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Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 53 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding.   What happens if we increase the time periods in our moving average forecast to six weeks opposed to three? Group of answer choices It would be more accurate because it includes more data. There would be no change. It would be less sensitive to changes. It would be better at predicting a trend.

Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 53 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding.   What happens if we increase the time periods in our moving average forecast to six weeks opposed to three? Group of answer choices It would be more accurate because it includes more data. There would be no change. It would be less sensitive to changes. It would be better at predicting a trend.

Purchasing and Supply Chain Management
Purchasing and Supply Chain Management
6th Edition
ISBN:9781285869681
Author:Robert M. Monczka, Robert B. Handfield, Larry C. Giunipero, James L. Patterson
Publisher:Robert M. Monczka, Robert B. Handfield, Larry C. Giunipero, James L. Patterson
ChapterC: Cases
Section: Chapter Questions
Problem 5.1SC: Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing...
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Based on the following equation for a moving average forecast, what would have been the three week moving average forecast for week 53 for Small Town Restaurant (see downloaded file for actual demand)? Provide two decimal places and use normal rounding.

 

What happens if we increase the time periods in our moving average forecast to six weeks opposed to three?
Group of answer choices

It would be more accurate because it includes more data.

There would be no change.

It would be less sensitive to changes.

It would be better at predicting a trend.

¶ ¶ Situation: The "Small Town Restaurant" uses a forecast of weekly sales revenue to plan their inventory ordering and staffing for the upcoming week. They have been using a regression forecast for the past year and wonder if it is the most accurate method. You have been brought in to determine if another forecasting technique would be more accurate. Data for their weekly sales for the past year is below: ¶ Week Revenue. X Week 1 2 $3,016 3 $3,062.¤ ¤ 4 $ 3,074. X 5 $3,002. 6 ----3,054-¤¤ 7 $3,002.¤ ¤ 8 9 $3,078¤ ¤ 10 $3,010- X 11 $3,148. 12 $3,146 $3,198. 13 X ¶ $3,157.** $ Forecasting S $ 2,984-¤¤ A - Small Town Restaurant Revenue. 14 $3,157 ¤ $3,016 ¤ 15 16 $3,019 ¤ 17 $3,045 ¤ 18 19 $3,046 ¤ $3,157 ¤ $3,016 ¤ $3,078 ¤ 22 $2,984 20 21 ¤ 23 $3,002 ¤ 24 $3,054 ¤ 25 $3,002 ¤ 26 $2,941 X A Week Revenue. 27 $2,899 ¤ 28 $2,942 ¤ 29 $3,062 X ¤ 30 $3,074 31 $3,013 ¤ ¤ 32 $3,067 33 $3,148 ¤ 34 $3,146 ¤ 35 $3,198 ¤ 36 $3,082 ¤ 37 $3,146 ¤ 38 $3,150 ¤ 39 $3,221 ¤ X B Week Revenue. ¤ 40 $3,298. 41 $3,201. 42 $3,156. 43 $3,017. X 44 $3,156. 45 $3,097. 46 $ 2,978. X 47 $3,120 48 $3,156. 49 $3,048. 50 $2,948. 51 $ 2,840. 52 $2,921. X $ 159,532 Annual total You will use this data in the next few steps to complete various time series forecasts and compare them to determine the most accurate method. ¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤ X{ X{ X{ X{
Transcribed Image Text:¶ ¶ Situation: The "Small Town Restaurant" uses a forecast of weekly sales revenue to plan their inventory ordering and staffing for the upcoming week. They have been using a regression forecast for the past year and wonder if it is the most accurate method. You have been brought in to determine if another forecasting technique would be more accurate. Data for their weekly sales for the past year is below: ¶ Week Revenue. X Week 1 2 $3,016 3 $3,062.¤ ¤ 4 $ 3,074. X 5 $3,002. 6 ----3,054-¤¤ 7 $3,002.¤ ¤ 8 9 $3,078¤ ¤ 10 $3,010- X 11 $3,148. 12 $3,146 $3,198. 13 X ¶ $3,157.** $ Forecasting S $ 2,984-¤¤ A - Small Town Restaurant Revenue. 14 $3,157 ¤ $3,016 ¤ 15 16 $3,019 ¤ 17 $3,045 ¤ 18 19 $3,046 ¤ $3,157 ¤ $3,016 ¤ $3,078 ¤ 22 $2,984 20 21 ¤ 23 $3,002 ¤ 24 $3,054 ¤ 25 $3,002 ¤ 26 $2,941 X A Week Revenue. 27 $2,899 ¤ 28 $2,942 ¤ 29 $3,062 X ¤ 30 $3,074 31 $3,013 ¤ ¤ 32 $3,067 33 $3,148 ¤ 34 $3,146 ¤ 35 $3,198 ¤ 36 $3,082 ¤ 37 $3,146 ¤ 38 $3,150 ¤ 39 $3,221 ¤ X B Week Revenue. ¤ 40 $3,298. 41 $3,201. 42 $3,156. 43 $3,017. X 44 $3,156. 45 $3,097. 46 $ 2,978. X 47 $3,120 48 $3,156. 49 $3,048. 50 $2,948. 51 $ 2,840. 52 $2,921. X $ 159,532 Annual total You will use this data in the next few steps to complete various time series forecasts and compare them to determine the most accurate method. ¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤ X{ X{ X{ X{
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