• No results found

Enclosure C: Assumptions Sales Forecast

N/A
N/A
Protected

Academic year: 2021

Share "Enclosure C: Assumptions Sales Forecast "

Copied!
16
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Page 1 of 16

ial model

13) Total Sales Forecast 24) Cash Flow SF

NPV Difference 26) Net Present Value (NPV) SF 5) Price

21) Net Present Value (NPV) SA 10) Set up Costs

18) Sales Forecast SA 23) Sales Forecast SF

6) Contribution Margin 5) Price

7) Commission Percentage 6) Contribution Margin 5) Price

17) Commission Forecast SA

16) Contirbution Margin Forecast SA 22) Contribution Margin Forecast SF 12) Total Cost / Year

19) Cash Flow SA 8) Discount Rate 25) Discounted Cash Flow SF

20) Discounted Cash Flow SA 9) Take Down Costs

11) Switching Costs

= C a lc u la ti o n = I n p u t

Note: SA= Sales Agent SF= Employee Sales Force

(2)

Page 2 of 16

Enclosure B: Explanation Financial Model

Assumptions:

All below mentioned calculations are performed for the years 2004, 2005, 2006 and one for the sum of the three years.

1) Volume Per Product Per Customer

• Volume Per product per customer can be found in enclosure A.

• All volume inputs modelled with a probability distribution can be found in enclosure B.

2) Volume at risk being lost by not going direct

• All inputs can be found in enclosure D

3) Volume at risk by going direct

• All inputs can be found in enclosure C

4) Potential extra volume by going direct

• All inputs can be found in enclosure E

5) Price

• Price Per product per customer can be found in enclosure A.

• All price inputs modelled with a probability distribution can be found in enclosure B.

6) Contribution margin

• Contribution margin per product can be found in enclosure H

7) Commission

• Commission % per product can be found in enclosure H

8) Discount rate

• A discount rate of 14% is used

9) Take down costs

• See enclosure F for assumptions and enclosure I for distributions

= goodwill compensation (= average commission last five years * estimated open orders * special compensation) + loss of sales (Agent Y sales + others).

10) Set up costs

(3)

• See enclosure F for assumptions and enclosure I for distributions

= number of employees to recruit * recruitment costs per employee + legal costs for setting up + one time office costs.

11) Swicthing costs

= Set Up costs + Take Down Costs

12) Total cost per year

• See enclosure F for assumptions and enclosure I for distributions

= salary costs ((salary cost per employee * number of empoloyees + salary costs secretary * number of adiministration employees) * (salary growth * social security * heath insurance * pensino fund)) + accounting & auditing costs (accounting costs + auditing costs) + office costs (rent per sq meter * square meters per employee + utilities) + home office (furnishing + car + mobile phone + desk phone and internet + IT services) + bonus + T&L costs (other + hotel and food + flight + number of trips)

13) Total Sales Forecast

= Volume (1) * Price (5)

14) Volume employee sales force

= Volume (1) + potential extra volume (4) - Volume at risk by going direct (3)

15) Volume sales agent

= Volume (1) + Volume at risk being lost by not going direct (2)

16) Contribution margin forecast SA

= Contribution margin (6) * volume sales agent (15)

17) Commission forecast

= Commission (7) * volume sales agent (15) 18) Sales forecast sales agent

(4)

Page 4 of 16 21) Net present value sales agent

= Discounted cash flow 2004 +2005 + 2006

22) Contribution margin forecast SF

= Contribution margin (6) * volume sales force (14)

23) Sales forecast SF

= Volume sales force (14) * price (5)

24) Cash flow SF

= Contribution margin – total one time costs – total cost per year

25) Discounted cash flow SF

= Cash flow SF * Discount rate (8)

26) Net present value SF

= Sum of discounted cash flow 2004, 2005, 2006 27) Net present value difference

= Net present value SA (21) – Net present value SF (26)

(5)

Enclosure C: Assumptions Sales Forecast

(6)

Page 6 of 16

(7)

Enclosure D: Assumptions Sales Forecast modeled with probability

distribution

(8)

Page 8 of 16

Enclosure E: Assumptions Volume at risk by not switching to employee

sales force

(9)

Enclosure F: Volume at risk by switching to an employee sales force

Assumption: Various Auto Customers Volume

Uniform distribution with parameters:

Minimum 0,00 Maximum 100,00

Assumption: Various electronics customer volume

Uniform distribution with parameters:

Minimum 1,00 Maximum 300,00

Assumption: Distribution Volume Sales Agent

Uniform distribution with parameters:

Minimum 0,00 Maximum 50,00

Assumption: CD Customer Volume

Uniform distribution with parameters:

Minimum 0,00 Maximum 1.000,00

0,00 25,00 50,00 75,00 100,00

Variaus Aut o Cust omers Volume at risk by

0,00 75,00 150,00 225,00 300,00

Various electronics customer volume at r

0,00 12,50 25,00 37,50 50,00

Distribution Vol Sales Agent at risk bei

0,00 250,00 500,00 750,00 1.000,00

CD Cust omer Vol at risk being lost by go

(10)

Page 10 of 16

Enclosure G: Potential extra volume by switching to employee sales force

(11)

Enclosure H: Cost assumptions

(12)

Page 12 of 16

Enclosure I: Costs assumptions modeled with probability distribution

Assumption: Estimated open orders

Triangular distribution with parameters:

