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ISYE 6501 Introduction to Analytic Modelling Homework 4 Georgia Institute of Technology $14.99   Add to cart

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ISYE 6501 Introduction to Analytic Modelling Homework 4 Georgia Institute of Technology

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ISYE 6501 Introduction to Analytic Modelling Homework 4 Georgia Institute of Technology

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  • August 28, 2024
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  • isye 6501
  • ISYE 6501
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ISYE 6501 Introduction to Analytic
Modelling Homework 4 Georgia
Institute of Technology.

, ISYE6501 HOMEWORK 4
Question 7.1


Describe a situation or problem from your job, everyday life, current events, etc., for which
exponential smoothing would be appropriate. What data would you need? Would you expect
the value of  (the first smoothing parameter) to be closer to 0 or 1, and why?



A good example of an exponential smoothing using trend might be a big box store
ordering the Christmas decorations stock to sell to the public. They will forecast needs based on
previous years sales as well as factoring in other variables such as unemployment rate and the
economy. Often, they plan the stock orders months in advance so last minute changes are not
always possible.

The reason this stands out to me so starkly is that I could not find decorations to save
my life last Christmas. It was so bizarre to me and at the time and I did not understand what
was causing these empty shelves.

Finally, I found a store manager at Lowes and asked him what was going on. He
explained that due to the Corona virus, many people had a lot of free time on their hands and
had the time to put a lot more effort towards their National Lampoon decorating skills. He
went on to tell me that his store had ordered their decorations 18 months prior.

I think we have all seen this with many material goods such as electronics, building
supplies, pool bleach, etc. This was one of those rare events that forecasting cannot anticipate
and illustrates one of the shortcomings of forecasting.

The data used in the forecast, barring rare events, would be the type of decoration (i.e.
outdoor lights, ornaments, inflated yard characters, etc.) If we stick with Lowes as an example,
they are a store driven by the seasons. So, our data should be found in the fall and/or winter
quarterly sales for prior years. Additionally, we would need to build in the buying power of the




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