supply chain integration: practices & customer values
TRANSCRIPT
UNIDEMI – R&D Unit in Mechanical & Industrial EngineeringUnidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
UNIVERSIDADE NOVA DE LISBOA
October 17, 2013
Supply Chain Integration:
Practices and Customer Values
Meysam Maleki
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UNIVERSIDADE NOVA DE LISBOA
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Agenda
1. Introduction
– Supply chain integration
– Research questions and objectives
– Methodology
2. Proposed approach
– Data collection
– Data analysis
– Expectations
3. Case studies
– Case study I: Customer values
– Case study II & III: SCI model in the fashion and food industries
4. Conclusion
– Contribution to theory
– Managerial contributions
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Definition of Supply Chain Management
Lambert, 2008
SCM is the integration of key business processes across
the supply chain for the purpose of creating value for
customers and stakeholders.
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Definition of Supply Chain Management
Lambert, 2008
SCM is the integration of key business processes across
the supply chain for the purpose of creating value for
customers and stakeholders.
Integration is embedded in
the definition of SCM
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Definition of Supply Chain Management:
Year wise frequency distribution of the keyword "supply chain integration": 2005 till
March 01, 2013 – Generated by Google Trends (the figure is on the scale of 100):
Figure 1.1; Page 3
Lambert, 2008
SCM is the integration of key business processes across
the supply chain for the purpose of creating value for
customers and stakeholders.
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Positioning the research are
The literature agrees that a comprehensive SCI is not feasible
owing to barriers such as lack of financial integration, cultural
barriers, and lack of customer-centred metrics, inconsistent
relationships with customers and suppliers and inequality in risk
sharing.
Carter et al., 2009
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Positioning the research area
The literature agrees that a comprehensive SCI is not feasible
owing to barriers such as lack of financial integration, cultural
barriers, and lack of customer-centred metrics, inconsistent
relationships with customers and suppliers and inequality in risk
sharing.
Carter et al., 2009
Supply chain
practices
Customer
values
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Thesis structure
CHAPTER 1
Overview of the research
issues and objectives
CHAPTER 2
Supply chain integration,
integration models,
Bayesian network,
analytic network process
CHAPTER 3
Comparative analysis,
data collection methods
CHAPTER 4
Proposed model,
framework
CHAPTER 5
Customer value data
analysis, SCI model for
fashion and food
industries
CHAPTER 6
Conclusion and future
work
INTRODUCTION LITERATURE REVIEW METHODOLOGY
CONCEPTUAL MODEL CASE STUDIES CONCLUSION
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Research questions
CHAPTER 1
Overview of the research
issues and objectives
CHAPTER 2
Supply chain integration,
integration models,
Bayesian network,
analytic network process
CHAPTER 3
Comparative analysis,
data collection methods
CHAPTER 4
Proposed model,
framework
CHAPTER 5
Customer value data
analysis, SCI model for
fashion and food
industries
CHAPTER 6
Conclusion and future
work
INTRODUCTION LITERATURE REVIEW METHODOLOGY
CONCEPTUAL MODEL CASE STUDIES CONCLUSION
o How can we integrate customer values and SCM practices?
o How can we quantify relations between customer values and SCM
practices?
o What tools can be used in this approach?
