Using Netchain Analysis to Explore the Formation
ofRobust Supply Chains for Innovative
Technologies
Jonathon Mote
The George Washington University
Gretchen B Jordan
360 Innovation LLC
Rosalie Ruegg
TIA Consulting
Work prepared for the U.S. Department of Energy (DOE), under contract with Lawrence
Berkeley National Laboratory (LBNL). Opinions expressed do not necessarily reflect those of the
U.S. DOE or LBNL.
Introduction
 Team brought together to develop evaluation framework for
specific set of the DOE’s Office of Energy Efficiency and
Renewable Energy initiatives:
 Includes Thomas Choi (Arizona State) and Angela Becker-
Dippmann (Pacific Northwest)
 Establish an evaluation framework that will guide impact and
process assessments of DOE/EERE R&D and related investments
aimed at accelerating innovation, advancing manufacturing and
creating a domestic supply base and early markets in the U.S.
 Where resulting evaluations speak to multiple audiences including
2
program managers, senior managers, industry partners, OMB and
Congress.
Evaluation perspective: focus on interim period
Net Cash Flow
With DOE Initiative
FOCUS
Without DOE Initiative
0
Time
Types of initiatives – supply chain
Interim Effects Covered
Brief Description
Related EERE Program Areas
and Activities
Accelerated development
and commercialization of
energy technologies in the
U.S.
New products, processes and/or
business models are introduced and
accepted faster than without EERE
support
BioRefinery Initiative
Growth in U.S.
manufacturing
Growth in U.S. production within the
target areas
Advanced Manufacturing Office
Capabilities for continued
innovation
Flow of new ideas and invention
within a supportive environment as
technologies and markets change
Innovation Ecosystem Initiative
Added value to
characteristics of a new
product
New or better performance
characteristics and functions, or costs,
or both
SunShot for Photovoltaics
More and stronger firms
in the product value chain
Development of a viable industry base
in the area of the product of focus
Buildings Innovation Hub
Stronger product supply
chain
An emergent supply chain begins to
deliver products to early adopters.
Advanced Batteries for Vehicles
Current evaluation guides and
practices not sufficient
Existing Evaluations
Government R&D
to develop &
prototype
technology
Peer Review
Stage Gate Review
Technical Milestones
Industry scales
up, validates &
commercializes a
technology
Government
encourages
deployment of a
technology
Energy, environment,
economic & security
benefits
Deployment Impact Studies
Retrospective Cost Benefit Studies
(minimal attention to intermediate events)
New Evaluation Guide
Government R&D
to develop &
prototype
technology
Public-Private
Partnerships help
Industry scale up
& commercialize a
technology
Government
encourages
deployment of a
technology
Evaluate Interim Impacts
5
Accelerated
innovation;
Domestic
suppliers &
producers in the
supply chain
Energy, environment,
economic & security
benefits
A logic model – theory of change
 A tool for evaluation planning
 Describes what an intervention aims to do and how
 Thus illuminates what to measure, from inputs to outcomes
 This draft is high level, attempts to summarize detail
 of the R&D process,
 supply chain management,
 netchains, and
 partially, government role.
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A logic model – theory of change
U.S. Global Competitiveness in Manufacturing Energy Technologies
National Energy and Economic Benefits
Ultimate Impacts
O3
Broader
Interim
Outcomes
Growth in US
manufacturing
O1
Accelerated new
product commercialization, adoption
C6
Strengthened
product value chain
Short & Interim
Outcomes Conditions for
Progress
C1
Added capabilities
(product process,
business model)
Inputs
O2
Capabilities
for continued
product innovation
C4
C5
Added value
to characteristics of new
product
Stronger networks,
knowledge exchange
C2
C7
Strengthened
/ developed
supply chain
Available
capital for
R&D, scale up,
production
EERE Investments/Activities and Collaborations
(Technical, Information/Relationships, Business, Policy)
C3
Supportive supply
chain practices,
gov’t. policies
External
Influences:
Technical.
Information/
Networks,
Economic,
Policy
Supply chains, value chains and
networks

Previous work on supply chain networks (Choi) and
netchains (Lazzarini) suggested the use of social
network analysis to assess impact on value chains for
specific alternative energy industries



Focus on near-term (early stage) changes and
intermediate indicators
Focus on connectedness of firms within value chain, as
well as other relevant actors (R&D, finance, etc)
Is the DOE-EERE fostering networks that lead to
positive outcomes?
Network indicators


Netchain – set of networks comprised of
horizontal ties between firms in an
industries, such that networks (or layers)
are sequentially arranged based on vertical
ties between firms in different layers
(Larrarini et al, 2001)
Focus primarily on the “horizonal” level of
industry netchains – ties within the same
layer


Three principal areas of indicators



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Also ties outside layer to other actors –
financial, R&D
Connectivity
Overall Network Health
Intended Interim Outcomes/Impacts
Network indicators - connectivity

