Pam is a healthcare administration leader for a large network of hospitals and health service centers that is attempting to predict future healthcare utilization at their centers over the next 5 years. She obtains data regarding patient use across the hospital network and health service centers and projects forward over the next 5 years to determine which areas might experience continued growth. After applying her time series model, she is able to demonstrate that, indeed, the hospital network and health service centers will experience significant growth. As she prepares to share her results and findings with the board, she also considers advocating for the development of a new regional health service center to fill one of the areas that will experience the most growth according to her forecast projections.
As a current or future healthcare administration leader, you may be asked to assess strategic planning and decision making using time series analysis.
Review the resources and reflect on time series models and forecasting. Think about how you might implement these methods for healthcare administration practice.
Describe in 2 or 3 parapgraphs some variables that you might evaluate using time series in your health services organization or one with which you are familiar. Then, explain what types of models might be most appropriate to measure, and analyze these variables. Be specific, and provide examples.
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 and consider how you might apply time series analyses to address the case questions. Use the attach dataset to answer the following questions. Provide complete analysis and graphs, as appropriate
Note: For this Assignment, you will be using SPSS.
The Assignment: (3–5 pages)
- Is it possible to forecast either series with some degree of accuracy or an extrapolation method (where only past values of that series are used to forecast current and future values)? Which method appears to be best? How accurate is it?
- Is it possible, when trying to forecast sales of one product, to somehow incorporate current or past sales of the other product in the forecast model?
- Are these products “substitute” products or are they “complementary” products? Conduct appropriate analyses to support your argument.
Albright, S. C., & Winston, W. L. (2015). Business analytics: Data analysis and decision making (5th ed.). Stamford, CT: Cengage Learning.
- Chapter 12, “Time Series Analysis and Forecasting” (pp. 590–653)