SMART MANUFACTURING, LOGISTICS & MAINTENANCE

How can we use data to make manufacturing, logistics and maintenance processes more efficient and able to support new models of use and repair

 

Challenge identifier: SC5-2017

 

Description

 

New technologies and approaches such as on demand, distributed manufacturing (including technologies such as 3D printing), artificial intelligence, the Internet of Things and automation are being incorporated into traditional manufacturing, supply chain and logistics processes.

At the same time new models of use are developing where ownership of manufactured goods is giving way to subscription and other access models. For instance in transport, car ownership is increasingly having to compete with new models of rental (Zip Car) and access services (e.g. Uber). These models will also require a new cadre of services which are focused on maintenance and repair of goods.

The combination of these two trends presents an opportunity for the development of data based products and services which help co-ordinate and make more efficient the manufacturing, use, maintenance and repair of goods.

We would be interested in products and services which span across traditional boundaries of manufacturing, logistics and maintenance, providing data informed solutions to make these processes better co-ordinated and more efficient and provide platforms for new models of use.

We are particularly interested in solutions that leverage closed and shared data to:

  • Help ensure the integrity of interactions within supply chains;
  • Integrate data of different modalities (sensors, acoustic data, historical records, thermal maps) to produce useful products and services;
  • Use data to predict maintenance needs and create more efficient servicing and repair services; and
  • Predict and help optimise consumption and stock level, including in multi-countries operational scenarios.

 

Data

Examples of data include but are not limited to:

  • sensor data
  • failure cases of machines
  • maintenance and usage history
  • environmental data
  • geospatial data

 

Expected outcomes

Examples of outcomes may include but are not limited to:

  • new apps and services (web, mobile etc)
  • new algorithms (failure prediction, anomaly prediction, complex event processing, device management, noise detection)
  • tools for data reporting and visualisation of analytics results.

 

Expected impacts

Participants will need to provide details on how they would develop an impact measurement framework they would use to show how their solution:

  • improves overall equipment efficiencies;
  • minimizes risks; and/or supports operational and business decisions.

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement 732506. Unless otherwise indicated, all materials created by the Data Pitch consortium are licensed under CC-BY 4.0