a. Compute a three-semester moving average forecast for semesters 4 through 9. b. Compute the exponentially smoothed forecast (α = 0.2) for the enrollment ata. c. Compare the two forecasts using MAD and indicate the most accurate.
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- At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.Professor Z needs to allocate time among several tasks next week to include time for students' appointments. Thus, he needs to forecast the number of students who will seek appointments. He has gathered the following data: Week Number of students6 Weeks ago 515 Weeks ago 794 Weeks ago 833 Weeks ago 892 Weeks ago 71Last Week 93 What is this week's forecast using exponential smoothing with alpha = 0.3 , if the forecast for two weeks ago was 77 ? a. 75.2 b. 78.8 c. 69.86 d. 80.54A police station had to deploy police officers for emergencies multiple times the lastfour evenings. The numbers of emergencies for Monday, Tuesday, Wednesday, andThursday were 7, 4, 8, and 11, respectively. What would be the station’s forecast for theemergencies on Friday using a two-day moving average approach
- Dave F letcher, the general manager of NorthCarolina Engineering Corporation (NCEC), thinks that his firm'sengineering services contracted to highway construction firms aredirectly rela ted to the volume of highway construction businesscontracted with companies in his geographic area. He wondersif thjs is really so, and if it is, can this information help him planhjs operations better by forecasting the quantity of his engineeringservices required by construction firms in each quarter of theyear? The following table presents the sales of his services andtotal amounts of contracts for hjghway construction over the pasteight quarters:…Tom Glass forecasts electrical demand for the FlatlandsPublic Power District (FPPD). The FPPD wants to take itsComstock power plant out of service for maintenance whendemand is expected to be low. After shutdown, performingmaintenance and getting the plant back on line takes twoweeks. The utility has enough other generating capacity tosatisfy 1,550 megawatts (MW) of demand while Comstockis out of service. Table 8.5 shows weekly peak demands(in MW) for the past several autumns. When next in year 6should the Comstock plant be scheduled for maintenance?4 Trinity General Hospital had the following number of patient admissions during the past 8 weeksWeek Patient Admissions1 1202 1453 954 1125 1306 1107 1008 140 a. Develop a 3-week weighted average forecast forWeek 4 through 9 with weightsW1 = 0.2W2 = 0.3W3 = 0.5 b. Forecast patient admissions for week 9 using simpleexponential smoothing with α = 0.2. Assume that the forecast for Week 2 (F2) is the naïve forecast.
- Naïve method versus three period Moving Average The number of major plumbing repair jobs performed by Augur’s Plumbing Service in each of the last six months is listed below. Month Jobs October 92 November 94 December 98 January 95 February 99 March 104 Use the ‘textbook’ naïve method to forecast April jobs (Ft+1 = Xt + (Xt – Xt-1). Use the three- period moving average to forecast jobs in April Based upon the MAD (mean absolute deviation), determine whether the three-period moving average or textbook naïve method is more accurate, based upon forecasts for January through March.The chairperson of the department of management at Tech wants to forecast the number of students who will enroll in operations management next semester in order to determine how many sections to schedule. The chair has accumulated the following enrollment data for the past 15 semesters: Using Exponential Smoothing with (α = 0.6) compute the forecasted number of student for semester 16 Semester Students Enrolled in OM 1 270 2 310 3 250 4 290 5 370 6 410 7 410 8 450 9 440 10 470 11 525 12 515 13 538 14 544 15 504Tom Glass forecasts electrical demand for the FlatlandsPublic Power District (FPPD). The FPPD wants to take itsComstock power plant out of service for maintenance whendemand is expected to be low. After shutdown, performingmaintenance and getting the plant back on line takes two weeks. The utility has enough other generating capacity tosatisfy 1,550 megawatts (MW) of demand while Comstockis out of service. Table 8.5 shows weekly peak demands(in MW) for the past several autumns. When next in year 6should the Comstock plant be scheduled for maintenance?
- Suppose a four-period weighted average is being used to forecast demand. Weights for the periods are as follows: wt-4 = 0.1, wt-3 = 0.2, wt-2 = 0.3 and wt-1 = 0.4. Demand observed in the previous four periods was as follows: At-4 = 380, At-3 = 410, At-2 = 390, and At-1 = 400. What will be the demand forecast for period t?In the past, Peter Kelle's tire dealership in BatonRouge sold an average of I ,000 radials each year. In the past2 years, 200 and 250, respectively, were sold in fa ll, 350 and 300 inCHAPTER 4 FORECASTING 149winter, ISO and 165 in spring, and 300 and 285 in summer. With amajor expansion planned, Kelle projects sales next year to increaseto I ,200 radials. What will be the demand during each season? ~(Mylab Operations Management also includes a shorter (brief)version of this problem.)Assume that the actual sales volume were 1,800; 2,000; 1,700; 1,600; 1,900; and 2,100 for Days 1 to 6, respectively. Assume further that w = 0.10, 0.30, 0.60 for use in weighted moving averages forecasting, How much would be the forecasted sales volume for Day 6? * A. 1,800 B. 1,670 C. 1,990 D. 1,790