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� Chapter 7– Some case examples
– A framework of supply chain decision databases
17/2/2009 SCM 2
“(…) In most instances, more than 80% of the data in transactional databases are irrelevant to decision making. Data aggregations and other analyses are needed to transform the remaining 20% or less, into useful information in the supply chain decision database (…)”
Case example the home/furniture sector
Independent
Stores
Wholesales
Suppliers
Wholesales
Suppliers
Wholesales
Suppliers
E-Procurement organization
Independent
StoresIndependent
StoresIndependentStores
Wholesales
Suppliers
19/2/2007 Logistics 3
Wholesales
Suppliers
Wholesales
Suppliers
Web Order
Database
Management
Web Catalog
Database
Management
Franchise
StoresFranchise
StoresFranchise
StoresFranchiseStores
Suppliers
Wholesales
SuppliersWholesales
Suppliers
GS1 / DAS
Furniture
Product
database
Starting Point:The organization as a supply chain model
SUPPLIERS CUSTOMERSFACILITIES
17/2/2009 SCM 4
Raw materials Finished
Intermediate
productsProcesses
Intermediate
products
Model assumptions:
• no mutual exchange
• no disintermediation
• no external markets
• no time dependencies
Conditions for modeling organizations as asupply chain model
� Model assumptions:– no mutual exchange– no disintermediation– no external markets– no time dependencies
17/2/2009 SCM 5
� Four key Data Elements for decision DB:– Products– Place– Price– Policy
A framework for supply chain data and decisions
Suppliers Facilities Customers
A. Products
17/2/2009 SCM 6
B. Place
C. Price
D. Policy
A. Product data
� Customer– Product aggregation, categories
– Customer value
– BCG-matrix
� Facility
17/2/2009 SCM 7
� Facility– Product aggregation, categories
– Processes, product flows
� Supplier– Product aggregation, categories
– Kraljic matrix
Cf. The socio-technical approach
Production structure Governance structureMacro
Customer order
Segmentation
17/2/2009 SCM 8
Micro
implies
parallel product
streams
Product aggregation bycustomer value portfolio:
calc the Gini coefficient / Lorentz Curve
17/2/2009 SCM 9
Decomposition of Customer Value
Increase the Number ofCustomer
Increase
Customer Gross Profit
Decrease
Cost per Customer
Increase
RelationshipDuration
17/2/2009 SCM 10
$$ Customer Value $$
Decisions based on customer value portfolio management
Market share
High Low
19/2/2007 Logistics 11
Market
growth
High ‘Star’ ‘Wild cat’
Low ‘Cash cow’ ‘Dog’
Decisions based on supplier value portfolio management
Supply risk
High Low
19/2/2007 Logistics 12
Financial importance
High ‘Strategic ‘Lever’
Low ‘Bottle neck’ ‘Routine’
(Kraljic-matrix)
Class Exercise:What Product Data
Aggregation/Categorization applies to Utrecht University?
Customer Facility Supplier
17/2/2009 SCM 13
Strategic ?? ?? ??
Tactical ?? ?? ??
