PERTEMUAN 5-6
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Transcript of PERTEMUAN 5-6
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1 11-1
PERTEMUAN 5-6
SALES FORECASTING AND FINANCIAL ANALYSIS(Perkiraan Penjualan dan
Analisis Keuangan)
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2 11-2
Tujuan Instruksional Khusus
• Mahasiswa mampu memperkirakan penjualan produk baru
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Proses Pengambilan Keputusan yang RasionalProses Pengambilan Keputusan yang Rasional
• Mengenali Problem Keputusan• Mendifinisikan ‘goal’ atau
obyektif• Mengumpulkan semua
Informasi yang Relevan• Mengindentifikasikan satu set
alternatif keputusan yang layak • Memilih Kriteria Keputusan
yang digunakan• Memilih alternatif yang paling
baik?
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4 11-4 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.
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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
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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.)
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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
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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.
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9 11-9
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
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10 11-10 Bass Model Forecast ofProduct Diffusion Figure 11.4
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11 11-11
The Life Cycle of AssessmentFigure 11.5
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12 11-12 Calculating New Product’s Required Rate of Return
Risk
% ReturnReqd. Rateof Return
Cost ofCapital
Avg. Riskof Firm
Risk on Proposed Product
Figure 11.6
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13 11-13 Hurdle Rates on Returns and Other Measures Figure 11.8
Hurdle RateProduct Strategic Role or Purpose Sales Return on
InvestmentMarket Share
IncreaseA Combat competitive entry $3,000,000 10% 0 PointsB Establish foothold in new
market$2,000,000 17% 15 Points
C Capitalize on existingmarkets
$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.
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Return to the PIC
• Seven-point scoring model:- Management interest- Customer interest- Sustainability of competitive advantage- Technical feasibility- Business case strength- Fit with core competencies- Profitability and impact
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15 11-15
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
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Summary
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