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“Data Sharing Spaces for Manufacturing in 2025: opportunities and challenges”, Madrid, November 15th 2019
Data Spaces for Smart ManufacturingSergio Gusmeroli (Politecnico di Milano)
The 2018 Data PACKAGE: towards a common EU Data Sharing Space
Yvo Volman, European Commission at EBDVF19
Business Models for sharing Private sector DataModels to B2B Data Exchange
a) An Open Data approach: The data in question are made available bythe data supplier to an in principle open range of (re-)users with as fewrestrictions as possible and against either no or very limitedremuneration.b) Data monetization on a data marketplace: Data monetization ortrading can take place through a data marketplace as an intermediaryon the basis of bilateral contracts against remuneration. Suitable wheneither (1) there are limited risks of illicit use of the data in question, (2)the data supplier has grounds to trusts the (re-)user, or (3) the datasupplier has technical mechanisms to prevent or identify illicit use.c) Data exchange in a closed platform: Data exchange may take placein a closed platform, either set up by one core player in a data sharingenvironment or by an independent intermediary. The data in this casemay be supplied against monetary remuneration or against added-value services, provided e.g. inside the platform.
Data Exchange in Closed Platform: Predictive Maintenance in PRIMA Welding Station at FIAT Melfi Campus Plant
FIAT Campus Melfi innovative welding station composed by equipment of differentmanufacturers, including PRIMA Laser Welding Machine and multiple MobileRobotics (AGVs) internal logistics systemsImplementing a “powered by FIWARE” multi-stakeholder Industrial Data Space inSIEMENS MINDSPHERE IoT Platform, under POLIMI methodology for Industry 4.06Ps Digital Transformation of ManufacturingAn IDSA Data Sovereignty reference implementation based on configurable andflexible integration of Orion Context Broker with Big Data AI-based MINDAPPs forPredictive MaintenanceMindSphere World Italia experimental Facility (end 2019)
i. No cycle time for part downloadingii. Increasing productivity: more than 14% of time savingiii. No fixed clamping system. The machine could be used to cut different
part numbers
iv. Using the same machine for different body car parts.v. Working tolerances measured & certified towards a Zero Defect
Manufacturing welding stationvi. Better customer service and predictive maintenance.
BUSINESS IMPACT
The Value Network
Mindsphere PaaS
MachinesOperator
AGV
MES
PRIMA Laser Machine
1 0 10 1 0
providesdata
provides optimization
Load/Unload Area
provider IT infrastructureprovider application
Provides data
PRIMA supplier of machine
various services
Signed Contract
Data Usage
CRF Tenant Owner share your Asset Prima Asset
IoT hardwareintegrator 1 0 1
0 1 0provides optimization
FIAT Plant
SME System Integrator/DevOps
Custom App Transfer
User
User
User
MINDSPHERE API
FIAT: Plant OwnerPRIMA: Machine ManufacturerMINDSPHERE: IIOT PlatformrmSME: App Developer
Business Model Innovation
CRF
What
Value How
Who
valueproposition
revenuemechanism
valuechain
MINDSPHERE
What
Value How
Who
valueproposition
revenuemechanism
valuechain
PRIMA
What
Value
Who
valueproposition
revenuemechanism
valuechain
How
What
Value How
Who
valueproposition
revenuemechanism
valuechain
SME
Significant change
No Significant change
FIAT: Plant Owner, significant change in the HOW dimension, a new business model is foreseen which also affects the VALUE
PRIMA: Machine Manufacturer, significant change in the business model (HOW) and in the WHAT for a new generation machines
MINDSPHERE: IIOT Platform, no significant changes as the case is fully in line with MSPHERE mission
SME: App Developer, a new company (SME or STARTUP) taking advantage of Open Source (FIWARE) and Proprietary (MINDSPHERE) platforms to develop highly innovative AI-based applications
Open Data: the vision of a Didactic Factories NetworkOpen Data in Manufacturing
a) Open Data Models for SI entities (such as robots, AGVs, machines, conveyors), Data in Motion Data at Rest
b) Network of open Didactic Factoriesproducing and sharing their data thanks to standard protocols and data formats (e.g. OPC-UA, MQTT, ROS, AMQP).
c) Data Transformation techniques for non-public Data such as Aggregation, Filtering, Anonymization, Pseudonymiz.
d) One stop shop for search-discovery-selection, Distributed Repositories for Data Storage (iSpaces)
e) Ecosystem of Innovators testing and experimenting their solutions on open data (Data-AI Community)
Open / Closed Data Spaces: B2G & B2C Data Sharing SpacesURBAN MANUFACTURING: SMART FACTORIES in SMART CITIES
Twin UM factory and city grew together and shared very similar socio-business experiences.
New-borne UM a new factory with no negative impact on the surrounding environment.
Proxy UM employees have been mostly hired locally, supply-distribution chains are extremely short.
Garage UM Do-It-Yourself FabLab experiences 3D Printing and the Makers movement.
Home UM production is spread in an ecosystem made of micro plants, often home based.
ZeroKM UM slow / organic food against tastes massification and global food chains.
City centre
Data Monetization: from DIH to Marketplaces
Access to Expertise Access to Financing Collaborate
Access to Knowledge Access to Technology Find Connections
Data Monetization: enabling Product-Services
Pure Product Product
Product Product Service
The servitization continuum
ServiceP
Servitization is the evolutionary pathway of the business model of a manufacturing company, moving from a product-centric perspective towards Product-Service Systems (PSSs), based on
the provision of integrated bundles consisting of both physical goods and services. (Tukker 2004)
10
The Fourth Industrial Revolution is characterized by the introduction of “Internet of Things” and “Servitization” concepts (Thoben et al. 2017)
Challenges for Data/Service Sharing Spaces1. Open Data (operational but in other domains )• Manufacturing Industry Managers and Decision Makers need to understand the
value of Open Innovation• Easy-to-use and self-explanatory algorithms for Data Preparation
• Interoperability with existing Open Data sources like in Urban Manufacturing
2. Data Marketplaces (dynamic and SME-oriented, new business models)• DIHs as trusted Data Marketplaces for SMEs (AI ecosystem)
• Distributed P2P Marketplaces and dynamic on-the-fly Smart Contract adaptations.
• Enabling new Business Models such as Servitization
3. Trusted Data Networks (often dominated by Large Companies)• Platform Economy and multi-stakeholders Innovation Models
THANK YOU
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 822064
MARKET4.0 Kick-off meeting, Brussels, 27-28/11/2018
THANKS!!!!!Sergio Gusmeroli (Politecnico di Milano)