B. Place data
� Customer– Outbound transportation networks
– Location decision
� Facility
17/2/2009 SCM 14
� Facility– Shop floor design
– Location decision
� Supplier– Inbound transportation networks
– Location decision
Location costs are transportation costs - generic determinants
� Shape, weight or size (SKU)
� Value
� Time critical (asap, JIT)
17/2/2009 SCM 15
� Temperature/conservation critical
� Combinatory, sequential conflicts
� Transport-processes combinations
� …
Saving supply chain costs by location optimization: the case of Yamaha
Spare parts factory
Yamaha Motor Europe
Spare parts factory
Yamaha Motor Europe
Before After
delivery deliveryordering ordering
Before After
19/2/2007 Logistics 17
25 Distributors
6000 Dealers
Customers
25 Distributors
6000 Dealers
Customers
C. Price data
� Customer
– Market prices
– Margins
� Facility
17/2/2009 SCM 18
� Facility
– Internal transfer prices
– Direct/indirect costs, ABC
� Supplier
– External transfer prices
Generic Cost Decomposition
Direct
costs
Product
costs
process
costs
17/2/2009 SCM 19
Indirect
costs
Facility
resources
Facility
overhead
costsCosts =
Units * Price/Unit
Generic Cost Relationships
(“gradation”)
costs
costs
unitscosts
17/2/2009 SCM 21
Costs = Units * Price/Unit
Costs =
Units * Price_a/Unit for Units < x
Units * Price_b/Unit for Units > x
(“gradation”)
unitsx
unitscosts
units
Direct facility costs
Process Discrete parts
manufacturing
Packaging Distribution
centers
Units Continuous Discrete Discrete Discrete
Cost driver Raw material
price
Raw material
price and labor
Labor Labor
17/2/2009 SCM 22
price price and labor
Cost
relationship
Linear, simple Linear,
complex
Linear,
complex
Complex
•Very complex: indirect costs – are not related to resource units, but to activities/objectives
•Method: Activity Based Costing
Example: Transfer prices Tasty Chips Supply Chain (Shapiro p. 281-286)
Iowa Cincinatti
Nashville
Chicago
Cleveland
41 M
arkets/cities
17/2/2009 SCM 23
Kanses
Maine
Texerkana
Peoria
Farm
CooperativesPlants
Kanses City
Louisville
Little Rock
41 M
arkets/cities
DCs
Conclusions from the Tasty Chips Supply Chain example
� FROM SUPPLIER TO PLANT– Purchase prices differ between corn and potatoes (corn is more expensive)
– Purchase prices differ between supplier-product combination (Iowa have potato shortages)
– Transport prices differ between corn and potatoes (corn is more expensive)
– Transport prices differ between supplier-plant combination (Maine is further away)
17/2/2009 SCM 24
– Transport prices differ between supplier-plant combination (Maine is further away)
� FROM PLANT TO PLANT– Unit change prices differ between corn and potatoes (potato is more expensive)
– Baking and packaging prices differ between corn and potatoes (corn is more expensive)
– Baking and packaging prices differ between factories (Peoria and Texarkana are ‘inefficient’)
� FROM PLAN TO DC AND MARKET– No substantial differences between product, location or production-location prices
D. Policy data
� Customer
– Demand forecasting
� Facility
– Production planning
17/2/2009 SCM 25
– Production planning
� Supplier
– Procurement
Policy data - customer:how to forecast/predict demands
annual beer consumption
86
88
90
annual beer consumption
?
17/2/2009 SCM 26
74
76
78
80
82
84
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
?
Demand Forecasting Methods
Quantitative
Forecasting
Causal
Models
Time Series
Models
17/2/2009 SCM 27
Linear
Regression
Models
ExponentialSmoothing
MovingAverage
Models
Trend
Projection
TimeTimeResponseResponse
YYii
Moving TotalMoving Total((nn = 3)= 3)
MovingMovingAvg. (Avg. (nn = 3)= 3)
19931993 44 NANA NANA
Moving Average Solution
MAMAnn
nn==∑∑ Demand in Demand in Previous Previous PeriodsPeriods
17/2/2009 SCM 28
