RECODE Consumer Goods, Big Data and Re-Distributed Manufacturing

重新编码消费品、大数据和重新分布式制造

• 批准号: EP/M017567/1
• 负责人: Fiona Charnley
• 金额: $ 59.56万
• 依托单位: CRANFIELD UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM017567%2F1
• 关键词: RECODE; Consumer; Goods; Big; Data

The EPSRC-ESRC Network in Consumer Goods, Big Data and Re-Distributed Manufacturing (RECODE) aims 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.Transforming the consumer goods industry through the use of big data and re-distributed models of manufacture poses entirely new challenges inherent to the capture, storage, analysis, visualisation and interpretation of big data. Combined with this is the cross-disciplinary requirement for radically new methods of engaging end-users, empowering customer interaction, facilitating ad-hoc supply chains, re-capturing and re-deploying valuable materials, optimising manufacturing processes, informing new user-driven design of customised goods and services, developing novel business models and implementing data-driven open innovation.The world generates 1.7 million billion bytes of data every day and global big data technology and services is growing by 40% per year, predicted to reach USD 16.9 billion in 2015. The exponential growth of available and potentially valuable data, often referred to as big data, is already facilitating transformational change across sectors and holds enormous potential to address many of the key challenges being faced by the manufacturing industry including increasing scarcity of resources, diverse global markets and a trend towards mass customisation. The consumer goods industry, one of the world's largest sectors worth approximately USD3.2 trillion, has remained largely unchanged and is characterised by mass manufacture through multi-national corporations and globally dispersed supply chains with 80% of materials ending up in landfill. The role of re-distributed manufacturing in this sector is often overlooked, yet there is great potential, when combined with timely advancements in big data, to re-define the consumer goods industry by changing the economics and organisation of manufacturing, particularly with regard to location and scale. RECODE will develop novel methods to engage communities of academics, international experts, user groups, government and industrial organisations to define and scope the shared multi-disciplinary vision and research agenda. New perspectives and contributions from user groups and stakeholders will be used to ensure that the vision of the network is fully inclusive and sensitive to regional trends, variances and scales. Short-term studies will be undertaken across the breadth of the theme to test and evaluate the feasibility of specific research challenges, the findings of which will contribute to an interactive roadmap representing local and global communities and research agendas of the network. Closing the gap between manufacturers, suppliers and consumers will provide opportunities for personalisation of products and services, up scaling of local enterprise and the development of user-driven products tuned to the requirements of local markets providing economic competitiveness for the UK. Improved understanding of skills and training required for interpreting big data and transforming industries will ensure that the UK can take full advantage of opportunities for job creation. Moving towards a localised and regenerative model of consumer goods manufacture will create more efficient and effective supply chains capable of on-demand responses; increasing productivity and competitiveness of the manufacturing industry. This challenging two year network will bring together an internationally renowned team of experts from Cranfield, Brunel, Cambridge, Manchester and Teesside universities drawing on leading-edge strengths of the host institutions and international connections with research communities, companies, business intermediaries and governance at local, national and international scales.

EPSRC-ESRC消费品、大数据和再分销制造网络（RECODE）旨在建立一个积极参与的社区，通过该社区，测试和评估与大数据应用相关的多学科愿景和研究议程，以向消费品重新分配制造模式转型。通过使用大数据和重新分配来改造消费品行业分布式制造模型对大数据的捕获、存储、分析、可视化和解释提出了全新的挑战。与此相结合的是对全新方法的跨学科要求，这些方法包括吸引最终用户，增强客户互动，促进特设供应链，重新捕获和重新部署有价值的材料，优化制造流程，为定制商品和服务的新用户驱动设计提供信息，开发新的商业模式和实施数据-驱动开放式创新。全球每天产生170万亿字节的数据，全球大数据技术和服务每年增长40%，预计2015年将达到169亿美元。可用和潜在有价值的数据（通常称为大数据）的指数级增长已经促进了各行业的转型变革，并具有巨大的潜力来解决制造业面临的许多关键挑战，包括资源日益稀缺，全球市场多样化和大规模定制的趋势。消费品行业是世界上最大的行业之一，价值约3.2万亿美元，基本保持不变，其特点是通过跨国公司和全球分散的供应链进行大规模生产，80%的材料最终被填埋。重新分配制造业在这一领域的作用往往被忽视，但当与大数据的及时进步相结合时，通过改变制造业的经济和组织，特别是在位置和规模方面，重新定义消费品行业有很大的潜力。RECODE将开发新的方法，让学术界、国际专家、用户团体、政府和工业组织参与进来，以定义和界定共享的多学科愿景和研究议程。将利用用户群体和利益攸关方的新观点和贡献，确保网络的愿景具有充分的包容性，并对区域趋势、差异和规模敏感。将在整个主题范围内进行短期研究，以测试和评估具体研究挑战的可行性，研究结果将有助于制定一个代表地方和全球社区的互动路线图以及网络的研究议程。缩小制造商、供应商和消费者之间的差距将为产品和服务的个性化、当地企业的规模扩大以及开发用户驱动的产品提供机会，这些产品符合当地市场的要求，为英国提供经济竞争力。提高对解读大数据和改造行业所需技能和培训的理解，将确保英国能够充分利用创造就业机会的机会。转向本地化和再生的消费品制造模式将创造更高效和更有效的供应链，能够按需响应;提高制造业的生产力和竞争力。这个具有挑战性的为期两年的网络将汇集来自克兰菲尔德，布鲁内尔，剑桥，曼彻斯特和提赛德大学的国际知名专家团队，利用主办机构的领先优势以及与当地，国家和国际规模的研究社区，公司，商业中介和治理的国际联系。

项目成果

A vision of re-distributed manufacturing for the UK's consumer goods industry

英国消费品行业重新分配制造的愿景

• DOI: 10.1080/09537287.2018.1540053
• 发表时间: 2019
• 期刊: Production Planning & Control
• 影响因子: 8.3
• 作者: Bessière D

Transforming the landscape of consumer goods through big data and RdM

通过大数据和 RdM 改变消费品格局

• 发表时间: 2015
• 作者: Armes,R

Molecular Characterization and Designing of a Novel Multiepitope Vaccine Construct Against Pseudomonas aeruginosa.

针对铜绿假单胞菌的新型多表位疫苗结构的分子表征和设计。

• DOI: 10.1007/978-3-319-57078-5_49
• 发表时间: 2022
• 期刊: International journal of peptide research and therapeutics
• 影响因子: 2.5
• 作者: Dey J

A framework for analysing the impact of the Internet of Things on consumer goods manufacturers

分析物联网对消费品制造商影响的框架

• 发表时间: 2015
• 作者: Barbesta, A

Circular Innovation and Re-distributed Manufacturing

循环创新与再分配制造

• 发表时间: 2016
• 作者: Charnley F