International Industrial Management I

Transcrição

IEU
Lehrstuhl für
Industrie, Energie und Umwelt
Location Decisions I
| Introduction | Examples | Check List |
| Steiner-Weber Model|
Universität Wien
Fakultät für Wirtschaftswissenschaften
Lehrstuhl für Industrie, Energie und Umwelt
Brünner Straße 72, 1210 Wien
IEU
Lehrstuhl f. Industrie, Energie und Umwelt | Mag. Florian Pützl |
Overview | Location Decisions
F Choice of locations
F
F
F
F
Introduction
A class of locational choice problems
Factors for locations
Methods
F
F
F
F
F
F
F
Check lists
Benefit analysis (simple, additive)
Location break-even analysis
Transportation method
Steiner-Weber Model
Location-alloction Model of Cooper and extensions
Hotelling Model
F Choice of locations with the firm
F The basic problem
F CRAFT
F Layouts
| Florian Pützl © 2009 | International Industrial Management I, WS 2009
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Location Decisions - Objective
Choose the location
that maximizes
the firm’s benefit
There are only three important things concerning locations:
1) Location, 2) Location, 3) Location.
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Location Decisions
F Importance:
Location decisions do not only result in real estate and investment
costs but influence in particular fixed and variable costs.
F Transportation costs
F rentals
F Wages
F Taxes, etc.
F Links to the environment (universities, clusters, etc.)
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Example: Automobile Industry I
Source: Dyer, J.H.,Dedicated Assets: Japan‘s Manufacturing Edge , Harvard Business Review, Nov.‐Dec., 1994, S.174ff.
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Example: Automobile Industry II
Source: Dyer, J.H.,Dedicated Assets: Japan‘s Manufacturing Edge , Harvard Business Review, Nov.‐Dec., 1994, S.174ff.
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Example: Automobile Industry III
Comparing locations of production and organizational units of the
automobile industry in Japan and the US (Dyer, 1994) finds and argues
the following:
F Thesis: The huge success of the Japanese car industry is to a large extent
due to the close relations with its suppliers and in particular the
geographical vicinity.
F There exist large differences concerning the average distance between
locations of Japanese and American car manufacturers. In particular, the
entire ring of Toyota and its suppliers fits between two GM locations.
F Inventory as a share of turnover is much larger at US car producers which
implies more bound capital. These distances in turn make it hard to
implement Just-in-Time Management (see later), which further increases
inventory costs.
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Example: Automobile Industry IV
F Closeness implies much better contacts between relevant people
and organizations (including suppliers). It is a grave
misunderstanding that personal contacts become redundant once
an industry can actually choose its supply in aglobally integrated
world by a mouse click; remember this for the following discussion
about clusters.
F If GM had a similar ratio between inventory and sales it would save
US$ 6 billions which amounts to annula cost saving of the order
between US$ 400 – 500 Millions depending on costs of capital
ranging between 6% to 8%.
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Introductory Example
Assumptions: Procurement centered around Dortmund.
Volume = 1.500 t and transport cost are 1 MU per t and km.
Demand in Munich, Volume = 1000 t, transport cost are 1.2 MU per t and km.
Table: Example of a location decision l
Feasible
locations
Distance from
procurement center
Transport costs
(in 1000 MU)
Distance to
center of demand
Transport costs
Sums
(in 1000 MU)
Aachen
150
(150*1500*1)=
225
650
(650*1000*1,2)=
780
1005= 4th rank
Braunschweig
270
405
625
750
1155= 6th rank
Dortmund
10
15
650
780
795= 1st rank
Erfurt
420
630
380
456
1086= 5th rank
Munich
650
975
10
12
987= 3rd rank
Nürnberg
480
720
170
204
904= 2nd rank
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Interdependences
Produktionsprogramm
*:
Optimierungsproblem
Ertrag
Produktpreise
Externe Transportkosten
Profitability
of a firm for
a given
configuratio
n of
locations
Aufwand
Innentransportkosten
Sonstige
aufwandsgleiche Kosten
+ (Aufwand≠Kosten)
Transportwege
Transportmenge
n
Innerbetriebliche
Standorte*
Transportmengen
Löhne, Steuern usw.
