Month 1 2 3 4 5 6 7 8 9 10 Sales 835 Forecast 1 Forecast 2 MAPE F1 MAPE F2 845 850 820 785 840 815 840 785 800 MSE MAD Forecast 1. 835 830 840 790 815 785 770 835 MSE Tracking signal Control limits 805 785 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) 869.44 1,250.67 Forecast 2 765 830 2.7519 % 3.4380 % MAD 0 t 815 830 b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) 800 806 775 810 815 805 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 be indicated by a minus sign. Round your answers to 2 decimal places.) -1.25 69.91 22.50 28.40

Month 1 2 3 4 5 6 7 8 9 10 Sales 835 Forecast 1 Forecast 2 MAPE F1 MAPE F2 845 850 820 785 840 815 840 785 800 MSE MAD Forecast 1. 835 830 840 790 815 785 770 835 MSE Tracking signal Control limits 805 785 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) 869.44 1,250.67 Forecast 2 765 830 2.7519 % 3.4380 % MAD 0 t 815 830 b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) 800 806 775 810 815 805 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 be indicated by a minus sign. Round your answers to 2 decimal places.) -1.25 69.91 22.50 28.40

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

See similar textbooks

Similar questions

Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons: Week 1 2 3 4 Forecast Method 11 0.90 1.08 0.97 1.17 Actual Demand 0.72 1.00 1.07 1.00 Week 1 4 Forecast Method 2 0.82 1.20 0.88 1.17 The MAD for Method 1 = thousand gallons (round your response to three decimal places). Actual Demand 0.72 1.00 1.07 1.00
Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in thousands of gallons: Week Forecast Method 1 Actual Demand 1 0.95 0.72 2 1.08 0.98 3 0.92 1.07 4 1.17 1.04 Week Forecast Method 2 Actual Demand 1 0.80 0.72 2 1.21 0.98 3 0.88 1.07 4 1.17 1.04 Part 2 The MAD for Method 1 = enter your response here thousand gallons (round your response to three decimal places).
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period simple moving average method to forecast the number of sales to expect next month. Month Demand Forecast 1 308 341 388 343.3 3 344 364.7 4 400 346.7 341 377.3
Sales of tablet computers at Marika Gonzalez's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week Demand 1 20 2 20 Week 1 Demand 20 Forecast 20.0 3 29 4 37 4 37 5 24 2 3 29 20 6 30 7 8 37 24 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): 5 6 30 24 7 37 9 24 8 24 10 28 9 24 10 28
A Use a simple moving average model. Experiment with the models using five weeks and three weeks past data. 3 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 5 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 B evaluate the forecast that would have been made over the 13 weeks using overall mean absolutely deviation, mean absolute percent error, and tracking signal as criteria.
- Sales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: 6 10 Week 1 Demand 20 2 3 23 27 4 5 37 26 7 8 9 30 35 22 24 29 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.55 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): Week 1 Demand 20 Forecast 20.0 2 3 23 27 4 37 5 26 6 30 7 35 8 22 9 24 10 29
National Standard, Inc. sells radio frequency identification (RFID) tags. Monthly demand for a seven-month period is reported below: Sales (1000 units) Forecast Observation Month Yt Ft 1 February 19 2 March 18 3 April 15 4 May 20 5 June 18 6 July 22 7 August 20 8 September ? Use Excel to plot the data and forecast September sales using the following methods: The naïve forecast A three-month moving average Exponential smoothing with a smoothing coefficient of α = 0.2, assuming a February forecast of 19 A 3-month weighted moving average, with weights 0.60, 0.3, and 0.1. With 0.6 applied to the most recent past.
The following table shows predicted product demand using your particular forecasting method along with the actual demand that occurred: FORECAST ACTUAL 1,480 1,530 1,380 1,480 1,680 1,580 1,730 1,780 1,630 1,680 Compute the tracking signal using the mean absolute deviation and running sum of forecast errors. Note: Negative values should be indicated by a minus sign. Round your "Mean Absolute Deviation", "Tracking Signal" to 2 decimal places and all other answers to the nearest whole number. Period Forecast Actual Deviation RSFE Absolute Deviation Sum of Absolute MAD TS Deviation 1 1,480 1,530 -50 -50 50 50 50.00 -1.00 2 1,380 1,480 -100 -150 100 150 75.00 -2.00 3 1,680 1,580 100 -50 100 250 83.33 -0.60 4 1,730 1,630 100 50 100 350 87.50 0.57 5 1,780 1,680 100 150 100 450 90.00 1.67
snip
The optimal solution of a minimization problem is at the extreme point closest to the origin. Analyze this statement
Given the table below, complete the missing cell values applying the Exponential Smoothing method of forecasting and Mean Absolute Deviation. Answer also the other 2 related questions below the table. Numbers with decimal should take 2 decimal places. Actual Quarter Tonnage Unloaded 1 2 3 4 5 6 7 8 175 160 170 160 160 170 180 200 Forecast for a=3 175 175.0 170.5 167.2 165.1 166.6 170.6 Sum of absolute deviation = Absolute Deviation for a = .3 Other questions: (1) What is the MAD for a = .3? (2) Which smoothing constant would you prefer? 0.00 15.00 0.50 10.35 4.93 13.45 29.41 80.89 Forecast for a = .6 175 166.0 168.4 163.4 161.3 166.5 174.6 Absolute Deviation for a = .6 0.00 15.00 4.00 8.40 3.36 8.66 13.46 25.38
Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table: Year Year Forecast (000) 1 2 4.00 7.00 1 5.00 3 5.00 Registrations (000) a) Calculate the forecasted registrations for years 2 through 12 using exponential smoothing, with a smoothing constant (a) of 0.40 and a starting forecast of 5.00 for year 1 (round your responses to one decimal place): 2 3 4.6 5.56 4 4.00 4 5.34 11 9 10 7 8 6 5 9.00 9.00 6.00 10.00 13.00 14.00 12.00 5 4.80 6 7 6.48 7.49 8 6.89 9 8.14 10 10.08 11 11.64 12 11.79 b) Mean absolute deviation based on the forecast developed using the exponential smoothing method (with a smoothing constant (a) = 0.40 and a starting forecast of F₁ = 5.00) is response to one decimal place). registrations (round your