Future manufacturing informatics - typology of manufacturing data

Future Manufacturing InformaticsISWC Semantic Web Sydney-Canberra Meetup

COMPUTATIONAL INFORMATICS, CANBERRA

Laurent Lefort 21 October 2013

• 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

Processing Informatics RoadmapStarting point

2 |

Presentation title | Presenter name3 |

Material, Products and Processes

Human Services ICT Automation,

IoT, Robotics

Supply chain management

Sustainable manufacturing

End to end data

Design, customisation

Assistive technologies

RelationshipManagement

Product data

Client data

Supply chain events Factory events

Process data

Factory data

Workforce skills

InformaticsSupplier data

Processing Informatics Roadmap| Laurent Lefort | Page

CROSS-CLASSIFICATION

CUSTOMISED SYSTEMS

COMPLEX SYSTEMS

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

Differentiating factors



CUSTOMISED SYSTEMSCOMPLEX SYSTEMS

HIGH-END SYSTEMSSOPHISTICATED COMPONENTS

PROCESS-BASED INDUSTRIES “NON-SYSTEMS”


Global supply chain capabilities: large series, high quality, low cost

Compliance to regulationsReputation (end customers)

Regional processing

Proximity to large number of customers (ability to meet specific requirements)

Proximity to primary producers (agriculture regions) Reputation (end customers)


Proximity to small number of leading customers (ability to meet specific requirements)

Protected know howEnergy and resources costs, proximity to energy/resources or end use


Support by scientific clustersImitation barriers

Support by scientific clustersGlobal supply chain capabilities: small series, high quality, high cost

Support by scientific clustersProtected know how

Labor-intensive-tradables

Availability of cheap labour

Enterprise Managed

Data

Typology of Manufacturing Data (v1)

Dagstuhl Seminar on Semantic Data Management 22-27 April 2012 | Kerry Taylor | Page

Innovation data (product, process, machine, factory,

supply chain)

Live stream (machine, factory, supply chain)

Social input and output (end user

input, customisation, …)

Analytics

Sub-tiers

Super-tiers

• 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.

Typology of Manufacturing data


Dagstuhl Seminar on Semantic Data Management 22-27 April 2012 | Kerry Taylor | Page

DATA CATEGORY

DIVERSITY VOLUME TIME FACTOR STRUCTURE PERIMETER STANDARDS/ PRODUCTSInnovation data

Very high Small to medium

Number of design cyclesGains at production time

Complex (drawings, bill of materials)

Design-task dependent

CAD, CAM

Live Stream Medium to high

High Response time to planned/unplanned changes

Tables/Graphs (Measurements, Events)

Physical world-dependent (sensors, machines)

M2M, IoT

Analytics Very high High Length of historyGains at design and/or production time

Multi-slices data cubes with links to data from all other categories

Mono or Multi-companies

ERPOLAP

Social input and output

Medium to high

Small to medium

Consumer/client/partner expectation

Partially unstructured (contract doc., emails, tweet, promo material)

Social network

CRM

Typology of Manufacturing data

Importance of data



CUSTOMISED SYSTEMSCOMPLEX SYSTEMS

HIGH-END SYSTEMSSOPHISTICATED COMPONENTS

PROCESS-BASED INDUSTRIES “NON-SYSTEMS”


Innovation data (leader and/or follower)Live streamAnalytics (leader and/or follower)

Social input and output

Regional processing

Live streamSocial input and output

Live streamSocial input and output


Innovation data Innovation data (leader)Live streamAnalytics (leader)


Innovation data (leader)Social input and output

Innovation data (leader and/or follower)Live streamAnalytics (leader and/or follower)

?

Ontologies and “echelons”Integration/Combination needs

Presentation title | Presenter name10 |

Product data

Client data

Process data

Factory data

Supplier data

Material data

Ext Interfaces.data

Market data

Techno data

Ext Rel..data

Supply Chaindata

Strategicdata

The interface between “product design and engineering” and manufacturing (Dekkers et al. 2013)

Analytics data

(multi-views)

Innovation Data (complex, technical)

SocialData (diverse, unstructured)

Live Data (big, urgent)

Computational InformaticsLaurent LefortPresenter Titlet +61 2 6216 7046e [email protected] www.csiro.au/

COMPUTATIONAL INFORMATICS

Thank you

Future manufacturing informatics - typology of manufacturing data

Technology

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