Investitionsprogramm
Kapitaleinsatz
Investitionsausgaben je Projekt
* Optimization problem
Source: Lüder, p.31
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Mathematical Formulation of Location Decisions I
F Assumptions (given):
F A set of locations:
S = {s j ;
j = 1, K , n }
F A set of organisational units:
B = {bi ;
i = 1, K , m }
F Location of the organisational units:
s (bi ) = s j ∈ S
F Objective:
F A mapping that assigns to each unit bi a location from the set S:
∑:B → S
bi → s (bi ) ⊂ S
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Mathematical Formulation of Location Decisions II
F Further assumptions:
F O is the set of given locations that are connected to locations bi e.g. customers:
O = {o p , p = 1, K , r }
F The sets O, S and B are independent (in practice however there are many
interdependencies and feedbacks).
F Further constraints such as those on capacity.
F Objective:
Z = f [s (b1 ), s (b2 ), K , s (bm )]
F Maximize/Minimize
F e.g. the locations of the organizational units are chosen in order to minimize the
transport costs.
F e.g. the (incremental) benefit from the choice of a particular location or locations should
be maximized.
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Examples of Constraints
Organizational units that must be close to the market
F Public
F Police, ambulance, fire brigade
F post
F Retail and Services
F Fast food, gas stations, super markets
F Pharmacies, shopping malls.
F Services
F M.D., lawyers, barbers
F Banks (?), mechanics, hotels
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Evaluation of Locations
F Methods
F Check lists
F (Cost-) Benefit analysis
F Location Break-Even Analysis
F Transportation Method
F Steiner-Weber Model
F Location-Alloction Model von Cooper
F Hotelling
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Factors Crucial for Locations
Arthur Anderson
Deloitte Touche
F Market size
F Availability of qualified labor
F Expected profits
F Reserach institutes
F Openess of markets
F Quality of life
F Stability
F Venture-Capital
F Costs
F bureaucracy
F Labour (quality and work ethics)
F Infrastructure
F Technological know how
F Labor costs
F Technology
F IT-Infrastructure
F Bureaucracy
F Suppliers & partners
F Subsidies
F Resources, primary inputs
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Match Product & Country
1. Braun Household
Appliances
2. Firestone Tires
3. Godiva Chocolate
4. Haagen-Dazs Ice Cream
5. Jaguar Autos
6. MGM Movies
7. Lamborghini Autos
8. Alpo Petfoods
1.
2.
3.
4.
5.
6.
7.
8.
Great Britain
Germany
Japan
United States
Switzerland
India
Italy
Denmark
Page 16
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Match Product & Country
1. Braun Household
Appliances
2. Firestone Tires
3. Godiva Chocolate
4. Haagen-Dazs Ice Cream
5. Jaguar Autos
6. MGM Movies
7. Lamborghini Autos
8. Alpo Petfoods
1.
2.
3.
4.
5.
6.
Great Britain
Germany
Japan
United States
Switzerland
India
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Page 17
A Few International Comparisons
FProductivity
FGrowth
FCountries
FCities
FEconomic liberty
FCorruption
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Growth Competitiveness Index: 2006-2007 | Top 20
Source: World Economic Forum
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Growth Competitiveness Index: 2006-2007 | Rank > 100
Growth Competitiveness Index – 2006-2007, Rank >100
Source: World Economic Forum
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Business Competitiveness Index: 2006-2007
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Business Competitiveness
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Productivity by Sectors
Martin Neil Baily und Robert M.
Solow, International Productivity
Comparisons Built from the Firm
Level, Journal of Economic
Perspectives, 15/3, 151-172,
2001.