Connectivity





10
Does the structure enable efficient sharing of info, ideas
and resources?
Is the network growing (new actors, but new links as
well)?
Is the network more interconnected (more dense)?
Does the network bridge clusters?
How are actors connected? – suppliers, buyers,
communication, collaboration, alliances, joint ventures
Network indicators – overall health

Overall Network Health




11
Who are the primary leading actors (organizational
leaders in horizontal networks)? What role are they
playing—controllers or collaborators?
How diverse is the network? Small/large (horizontal
networks), suppliers, manufacturers, distributors, R&D,
universities, agencies, venture capital/private equity
(netchains)
Is the network balanced and growing – able to grow more
inclusive and sustain collaboration?
Is the structure appropriate for the work of the network
(different horizontal networks may require different
structures)? Assumed core/periphery is optimal, but may
not be the case.
Network indicators – interim impacts

Intended Outcomes/Impacts



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Evidence of greater coordination and collaboration –
alignment of priorities/R&D, working agreements,
alliances, joint ventures, etc.
Identification of key actors (either within or outside the
networks) for future network weaving.
More innovative products being developed for market
and deployed – movement through the TRLs and MRLs.
Network “Weaving”


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Identifying important actors and assessing emerging
network patterns could allow for network
“weaving”
Strategic interventions to make connections that
strengthen the network
The innovation ecosystem
Innovation Ecosystem
Technical:
Competing,
complementary
technologies…
Economic:
Market
characteristics,
NGOs...
Government:
Policies,
procurement …
(including EERE)
Information,
Culture:
Human resources,
networks, beliefs…
Product Value Chain
Raw Material
Suppliers
Other actors involved
with product
Component,
Sub system
Suppliers
Supply chains, value chains and networks
R&D
Institutions
Sources of
Capital, Other
Resources
Manufacturers
/Assemblers
Distributors, Sellers
Service Providers
Consumers
Product & Market
of Focus
Product Supply Chain firms
Other firms in the industry
Other elements contributing to product, market
Opportunities and challenges

Opportunities



Network analysis able to capture complexity of
innovation ecosystem (one-mode, two-mode and
multi-level)
Suggests interim indicators based on how firms are
interacting (or not)
Challenges


Network theory and methodology still nascent
Data gathering of this magnitude
A hypothetical example – li-ion
batteries for vehicles

Application of netchain analysis to real-world



Hypothetical dataset based on parameters of li-ion
battery industry and the EERE li-ion batteries for
vehicles initiative
Utilized existing industry analysis conducted by Marcy
Lowe at Center on Globalization Governance &
Competitiveness (Duke University)
Constructed a hypothetical value chain with linkages
that attempted to mirror real-world linkages
Hypothetical Value Chain
Firm Type by Category
OEM
Supplier – Battery Pack
Supplier - Anode
Supplier - Cathode
Supplier - Lithium
Supplier – Other
Number of Firms
7
6
9
8
4
41
Li-ion value chain, firms and
recipients
Li-ion value chain, firms and
recipients – time 1
Li-ion value chain, firms and
recipients – time 2
Network Measures Over Time
Measures
Size
Network Density
Network Centralization
Network Closeness
Degree Centrality (individual
firms)
Closeness (individual firms)
Betweenness (individual firms)
Isolates
Time 1
75
.024
.323
.016
Top 5
.338 Firm 43 (Supplier-Battery)
.189 Firm 17 (Supplier-Battery)
.135 Firm 33 (OEM)
.122 Firm 1 (OEM)
.068 Firm 9 (Supplier-Battery)
Top 5
.039 Firm 38 (Supplier-Lithium)
.039 Firm 33 (OEM)
.039 Firm 43 (Supplier-Battery)
.038 Firm 17 (Supplier-Lithium)
.038 Firm 34 (Supplier-Anode)
Top 5
.333 Firm 43 (Supplier-Battery)
.289 Firm 33 (OEM)
.232 Firm 17 (Supplier-Battery)
.231 Firm 38 (Supplier-Lithium)
.039 Firm 1 (OEM)
7
Time 2
74
.025
.322
.083
Top 5
.338 Firm 43 (Supplier-Battery)
.196 Firm 17 (Supplier-Battery)
.135 Firm 33 (OEM)
.122 Firm 1 (OEM)
.074 Firm 9 (Supplier-Battery)
Top 5
.111 Firm 73 (Supplier-Lithium)
.107 Firm 17 (Supplier-Battery)
.103 Firm 33 (OEM)
.102 Firm 19 (Supplier-Anode)
.101 Firm 38 (Supplier-Lithium)
Top 5
.409 Firm 43 (Supplier-Battery)
.391 Firm 17 (Supplier-Battery)
.326 Firm 33 (OEM)
.244 Firm 38 (Supplier-Lithium)
.193 Firm 1 (OEM)
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Conclusion



Need for interim indicators suggested new
methodology
Development of new products in emerging
industries does not happen in isolation – supply
chains
Netchain analysis (network analysis across
product value chains) offers one potential avenue
Questions?

Gretchen Jordan – [email protected]

Jonathon Mote – [email protected]

Thanks for your time!