The document discusses developing a roadmap for manufacturing and processing informatics in Australia. It would survey the current situation and international trends to help Australian industry develop use of digital technologies and maintain competitiveness. The roadmap would propose future directions for CSIRO's efforts in manufacturing informatics. It also discusses different types of manufacturing data and the importance of data for different industry types.

1. Future Manufacturing Informatics
ISWC Semantic Web Sydney-Canberra Meetup
Laurent Lefort
21 October 2013
COMPUTATIONAL INFORMATICS, CANBERRA

2. Processing Informatics Roadmap
Starting point
• Develop a roadmap (white paper) for Manufacturing/Processing
Informatics based on industry consultation which highlights the
opportunities for CSIRO to support the Manufacturing sector in
Australia.
• Survey the current situation of Australian Industry as well as
international trends.
• Help the Australian Process and Manufacturing industries to
develop their use of digital technologies and maintain or improve
their competitiveness on increasingly connected global and
domestic markets.
• Propose the future direction of CSIRO’s effort in Process and
Manufacturing Informatics.
Presentation title | Presenter name
2 |

3. Supplier data Informatics
Presentation 3 | title | Presenter name
Material, Products
and Processes
Process data
Factory
data
Human
Services ICT Automation,
IoT, Robotics
Supply chain management
Sustainable
manufacturing
End to end
data
Design,
customisation
Assistive
technologies
Relationship
Management
Product data
Client data
Supply chain events Factory events
Workforce skills

4. CROSS-CLASSIFICATION
CUSTOMISED
SYSTEMS
COMPLEX
SYSTEMS
Processing Informatics Roadmap| Laurent Lefort | Page
HIGH-END
SYSTEMS
SOPHISTICATED
COMPONENTS
“NON-SYSTEMS”
Global goods
for local
markets
Scale-Intensive (automotive) Dynamic Increasing
Returns
(chemical/pharma)
Regional
processing
Digital Manufacturing (digital
printing, custom-made furniture)
Traditional (printing)
Traditional (food)
? (plastics)
Energy-and-resource-intensive
Specialised Supplier (Mining) ? Process industries
(coke, nuclear, refined-petroleum
products,
paper/pulp)
Technology
innovators
(high R&D
intensity)
Science Based (Medical
Instruments)
Variation Intensive (Telecoms
equipment)
Specialised Supplier (Aerospace)
?
Labor-intensive-tradables
Traditional
(apparel/textile,
furniture, toys)
Typology of Australian companies

5. Differentiating factors
CROSS-CLASSIFICATION
CUSTOMISED SYSTEMS
COMPLEX SYSTEMS
Processing Informatics Roadmap| Laurent Lefort | Page
HIGH-END SYSTEMS
SOPHISTICATED COMPONENTS
PROCESS-BASED INDUSTRIES
“NON-SYSTEMS”
Global goods for
local markets
Global supply chain capabilities:
large series, high quality, low cost
Compliance to regulations
Reputation (end customers)
Regional
processing
Proximity to large number of
customers (ability to meet
specific requirements)
Proximity to primary
producers (agriculture
regions)
Reputation (end customers)
Energy-and-resource-intensive
Proximity to small number of leading customers (ability to meet
specific requirements)
Protected know how
Energy and resources costs,
proximity to energy/resources
or end use
Technology
innovators (high
R&D intensity)
Support by scientific
clusters
Imitation barriers
Support by scientific clusters
Global supply chain capabilities:
small series, high quality, high
cost
Support by scientific clusters
Protected know how
Labor-intensive-tradables
Availability of cheap labour

6. Typology of Manufacturing Data (v1)
Live stream (machine,
factory, supply chain)
Enterprise
Managed
Data
Innovation data
(product, process,
machine, factory,
supply chain)
Dagstuhl Seminar on Semantic Data Management 22-27 April 2012 | Kerry Taylor | Page
Social input and
output (end user
input, customisation,
…)
Analytics
Sub-tiers
Super-tiers

7. Typology of Manufacturing data
• The Innovation data category groups the data created during the design and
planning phase at all levels: definition of a product, a process, a machine or
factory, a supply chain. This is a category which requires
• The Live Stream data category groups the data captured on the factory floor
and/or within the supply chain in relation to a product or part at an
intermediate or final stage of production (discrete manufacturing) or to a
machine transforming bulk material (process manufacturing).
• The Social Input and Output data category groups all the data which supports
the standing of a company in a world where supply chain partners and end
consumers have increasing expectations of transparency and responsiveness.
• The Analytics data category groups any types of aggregated data. Current ERP
systems are generally designed to match the business needs of one specific
company. Business intelligence across a whole supply chain is increasingly
important both for the optimisation of day to day operations and for the
prevention and handling of crisis situations in multi-tiered contractual
arrangements.
Processing Informatics Roadmap| Laurent Lefort | Page

8. DATA
CATEGORY
DIVERSITY VOLUME TIME FACTOR STRUCTURE PERIMETER STANDARDS/
Dagstuhl Seminar on Semantic Data Management 22-27 April 2012 | Kerry Taylor | Page
PRODUCTS
Innovation
data
Very high Small to
medium
Number of
design cycles
Gains at
production
time
Complex
(drawings, bill of
materials)
Design-task
dependent
CAD, CAM
Live Stream Medium to
high
High Response
time to
planned/unpl
anned
changes
Tables/Graphs
(Measurements,
Events)
Physical
world-dependent
(sensors,
machines)
M2M, IoT
Analytics Very high High Length of
history
Gains at
design and/or
production
time
Multi-slices data
cubes with links
to data from all
other categories
Mono or
Multi-companies
ERP
OLAP
Social input
and output
Medium to
high
Small to
medium
Consumer/clie
nt/partner
expectation
Partially
unstructured
(contract doc.,
emails, tweet,
promo material)
Social
network
CRM
Typology of Manufacturing data

9. Importance of data
CROSS-CLASSIFICATION
CUSTOMISED SYSTEMS
COMPLEX SYSTEMS
Processing Informatics Roadmap| Laurent Lefort | Page
HIGH-END SYSTEMS
SOPHISTICATED
COMPONENTS
PROCESS-BASED INDUSTRIES
“NON-SYSTEMS”
Global goods for
local markets
Innovation data (leader and/or
follower)
Live stream
Analytics (leader and/or
follower)
Social input and output
Regional
processing
Live stream
Social input and output
Live stream
Social input and output
Energy-and-resource-
intensive
Innovation data Innovation data (leader)
Live stream
Analytics (leader)
Technology
innovators (high
R&D intensity)
Innovation data (leader)
Social input and output
Innovation data (leader and/or
follower)
Live stream
Analytics (leader and/or
follower)
?

10. Ontologies and “echelons”
Integration/Combination needs
Material data
Product data
Client data
Live Data
(big, urgent)
Presentation title | Presenter name
10 |
Process data
Factory
data
Supplier data
Ext
Interfaces.
data
Techno data
Market data
Supply
Chain
data
Ext Rel..
data
The interface between “product design and
engineering” and manufacturing (Dekkers et al. 2013)
Strategic
data
Analytics
data
(multi-views)
Innovation Data
(complex, technical)
Social
Data (diverse,
unstructured)

11. Thank you
Computational Informatics
Laurent Lefort
Presenter Title
t +61 2 6216 7046
e laurent.lefort@csiro.au
w www.csiro.au/
COMPUTATIONAL INFORMATICS