9. Problem 18-28 (Algo)The following are historical demand data:ACTUALYEAR SEASONDEMAND2011 Spring 203Summer 144Fall382Winter 5652012Spring 471Summer 271Fall686Winter 955Use regression analysis on deseasonalized demand to forecast demand in summer 2013. (Do notround intermediate calculations. Round your answer to the nearest whole number.)Forecast for summer 2013 = 364

Calculate Seasonal Indices:Determine the seasonal index for each season. By dividing the actual…

Answered: 9. Problem 18-28 (Algo) The following…

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...

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The following data show the number of liters of gasoline sold by Mackrin's in Colorado for the past 12 weeks. Which forecasting model would you recommend to Mackrin's manager? Why? What demand forecast would you recommend for week 13? Week Actual Demand (1000 liters) Forecasting (1000 liters) FSMA /A-F/ FWMA /A-F/ FES, α=0.3 /A-F/ 1 17 2 21 17 4 3 19 18.2 0.8 4 23 19 4 18.44 4.56 5 18 21 3 20.8 2.8 19.81 1.81 6 16 20 4 20 4 19.3 3.3 7 20 19 1 18.3 2.3 18.31 1.69 8 18 18 0 18.7 0.7 18.82 0.82 9 22 18 4 18.2 4.2 18.6 3.4 10 20 20 0 19.8 0.2 19.62 0.38 11 15 20 5 20.2 5.2 19.73 4.73 12 22 19 3 18.2 3.8 18.3 3.7 13 19 19.5 19.41 MAD 2.67 2.9 2.7 MSE 10.22…
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.
1. Discuss the differences between Qualitative and Quantitative forecasting models. How do Associative and Time Series techniques differ? 2. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Actual (A:) Forecast (Ft) Forecast Error Absolute (Deviation) Month Forecast Eror January February March 45 45 42 50 34 45 April 48 40 Мay 38 45 MAD =
Use simple exponential smoothing with a -0.8 to forecast electric scooter sales at Guelph- Humber Inc. for July. Assume that the forecast for May was for 45 electric scooters. Round your answer to 1 decimal Electric Scooter Sales Month May 42 June 47 July 45 August 40 Weekly demand for medical masks for the last five weeks at Chopper Drug Store has been as follows: 92, 96, 100, 100 and 102 (listed from the oldest to most recent). Suppose a naive forecast was used to forecast demand. What would the MAD be for this situation? Enrollment in a particular Yoga class at Guelph Fitness Centre for the last five months has been 80, 86, 88, 88 and 90(listed from the oldest to most recent). Suppose a two-month mov average was used to forecast enrollment. What would the MSE be for this situation?
14.5. For the data in Exercise 14.4, use an a of 0.1 to make a forecast for July F Ft-1 + 0.1(At-1-Ft-1) Months January 15 February 18 March 22 23 27 26 April May June Demand Smoothing unit (At) (St) July 15 15.3 15.97 16.67 17.7 18.53 Forecast for July= 18.53 Forecast (Ft) 15 15.3 15.97 16.67 17.7 18.53
4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 600 600 775 10 775 3 600 11 600 4 650 12 575 700 13 625 800 14 650 550 15 600 8 775 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.1. Which provides the better forecasts based on MSE? b. Find the forecast for the next month using the best forecast method. 2.
A firm uses simple exponential smoothing with trend adjustment to forecast demand, "a = 0.6" and "B=0.6". The Forecast including Trend (FIT) for the 1st month of 2022 was 434 units whereas the actual demand turned out to be 488 units, the trend over that period was 26. Calculate the demand forecast including trend for the 2nd month.
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FORECASTING- EXPONENTIAL SMOOTHING QUESTION 3 PERIOD АCTUAL 1 42 2 40 3 43 4 40 5 41 39 7 46 8 44 9. 45 10 38 11 40 12 a) Consider exponential smoothing forecasts using smoothing constants (a) 0.1 and 0.4
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Please consider the following information and make forecasts for January, February, March, and April of Year 2. Use second-order polynomial regression to make forecasts. Adjusted seasonal index Actual sales Deseasonalized sales 836 825 Year 1 Year 2 January February March April May June July August September October November December January February March April 0.72 0.75 1:22 1.48 1 1.23 0.7 0.78 0.92 1.13 1.28 1.33 0.72 0.75 1.22 1.48 1220 1319 1020 1184 685 641 875 1008 1068 1141
Help Saved Mary, Susan, and Sarah are running a beach boutique on the board walk of Ocean City. Their favorite product is a red lifeguard hoody. Mary believes it will sell 318 times next season. Susan forecasts sales of 518, and Sarah forecasts 199. What would be the result of a simple forecast combination? (Round to two decimal places)

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