19941994 66 NANA NANA
19951995 55 NANA NANA
19961996 33 4 + 6 + 5 = 154 + 6 + 5 = 15 15/3 = 5.015/3 = 5.0
19971997 77 6 + 5 + 3 = 146 + 5 + 3 = 14 14/3 = 4.714/3 = 4.7
19981998 NANA 5 + 3 + 7 = 155 + 3 + 7 = 15 15/3 = 5.015/3 = 5.0
Moving averages
84
86
88
90
annual beer consumption MA (n=4) MA (n=3) MA (n=2) MA (n=1)
17/2/2009 SCM 29
74
76
78
80
82
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Ft= F
t-1 + a· (At-1 - F
t-1)
Time ActualForecast, Ft
(a = .10)
1993 180 175.00 (Given)
Exponential Smoothing Solution
17/2/2009 SCM 30
1993 180 175.00 (Given)
19941994 168168 175.00 + .10(180 175.00 + .10(180 -- 175.00) = 175.50175.00) = 175.50
19951995 159159 175.50 + .10(168 175.50 + .10(168 -- 175.50) = 174.75175.50) = 174.75
19961996 175175 174.75 + .10(159 174.75 + .10(159 -- 174.75) = 173.18174.75) = 173.18
19971997 190190 173.18 + .10(175 173.18 + .10(175 -- 173.18) = 173.18) = 173.36173.36
19981998 NANA 173.36173.36 + + .10.10((190190 -- 173.36173.36) = 175.02) = 175.02
Exponential smoothing
84
86
88
90
annual beer consumption ES (a=.1) ES (a=.2) ES (a=.3) ES (a=.4)
17/2/2009 SCM 31
74
76
78
80
82
84
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Trend Projection
annual beer consumption
86
88
90
annual beer consumption
17/2/2009 SCM 32
74
76
78
80
82
84
86
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
r = 1r = 1YY
XX
YYii
= = aa + + bb XXii
^̂
Linear Regression
- New products
- Life cycle products
- Season products
- Trendy products
17/2/2009 SCM 33
r = .89r = .89 r = 0r = 0
XX
YY
XX
YY
XX
YYii
= = aa + + bb XXii
^̂YY
ii= = aa + + bb XX
ii
^̂
- Trendy products
YY a i+ b Xi = + Error
Error
Linear Regression Model
17/2/2009 SCM 34
X
^i i
Error
Observed value
Y a b X= +
Regression line
Prediction based on experience,
74
76
78
80
82
84
86
88
90
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
annual beer consumption
17/2/2009 SCM 35
experience, theory or
assumptions
0,00
2,00
4,00
6,00
8,00
10,00
12,00
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Year average tempature
Exponential smoothing versus ‘temperature’ hypothesis
(1992-2003)
85
86
87
Ex
po
ne
nti
al
sm
oo
thin
g (
a=
.4)
10,50
11,00
11,50
Ye
ar
av
era
ge
te
mp
atu
re
17/2/2009 SCM 36
80
81
82
83
84
78 80 82 84 86 88 90
annual beer consumption
Ex
po
ne
nti
al
sm
oo
thin
g (
a=
.4)
8,00
8,50
9,00
9,50
10,00
78 80 82 84 86 88 90
annual beer consumption
Ye
ar
av
era
ge
te
mp
atu
re
In this case:Moving Averages performs best
0,94
0,96
0,98
1
Correlation with actual trend
17/2/2009 SCM 37
0,84
0,86
0,88
0,9
0,92
MA (n=4) MA (n=3) MA (n=2) MA (n=1) ES (a=.1) ES (a=.2) ES (a=.3) ES (a=.4)
Policy data - facilities
• Production planning
• Capacity planning
• Resource planning
17/2/2009 SCM 38
� Way of working� Formalized procedures� Quality systems� Scenarios� Make or buy
A framework for supply chain decisions – finalCustomers Facilities Suppliers
Products Customer value –portfolio
Product aggregation
Supplier portfolio
17/2/2009 SCM 39
Place Inbound transportation network
Shop floor design Outbound transportation network
Price Margins Direct/indirect costs
External transfer prices
Policy Demand forecasting
Quality management
Make-or-buy
Supply Chain Decision Database support integrative SCM
“(…) In most instances, more than 80% of the data in transactional databases are irrelevant to decision making. Data aggregations and other analyses are needed to transform the remaining 20% or less, into useful information in the supply chain decision database (…)”
17/2/2009 SCM 40
chain decision database (…)”
– What are the overall consequences of reducing 1 or n suppliers?
– What are the overall consequences of increasing 1 or n employees?
– What are the overall consequences of gaining 1 or n customers?