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answer all the questions below and show each and every woorkings
You have just been given the following production reports as a new manager of a company;
|
January |
February |
March |
April |
Units Produced |
230 |
1,800 |
2, 800 |
3,000 |
Hours Per Machine |
325 |
200 |
400 |
320 |
No of machines |
3 |
5 |
4 |
4 |
show working and formulars used
- What percentage of production is February production?
- If you are to
forecast for May using four month simple moving average, what would be your forecast - Determine the average productivity unit for the first three months.
- Determine the average productivity units per machine.
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- Under what conditions might a firm use multiple forecasting methods?Given the following data, use exponential smoothing (a=0.20) to develop a demand forecast. Assume the forecast for the initial period is 6. Period Demand Period Forecast 2 2 1 3 7 9 8 The exponential smoothing forecast is (round your responses to two decimal places): 1 5 6 6.00 2 3 4 8 4 5 13 6 s D 6 7Week Sales1 27502 31003 32504 28005 29006 30507 33008 31009 295010 300011 320012 3150Develop a three-week moving average. A.What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) B.What is the value of MSE (mean squared error) with regard to a three-week moving average? (Make sure no decimal place and no comma. EX: 1234) C.Develop the exponential smoothing with the alpha value of 0.20. What is the forecast for week 5? (Make sure no decimal place and no comma. EX: 1234) D.What is the value of MSE (mean squared error) with regard to the exponential smoothing? (Make sure no decimal place and no comma. EX: 1234) E.Which method is more accurate? (1 for Moving average, 2 for Exponential Smoothing)
- Use simple exponential smoothing with a=0.6 to forecast chargers sales for February through May. Assume that the forecast for January was 24 chargers. *** Month January Charger Sales 42 February 33 March 28 April 59 May ? a) What will be the forecast for May? b) Calculate the MSE.Weekly demand for an item averaged 100 units over the past year. Actual demand forthe next eight weeks is shown in what follows:a. Plot the data on graph paper.b. Letting a = 0.25, calculate the smoothed forecast for each week.c. Comment on how well the forecast is tracking actual demand. Is it lagging or leading actual demand?.A check-processing center uses exponential smoothing to forecast the number of incoming checks each month. The number of checks received in June was 570 million, while the forecast was 633million.A smoothing constant of 0.2 is used. What is the forecast for July? If the center received 498 million checks in July, what would be the forecast for August?
- B). Using exponential smoothing, calculate the forecasts for months 2, 3, 4, 5, and 6. The smoothing constant is 0.2, and the old forecast for month I is 245. Month Actual Demand Forecast Demand 1 260 2. 230 3 225 4 245 5 250 6. C). Given the following average demand for each month, calculate the seasonal indices for each month. Month Average Demand Seasonal Index January 30 February 50 March 85 April 110 May 125 June 245 July 255 August 135 September 100 October 90 November 50 December 30 Total 3 D). Using the data in part A and the seasonal indices you have calculated, calculate expected monthly demand if the annual forecast is 2000 units. Month Seasonal Forecast Index January February March April May June July August September October November DecemberState the Common features that describe principles of forecasting. The lost Paradise of Dilmun Park in Bahrain, is a waterpark activities, experiences seasonal attendance. It has collected two weeks of attendance data and made a forecast of weekly attendance for the coming week. Compute the seasonal indexes for the days /week and generate forecasts for the coming week, assuming weekly attendance for the coming week to be 2300 person. Wahoo water Park attendance Days/Week Week 1 Week 2 Saturday 500 600 Sunday 110 80 Monday 90 60 Tuesday 100 50 Wednesday 100 140 Thursday 400 370 Friday 700 800Please do not give solution in image format thanku supose that the demand in period 1 was 7 units and the demand in period 2 was 9units . Assume that the forecast for period 1 was for 5 units .If the firm uses exponential smoothing with an alpha value of .20. what should be the forecast for period 3
- 15 5 Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 10 months. The forecasts and actual sales are as follows: 1 Month Sales Forecast 1 835 845 2 1 2 3 4 5 6 7 8 9 10 Forecast 850 820 785 840 815 840 785 800 Forecast 835 830 840 790 815 785 770 835 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) MSE MAPE F1 MAPE F2 805 785 Answer is complete but not entirely correct. MAD 2,942.50 x Forecast 2 765 830 1,125.60 x 815 830 800 806 775 810 815 805 4.2153 X % 3.4380 % 22.50✔ Return to question 28.40✔ b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) > Answer is complete but not entirely correct. c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. (Negative values should…The table below shows the sales of widgets (in units) over the past 5 years. Using exponential smoothing with a smoothing constant of 0.2, what is the forecast for next year? The initial forecast (for 5 years ago) was 1,200 units. Forecast: Time Sales 5 years ago 1,140 4 years ago 1,220 3 years ago 1,450 2 years ago 1,090 Last year 1,150 (Do not round intermediate calculations, round your final answer to the nearest whole number.)Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 5 months. The forecasts and actual sales are as follows: Month 1 2 3 4 5 6 sales 77 75 78 76 74 71 Forecast1 75 72 81 75 75 73 Forecast2 70 73 79 74 75 72 a. Compute the MAD, MSE and MAPE for each forecast. Does either forecast seem to be superior? Explain. (Round off your numbers to two decimal places, e.g., 2.674 → 2.67) b. Compute 2s control limits for each forecast and draw a control chart. Are the forecast errors with in control limits? Round off your numbers to two decimal places.