This document discusses SimQRi, a query-oriented tool for efficiently simulating and analyzing process models to optimize industrial processes and quantify risks. It allows modeling supply chains, identifying delays, quantities, and bad quality. Risks are expressed as queries over the supply chain model at different levels and the tool supports simulation, analysis, risk mitigation, and guidance for risk identification.

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Centred’ExcellenceenTechnologiesde
l’InformationetdelaCommunication
www.cetic.be
Optimisation of Industrial Processes
SimQRi - A Query-oriented Tool for the Efficient
Simulation and Analysis of Process Models
Christophe Ponsard
TEKK tour Digital Wallonia, 06/11/17, Mons

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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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State of practice in SMEs (survey performed in
2015)
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
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Risk Modelling
• Risk Management Process
(ISO 31000)
• Expressing Risks as Queries
• over a Supply Chain Model
• at different levels

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Modelling Supply Chains and Risks
• Suppliers + supply policies
• Storages
• Processes
• Queries (for risks but also other purposes)
• 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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Web-based Version (« beer game » model)
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Eclipse Version (Green Supply Chain)
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Guidance for Risk Identification

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Behind the Scene: Discrete Event Simulation
and Monte Carlo Aggregation
• Simulation using OscaR.DES (Open Source)
• Built on top of a task-resource model
• Incremental evaluation throughout the simulation run
• Minimal updates: only the relevant fragment of queries
• Non accumulating expressions: evaluated only at the end
• Accumulating expressions: evaluated at each step
• 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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Analysis Capabilities
• Raw Output
• Dashboard and reports
(data visualisation) Customisable reports (Birt)

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Risk Mitigation (Optimisation)
• 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.or
g

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QUESTIONS ?
Tool homepage: https://simqri.cetic.be
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linkedin.com/company/cetic
info@cetic.be
+32 71 490 700
twitter.com/@CETIC
twitter.com/@CETIC_be
www.cetic.be
Aéropole de Charleroi-Gosselies
Avenue Jean Mermoz 28
B-6041 Charleroi - Belgique
Questions?
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