The final report from this 9-month feasibility study is now available, which seeks to address the key research question: 'How can big data impact redistributed manufacturing in the consumer goods industry?'
The final report from the RECODE Network Feasibility Study into Big Data Ecosystem in Re-distributed Manufacturing (RdM) Past & Future, by Mohamed Zaki, Matthias Friedrich Tepel, Babis Theodoulidis, Philip Shapira, Andy Neely
The 9-month feasibility study aimed to identify the challenges and opportunities to effectively leverage big data in consumer goods and provide a better understanding of the drivers and the value that redistributed manufacturing can deliver for a manufacturer as well as for a customer. The enormous data, which can include anything from online chatter about a brand or product to real-time feeds from cyber-physical systems, machine tools and robots, have a great potential to facilitate and enable the redistribution of manufacture.
Therefore the study aimed to investigate Data-Driven Pathways in Re-Distributed Manufacturing that target and engage different consumer goods industries. For example, a case study for redistributed manufacturing is the furniture company AtFAB. This company provides the digital design for furniture, which can be downloaded and then used by anyone, anywhere to manufacture the product with local raw materials and a CNC machine. This example illustrates clearly how the concept of redistributed manufacturing is changing the role of entities in the supply chain for consumer goods and how data is replacing the physical supply chain.
The feasibility study aim was fulfilled through undertaking the following objectives:
a) Develop a Big Data Ecosystem Blueprint1, which will include the different data sources and activities that are needed to enable the redistributed manufacturing in CG industries.
b) Examine the future outlook in relation to Data-Driven Pathways in Re-distributed Manufacturing:
What are the different scenarios to drive innovations?
What are the barriers that should be considered?
c) Use the findings to contribute to the RECODE research agenda.