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Research objectives
CHAPTER 1
Overview of the research
issues and objectives
CHAPTER 2
Supply chain integration,
integration models,
Bayesian network,
analytic network process
CHAPTER 3
Comparative analysis,
data collection methods
CHAPTER 4
Proposed model,
framework
CHAPTER 5
Customer value data
analysis, SCI model for
fashion and food
industries
CHAPTER 6
Conclusion and future
work
INTRODUCTION LITERATURE REVIEW METHODOLOGY
CONCEPTUAL MODEL CASE STUDIES CONCLUSION
o Quantify correlations
among customer values and
practices
o Provide possibility to plan
scenarios
o Study paiwise comparisons
among customer values
o Connect practices in supply
chain with customer values
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Flow of materials and information in SC End Customer
Customer valuesManufacturing /
Assembly PracticesLogistics Practices
Interview with
expertsQuestionnaire
ANP Model BN Model
Supply Chain Integration
Model
PHASE 1PHASE 2
PHASE 3
Shared
factors
InputInput
InputInput
Research Methodology
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CHAPTER 1
Overview of the research
issues and objectives
CHAPTER 2
Supply chain integration,
integration models,
Bayesian network,
analytic network process
CHAPTER 3
Comparative analysis,
data collection methods
CHAPTER 4
Proposed model,
framework
CHAPTER 5
Customer value data
analysis, SCI model for
fashion and food
industries
CHAPTER 6
Conclusion and future
work
INTRODUCTION LITERATURE REVIEW METHODOLOGY
CONCEPTUAL MODEL CASE STUDIES CONCLUSION
Case studies
CASE STUDY 1
Data collection and data
analysis of customer values
CASE STUDY 2
Development of SCI for the
fashion industry
CASE STUDY 3
Development of SCI for the
food industry
Input
Input
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Agenda
1. Introduction
– Supply chain integration
– Research questions and objectives
– Methodology
2. Proposed approach
– Data collection
– Data analysis
– Expectations
3. Case studies
– Case study I: Customer values
– Case study II & III: SCI model in the fashion and food industries
4. Conclusion
– Contribution to theory
– Managerial contributions
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
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UNIVERSIDADE NOVA DE LISBOA
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
Customer value is the
perception of a customer
about a product or service.
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UNIVERSIDADE NOVA DE LISBOA
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
Customer value is the
perception of a customer
about a product or service.
1. Quality
2. Cost
3. Time
4. Customization
5. Know-how
6. Respect for the environment
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
Sig
nif
ican
tly
mo
reim
po
rtan
t
Mo
rere
imp
ort
ant
Th
esa
me
imp
ort
ance
mo
reim
po
rtan
t
Sig
nif
ican
tly
mo
reim
po
rtan
t
Cost Quality
4 : 0 3 : 1 2 : 2 1 : 3 0 : 4
Pairwise comparison of
customer values
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
SCM practices:
Actions employed by firms
in order to achieve their
goals along the chain
• Just in time
• Decrease work in process
• Mixed production planning
• …
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
Pairwise comparison of
SCM practices
[PM1] [PMi]
[CV1] [CVn]
[PL1] [PLj]
Manufacturing / Assembly
practices
Logistics practices
Customer values
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UNIVERSIDADE NOVA DE LISBOA
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SCI
conceptual model
Identify industry sector
Identify customer values
Identify manufacturing
practicesIdentify logistics practices
SCI Conceptual Model
Pairwise analyses
[CV1…CVn]
[PM1…PMi] [PL1…PLj]
[PM1] [PMi][CV1…CVn]
[PL1] [PLj][CV1…CVn]
PMi: Manufacturing practices
PLj: Logistics Practices
CVn: Customer values
Data collection from end
customers
Data collection through
interview with experts
[CV1] [CVn]
SCM practices
BN data mining
ANP Analysis
PMi / PLj is
sensitive to CVn
Set BN state as
“Recommended”Yes
Set BN state as “Not
Recommended”
Dat
a C
oll
ecti
on
Dat
a A
nal
ysi
s an
d M
od
el D
evel
op
men
t
Customer values have three states:
• Important
• Neutral
• Not important
SCP practices have two states:
• Recommended
• Not recommended
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Expectations from the SCI
conceptual model
1. Present the current state
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Expectations from the SCI
conceptual model
1. Present the current state
2. Monitor scenarios
If customer value X is important, which SCM practice is
more recommended
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Expectations from the SCI
conceptual model
1. Present the current state
2. Monitor scenarios
If customer value X is important, which SCM practice is
more recommended
If we implement practice Y, how does it contribute to the
customer values.