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Labor Productivity and Wages
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International Labor Costs in Automobile Industry
(in €/h)
n
A
pa
Ja
ia
S.
U.
l
st r
ga
Au
rtu
en
ai n
Po
Sp
ed
UK
Sw
Th
e
Ne
th
e
r la
n
B e ds
l gi
um
ly
Ita
ce
an
Fr
Ge
rm
an
y
€ 50.00 42.29
36.61
35.52
31.46 31.66
30.71 34.51
€ 40.00
28.94
24.68
23.95
21.99
€ 30.00
12.06
€ 20.00
€ 10.00
€ 0.00
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Corruption
F 5 Best:
F
F
F
F
F
Finland
USA
Canada
Singapur
Australia
F Corruption index:
FFinland
FDenmark / New Zealand
FCanada
FUK
FUSA
FGermany / Israel
FJapan
FItaly
FChina
FEgypt
FIndia / Russia
FNigeria
FBangladesh
F 5 Worst:
9,7
9,5
9,0
8,7
7,7
7,3
7,1
5,2
3,5
3,4
2,7
1,6
1,2
FBangladesh
FParaguay
FNicaragua
FNigeria
FZimbabwe
10 = No corruption
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Economic Freedom I
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Economic Freedom II
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Economic Freedom - Graphical
HONG KONG keeps its top position
in the Economic Freedom Index
compiled by the Fraser Institute, a
Canadian think-tank. The index
ranks the policies of 141 countries
according to how much they
encourage free trade, both internally
and with other territories. Countries
with fewer taxes, strong property
rights, low regulation and sound
money score best. Britain and
America tie with Canada for fifth
place in the list. Germany is ranked
18th—on a par with El Salvador but
above Japan. Most of the lowranking countries are African, except
Myanmar and Venezuela, which are
both in the bottom ten.
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Page 29
Ranking of Cities
Citiy
2006
2005
1990
Index2006
London
1
1
1
0,91
París
2
2
2
0,59
Frankfurt
3
3
3
0,36
Barcelona
4
5
11
0,27
Brussels
5
4
4
0,24
Amsterdam
6
6
5
0,23
Madrid
7
7
17
0,20
Berlín
8
8
15
0,18
Munich
9
9
12
0,18
Zurich
10
10
7
0,16
Source: European Cities Monitor 2006:
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Another Ranking of Cities
London and Paris maintain their position as
Europe's two top cities to locate a business
Warsaw can expect the biggest influx of
international companies over the next five
years.
Barcelona is also the city perceived as doing
the most in Europe to improve itself as a
business location, followed by Prague and
Madrid
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Hardship Rating
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Austria (WIFO)
Advantages
Disadvantages
Part of the common market
Bureaucracy
Active in Eastern Europe
Duration until receiving operating permits
Availability of high quality labor
Costs of telecomm (not any more!)
Availability of highly qualified labor
High costs for unskilled labor
Consensus seeking society
Environmental regulations
Rule of law
Lack of venture capital
Environmental quality
Little competition in energy markets
Cultural and leisure activities
Lack of reforms (and willingness to reform)
Public safety
Public administration
Political stability (?)
Energy costs
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Page 33
Austria (US-Investor Confidence Study)
Advatantages
Disadvatantages
Knowledge of foreign languages
Tax deductions of investments
Access to Eastern European markets
Real estate prices
Sales in Austria
Cost of living
Qualifications and effort (of labor)
Operating permit (length)
Transport
Labor permits for Non-EU residents
Culture and leisure activities
Opening hours
Political stability
Income taxes
Social peace
Labor laws
Costs of electricity
Wages, payroll taxes and other ancillary labor costs
Page 34
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Topical (Economist, 2008): Eastern Europe vs Asia
• As in the previous five years, economies in eastern Europe and Central Asia have
consistently seen the fastest pace of positive reform.
