SMART MANUFACTURING

Harnessing IoT data for tomorrow’s smart factories

 

Challenge identifier: DPC3-2018

 

Proposed by

Greiner Packaging International GmbH (GPI). GPI manufacture and markets plastic packaging solutions for food and nonfood industries.

 

Background

Sensor data and other Internet of Things (IoT) technologies used in industrial processes (known as the Industrial Internet of Things (IIoT)) are providing manufacturers with increased opportunities to optimise their operations and business processes, as well as engaging with their customers.

The smart factory not only represents a step forward from traditional automation, but also rests on a fully connected and flexible system—one that can use a constant stream of data to learn and adapt to new demands.

Manufacturers who are able to access such insights are able to optimise business and manufacturing processes better than ever before. The market for big data manufacturing software is estimated to be one of the largest opportunities in any industrial category.

 

Description

GPI has recently invested in extensive sensors across 3 manufacturing plants. We are now looking for ways to utilise this data, along with our existing data, to best support and enhance our business. We are looking to develop applications which span across traditional boundaries of manufacturing, logistics and supply chain (perhaps even sales), providing data-informed solutions to better coordinate these processes, make them more efficient, and platforms supporting these new processes.

We are particularly interested in solutions that:

  • Define new relationships between data to provide new insights and understanding;
  • Discover new business opportunities and improve production efficiency;
  • 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-country operational scenarios
  • Capture and interpret data to produce answers to commonly asked questions and reduce human intervention;
  • Are able to integrate poor, inconsistent or fuzzy data or information and provide interfaces that communicate key findings and effectively engage users.

Data

Examples of data include but are not limited to:

  • Production orders
  • Logistics  (order process)
  • Sensor data in production (machine, energy consumption, cooling water)
  • Environmental data (shop floor temperature/humidity)
  • Quality data (product properties, scrap rate)
  • Failure cases of machines
  • Maintenance and usage history

More detailed information about the data can be found in our data catalogue

 

Expected outcomes

Examples of outcomes include but are not limited to:

  • Prediction algorithms that help decrease total stock holdings and lost sales.
  • Supply chain optimisation algorithms
  • Algorithms and applications that integrate different sources of data in interesting, novel ways
  • Insights across business functions such as marketing, operations, product and development, sales, etc.
  • Repeatable analytic processes that accelerate the adoption of analytics
  • Ability to gain a better understand of what data is currently not collected;
  • Ability to develop benchmarks that over time contribute to the optimization of future decisions.
  • waste and lead to better carbon footprints.
  • Reporting, analytics and visualization tools that help to:
    • Absorb information in new and more constructive ways
    • Visualize relationships and patterns between operational and business activities.
    • Manipulate and interact directly with data
    • Allow other stakeholders to engage with the data

Expected impacts

  • Ability to make better informed decisions e.g. strategies, recommendations
  • Ability to discover hidden insights e.g. anomalies forensics, patterns, trends
  • Facility for automating business processes e.g. complex events, translation
  • Performance payoffs
  • New business processes
  • Improved decision making

 

1. Do all the expected outcomes have to be covered ?

Innovative solutions (apps) that address only some of the objectives are welcome.

2. Do you use anything from Lean / Six Sigma / Lean Six Sigma methods for the process analysis and improvement?

Greiner Packaging is already using lean methodology and has an initiative to expand and broaden the use of lean methodology in all its factories. Six Sigma is known but not implemented on a company level.

3. Have you planned to apply a Digital Twin approach (or you have it already), as a part of Industry 4.0 or a similar initiative? and improvement?

Digital Twin is of interest to Greiner Packaging but it is “Digital Twin of an Organisation” rather than the virtual twin of a machine.

4. Are 2. and 3. interesting for your use case?

Lean / Six Sigma / “Digital Twin of an Organisation” are relevant and interesting use cases for Greiner Packaging. Apps that use/ and integrate data with these methods are definitely within the scope of the challenge.

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