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Agenda
1. Introduction
– Supply chain integration
– Research questions and objectives
– Methodology
2. Proposed approach
– Data collection
– Data analysis
– Expectations
3. Case studies
– Case study I: Customer values
– Case study II & III: SCI model in the fashion and food industries
4. Conclusion
– Contribution to theory
– Managerial contributions
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Case studies Pairwise comparison of
customer values
Data analysis
CASE STUDY 1
CASE STUDY 2 & 3
Import analysis of customer value dataInterview with experts in the respected
industry
Identify importance of SCM practices
with respect to customer values
Development of the integration model
Imp
ort
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Case Study # 1: Customer values
Time Customization
Know-how
Respect
environment
CostQuality
Automotive Electronics Furniture Food Fashion Pharmaceutical
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Case Study # 1: Customer values
Time Customization
Know-how
Respect
environment
CostQuality
Automotive Electronics Furniture Food Fashion Pharmaceutical
Customer value coefficient in 6
industries
Coefficient of Cost in
Electronics industry is: 0.23
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Case Study 2 & 3: SCI model in Fashion and
Food industries
Company namePosition of
intervieweeCountry Industry sector
Upward UnlimitedSupply chain
specialistUSA Fashion
Sopplan Director New Zealand Food
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29
Case Study 2 & 3: SCI model in Fashion and
Food industries
Company namePosition of
intervieweeCountry Industry sector
Upward UnlimitedSupply chain
specialistUSA Fashion
Sopplan Director New Zealand Food
Food industry
Constant products
Local market influences
Short time to market
Complex network of suppliers
Not season sensitive products
Fashion industry
Highly changing products
Global market influences
Long time to market
Simple network of suppliers
Season sensitive products
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Case Study # 2: SCI model in fashion industry
Quality
Not important
Neutral
Important
15%
29%
56%
Cost
Not important
Neutral
Important
21%
29%
50%
Know-how
Not important
Neutral
Important
46%
36%
17%
Customization
Not important
Neutral
Important
37%
33%
30%
Respect environment
Not important
Neutral
Important
26%
35%
39%
Time
Not important
Neutral
Important
58%
29%
13%
Phase1: Analysis of customer values
Bayesian network of customer values in the
fashion industry- Generated by GeNIe 2.0
Dataset volume: 131
Order of customer
values with respect to
“important” priority
1. Quality
2. Cost
3. Respect Env.
4. Customization
5. Know-how
6. Time
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Case Study # 2: SCI model in fashion industry
Quality
Not important
Neutral
Important
15%
29%
56%
Cost
Not important
Neutral
Important
21%
29%
50%
Know-how
Not important
Neutral
Important
46%
36%
17%
Customization
Not important
Neutral
Important
37%
33%
30%
Respect environment
Not important
Neutral
Important
26%
35%
39%
Time
Not important
Neutral
Important
58%
29%
13%
Phase1: Analysis of customer values
Bayesian network of customer values in the
fashion industry- Generated by GeNIe 2.0
Dataset volume: 131
End customers prefer
to sacrifice Time to
get higher value on
other dimensions.
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Case Study # 2: SCI model in fashion industry
Phase1: Analysis of customer values
Bayesian network of customer values in the fashion industry- Generated by GeNIe 2.0
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Case Study # 2: SCI model in fashion industry
Phase 2: Pairwise comparison of SCM
practices with respect to customer values
Criteria
Cost Customization Know-how Quality Respect env Time
Alternative Manufacturing Practices
Cross functional oprations
Decrease work in process
Implement standards
Mixed production planning
Use recyclable materials
Alternative Logistics Practices
Implement standards
Information sharing with customer
Just in time
Visibility to up/down stream inventories
The ANP clusters: criteria and alternatives
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Case Study # 2: SCI model in fashion industry
Phase 2: Pairwise comparison of SCM
practices with respect to customer values
The ANP clusters: criteria and alternatives
Screenshot from SuperDecisions 2.2.6
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ClustersNorm.
