(4) The Great Northwest Outdoor Company is a catalog sales operation that specializes in outdoor recreational clothing. Demand for its items is very seasonal, peaking during the holiday season and during the spring. It has accumulated the following data for orders per season (quarter) during the past five years. Orders (1,000s) Year 1 Year 2 Year 3 Year 4 Year 5 Winter (Q 1) 18.6 18.1 22.4 23.2 24.5 Spring (Q 2) Summer (Q 3) 23.5 24.7 28.8 27.6 31.0 20.4 19.5 21.0 24.4 23.7 Fall (Q 4) 41.9 46.3 45.5 47.1 52.8 Total 104.4 108.6 117.7 122.3 132.0 Develop a seasonally adjusted forecast model for these order data. Forecast demand for each quarter for year 6, using the annual trend line as given: Y(t) = 96.33 + 6.89t %3D (a) Find seasonal index for each quarter (b) Find seasonally adjusted demand forecast for each quarter for year 6 (c) Find MAD for seasonally adjusted forecasting for year 1 through 5.
(4) The Great Northwest Outdoor Company is a catalog sales operation that specializes in outdoor recreational clothing. Demand for its items is very seasonal, peaking during the holiday season and during the spring. It has accumulated the following data for orders per season (quarter) during the past five years. Orders (1,000s) Year 1 Year 2 Year 3 Year 4 Year 5 Winter (Q 1) 18.6 18.1 22.4 23.2 24.5 Spring (Q 2) Summer (Q 3) 23.5 24.7 28.8 27.6 31.0 20.4 19.5 21.0 24.4 23.7 Fall (Q 4) 41.9 46.3 45.5 47.1 52.8 Total 104.4 108.6 117.7 122.3 132.0 Develop a seasonally adjusted forecast model for these order data. Forecast demand for each quarter for year 6, using the annual trend line as given: Y(t) = 96.33 + 6.89t %3D (a) Find seasonal index for each quarter (b) Find seasonally adjusted demand forecast for each quarter for year 6 (c) Find MAD for seasonally adjusted forecasting for year 1 through 5.
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 Great Northwest Outdoor Company: Seasonal Order Analysis**
The Great Northwest Outdoor Company specializes in catalog sales of outdoor recreational clothing. The demand for its products is highly seasonal, with peaks during the holiday season and spring. The following table presents the order data (in thousands) by quarter over the past five years.
| Orders (1,000s) | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|-----------------|--------|--------|--------|--------|--------|
| Winter (Q1) | 18.6 | 18.1 | 22.4 | 23.2 | 24.5 |
| Spring (Q2) | 23.5 | 24.7 | 28.8 | 27.6 | 31.0 |
| Summer (Q3) | 20.4 | 19.5 | 21.0 | 24.4 | 23.7 |
| Fall (Q4) | 41.9 | 46.3 | 45.5 | 47.1 | 52.8 |
| **Total** | 104.4 | 108.6 | 117.7 | 122.3 | 132.0 |
**Task: Develop a Seasonally Adjusted Forecast Model**
To better anticipate future demands, specifically for year 6, a seasonally adjusted forecast model must be developed using the following steps:
1. **Seasonal Index Calculation**: Determine the seasonal index for each quarter.
2. **Seasonally Adjusted Demand Forecasting**: Use the indices to adjust demand forecasts for each quarter of year 6, based on the given annual trend line:
\[
Y(t) = 96.33 + 6.89t
\]
3. **Mean Absolute Deviation (MAD) Calculation**: Calculate the MAD for seasonally adjusted forecasts for years 1 through 5.
By following these steps, the company can optimize inventory levels and improve sales strategies in response to seasonal demand fluctuations.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3b91253d-6cd1-478c-987b-3f2c8d885106%2F6a495849-e13f-4404-b1f6-51d41a2428e0%2Fnp22wn_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**The Great Northwest Outdoor Company: Seasonal Order Analysis**
The Great Northwest Outdoor Company specializes in catalog sales of outdoor recreational clothing. The demand for its products is highly seasonal, with peaks during the holiday season and spring. The following table presents the order data (in thousands) by quarter over the past five years.
| Orders (1,000s) | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|-----------------|--------|--------|--------|--------|--------|
| Winter (Q1) | 18.6 | 18.1 | 22.4 | 23.2 | 24.5 |
| Spring (Q2) | 23.5 | 24.7 | 28.8 | 27.6 | 31.0 |
| Summer (Q3) | 20.4 | 19.5 | 21.0 | 24.4 | 23.7 |
| Fall (Q4) | 41.9 | 46.3 | 45.5 | 47.1 | 52.8 |
| **Total** | 104.4 | 108.6 | 117.7 | 122.3 | 132.0 |
**Task: Develop a Seasonally Adjusted Forecast Model**
To better anticipate future demands, specifically for year 6, a seasonally adjusted forecast model must be developed using the following steps:
1. **Seasonal Index Calculation**: Determine the seasonal index for each quarter.
2. **Seasonally Adjusted Demand Forecasting**: Use the indices to adjust demand forecasts for each quarter of year 6, based on the given annual trend line:
\[
Y(t) = 96.33 + 6.89t
\]
3. **Mean Absolute Deviation (MAD) Calculation**: Calculate the MAD for seasonally adjusted forecasts for years 1 through 5.
By following these steps, the company can optimize inventory levels and improve sales strategies in response to seasonal demand fluctuations.
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