“So that’s the simplified version of the decision tree based on what appear to be the critical issues,” Nancy Milnor concluded. “Calculating expected values, it looks as though we should introduce the new product. Now, I know that we don’t all agree on the numbers in the tree, so why don’t we play around with them a little bit. I’ve got the data on the computer here. I can make any changes you want and see what effect they have on the expected value.”

Nancy had just completed her presentation to the board of directors of DuMond International, which manufactured agricultural fertilizers and pesticides. The decision the board faced was whether to go ahead with a new pesticide product to replace an old one or whether to continue to rely on the current product, which had been around for years and was a good seller. The problem with the current product was that evidence was beginning to surface that showed that the chemical’s use could create substantial health risks, and they're even was some talk of banning the product. The new product still required more development, and the question was whether all of the development issues could be resolved in time to meet the scheduled introduction date. And once the product was introduced, there was always the question of how well it would be received. The decision tree (Figure 5.28) that Nancy had presented to the board captured these concerns.

The boardroom was beginning to get warm. Nancy sat back and relaxed as she listened to the comments.

“Well, I’ll start,” said John Dilts. “I don’t havemuch trouble with the numbers in the top half of the tree. But you have the chance of banning the current product fixed or pinned at 30%. That’s high. Personally, I don’t think there’s more than a 10%chance of an out-and-out ban.”

“Yeah, and even if there were, the current product ought to be worth $300,000 at least,” added Pete Lillovich. “With a smaller chance of a ban and a higher value, surely we’re better offwith the old product!”

“Well, I don’t know about you two,” said Marla Jenkins. “I think we have a pretty good handle on what’s going on with the current product. But I’d like to play the new product a little more conservatively. I know that the values at the ends of the branches on the top half of the tree are accounting’s best guesses based on a complete analysis, but maybe they should all be reduced by $100,000 just to play it safe. And maybe we should just set the probability of high sales equal to 50% regardless of the delay.”

Steven Kellogg had been involved in the preliminary development of the new product more than anyone else. He piped up, “And the delay is actually more likely than no delay. I’d just reverse those probabilities so that there’s a 60% chance of a delay. But I wouldn’t make any changes on the lower part of the tree. I agree with Marla that we have a good idea about the performance of the current product and the prospects for a ban.”

“I don’t think it matters,” countered Lillovich. “The changes John and I suggest make the current product look better than it does in Nancy’s analysis. Marla’s and Steven’s changes make the new product look worse. Either way, the effect is the same.”

Nancy had kept track of the comments and suggested changes. She sat down at the computer and started to enter the changes. After a few moments, she grinned and turned to the board. “In spite of your changes,” she said, “I believe I can persuade you that DuMond should go with the new product.”

Explain why Nancy believes that DuMond should go with the new product.

FIGURE 5.28 DuMond’s new product decision.

 

New Product EV = 1.10 Current Product EV = 0.76 No Delay (0.6) Delay (0.4) Ban (0.3) No Ban (0.7) Sales High (0.64) Sales Low (0.36) Sales High (0.57) Sales Low (0.43) Value ($ Millions) 1.35 0.85 1.20 0.75 0.20 1.00 Cengage Learning o