What is the RECODE Network - Fiona Charnley, Cranfield University
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Fiona Charnley is the PI of the RECODE Network
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What is the RECODE Network - Fiona Charnley, Cranfield University
- 1. EPSRC-ESRC Network for Re-Distributed Manufacturing, Consumer Goods and Big Data Fiona Charnley, Cranfield University RECODE #RECODE
- 3. RECODE: Vision Flexible digitally connected supply chains On-demand supply Up-scaling of local enterprise Consumer Insight Durable, Geographically Tuned Relationships Smart Analytics Informed real-time decisions Insight-driven personalised goods & services 'Inventory of Everything’ Hypothesis: The acquisition, analysis and interpretation of big data will facilitate an insight-driven and localised model of consumer goods manufacture transforming productivity, competitiveness and opportunities for innovation
- 4. RECODE:Objectives AIM: To develop an active and engaged community through which to identify, test and evaluate a multi-disciplinary vision and research agenda associated with the application of big data in the transition towards a re-distributed manufacturing model for consumer goods. OBJECTIVES: • Identify and seek new perspectives, contributions and requirements from an inclusive and active multi-disciplinary research community of local and global academics, industrialists, experts, government representatives and user groups (consumers and manufacturers), • Develop, test and evaluate a multi-disciplinary vision and research agenda for big data and re-distributed manufacture within the context of the consumer goods sector at the interfaces of technology, society and the global economy, • Significantly contribute to the development of a long term cross-network vision, research agenda and strategies for re-distributed manufacturing, • Maximise the impact of the network through innovative methods of dissemination and knowledge transfer to the broader community, relevant stakeholders, other successful networks and research funders.
- 5. Connected Manufacture Themes RECODE:Opportunity How can data and future connection technology (web 4.0, the industrial Internet, connected devices and the internet of things) be used to enhance an interconnected model of production and consumption, and close the gap between the supply chain, manufacturer and consumer?
- 6. Connected Manufacture Open Innovation Themes RECODE:Opportunity • How can consumer goods companies tap into distributed knowledge? • Can innovative combinations and analytics of big data inform transformational models of manufacture? • How could data sharing within consumer goods service network facilitate open innovation? • What are the risks and opportunities presented by open innovation for novel business models and even the discovery of new business sectors? • What is the right data driven open innovation strategy for consumer goods companies? • What are effective methods of data-driven open innovation?
- 7. Connected Manufacture Open Innovation Regenerative Economy Themes RECODE:Opportunity Can data-driven insight be used to create opportunities for regenerative models of production and consumption such as monitoring and re-capturing valuable materials, incentivising take-back and reward schemes and innovating models of collaborative consumption?
- 8. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Themes RECODE:Opportunity How can data inform more tailored design of products and services for local markets, engage local innovation and entrepreneurship within the consumer goods supply chain, and address the re- distributed supply and demand of consumer products?
- 9. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes RECODE:Opportunity Where and how is data being used to inform and innovate developed and emerging global markets, policy, legislation and new manufacturing approaches and how can this knowledge be used in the transition towards a re-distributed model of consumer goods production in the UK?
- 10. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes Production Community RECODE:Opportunity What will production communities in consumer goods sectors look like in the future? What are the organisational challenges and skill requirements of data-driven re-distributed production and consumption? What can be localised, and how will micro factories of the future be organised and linked with geographical regions?
- 11. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes Production Community RECODE:Opportunity Additional Themes
- 12. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes Cross-cutting Challenges Production Community RECODE:Opportunity
- 13. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes Cross-cutting Challenges Activities Production Community Industrial Steering Group RECODE:Opportunity Road Show Engagement Study Community Sandpit Feasibility Studies Symposia Cross Network Events
- 14. Connected Manufacture Open Innovation Regenerative Economy User-Driven Innovation Internationalisation Themes Cross-cutting Challenges Activities Outcomes Production Community Industrial Steering Group User Engagement Methodologies RECODE:Opportunity Road Show Multidisciplinary Research Roadmap Insight-Driven Product & Service Development Engaged Network of Research Active Partners Examples of Impact in Industry Engagement Study Community Sandpit Feasibility Studies Symposia Cross Network Events New Multi- disciplinary Collaborations New Knowledge in Data Acquisition, Analysis and Application Novel Contribution to Re-Dis Man Strategy
- 15. RECODE: Feasibility Studies Circular Innovation and Re-Distributed Manufacturing Big Data Ecosystem in Re-Distributed Manufacturing To be allocated through a Community Sandpit F1 F2 F3-5 Feasibility studies that involve academic – industrial collaborations Meet the objectives of the Network £35k - £65k per study Call for sandpit participation is to be announced November 2015
- 16. RECODE Network
- 20. Name: ……………………………………………. Organisation: …………………………………. Role: ………………………………………………. Email: ……………………………………………… Three things that interest me about big data, re-distributed manufacturing and consumer goods: 1. …………………………………………………………………… 2. …………………………………………………………………… 3. …………………………………………………………………… NetworkingActivity
- 21. RECODE:WorkshopActivity 1. Design a re-distributed model of production enabled through the use of big data for a product within your group’s pre-determined category, 2. Interrogate your model to identify the opportunities and challenges posed by RdM, 3. Identify a set of research challenges for the future, 4. Pitch your idea and challenges in the plenary.
- 23. RECODE