Time Series Analysis for Blayer Pharm

 
Time Series Analysis for Blayer Pharm
Blayer Pharm sells two types of blood  pressure cuffs at more than 50 locations in the Midwest. The first style  is a relatively expensive model, whereas the second is a standard, less  expensive model. Although weekly demand for these two products is  fairly stable from week to week, there is enough variation to concern  management. There have been relatively unsophisticated attempts to  forecast weekly demand but they haven’t been very successful. Sometimes  demand (and the corresponding sales) is lower than forecasts, so  inventory costs are high. Other times, the forecasts are too low. When  this happens, and on-hand inventory is not sufficient to meet customer  demand, Blayer requires expedited shipments to keep customers happy—and  this nearly wipes out Blayer’s profit margin on the expedited units.  Profits would almost certainly increase if demand could be forecast more  accurately. Data on weekly sales of both products appear in the file  for this week. A time series chart of the two sales variables indicates  what Blayer management expected—namely, there is no evidence of any  upward or downward trends or of any seasonality. In fact, it might  appear that each series is an unpredictable sequence of random ups and  downs.
For this Assignment, reflect on  the scenario presented. Review the resources for this week and consider  how you might apply time series analyses to address the case questions.
Note: For this Assignment, you will be using SPSS.
 

Document: Week 6 Assignment Dataset (Excel file)
 
The Assignment: (3–5 page)
Use the dataset to answer the following questions. Provide complete analysis and graphs, as appropriate.

It is possible to forecast either series with some degree of  accuracy or with an extrapolation method (where only past values of that  series are used to forecast current and future values).    

Perform an analysis with at least two different methods. Show  your forecast results in table form (include your SPSS output tables).
Which method appears to be best? In narrative form, defend your  choice of best method. Include a description of the level of accuracy of  the chosen method.

Is it possible, when trying to forecast sales of one of your blood  pressure cuff products, to somehow incorporate current or past sales of  the other blood pressure cuff product in the forecast model? Why or why  not? Explain your reasoning and how you would go about doing this.
Are these products “substitute” products or are they  “complementary” products? Why? Conduct appropriate analyses to support  your argument and include tabular results (i.e., your SPSS output).

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