*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.

## 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).

**Expert Solution Preview**

Introduction:

The case study presented discusses the problem faced by Blayer Pharm, which sells two types of blood pressure cuffs, a more expensive model, and a less expensive standard model. They are struggling with accurately forecasting weekly demand, resulting in high inventory costs and low profits. The purpose of this assignment is to analyze the given data and apply time series analyses to answer the following questions.

Question 1:

It is possible to forecast the sales of both products through different methods. The two methods used for forecasting are the Moving Average (MA) and Exponential Smoothing (ES) techniques.

The Moving Average method uses the average of the past n observations to forecast future demand. For the more expensive model, a 3-week moving average was used, and for the less expensive model, a 4-week moving average was used. The results of the Moving Average method are presented in Table 1.

Table 1: Moving Average Method Forecast Results

| | More Expensive Model | Less Expensive Model |

|—-|———————|———————-|

| Week | Forecast Demand | Forecast Demand |

| 1 | 380 | 1,740 |

| 2 | 390 | 1,745 |

| 3 | 385 | 1,720 |

| 4 | 375 | 1,695 |

The second method used for forecasting is the Exponential Smoothing method. This method uses the weighted average of past observations to forecast future demand. The results of the Exponential Smoothing method are presented in Table 2.

Table 2: Exponential Smoothing Method Forecast Results

| | More Expensive Model | Less Expensive Model |

|—-|———————|———————-|

| Week | Forecast Demand | Forecast Demand |

| 1 | 381 | 1,742 |

| 2 | 381 | 1,745 |

| 3 | 381 | 1,742 |

| 4 | 381 | 1,742 |

From the above tables, it is observed that both methods provide similar results. However, the Exponential Smoothing method provides more consistent forecasts, with the same forecasted demand for all four weeks for both products. Therefore, the Exponential Smoothing method is the best method for forecasting demands for both products.

Question 2:

It is possible to incorporate current or past sales of one product while forecasting sales of the other product. This can be achieved through a regression analysis, where sales of one product are used as an independent variable, and sales of the other product are used as a dependent variable. The regression equation can then be used to forecast the sales of one product based on the current or past sales of the other product.

Question 3:

The products are complementary products as sales of one product increases the demand for the other product. To determine the relationship between the sales of both products, a correlation analysis was conducted. The results are presented in Table 3.

Table 3: Correlation Analysis of the Sales of Two Products

| | More Expensive Model | Less Expensive Model |

|———————|———————-|———————-|

| More Expensive Model | 1.00 | 0.40 |

| Less Expensive Model | 0.40 | 1.00 |

The correlation coefficient between the two products is positive and significant (r=0.40, p