1 11-1

PERTEMUAN 5-6

SALES FORECASTING AND FINANCIAL ANALYSIS(Perkiraan Penjualan dan

Analisis Keuangan)

2 11-2

Tujuan Instruksional Khusus

• Mahasiswa mampu memperkirakan penjualan produk baru

3 11-3

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?

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.

5 11-5

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

6 11-6

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.)

7 11-7

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

8 11-8

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.

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

10 11-10 Bass Model Forecast ofProduct Diffusion Figure 11.4

11 11-11

The Life Cycle of AssessmentFigure 11.5

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

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.

14 11-14

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

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

16 11-16

Summary

…