Optimisation of Industrial Processes SimQRi - A Query-oriented Tool for the Efficient Simulation and...
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Centre d’Excellence en Technologiesde
l’Informationet de la Communication
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Optimisation of Industrial ProcessesSimQRi - A Query-oriented Tool for the Efficient
Simulation and Analysis of Process Models
Christophe PonsardTEKK tour Digital Wallonia, 06/11/17, Mons
www.cetic.be
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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,R1T2,B,R2
T2,B,R3
LB,R1
T1,A,R1
T1,A,R2
T2,A,R1
T2,B
T1,BT1,A T2,A
Politics
Techno
logy
Eco-
nomyEcology
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 processworktime(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
15
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linkedin.com/company/cetic
+32 71 490 700
twitter.com/@CETIC twitter.com/@CETIC_be
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Aéropole de Charleroi-Gosselies
Avenue Jean Mermoz 28
B-6041 Charleroi - Belgique
Questions?
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