The Decision tools with prior probabilities (source created by author) Using the EVPI method: For the method For the method Thus with the condition of prior probabilities the EVPI for the method a1 is more than a2. The EVPI for a1 is 20,900 and for a2 is 19,100. Thus with the condition of prior probabilities the management should choose method a1. Part c According to the marketing consultant considering posterior probabilities the demand being good s1 is 0.80 and the demand being poor s2 = 0.20 p(s1) = 0.80 p(s2) = 0.20 s1 s2 a1 30,000 16,000 a2 10,000 24,000 Table 3: The Decision tools with posterior probabilities (source created by author) Using the EVPI method: For the method For the method Thus with the condition of posterior probabilities the EVPI for the method a1 is more than a2. The EVPI for a1 is 27,200 and for a2 is 12,800. Thus with the condition of prior probabilities the management should choose method a1. Part d In all the above three methods when the management has made has done his own calculations, with the prior and posterior probabilities (as defined by the consultant) the method a1 provides the maximum benefit to the manufacturer. Thus we do not find any reason to hire the consultant (Kerzner, 2014). Step 1 The first step is to calculate the cumulative probabilities for both the time between arrivals of the patients and the service time of the patients. The random numbers are generated for the cumulative probabilities. Figure 1: Screenshot of the Simulation 1 (source created by author) From the above simulation we see that the first patient arrives 25 minutes after the service starts. Since the nurse is then free hence the service of the patient starts immediately. The service nurse was then waiting for 15 minutes. For the first patient the time taken by the nurse was 25 minutes. The service time for the patient ends 40 minutes after the start of service. The second patient arrives at 40 minutes. Since by this time the nurse has finished her work with the first patient, she can start her nursing with the 2nd patient immediately. The nursing activity on the 2nd patient takes place for 30 minutes. She finishes her work at 70 minutes after the start of the service. The 3rd patient arrives 65 minutes after the start of service. Since the nurse has not finished her task with the 2nd patient hence the 3rd patient has to wait for 5minutes before activity on the patient can start. From the simulation, we can see that from 65 minutes till 120 minutes there is a patient always waiting since the nurse has not finished her activity with the previous patient. Thus there is a requirement of a second nurse accordingly (Render et.al., 2014). References Kerzner, H.R., (2014). Project Management 2.0. John Wiley Sons Render, B., Ralph, M.S.J, Hanna, M.E., Hale, T.S. (2014). Quantitative analysis for management. 12th Ed. Pearson Education