• On average, it takes 21 days to register a business in eastern Europe, which is 27
days faster than in East Asia. Setting up a company in Indonesia costs 77.9% of the
average annual income per person; in Georgia it costs 4%—though there is the small
matter of political risk to factor in. Firing a worker costs an average of 53 weeks’
salary in East Asia, compared with 27 in eastern Europe. All this cutting of red tape
has brought results: Poland now has as many registered businesses relative to its
population as Hong Kong does.
• Eastern Europe’s rapid progress due to the accession requirements imposed by
the European Union (EU), e.g., the EU requires new members to create a “one-stop
shop”. Before Macedonia became a candidate for EU membership in 2005, it took 48
days to start a business there. After three years of reforms, it now takes nine days.
• East Asian countries still have the edge in some respects: it is easier to move
goods across their borders, for example. Businesses in East Asia also face lower
taxes. Taxes on profits in eastern Europe are among the lowest in the world, typically
around 10%, but labour taxes and compulsory pension contributions increase the
overall tax burden on business.
• Of course, a few East Asian economies are still miles ahead of eastern Europe.
Singapore 1st, Hong Kong 4th but Georgia, Estonia, Lithuania and Latvia in the top
30, Russia = 120th and Azerbaijan was the top reformer.
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Page 35
(Cost-) Benefit Analysis - Additive
Four steps:
1. Scaling: Evaluation via scale (say 1 – 10) the benefits of location with respect
to a particular factor which allows to rank and evaluate also qualitative factors.
2. Weighting: Assign weights {gi} to each individual factor entering the analysis
and to differentiate between important and less important ones.
3. Aggregate: Multiply points the scale in (1) with the weights in (2) and add up
which gives the measure of benefit associated with a particular location:
N(sj) = n1j*g1 + n2j*g2 + .....
4. Choose the location with the highest benefit = highest value of N.
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A Simple Example
Labor
Sales
Subsidies
Locations s1
7
2
1
s2
5
1
9
s3
10
4
6
s4
Weights
5
7
8
0.3
0.5
0.2
N(s1) = 7*0,3 + 2*0,5 + 1*0,2 = 3,3
N(s2) = 5*0,3 + 1*0,5 + 9*0,2 = 3,8
N(s3) = 10*0,3 + 4*0,5 + 6*0,2 = 6,2
N(s4) = 5*0,3 + 7*0,5 + 8*0,2 = 6,6
Therefore, location 4 should be chosen.
Extension: Multi-criteria optimization (or decision making)
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Check Lists
F Precise evaluation of the benefits associated with each factor.
F Similarly to the the above benefits analysis – hierarchical
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Benefit Analysis
F Weights allow to mitigate for outliers
F Ranking without weights:
C–B–A-D
F Ranking with weights:
A–C–D–B
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Page 39
Location Break-Even Analysis I
F Comparison of the costs at different locations depending on the rate
of utilization.
F Example
F
F
F
F
Production of a new Volkswagen model.
3 potential locations with their respective costs
Assume a sales price
Profit:
Standort
Poznan
Pamplona
Wolfsburg
Fixe Kosten
€ 3.000.000
€ 6.000.000
€ 11.000.000
Variable Kosten
€ 7.500,00
€ 4.500,00
€ 2.500,00
Verkaufspreis
€ 12.000,00
Standort
Poznan
Pamplona
Wolfsburg
E(Menge)
2.000
Gesamtkosten*
Gewinn*
€ 18.000.000
€ 6.000.000
€ 15.000.000
€ 9.000.000
€ 16.000.000
€ 8.000.000
* bei einer Menge von 2.000 Stk.
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Location Break-Even Analysis II
1. Given a fixed quantity of 2000 pieces Pamplona yields the highest
profits.
2. Since the assumption of 2000 pieces depends on a marketing study
it is unreliable. Therefore, the management wants to know whether
Pamplona remains the most profitable with less or higher
production.