values
Logis
tics
Pra
ctic
es
Implementing logistics standards0.03
Information Sharing with
Customer 0.09
Just in Time 0.10
Visibility to upstream /
downstream inventories 0.11
Man
ufa
ctu
rin
g
Pra
ctic
es
Cross functional operations 0.14
Decrease work in process 0.20
Implement standards 0.14
Mixed production planning 0.12
Use recyclable materials 0.05
Com
par
iso
n c
rite
rio
n Cost 0.29
Customization 0.11
Know-how 0.06
Quality 0.16
Respect Environment 0.07
Time 0.30
Case Study # 2: SCI model in fashion industry
Phase 2: Pairwise comparison of SCM
practices with respect to customer values
The ANP clusters: criteria and alternatives
Notice: Time was the least
important value from
customer’s perspective,
while it is the most
important from expert’s
perspective.
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Case Study # 2: SCI in fashion industry
Phase 3: Development of SCI model in the fashion industry
Quality
Not important
Neutral
Important
15%
29%
56%
Cost
Not important
Neutral
Important
21%
29%
50%
Customization
Not important
Neutral
Important
37%
33%
30%
Respect environment
Not important
Neutral
Important
26%
35%
39%
Time
Not important
Neutral
Important
58%
29%
13%
Q 3
Recommended
Not Recommended19%
81%
Q 4
Recommended
Not Recommended11%
89%
Q 5
Recommended
Not Recommended4%
96%
Q 6
Recommended
Not Recommended3
97%
Q 7
Recommended
Not Recommended6%
94%
Q 8
Recommended
Not Recommended6%
94%
Q 9
Recommended
Not Recommended7%
93%
Q 1
Recommended
Not Recommended13%
87%
Q 2
Recommended
Not Recommended31%
69%
T 1
Recommended
Not Recommended14%
86%
T 2
Recommended
Not Recommended21%
79%
T 3
Recommended
Not Recommended14%
86%T 4
Recommended
Not Recommended12%
88%
T 6
Recommended
Not Recommended3%
97%
T 5
Recommended
Not Recommended6%
94%
T 7
Recommended
Not Recommended9%
91%
T 8
Recommended
Not Recommended10%
90%
T 9
Recommended
Not Recommended11%
89%
C 1
Recommended
Not Recommended11%
89%
C 4
Recommended
Not Recommended12%
88%
C 3
Recommended
Not Recommended11%
89%
C 2
Recommended
Not Recommended21%
79%
C 5
Recommended
Not Recommended4%
96%
C 7
Recommended
Not Recommended5%
95%
C 8
Recommended
Not Recommended12%
88%
C 6
Recommended
Not Recommended3%
97%
C 9
Recommended
Not Recommended14%
86%
Cu 2
Recommended
Not Recommended18%
82%
Cu 1
Recommended
Not Recommended22%
78%
Cu 4
Recommended
Not Recommended13%
91%
Cu 3
Recommended
Not Recommended13%
87%
Cu 5
Recommended
Not Recommended5%
95%
Cu 6
Recommended
Not Recommended3%
97%
Cu 7
Recommended
Not Recommended8%
92%
Cu 8
Recommended
Not Recommended9%
91%
Cu 9
Recommended
Not Recommended9%
91%
R 1
Recommended
Not Recommended11%
89%
R 4
Recommended
Not Recommended10%
90%
R 2
Recommended
Not Recommended18%
82%
R 3
Recommended
Not Recommended23%
77%
R 5
Recommended
Not Recommended11%
89%
R 6
Recommended
Not Recommended2%
98%
R 7
Recommended
Not Recommended6%
94%
R 8
Recommended
Not Recommended5%
95%
R 9
Recommended
Not Recommended8%
92%
Know-how
Not important
Neutral
Important
46%
36%
18%
K 1
Recommended
Not Recommended13%
87%
K 2
Recommended
Not Recommended18%
82%
K 3
Recommended
Not Recommended14%
86%
K 4
Recommended
Not Recommended12%
88%
K 5
Recommended
Not Recommended5%
95%
K 6
Recommended
Not Recommended4%
96%
K 7
Recommended
Not Recommended13%
87%
K 8
Recommended
Not Recommended10%
90%
K 9
Recommended
Not Recommended11%
89%
UNIDEMI – R&D Unit in Mechanical & Industrial EngineeringUnidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
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Case Study # 3: SCI model in food industry
