Although venturing into this option would require repackaging, relabeling, and reindicating the brand and it might accelerate the cannibalization of Pharma's sales, reintroducing the product could attract new users from competitive brands. As Joachim Zander analyzed the two scenarios under when they reintroduce their product: “Under the first scenario, we essentially create attention for ourselves and the market reacts. As a result, Pharma's high-end product, CardioASPIRIN® , loses 10% of its sales due to our cheaper alternative.
However, we also appropriate incremental sales of 10% from the competition. Beyond that, by keeping the product on the market, though in a new form, we are able to keep 20% of the children's ASPIRIN customers. Under the second scenario, I envision that the market does the opposite and reacts very little to our product change. Thus, Pharma doesn't lose any volume, and Consumer Care is unable to gain any incremental volume from the competition. Either way, we still keep 20% of the children's ASPIRIN customers.
” Comparing Exhibit 6 with Exhibits 7A and 7B, the forecast losses in Option 1 will be substantially greater given the Middle and Worst Case Scenarios than what is featured in losses in Option 2. Although in the Best Case Scenario in Option 1 could appear to be advantageous, assuming that scenario to happen would be faulty. Also, as emphasized in Exhibit 5, although the sales of children’s aspirin is declining, its margin against the Prevention CardioASPIRIN® is still significantly higher.
Keeping their seasoned and most valued customers who give them more profits will be the optimal approach. Thus, the win-win solution would be Option 2 because either way 20 percent of the children’s aspirin customers will be maintained. This business decision will be less damaging to the company, either best or worse case happens. 2. ) Do you agree with quantitative assumptions used to develop the forecasts? Why or Why not? Yes, I agree.
In order to come up with the best decision, managers must make a number of educated assumptions about future trends and events and modify those assumptions once new information becomes available. Quantitative forecasts are typically based on historical data or tests and which involve complex statistical computations, which the Bayer Aspirin Case presented to represent the financial outcomes of the two options. Although quantitative forecasting is not foolproof, it is a valuable tool that enable managers to fill in the unknown variables that inevitably crop up in the planning process.
For instance, the case study obtained the trend from 1994 to 1998 to formulate the outcomes of the trends in 1999 to 2003. The results they obtained indicated that prevention customers had been rapidly replacing children customers over the five years from 1994 through 1998. Forecast indicated by 2003 that approximately 27% of Children’s ASPIRIN sales would be for children and 73% for prevention customers, effectively reversing the 80% / 20% split, respectively, from 1994.
Upon seeing the trends, Zander and Merker had the ability to conclude that Children’s ASPIRIN was naturally evolving into a low-cost, low-dosage prevention product. And it looked as if the combination of the rapid growth in the prevention market and the decline of the children’s market would be the death of the Children’s ASPIRIN brand. Of course, these are just assumptions and these could be wrong but at least the present trends could spell a lot with what will happen in the future.