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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

6

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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)

9

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Eclipse Version (Green Supply Chain)

10

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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

info@cetic.be

+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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