Phase1: Analysis of customer values
Bayesian network of customer values in the
food industry- Generated by GeNIe 2.0
Dataset volume: 131
Quality
Not important
Neutral
Important
12%
25%
63%
Cost
Not important
Neutral
Important
28%
25%
47%
Know-how
Not important
Neutral
Important
43%
34%
23%
Customization
Not important
Neutral
Important
48%
29%
24%
Respect environment
Not important
Neutral
Important
27%
33%
41%
Time
Not important
Neutral
Important
54%
26%
19%
Order of customer
values with respect to
“important” priority
1. Quality
2. Cost
3. Respect Env.
4. Customization
5. Know-how
6. Time
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Case Study # 3: SCI model in food industry
Phase 2: Pairwise comparison of SCM
practices with respect to customer values
Criteria
Cost Customization Know-how Quality Respect env Time
Alternative Manufacturing Practices
Cross functional oprations
Decrease work in process
Implement standards
Mixed production planning
Use recyclable materials
Alternative Logistics Practices
Implement standards
Information sharing with customer
Just in time
Visibility to up/down stream inventories
The ANP clusters: criteria and alternatives
ClustersNormalize
d values
Logis
tics
Pra
ctic
es
Implementing logistics
standards 0.01
Information Sharing with
Customer 0.04
Just in Time 0.02
Visibility to upstream /
downstream inventories 0.02
Man
ufa
cturi
ng
Pra
ctic
es
Cross functional operations 0.22
Decrease work in process 0.15
Implement standards 0.42
Mixed production planning 0.09
Use recyclable materials 0.02
Com
par
ison
cri
teri
on Cost 0.25
Customization 0.05
Know-how 0.11
Quality 0.51
Respect Environment 0.015
Time 0.06
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Case Study # 3: SCI in food industry
Phase 3: Development of SCI model in the food industry
Quality
Not important
Neutral
Important
12%
25%
63%
Cost
Not important
Neutral
Important
28%
25%
47%
Customization
Not important
Neutral
Important
48%
29%
23%
Respect environment
Not important
Neutral
Important
27%
33%
41%
Time
Not important
Neutral
Important
54%
26%
19%
Q 3
Recommended
Not Recommended48%
52%
Q 4
Recommended
Not Recommended9%
91%
Q 5
Recommended
Not Recommended2%
92%
Q 6
Recommended
Not Recommended1%
99%
Q 7
Recommended
Not Recommended3%
97%
Q 8
Recommended
Not Recommended2%
98%
Q 9
Recommended
Not Recommended2%
98%
Q 1
Recommended
Not Recommended18%
82%
Q 2
Recommended
Not Recommended16%
84%
T 1
Recommended
Not Recommended21%
79%
T 2
Recommended
Not Recommended17%
83%
T 3
Recommended
Not Recommended40%
60%T 4
Recommended
Not Recommended9%
91%
T 6
Recommended
Not Recommended1%
99%
T 5
Recommended
Not Recommended2%
98%
T 7
Recommended
Not Recommended4%
96%
T 8
Recommended
Not Recommended2%
98%
T 9
Recommended
Not Recommended2%
98%
C 1
Recommended
Not Recommended29%
71%
C 4
Recommended
Not Recommended9%
91%
C 3
Recommended
Not Recommended37%
63%
C 2
Recommended
Not Recommended12%
88%
C 5
Recommended
Not Recommended2%
98%
C 7
Recommended
Not Recommended4%
96%
C 8
Recommended
Not Recommended2%
98%
C 6
Recommended
Not Recommended1%
99%
C 9
Recommended
Not Recommended3%
97%
Cu 2
Recommended
Not Recommended14%
86%
Cu 1
Recommended
Not Recommended24%
76%
Cu 4
Recommended
Not Recommended9%
91%
Cu 3
Recommended
Not Recommended36%
64%
Cu 5
Recommended
Not Recommended2%
98%
Cu 6
Recommended
Not Recommended1%
99%
Cu 7
Recommended