Minimum 65,00

Likeliest 80,00

Maximum 95,00

Selected range is from 65,00 to 95,00

Assumption: Others

Triangular distribution with parameters:

Minimum 400,00

Likeliest 520,00

Maximum 640,00

Selected range is from 400,00 to 640,00

Assumption: Agent Y sales

Triangular distribution with parameters:

Minimum 45,00

Likeliest 50,00

Maximum 55,00

Selected range is from 45,00 to 55,00

Assumption: Number of business trips per Sales Employee

Triangular distribution with parameters:

Minimum 5,00

Likeliest 10,00

Maximum 15,00

Selected range is from 5,00 to 15,00

Assumption: Rent / sq m

Triangular distribution with parameters:

Minimum 0,02

65,00 72,50 80,00 87,50 95,00

Estimated open orders

400,00 460,00 520,00 580,00 640,00

O thers

45,00 47,50 50,00 52,50 55,00

Plast oplan sales

5,00 7,50 10,00 12,50 15,00

Number of business trips per Sales Emplo

(13)

Likeliest 0,03

Maximum 0,03

Selected range is from 0,02 to 0,03

Assumption: salary costs per employee

Triangular distribution with parameters:

Minimum 35,00

Likeliest 43,00

Maximum 60,00

Selected range is from 35,00 to 60,00

Assumption: Salary costs senior secretary

Uniform distribution with parameters:

Minimum 15

Maximum 23

Assumption: Bonus Per Sales Employee

Uniform distribution with parameters:

Minimum 10,00

Maximum 15,00

Assumption: Special compensation pp

Triangular distribution with parameters:

Minimum 0,00

0,02 0,02 0,03 0,03 0,03

Rent / sq m

35,00 41,25 47,50 53,75 60,00

salary costs per employee

15 17 19 21 23

Salary costs senior secretary

10,00 11,25 12,50 13,75 15,00

Bonus Per Sales Employee

(14)

Page 14 of 16

Enclosure K: Contribution margin and commission per product

(15)

References

Benito, G.R.G., Pederson, T, Petersen, B. (1999). Foreign operation methods and switching costs:

conceptual issues and possible effects.

Scandinavian Journal of Management,

15, 213-229.

Calof, J.L. (1993). The mode choice and change decision process and its impact on international performance.

International Business Review

, 2(1), 97-120.

De Leeuw, A.C.J. (1996).

Bedrijfskundige Methodologie, management van onderzoek

, Van Gorcum Economist Intelligence Unit (2003),

Country Forecast Czech Republic

.

Economist Intelligence Unit (2003),

Country forecast Slovakia

. Economist Intelligence Unit (2003),

Country Report Czech Republic

. Economist Intelligence Unit (2003),

Country Report Slovakia

.

Klein, S., Frazier, G.L., & Roth, V.J. (1990). A transaction costs analysis model of channel integration in international markets.

Journal of Marketing Research

, 27(2), 196-208.

Heide, J., & John, G. (1988). The role of dependence balancing in safeguarding transaction-specific assets in conventional channels.

Journal of Marketing

, 52(1), 20-35.

Pedersen, T., Petersen, B., Benito, G.R.G. (2002). Change of foreign operation method: impetus and switching costs.

International Business Review

, 11, 325-345.

Reeder, R.R., Brierty, E.G., B.H. Reeder (1987). Industrial

Marketing: analysis, planning, and control

, Englewood Cliffs, NJ: Prentice-Hall

Root, F.R. (1987).

Entry strategies for international markets.

Lexington, MA; Lexington Books Stern, L.W., & El-Ansary, A.T. (1992).

Marketing Channels

. Englewood Cliffs, NJ: Prentice-Hall.

(16)

Page 16 of 16 www.czechinvest.org

www.csmauto.com www.globalinsight.com

List of interviewed persons:

• James Neuling, ABC, General Manager Central & Eastern Europe, April – August 2003

• John Van Der Stoop, ABC, Sales Manager EMEA, April 2003

• Erhard Bruss, ABC, Account Manager, July 2003

• Paul Maclean, ABC, European Sales Manager Polymers, July 2003

• Nico Egelman, ABC, Sales Director Automotive Central & Eastern Europe, July 2003

• Julian Cass, ABC, account manager, July 2003

• Paul Niesing, ABC, Finance Manager, July 2003

• Henriette Cegledi, ABC, Finance Manager, July 2003

• Jack Govers, ABC, Business Development Manager, August 2003

• Giles Thomas, ABC, Area Sales Manager Central Europe, April – August 2003

• Marijke Aalders, ABC General Counsel, April 2003

Referenties

GERELATEERDE DOCUMENTEN

[r]

Op basis van de kansen in tabel 1 is voor de totale hoeveelheid Nederlandse munten een model te maken dat voor elk tijdstip voorspelt hoeveel van deze munten in Nederland zijn

[r]

The Gauss–Newton type algorithms cpd and cpdi outperform the first-order NCG type algorithms as the higher per-iteration cost is countered by a significantly lower number of

This can be useful in large projects, where value can be entered once and automatically updated throughout the document, without having to maintain a seperate file full of

Of course, mhequ can be used in conjunction with the usual equation environment, but mhequ is great, so why would you want to do this. x = y +

By just specifying a graphics file, the macros provided by this package will render it and its reflection automatically..

Given the fact that the Ermelo structure is not situated on the löss, that it is very large, has no clear ditches, and consists for the largest part of natural ridges, an