F Location Break-Even Analysis
F This procedure calculates the intersections of the cost curves
associated with the different locations depending on the rate of
production.
F This yields capacity bounds where which location is efficient as well as
the minimal costs over all locations contingent on production.
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Location Break-Even Analysis III
F Depending on production, different locations turn out to be the most
profitable ones:
22.000.000
Poznan
Pamplona
Gesamtkosten
19.000.000
16.000.000
Wolfsburg
13.000.000
10.000.000
7.000.000
4.000.000
Poznan
Pamplona
Wolfsburg
1.000.000
0
500
1000
1500
2000
2500
3000
3500
Menge (Stk.)
Page 42
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Sequential (Hierarchical) Location Decisions I
1) Countries
2) Regions
3) Final location
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Sequential (Hierarchical) Location Decisions II
Countries
1.
2.
3.
4.
5.
6.
Political risks, regulation, incentives.
Cultural and economical.
Location relative to markets (sales and procurement).
Labor: availability, productivity, costs, behavior.
Availability of suppliers, communication, energy.
Exchange rates and their risks.
Regions
1.
2.
3.
4.
5.
6.
7.
8.
Specifics views and demands of the firm.
Atractiveness of region (culture, climate, taxes, etc.)
Labor: availability, productivity, costs, unions.
Availability and costs of materials.
Environmental regulation at federal and city level.
Incentives (by regions, cities).
Availability and distance to primary inputs.
Costs for real estates and construction.
Specific location
1.
2.
3.
4.
5.
Location specific costs contingent on the size of the unit.
Air, rail, road and water transport networks.
Zoning, in particular restrictions.
Availability and distance to suppliers and service providers.
Environment and environmental constraints.
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Case Study | BMW Spartanburg, USA I
Location decision of BMW
F Production of BMW Z3
in the USA, mid 1990ies.
F Expansion to include
BMW X5 und Z4.
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Case Study | BMW Spartanburg, USA II
1) Choice of the US
2) Choice of South Carolina
3) Final decision for Spartanburg
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Case Study | BMW Spartanburg, USA III
Factors decisive for choosing USA
F Market:
F Market size: USA is the world‘s largest market for luxury cars
F Growing versus declining markets: US growing market due to baby boomers.
F Labor:
F Lower labor and production costs:
(US: US$ 17/h, DEU: US$ 27/h)
F High labor productivity
(Holidays: US 11, Germany: 31)
F Additionally:
F Low transport costs (US$ 2.500,- less per vehicle)
F New facilities improve productivity and thus lower the costs per car (around US$
2.000-3.000.-)
F Insurance (‚hedging‘) against high Dollar-Euro exchange rates.
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Case Study | BMW Spartanburg, USA IV
Factor in favor of South Carolina
85
F Labor:
F Low wages in South Carolina
F Public subsidies:
F US$ 135 Mio.in terms of tax deductions.
F Free trade zone:
F Duties neither on imported goods nor on car
exports.
85
F Infrastructure:
F Access to freeway and via the freeway to the
airport Charlotte from which Lufthansa
operates direct flights to Munich. Port
Charleston serves for exports and the
imports of engines and gearboxes from
Europe
26
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Labor Productivity
F Keyword: level of labor costs
F Cheaper employees => higher profit?
F However, cheap labor is NOT everything – countries with cheap
labor are often characterized by low labor productity that can erode
partially or totally the advantage in terms of labor cost.
Compare: Gregory Clark, Farewell to Alms, textile production in
England and colonial India.
F ‚Skills‘ of workers, good infrastructure, and technology are often
more important to many industries than plain labor costs.
