Nowadays the way products are produced and consumed is not sustainable, people are replacing products are a fastest rate than ever. Several of these products are considered obsolete and as a consequence are replaced. There are other options that extend a product lifetime, such as product repair.
The aim of this study is to capture through ethnographic research different repair narratives to develop an understanding of how a range of consumer interactions in product repair can be expanded and built upon to inform more circular models of production and consumption.
Setting the vision for Re-distributed Manufacturing
This research focuses on exploring the opportunities, challenges and research question related to the shift from the current economic model to Re-distributed Manufacturing in the consumer goods industry. The study aims to set the vision for Re-distributed Manufacturing in this particular sector for the next few decades.
A framework for applying circular innovation and re-distributed manufacturing models
The aim of my research project is to create a conceptual framework that aids in the application of circular principles and re-distributed manufacturing models. To evaluate this framework a case study of its application to an Unilever asset is being conducted. The case involves analysing how changes in asset design and the addition of intelligence can improve efficiency, increase the value of the asset over its lifetime, and facilitate a re-distributed manufacturing model.
A framework for analysing the impact of the Internet of Things on consumer goods manufacturers
This research project aims to analyse the impact of the Internet of Things on consumer goods companies. With information technologies permeating products and organisations, businesses are becoming more and more digitalised.
The shift to provide smart products have an effect on different organisation’s components, from organisational structure to business strategy, with new business models and revenue streams possible.
Using the Strategic Alignment Model framework it is possible to analyse what is going to change and what are the possible measures businesses can take to succeed in the Internet of Things era.
Martin Rioja Falcone
Developing Circular Models of Consumption through the use of Big Data
This project aims to study how big data can enable the transition from linear business models to circular business models within the consumer goods market. Principles from the analysis of current examples in B2B and B2C markets were identified to assess which products are more likely to succeed through this transition and where Big Data and Intelligence adds value.Three case studies were created to show the potential of big data in mainstream products. And how this relation between circular business models and Big Data can generate new benefits for both manufacturers and users.
See the case studies videos by following the links:
The role of Big Data to facilitate Redistributed Manufacturing using Co-creation lens:patterns from Consumer Goods
Redistributed manufacture is a connected, local and inclusive model of production and consumption that is driven by exponential growth and embedded value of big data.
This research sheds light on the inclusiveness theme, presenting patterns on how businesses are starting to redistribute their functions among various stakeholders including consumers by co-creating value using data-driven methods. A co-creation framework was synthesised based on the literature for analysis. Many secondary data cases were analysed across 5 major consumer goods industries. These industries were chosen to be representative of the whole consumer goods. Elite interviews were conducted to evaluate the existing co-creation practices, and for how they can evolve to become more data-driven and local. Industry specific patterns and cross cutting themes were identified, presenting the RECODE network opportunity areas to explore and challenges to address as part of its future research agenda.
The impact of Big Data on the redistributed of manufacturing in the consumer goods industry
A study based on secondary data and interviews with manufacturing experts to understand the current consumer goods manufacturing landscape and how Big Data could have an impact on it with particular focus on production scale and location.