A manager of a trav company using the following data for the pacCkaged tours. Compute additive seasonality for the four quarters in 2020.

Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
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### Analyzing Seasonal Demand for Packaged Tours

A manager of a travel company is tasked with computing the 'additive' seasonality for the four quarters in 2020 using the historical demand data for packaged tours. 

### Historical Demand Data

Below is a table presenting the demand for each quarter over two years, 2018 and 2019:

| Period   | Demand |
|----------|--------|
| 2018 Q1  | 1800   |
| 2018 Q2  | 1525   |
| 2018 Q3  | 2100   |
| 2018 Q4  | 1940   |
| 2019 Q1  | 1848   |
| 2019 Q2  | 1456   |
| 2019 Q3  | 2172   |
| 2019 Q4  | 1950   |

This data shows the number of packaged tours demanded in each quarter over the specified years, highlighting potential seasonal trends.

### Task

The goal is to analyze this data to determine the additive seasonal factors for each quarter of 2020. 

### Note on Additive Seasonality

Additive seasonality involves determining a fixed amount to add or subtract to account for seasonal variations. This method is used to adjust forecasts based on past patterns observed in the data.

By analyzing these patterns, the manager can predict and prepare for demand fluctuations in 2020, aligning resources and strategies accordingly.
Transcribed Image Text:### Analyzing Seasonal Demand for Packaged Tours A manager of a travel company is tasked with computing the 'additive' seasonality for the four quarters in 2020 using the historical demand data for packaged tours. ### Historical Demand Data Below is a table presenting the demand for each quarter over two years, 2018 and 2019: | Period | Demand | |----------|--------| | 2018 Q1 | 1800 | | 2018 Q2 | 1525 | | 2018 Q3 | 2100 | | 2018 Q4 | 1940 | | 2019 Q1 | 1848 | | 2019 Q2 | 1456 | | 2019 Q3 | 2172 | | 2019 Q4 | 1950 | This data shows the number of packaged tours demanded in each quarter over the specified years, highlighting potential seasonal trends. ### Task The goal is to analyze this data to determine the additive seasonal factors for each quarter of 2020. ### Note on Additive Seasonality Additive seasonality involves determining a fixed amount to add or subtract to account for seasonal variations. This method is used to adjust forecasts based on past patterns observed in the data. By analyzing these patterns, the manager can predict and prepare for demand fluctuations in 2020, aligning resources and strategies accordingly.
Expert Solution
Step 1

D3=D1+D5+2(D2+D3+D4)8=1800+1848+2(1525+2100+1940)8=1847

By formula

Operations Management homework question answer, step 1, image 1

From above equation , first Deseasonalized demand

Then use regression and get

 

Regression=1937+2.43T

steps

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