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Page 49
Case Study | Labor productivity
F Quality Coils Inc., CT, USA
F Coil production for electrical equipments
F 2 potential locations:
F Expanding a the present location in Connecticut
F New factory in Juarez (one of the fastest growing cities in Mexiko along the US border
due to the Maquiladoras)
F Comparison based on labor costs:
Labor Productivity =
Labor Cost per day
= cost per unit
Productivity (units per day)
Wage/d
Output/
worker
Labor cost/
piece
Connecticut Plant
US$ 70.-
60 units
US$ 1.17/unit
Juarez Plant
US$ 25.-
20 units
US$ 1.25/unit
F Historical Example: Alfa Romeo - Alfasud
Page 50
IEU
Four International Operations Strategies
Cost Reduction Considerations
High
Global Strategy
Transnational Strategy
; Standardized product
; Economies of scale
; Cross-cultural learning
; Move material, people, ideas
across national boundaries
; Economies of scale
; Cross-cultural learning
Examples
Texas Instruments
Caterpillar
Otis Elevator
Examples
Coca-Cola
Nestlé
International Strategy
; Import/export or
license existing
product
Multidomestic Strategy
; Use existing
domestic model globally
; Franchise, joint ventures,
subsidiaries
Examples
U.S. Steel
Harley Davidson
Examples
Heinz
The Body Shop
McDonald’s Hard Rock Cafe
Low
Low
High
Local Responsiveness Considerations
(Quick Response and/or Differentiation)
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Page 51
Steiner-Weber Model I
F Geometric, continuous Model based on the works of the German
geometricians and regional scientists:
F Launhardt (1882), Steiner-Weber (1909) & Lösch, Christaller (1940er)
F Model assumptions:
{
}
F The set location where to delivery to, O = o p , p = 1, K r is given.
F The set of potential locations (S) is the entire plane (infinitely many, actually
uncountable many).
F The decision involves only a single location s.
F Transport costs are proportional to the Euclidean distance.
F Problem – minimize transport costs:
F Find the optimal location s * ∈ S determined by the coordinates x*, y*
that minimizes from the chosen location the transport costs to the sinks
Op with the coordinates xp, yp (p=1,…r).
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Steiner-Weber Modell II
F The objective function depends on:
F Volume (delivered or for delivery) from the given location p, ap,
F Normalized transport cost (e.g. EUR/ton/km) kt,
F Distance measures in terms of direct connection (Euclidean), dp.
dp =
(x − x ) + (y − y )
2
2
p
(xp,
yp)
p
dp
xp
F Objective
x
r
r
p =1
p =1
(x, y)
K T = ∑ K T p = ∑ kt * a p * d p
y
yp
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Page 54
Steiner-Weber Modell III
(x − x ) + (y − y )
r
F Objective Æ min:
min K T = k t ∑ a p *
x, y
2
2
p
p =1
p
F Differentiation with respect to x and y and equating to zero yields the first
order optimality condition.
r
∂K T
= kt ∑
∂x
p =1
r
∂K T
= kt ∑
∂y
p =1
ap (x − xp )
!
(x − x ) + (y − y )
2
p
2
p
ap(y − yp)
!
(x − x ) + (y − y )
2
p
=0
2
=0
p
F Result: two nonlinear equations that require numerical means (e.g.
Newton‘s iteration scheme)
F Fortunately, Excel does it! (via the Solver).
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Steiner-Weber Modell IV | Increments
F Nonlinear transport costs but distance depending, e.g., quadratic, or
including set up costs for each connection: k T p = k T p (d p )
F It can make sense to link the transport costs to particular sinks p to account
for differences in deliveries (e.g., scrap versus sensitive final prducts): k t = k t ( p )
A further extension is to allow for the deliveries of multiple goods from the
source (i.e., the sought location) s to sinks O.
Accounting for the last two recommendation leads to the following cost
minimization:
min K T =
x, y
q
r
∑ ∑k
p =1
q =1
T qp
* a qp * d p =
∑ (x − x ) + (y − y ) ∑ k
q
r
p =1
2
p
2
p
q =1
T qp
* a qp
where :
q = 1, K , q
k T qp
products
transport cost of product q to sink p
Page 56

International Industrial Management I

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