The Northville Post Office experiences a seasonal pattern of daily mail volume every week. The followingdata for two representative weeks are expressed in thousands of pieces of mail:Day Week 1 Week 2Sunday 5 8Monday 20 15Tuesday 30 32Wednesday 35 30Thursday 49 45Friday 70 70Saturday 15 10Total 224 210 a. Calculate a seasonal factor for each day of the week.b. If the postmaster estimates 230,000 pieces of mail to be sorted next week, forecast the volume foreach day of the week.

The Northville Post Office experiences a seasonal pattern of daily mail volume every week. The followingdata for two representative weeks are expressed in thousands of pieces of mail:Day Week 1 Week 2Sunday 5 8Monday 20 15Tuesday 30 32Wednesday 35 30Thursday 49 45Friday 70 70Saturday 15 10Total 224 210 a. Calculate a seasonal factor for each day of the week.b. If the postmaster estimates 230,000 pieces of mail to be sorted next week, forecast the volume foreach day of the week.

Contemporary Marketing

18th Edition

ISBN:9780357033777

Author:Louis E. Boone, David L. Kurtz

Publisher:Louis E. Boone, David L. Kurtz

Chapter14: Pricing Strategies

Section14.2: Forecasting Demand

Problem 1LO

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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 December
1. Solve manually. The following gives the number of pints of type A blood used at Woodlawn Hospital in the past 6 weeks. Pints Used 360 Week August 31 September 7 September 14 September 21 September 28 389 410 381 368 October 5 374 a. Forecast the demand for the week of October 12 using a 3-week moving average. b. Use a 3-week weighted moving average, with weights of 0.1, 0.3 and 0.6, using 0.6 for the most recent week. Forecast demand for the week of October 12. c. Compute the forecast for the week of October 12 using exponential smoothing with a forecast for August 31 of 360 and c=0.2. d. Compute the MAD for the 3-week moving average, 3-week weighted moving average and exponential smoothing models above.
Using the exponential smoothing forecast, is it possible to forecast a demand that isbigger than any previously observed demand?a. Yesb. No
Assume that your stock of sales merchandise is maintained based on the forecast demand. If the distributor's sales personnel call on the first day of each month, compute your forecast sales by each of the three methods requested here. ACTUAL June 154 July August 194 225 a. Using a simple three-month moving average, what is the forecast for September? (Round your answer to 2 decimal places.) Forecast for September 191.00 b. Using a weighted moving average, what is the forecast for September with weights of 0.30, 0.40, and 0.30 for June, July, and August, respectively? (Do not round intermediate calculations. Round your answer to 2 decimal places.) Forecast for September 191.30 c. Using single exponential smoothing and assuming that the forecast for June had been 142, forecast sales for September with a smoothing constant a of 0.30. (Do not round intermediate calculations. Round your answer to 2 decimal places.) Forecast for September
8.6 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 1 is 245. Month 1 2 3 4 5 6 LO Actual Demand 260 230 225 245 250 Forecast Demand
Consider the following yearly demand values for a particular product. Year Demand 1 360 389 3 410 4 381 368 374 (a) Produce forecast for year 4,5,6, and 7 by using 3-week moving average. (b) Produce forecast for year 4,5,6, and 7 by using 3-week weighted moving average where weights are as follows; wt–3 = .1, wt-2 = .3, and wt-1 = .6. (c) Produce forecast for year 4,5,6, and 7 by using exponential smoothing with a = .2. Please assume that the forecast for the first year is 360.
The Northville Post Office experiences a seasonal pattern of daily mail volume every week. The followingdata for two representative weeks are expressed in thousands of pieces of mail: a. Calculate a seasonal factor for each day of the week.b. If the postmaster estimates 230,000 pieces of mail to be sorted next week, forecast the volume foreach day of the week.
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven month period were as follows:Months Sales (in thousand units)February 15March 19April 12May 21June 23July 24August 27a. Using the Naïve method in order to forecast September sales volume.b. Using linear trend equation in order to forecast September sales volume. c. Using five-month moving average in order to forecast September sales volume.d. Using weighted moving average of 0.80 for August, 0.05 for July, and 0.15 for June in order to forecast September sales volume.e. Using Exponential smoothing with a smoothing constant equal to 0.25 and assuming a March forecast of 18(000) in order to forecast September sales volume.
11. Consider the following actual and forecast demand levels for Big Mac hamburgers at a local McDonald's restaurant: Day Actual Demand Forecast Demand Monday 88.00 88.00 Tuesday 72.00 88.00 Wednesday 68.00 84.00 Thursday 48.00 80.00 Friday − ? Part 2 The forecast for Monday was derived by observing Monday's demand level and setting Monday's forecast level equal to this demand level. Subsequent forecasts were derived by using exponential smoothing with a smoothing constant of 0.25. Using this exponential smoothing method, the forecast for Big Mac demand for Friday is __________ Big Macs (round your response to one decimal place).
3. The following tabulations are actual sales of units for 6 months and a starting forecast in January. a. Calculate forecasts for the remaining 5 months using simple exponential smoothing with a = 0.2. b. Calculate MAD for the forecasts. January February March April May June Actual 100 94 106 80 68 94 Forecast 80
Use exponential smoothing with trend adjustment to forecast demand for period 11. Let α = 0.5, β= 0.3, and let the initial trend value be 12 and the initial forecast be 200. Period Actual Demand 1 200 2 212 3 214 4 222 5 236 6 221 7 240 8 244 9 250 10 266
Forecast sales for the 7th period. For leveling, use exponential smoothing 0.20 and moving average 3 for averaging; and linear and exponential functions for trend data. Assume an initial exponential forecast of 60 units in period 2 if you decide to use it (i.e., no forecast for period 1). Period Demand 1 67 2 72 3 68 4 20 5 70 6 66 7 68