Hello again:)

Well..this lecture is service management, and the professor gave us group project.
As you can see the uploaded picture and ppt, the professor wants us to MAKE OUR OWN FICTION SERVICE COMPANY. So, our group decided to go with 24 hours restaurant. Our six members assigned each of task and I’m in charge of TASK 1_(4) ” Explain how those peripheral services are helping core service/s” part. You should know that I am group member 4 and I should write about  TASK 1_(4) = (slide 6) around 150~200 words.
Actually, one of our member already finished for TASK 1_(3) “Core service/s & Peripheral service/s (at least 4 peripheral services” part, so basically you can write my part based on the slide 5.

Simply saying, you should write about ‘Explain how those peripheral services are helping core service/s for 24 hours restaurant’. MY part is only for TASK 1(4) please just focus on this. And remember our group decided to go with 24 hours restaurant. So, you can actually add more for core services and peripheral services, if it’s not enough. 

If you have any questions or don’t understand anything please let me know.

Time Series Prediction Project for BA375: Two choices

Option 1: Use one of the US Census time series linked https://www.census.gov/econ/currentdata/dbsearch?program=MRTS&startYear=1992&endYear=2017&categories=44X72&dataType=SM&geoLevel=US&notAdjusted=1&submit=GET+DATA&releaseScheduleId=%C2%A0 (Links to an external site.) (note that the Census will allow you to check seasonally adjusted or not seasonally adjusted. Youll almost certainly want not seasonally adjusted since it will help you to show that you can deal with seasonality).

Option 2: Find another time series of interest to you on the internet (any time series OK, but be sure to include a link to the data. One option: choose any subcategory of the data from class!).

Whether you choose Option 1 or Option 2, perform a time series analysis. Consider is it horizontal? Does it show a trend? Seasonality?  How about a non-seasonal cycle?  Some textbooks including Business Analytics say that non-seasonal cycles must be longer than a year (and depending on your sampling frequency those might be the only cycles you can see in your data), but of course that’s not generally true.  Biological rhythms are generally cyclic and less than a year in length, and it’s entirely possible to have cycles in the supply chain that repeat more often than yearly.

Consider smoothing (moving average or exponential smoothing). Include graphs of both. Do you believe they improve the model? Why or why not?

Here’s a quick and imperfect video (I forgot the camera was on! be merciful and toggle my face to the smaller screen!) showing you the basic steps, if it helps.  I’m following Kylene Tyler’s “Project4Tutorials.docx” if you prefer that format.

Time Series Prediction Project for BA375: Two choices

Option 1: Use one of the US Census time series linked https://www.census.gov/econ/currentdata/dbsearch?program=MRTS&startYear=1992&endYear=2017&categories=44X72&dataType=SM&geoLevel=US&notAdjusted=1&submit=GET+DATA&releaseScheduleId=%C2%A0 (Links to an external site.) (note that the Census will allow you to check seasonally adjusted or not seasonally adjusted. Youll almost certainly want not seasonally adjusted since it will help you to show that you can deal with seasonality).

Option 2: Find another time series of interest to you on the internet (any time series OK, but be sure to include a link to the data. One option: choose any subcategory of the data from class!).

Whether you choose Option 1 or Option 2, perform a time series analysis. Consider is it horizontal? Does it show a trend? Seasonality?  How about a non-seasonal cycle?  Some textbooks including Business Analytics say that non-seasonal cycles must be longer than a year (and depending on your sampling frequency those might be the only cycles you can see in your data), but of course that’s not generally true.  Biological rhythms are generally cyclic and less than a year in length, and it’s entirely possible to have cycles in the supply chain that repeat more often than yearly.

Consider smoothing (moving average or exponential smoothing). Include graphs of both. Do you believe they improve the model? Why or why not?

Here’s a quick and imperfect video (I forgot the camera was on! be merciful and toggle my face to the smaller screen!) showing you the basic steps, if it helps.  I’m following Kylene Tyler’s “Project4Tutorials.docx” if you prefer that format.