Exploring the Use of Netchain Analysis in
Evaluating Emerging Markets
Jonathon Mote
The George Washington University
Gretchen B Jordan
360 Innovation LLC
Rosalie Ruegg
TIA Consulting
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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.
 Explored the use of network analysis to derive interim
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indicators of changes in supply chains/netchains
Supply chains, value chains and
networks
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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
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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?
Supply chains
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Sequence of processes across a value chain involved in
the production and distribution of a product.
Flexible, Robust and Resilient – many stable suppliers
can accommodate significant threats
Supply chain network
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Choi – supply
chains should be
considered
networks
Highlights
interconnections –
cross-linked and
two-way
exchanges
But few, if any,
companies (OEMs)
know the extent of
their supply chain
networks
Netchains
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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)
Highlights that supply chains overlap and
intersect
Conceptually offers a way to understand
the interaction of supply chain networks
across an industry, value chain or product
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
R&D
Institutions
Component,
Sub system
Suppliers
Sources of
Capital, Other
Resources
Manufacturers
/Assemblers
Distributors, Sellers
Service Providers
Consumers
Product & Market
of Focus
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Product Supply Chain firms
Other firms in the industry
Other elements contributing to product, market
Network indicators
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Both one-mode and two-mode networks
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Two-mode - network connection across the innovation ecosystem (“firms” and
“support organizations”)
Goal is robust and resilient domestic supply chains for energy technologies
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Suggests plentiful actors and plentiful connections among actors
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Suggests key actors playing positive roles (cooperative and collaborative)
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Suggests strong, stable actors
Three principal areas of indicators
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One-mode – network connections across the netchain (buyers/supplier as
“firms”)
Connectivity
Overall Network Health
Intended Interim Outcomes/Impacts
Network indicators - connectivity
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Connectivity
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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
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Overall Network Health
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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
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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.
Opportunities and challenges
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Opportunities
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Challenges
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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)
Possible to identify opportunities for network
“weaving”
Network theory and methodology still nascent –
suggestive, but not conclusive
Magnitude of data gathering
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
A hypothetical example – li-ion
batteries for vehicles
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Application of netchain analysis to real-world
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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
Note: Red indicates firms that received DOE investment
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Source: Center for Globalization, Governance & Competitiveness, Duke University
Li-ion value chain, firms and
recipients – time 1
Red = OEM
Green = Battery Pack
Supplier
Blue = Lithium Supplier
Teal = Component
Supplier
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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)
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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)
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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
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Need for early-stage indicators requires
exploration of new methodologies
Development of new products in emerging
industries does not happen in isolation – supply
chains and networks
Netchain analysis (network analysis across
product value chains) offers one potential avenue
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HOWEVER, provides a limited set of metrics which can
only answer a limited set of questions
Questions?
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
Gretchen Jordan – Gretchen.Jordan@Comcast.net

Rosalie Ruegg – ruegg@ec.rr.com
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Jonathon Mote – jmote@gwu.edu
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Thanks for your time!