Not Recommended7%
93%
Cu 8
Recommended
Not Recommended3%
97%
Cu 9
Recommended
Not Recommended3%
97%
R 1
Recommended
Not Recommended16%
84%
R 4
Recommended
Not Recommended8%
92%
R 2
Recommended
Not Recommended10%
90%
R 3
Recommended
Not Recommended30%
70%
R 5
Recommended
Not Recommended9%
91%
R 6
Recommended
Not Recommended3%
97%
R 7
Recommended
Not Recommended10%
90%
R 8
Recommended
Not Recommended7%
93%
R 9
Recommended
Not Recommended7%
93%
Know-how
Not important
Neutral
Important
43%
34%
23%
K 1
Recommended
Not Recommended23%
77%
K 2
Recommended
Not Recommended13%
87%
K 3
Recommended
Not Recommended42%
58%
K 4
Recommended
Not Recommended11%
89%
K 5
Recommended
Not Recommended2%
98%
K 6
Recommended
Not Recommended1%
99%
K 7
Recommended
Not Recommended4%
96%
K 8
Recommended
Not Recommended2%
98%
K 9
Recommended
Not Recommended2%
98%
UNIDEMI – R&D Unit in Mechanical & Industrial EngineeringUnidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
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Case Study # 3: SCI in food industry
Play the videoGenerated by GeNIe 2.0
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Case Study # 3: Scenario monitoring
PracticesNormal
state
“respect
environment”
is important
Difference
Logis
tics
Pra
ctic
es
Implementing logistics standards 3% 7% +4%
Information Sharing with Customer 10% 19% +9%
Just in Time 7% 13% +6%
Visibility to up/down stream inventories 7% 13% +6%
Man
ufa
cturi
ng
Pra
ctic
es
Cross functional operations 16% 7% -9%
Decrease work in process 10% 4% -6%
Implement standards 30% 13% -17%
Mixed production planning 8% 5% -3%
Use recyclable materials 9% 19% +10%
If “Respect Env” is grounded as “important”
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Agenda
1. Introduction
– Supply chain integration
– Research questions and objectives
– Methodology
2. Proposed approach
– Data collection
– Data analysis
– Expectations
3. Case studies
– Case study I: Customer values
– Case study II & III: SCI model in the fashion and food industries
4. Conclusion
– Contribution to theory
– Managerial contributions
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Conclusions
Although development of a comprehensive model is not feasible
but it is possible to shed light to some of its aspects.
Identifying correlations between customer values and SCM
practices is one step toward development of SCI model.
Combination of ANP and BN as tools leads to quantifying mutual
correlations between customer values and practices, also it provides
platform to monitor variety of scenarios.
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ConclusionsQuantifying mutual correlations between customer values and
SCM practices assist decision makers to:
Provides an illustrative presentation of the current state of
the chain
Align internal activities in their firms with expectations of
the end customers
Truly understand which value is more preferred by the end
customers
Predict the market influence of their practices
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Acknowledgments
• DEMIo Prof. V. Cruz Machado
o Teachers
o Staffs
o Students and friends
• Case companieso Eric Hargraves, Upward Limited Inc., USA
o John Chase, Sopplan, New Zealand
• Softwareo GeNIe 2.0: Decision Systems Laboratory, University of Pittsburgh
o Super Decisions 2.2.6: Creative Decisions Foundation, Pittsburgh