Manufacturing Industry Digital Innovation Hubs

Manufacturing Industry Digital Innovation Hubs
Summary

VISION: By 2023, Europe will set the reference for the Industry 4.0 market: European CPS/IOT Open Digital Platforms providers will be able to flexibly and dynamically connect the Real World with digital Enterprise Systems through common open standards; European ICT SMEs will be growing fast through leadership in data-driven smart Industry 4.0 services; European Manufacturing SMEs will successfully compete globally with innovative products and services, digitised Industry 4.0 processes and innovative business models, involving their workforce at all levels in this Digital Transformation innovations. MISSION.

The MIDIH 4.0 project aims at implementing the fast, dynamic, borderless, disruptive side of the I4MS innovation coin:

  • technological services (interactive try-on demos, webinars, challenges, hackathons and awards) will be driven by young and dynamic ICT talents virtually meeting older and experienced manufacturing engineers in a one-stop-shop global marketplace;
  • business services (ideas incubation, business acceleration, demand-offer matchmaking and brokerage, access to finance) will support SMEs, startups, web entrepreneurs as well as corporates in the delivery of innovative products and services, in accessing new markets, in fund-raising; skills building services (serious and role games, participative lessons and webinars, virtual experiments in physical teaching factories, professional courses for existing technicians as well as for executives) will not only help SMEs and corporates understand the new technologies, but also take full advantage of them, providing an operational framework that will stimulate trust, confidence and investments.

The MIDIH project is an inclusive Innovation Action of 21 beneficiaries coming from 12 EU Countries, including, Competence Centers, Digital Innovation Hubs, CPS/IOT Technology Providers as well as Lighthouse Manufacturing Industries. A two-iteration Open Call will help achieve a critical mass of cross-border experiments.

More information
Web resources: http://www.midih.eu/
https://cordis.europa.eu/project/rcn/211689/factsheet/en
Start date: 01-10-2017
End date: 30-09-2020
Total budget - Public funding: 8 524 832,00 Euro - 7 999 157,00 Euro
Call topic: ICT Innovation for Manufacturing SMEs (I4MS) (FoF.2017.12)
Twitter: @MIDIH_EU
Location

This is a set of Specific Objectives and Research & Innovation Objectives that is subject to a consultation in preparation of the Made In Europe Partnership.  For more guidance about the consultation, please see www.effra.eu/made-in-europe-state-play.

      Comment:

      MIDIH has an experiment in the cutting tool sector (NECO) focusing on smart management of product orders and their integration with the production, in particular to optimize data interoperability between logistic data, raw material costs and manufacturing critical paths with the goal to optimize the capability of the PSS (Production Service Systems) providing an optimal prediction of delivery time and associated costs and lifetime of the cutting tool.

      Clienrs will benefit from speed-up of the design process and faster offers --> improvement of brand image.

      Increases the commitment to the client: KPI: 30% reduction of broken promises

      Standardization of tool changes. Warehouse inventory reduction: KPI: 30% decrease on the average tap stock level in the facilities compared with the AS IS situation prior to MIDIH

      Repeatability of the process KPI: 50% decrease on the number of tools reworked compared with the "As-Is" situation prior to MIDIH. Improving the manufacturing process (KPI 10% defective product reduction) Lead time reduction (KPI 15% time reduction in the delivery).

      The most important business indicator will be the time per offer. The time of the team to prepare and launch an offer will decrease by 10% (KPI)

       

       

      Comment:

      In MIDIH we have two experiments in the area of Smart Factory, one in the automotive sector and one in the cutting tools sector.
      The scenario in the automotive sector is focused on the quality of the welding process in truck manufacturing. The experiments to be performed aim at testing a continuous monitoring of the welding cell and robots and Data analysis algorithm to detect unexpected behaviour or to plan special maintenance intervention based on historical data.                                                                           
      The scenario in the cutting tools sector is focused on the management of the cutting tools production process. The experiments aim at testing the integration of digital twins to improve quality control and optimize performance and maintenance of the manufacturing machines, monitor and keep track of the manufacturing costs.

      Predictive maintenance service implemented in the welding cell focuses on following KPIs:
      – 20–30% decrease in costs for the management of unpredicted maintenance events reduction
      – 5% increase in Mean Operating Time Between Failures (MTBF)
      – 0.5–1% OEE increase
      – 5–10% decrease in low quality bodies

      Comment:

      The experiment in the automotive sector in the area of Smart Factory (already described for the previous objective can also contribute to this objective:
      The scenario is focused on the quality of the welding process in truck manufacturing. The experiments to be performed aim at testing a continuous monitoring of the welding cell and robots and Data analysis algorithm to detect unexpected behaviour or to plan special maintenance intervention based on historical data.

      Investments, savings, efficiency growth expected etc. in 3 years (KPIs):
      – 20–30% decrease in costs for the management of unpredicted maintenance events reduction
      – 5% increase in Mean Operating Time Between Failures (MTBF)
      – 0.5–1% OEE increase
      – 5–10% decrease in low quality bodies

      Comment:

      The second Business Process for the Smart Factory scenario in the automotive sector consists in the application of the MIDIH solution in the Campus Melfi, involving a welding cell, equipped with the same sensors
      and SCADA system as in the Suzzara plant, other robots and AGVs.

      The KPIs related to this Business Process are the maintenance costs and the number of low quality products and its respective decrease.

      Comment:

      In MIDIH we have also experimentation in the Steel Sector covering the Smart Supply Chain scenario.
      This scenario focuses on the ecosystem of 3D printing services. There is a rising number of 3D Printing Hubs in the market, but there is sophisticated engineering know how needed to create a printable file to maximize the benefits of 3D printing. Beside the optimization of the part itself, there is the choice of the fitting production technologies and finally the availability of logistic services. All these aspects lead to complex decision-making process in optimizing the delivery of the part. The experiment aims to establish a self-organized distribution network as part of a digital self-organized supply chain. The dynamic forming of the supply chain should be realised by a service application. The application plans an order-related distribution by exploring an open market for logistic services, based on a customer designed, preferred distribution network.

      The targeted value of the experiment is to create Logistics vocabulary for smart supply chain, the creation of a distributed analytics service for the calculation of the estimated time of arrival and vocabulary for digital twin as foundation for smart product.

      Comment:

      DIHIWARE is the MIDIH Innovation and Collaboration Platform that will act as a facility intended to support   knowledge sharing and technology transfer, based on human to human interaction, communication and technology informationbetween CCs and DIHs.

      The MIDIH Open Source Reference Architecture (MIDIH RA) is a functional and modular architecture that  supports IoT, Big Data and Artificial Intelligence technologies, which are expected to drive the change in Manufacturing Industry by enabling smart products (digital inside), smart processes and smart business models.

      The MIDIH RA is composed by 39 components (34 as background open source assets, 5 as foreground assets) and was instanciated in three Industrial Experiments with specific needs. The DIHIWARE was experimented by 16 DIHs/CCs part of the MIDIH Consortium (plus three external experiments)