EPSRC Project (Robustness and Resilience of Dynamic Manufacturing Supply Networks) Meeting, Coventry, 2 July 2014 - Inoperability Models of Risk in FLXINET

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Inoperability Models of Risk in FLEXINET

Ali Niknejad, Prof. Dobrila Petrovic

2 July 2014 - The Futures Institute, Coventry University, Coventry

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FLEXINET

Intelligent Systems Configuration Services for Flexible Dynamic Global Production Networks

European Union Seventh Framework Programme FP7 project

Started on 1 July 2013 – Ends on 30 June 2016

Academic partners from UK, Germany, Switzerland and Spain

Industrial partners from food & drink (Spain), white goods (Italy) and pumps (Germany) industries

Coventry University is responsible for the risk module

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Risk Concepts

Risk Factor: A potential incident or failure that may influence a GPN adversely.

Disruptive Event: An unwanted event that is the result of a risk factor and has led to disruption in the normal operation of the GPN.

Risk Interdependency: A measure of the influences that risk factors have on each other’s likelihood and impact.

Propagation: The indirect effect of disruptive events on other parts of the GPN that can hinder a GPN node’s ability to operate.

Perturbation: The direct adverse effect of disruption on a GPN node that is propagated through the GPN and leads to inoperability.

Inoperability: The reduced percentage of operability of a GPN node as the result of disruption and risk propagation.

Resilience: The ability of a GPN to react to an unforeseen disturbance and to return quickly to their original state or move to a new, more advantageous one after suffering the disturbance.

Mitigation: An effort to reduce the impact and likelihood of risk.

Economic Loss of Risk: The expected loss of future income as a result of inoperability in an GPN due to a disruptive event.

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Risk Factors (1)

Risk Factor

Classification

Supply

Production

Informatio

n and Control

Logistics

Demand

External

1. Food Safety Issues ✓ ✓ ✓

2. Risk of Global Sourcing ✓ ✓ ✓

3. Inadequate Product/Service Quality ✓ ✓

4. Delayed Deliveries ✓ ✓

5. Unreliable Supply ✓

6. Dependency on Supplier(s) ✓

7. Financial Instability of Suppliers ✓

8. Unavailability of Ingredients/Materials ✓

9. Technological Challenge ✓ ✓

10. Machine Modification Issues ✓

11. Significant Changes to Business Model ✓

12. High Cost of Ownership ✓

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Risk Factors (2)

Risk Factor

Classification

Supply

Production

Informatio

n and Control

Logistics

Demand

External

13. Readiness to Adapt Technology ✓

14. Uncertainty in New Markets ✓

15. Unanticipated Level of Demand ✓

16. Insolvency of Clients ✓

17. Changes in Market Trends ✓

18. Import or Export Controls ✓

19. