Jonathon Mote The George Washington University Gretchen B Jordan 360 Innovation LLC Rosalie Ruegg TIA Consulting Exploring the Use of Netchain Analysis in Evaluating Emerging Markets 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. 1

Introduction 2 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 indicators of changes in supply chains/netchains 2

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? 3

Supply chains 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 4

Supply chain network 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 5

Netchains 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 6

The innovation ecosystem 7

Network indicators Both one-mode and two-mode networks One-mode – network connections across the netchain (buyers/supplier as “firms”) Two-mode - network connection across the innovation ecosystem (“firms” and “support organizations”) Goal is robust and resilient domestic supply chains for energy technologies Suggests plentiful actors and plentiful connections among actors Suggests key actors playing positive roles (cooperative and collaborative) Suggests strong, stable actors Three principal areas of indicators  Connectivity  Overall Network Health  Intended Interim Outcomes/Impacts 8

Network indicators - connectivity Connectivity  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 9

Network indicators – overall health Overall Network Health  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. 10

Network indicators – interim impacts Intended Outcomes/Impacts  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. 11

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)  Possible to identify opportunities for network “weaving” Challenges  Network theory and methodology still nascent – suggestive, but not conclusive  Magnitude of data gathering 12

13 Network “Weaving” 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 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 CategoryNumber of Firms OEM7 Supplier – Battery Pack6 Supplier - Anode9 Supplier - Cathode8 Supplier - Lithium4 Supplier – Other41 14

Li-ion value chain, firms and recipients 15 Source: Center for Globalization, Governance & Competitiveness, Duke University Note: Red indicates firms that received DOE investment

Li-ion value chain, firms and recipients – time 1 16 Red = OEM Green = Battery Pack Supplier Blue = Lithium Supplier Teal = Component Supplier

Li-ion value chain, firms and recipients – time 2 Network Measures Over Time MeasuresTime 1Time 2 Size7574 Network Density Network Centralization Network Closeness Degree Centrality (individual firms) Top Firm 43 (Supplier-Battery).189 Firm 17 (Supplier-Battery).135 Firm 33 (OEM).122 Firm 1 (OEM).068 Firm 9 (Supplier-Battery) Top Firm 43 (Supplier-Battery).196 Firm 17 (Supplier-Battery).135 Firm 33 (OEM).122 Firm 1 (OEM).074 Firm 9 (Supplier-Battery) Closeness (individual firms) Top Firm 38 (Supplier-Lithium).039 Firm 33 (OEM).039 Firm 43 (Supplier-Battery).038 Firm 17 (Supplier-Lithium).038 Firm 34 (Supplier-Anode) Top Firm 73 (Supplier-Lithium).107 Firm 17 (Supplier-Battery).103 Firm 33 (OEM).102 Firm 19 (Supplier-Anode).101 Firm 38 (Supplier-Lithium) Betweenness (individual firms) Top Firm 43 (Supplier-Battery).289 Firm 33 (OEM).232 Firm 17 (Supplier-Battery).231 Firm 38 (Supplier-Lithium).039 Firm 1 (OEM) Top Firm 43 (Supplier-Battery).391 Firm 17 (Supplier-Battery).326 Firm 33 (OEM).244 Firm 38 (Supplier-Lithium).193 Firm 1 (OEM) Isolates74 17

Conclusion 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 HOWEVER, provides a limited set of metrics which can only answer a limited set of questions 18

Questions? Gretchen Jordan – Rosalie Ruegg – Jonathon Mote – Thanks for your time! 19