1 11-1 mcgraw-hill/irwin © 2003 the mcgraw-hill companies, inc., all rights reserved
Post on 21-Dec-2015
219 views
TRANSCRIPT
3 11-3 Why Financial Analysis for New Products is Difficult
• Target users don’t know.
• If they know they might not tell us.
• Poor execution of market research.
• Market dynamics.• Uncertainties about
marketing support.
• Biased internal attitudes.
• Poor accounting.• Rushing products to
market.• Basing forecasts on
history.• Technology revolutions.
4 11-4
Forecasters Are Often Right
In 1967 they said we would have:
• Artificial organs in humans by 1982.
• Human organ transplants by 1987.
• Credit cards almost eliminating currency by 1986.
• Automation throughout industry including some managerial decision making by 1987.
• Landing on moon by 1970.
• Three of four Americans living in cities or towns by 1986.
• Expenditures for recreation and entertainment doubled by 1986.
Figure 11.1
5 11-5
Forecasters Can Be Very Wrong
They also said we would have:
• Permanent base on moon by 1987.
• Manned planetary landings by 1980.
• Most urbanites living in high-rises by 1986.
• Private cars barred from city cores by 1986.
• Primitive life forms created in laboratory by 1989.
• Full color 3D TV globally available.
Source: a 1967 forecast by The Futurist journal.
Note: about two-thirds of the forecasts were correct!
Figure 11.1(cont’d.)
6 11-6
Commonly Used Forecasting Techniques
Technique Time Horizon* Cost CommentsSimple Regression Short Low Easy to learnMultiple Regression Short-medium Moderate More difficult to
learn and interpretEconometricAnalysis
Short-medium Moderate to high Complex
Simple time series Short Very low Easy to learnAdvanced timeseries (e.g.,smoothing)
Short-medium Low to high,depending onmethod
Can be difficult tolearn but results areeasy to interpret
Jury of executiveopinion
Medium Low Interpret withcaution
Scenario writing Medium-long Moderately high Can be complexDelphi probe Long Moderately high Difficult to learn
and interpret
Figure 11.2
7 11-7
Handling Problems in Financial Analysis
• Improve your existing new products process.• Use the life cycle concept of financial analysis.• Reduce dependence on poor forecasts.
– Forecast what you know.
– Approve situations, not numbers (recall Campbell Soup example)
– Commit to low-cost development and marketing.
– Be prepared to handle the risks.
– Don’t use one standard format for financial analysis.
– Improve current financial forecasting methods.
8 11-8
0.9
0.603
0.0965 0.0614
0%10%20%30%40%50%60%70%80%90%100%
Aware Available Trial Repeat
A-T-A-R Model Results:Bar Chart Format Figure 11.3
11 11-11 Calculating New Product’s Required Rate of Return
Risk
% ReturnReqd. Rateof Return
Cost ofCapital
Avg. Riskof Firm
Risk on Proposed Product
Figure 11.6
12 11-12 Hurdle Rates on Returns and Other Measures Figure 11.8
Hurdle Rate Product Strategic Role or Purpose Sales Return on
Investment Market Share
Increase A Combat competitive entry $3,000,000 10% 0 Points B Establish foothold in new
market $2,000,000 17% 15 Points
C Capitalize on existing markets
$1,000,000 12% 1 Point
Explanation: the hurdles should reflect a product’s purpose,or assignment. Example: we might accept a very lowshare increase for an item that simply capitalized on ourexisting market position.
13 11-13
Hoechst-U.S. Scoring Model
Key Factors Rating Scale (from 1 - 10)1 ………. 4 ………. 7 ………. 10
Probability of TechnicalSuccess
<20% probability >90% probability
Probability of CommercialSuccess
<25% probability >90% probability
Reward Small Payback < 3 yearsBusiness-Strategy Fit R&D independent of R&D strongly supports
business strategy business strategyStrategic Leverage "One-of-a-kind"/ Many proprietary
dead end opportunities
Source: Adapted from Robert G. Cooper, Scott J. Edgett, and Elko J. Kleinschmidt. Portfolio Managementfor New Products, McMaster University, Hamilton, Ontario, Canada, 1997, pp. 24-28.
Figure 11.9