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Low-Cost Circular Economy Chain
MANUFACTURERS IN NORTHERN ITALY (8 PARTICIPATING COMPANIES)
University of Cambridge

Problem

As companies move towards circular manufacturing business models, they increasingly rely on data and information from across their supply chains. This information is crucial not only for manufacturers but also for users and end-of-life partners, such as recyclers. Effective information sharing requires collaboration and a clear understanding of what data each party finds valuable. However, this collaboration is becoming more complicated as some larger companies automate their operations with modern digital systems, while smaller companies often continue using outdated software or manual processes. Even for companies with modern IT systems, barriers to efficient information sharing persist.

Solution

A workshop was organised in collaboration with POLIMI University in Milan, Italy and had two key aims: 

 

Aim 1: To help manufacturers identify digital technologies that can be used to monitor and address their internal circular economy challenges. 

Aim 2: To identify the data that manufacturers need to collect and share to facilitate circular economy practices, as well as the barriers preventing effective data exchange.

 

 

 

 

 

The workshop, which took place over a morning session, was divided into two phases to tackle each objective. Some of the key outcomes were as follows:

 

Data to be shared and received included: 

  • Environmental Impact Information 

  • Product Information  

  • Quality Information 

 

Interestingly, similar to other supply chain-focused workshops, participants expressed a desire to receive data from suppliers but were less inclined to share their own data. This is notable, given their shared interest in receiving more data from customers.

 

The top five barriers include: 

  1. Lack of standardised formats for data exchange

  2. No data is collected to be shared 

  3. Digital tools for sharing data are not available 

  4. Need for employee training or recruitment of new personnel

  5. Difficulty in ensuring data accuracy

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