Our talk reports about how efficient Sirius was in supporting various tasks like creating the graphical editor, adding specific views and interfacing with the underlying simulation engine as well as a report generator. Our tooling was developed in the scope of a 2-year joined project with the Fraunhofer IPT and the IFU cybernetic lab of RWTH Aachen. It also involved Belgian and German end-user companies. It is now being transferred to more companies in the logistics and mechanical engineering domains. It is also available under a permissive Open Source license for all interested companies.

1. www.cetic.be Centred’ExcellenceenTechnologiesde
l’InformationetdelaCommunication
www.cetic.be
Sirius-powered Risk Modelling and
Simulation in Industry 4.0 Supply Chains
Christophe Ponsard
SiriusCON, Paris 2017

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Context – Risks Management in Supply Chains

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Example of Procurement Risks
Supplier A
in Seattle Supplier B
in Osaka
Customer
in Hamburg
Riskarea
Legend
LA,R1
T1,B,R1
T2,B,R1
T2,B,R2
T2,B,R3
LB,R1
T1,A,R1
T1,A,R2
T2,A,R1
T2,B
T1,B
T1,A T2,A
Politics
Techno
logy
Eco-
nomy
Ecology
Social
Risk of multinational enterprises
according to Dunning
Risk
types

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End-to-end Risk Management in Supply Chains
Warehouse risk
(Delay)
Quality
Production risk
Delay
Quality
Warehouse risk
(Delay)
Quality
[Production Risks (internal)]
[Procurement
Risks (external)]
[External Risks (external]

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StateofpracticeinSMEs(surveyperformedin2015)
https://www.cetic.be/Management-of-Procurement-Risks-on-Manufacturing-Processes

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SimQRi Project Context
• Towards Quantitative Risk Management in Supply Chains
• Main goal:
• Develop a practical tool-supported methodology to help SMEs
assessing the risks and mitigating their impact in the production
process
• Approach: modelling and simulation toolbox
• Modelling supply chains
• Identifying delay, quantity, bad quality,…
• Expressing them with model queries
• Efficiently simulate alternative designs to reduce risk
http://simqri.cetic.be and http://simqri.com
6

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Modelling Supply Chains and Risks
• Suppliers + supply policies
• Storages
• Processes

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Queries
• Probes on model element:
e.g. relativeContent(storage) totalWaitDuration(process), …
• Composed probes
• Percentage of working time for a process
worktime(p) := 100 * (time – totalWaitDuration(p)) / time
• Carbon footprint:
CF(order) := upplier(order).dist*CO2_FACTOR_TRUCK_PER_KM

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SimQRi - EMF Meta-model

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SimQRi - Tool Architecture
• Actually a dual tool with a common simulation engine
• Web-based
• Eclipse-based
• Technology mix:
10
BIRT(reporting)

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Web-based Version (« beer game » model)
11

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Eclipse Version (Green Supply Chain)
12

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SimQRi - Web-based UI
Not a Good Experience
Joint.JS selected as JavaScript among some candidates
Globally did the job, however:
• Not configurator-based as Sirius ⇒ JavaScript programming
• Need to buy full (non-open source) version or spend time reinventing the
wheel if you want features like UNDO/REDO
• Integrating more stuff, e.g. dashboard, was painful
• Proved hard to maintain (kept as “simplified tool” in the end)
Comparing pure UI development effort:
With joint-JS With Sirius
First usable prototype 3 months 3 weeks (1 week training)
Full scope Not achieved after 18 months 6 months

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Another feedback on learning curve

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SimQRi - Eclipse Version
Complexity of Project Set-up

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SimQRi - Ease of Evolution in Eclipse
Green Supply Chains
• Garbage production
• special type of output of a process through a "trash" port
• connected to a dedicated overflow storage named "Garbage“
• total quantity of garbage= Garbage.totalPut()
• Energy Usage:
• “energy counter” updated on processed/storages per processed item
• can depend on load, e.g. EnergyCounter+=MaxEnergyPerBatch*(1-Load)
• Carbon footprint:
• Transportation: OP_A.quantity*Supplier_A.dist*CO2_FACTOR_TRUCK
• Production: using specific attributes
success.CF=part_A.CF+part_B.CF+part_C.CF EnergyPerBatch*(1-Load)*CO2_FACTOR

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SimQRi - Ease of Evolution in Eclipse:
Simulation of Cloud Computing Elasticity
• total energy (on whole system) = SaaS level energy model
sum(Process.allInstances()->select(p:Process|p.energy*p.processedRequests))
• missed request ratio (on the FRONT process)
FRONT.processedRequests/FRONT.incommingRequests
• average response time (on a chain):
avg(sum(ProcessChain.allInstances()->select(p:Process | p.runtime)))

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SimQRI - Community Support
• Web-based:
• Join.js: good tutorials, forums
• Need to buy RAPPID commercial version to get all features
e.g. undo/redo
• Eclipse-based:
• Full editing features
• Good tutorials, forums
• Strong support from Obeo community
• Obeo training useful for a quick learning curve
• Collaborative work

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Integration with the OscaR engine
A library for operational research encompassing:
• State of the art optimization engines: CP, CBLS, MIP, DFO
• Discrete simulation engine
• Visualization
Scala 2.11, Open source LGPL
Release 4.0.0 in September 2016
• Simulation using OscaR.DES (Open Source)
• Built on top of a task-resource model
• Incremental evaluation throughout the simulation run
i.e. no trace stored, no post-processing
• Minimal updates of only the relevant fragment of queries
• Bottom-up updates to allow the sharing of sub-queries
• Monte Carlo techniques for aggregating results
• Availability of specific statistical operators (mean, avg, std dev,…)
• Computation of distributions
oscarlib.org

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OscaR integration
• AQL query to extract queries
• Model transformation to translate into OscaR.DES
• Running MCS
over DES engine
• Injecting result
back into model
• Generating
BIRT reports

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Integration with BIRT for Reporting
• First: tried to setup ecore as data source
• Finally used intermediate XML format

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On-going work: Optimised Risk Mitigation
• Tuning the model in order to minimise costs induced by risks
• Now: simple “explorer” functionality to change the values of a single
parameter to find out its optimal value (the other being unchanged) –
e.g. optimal ordering threshold
• Current work (PRIMa-q) : use optimisation (constantly) minimizing risks
• Risk robust strategies (scheduling)
• On-line Stochastic Optimisation
• Based on the Oscar.CBLS engine (efficient, scalable, online) oscarlib.org

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Conclusions and Ideas
• Sirius approach
• Proved evolvable to other domains
• Some learning curve but quickly recovered
• Easier to maintain/evolve
• Related technologies are quite widely used and well known
• Our work also released as open source to SME (and everyone)
GitHub: https://github.com/cetic/SimQRI
• Web version
• Too specific and hard to maintain
• Code not released (possible licence issues)
• But nice showcase, interesting SaaS mode
 what about a “web-based” Sirius in the future ?
• Text integration
• Xtext integration great
• Looking into Office suite integration like Open/Libre Office & Microsoft Office
see EclipseCON talk and more on-going work

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QUESTIONS ?
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Tool homepage
https://simqri.cetic.be
Open Source repo
https://github.com/cetic/SimQRI

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linkedin.com/company/cetic
info@cetic.be
+32 71 159 362
twitter.com/@CETIC
twitter.com/@CETIC_be
www.cetic.be
Aéropole de Charleroi-Gosselies
Avenue Jean Mermoz 28
6041 Charleroi - Belgique
Questions?
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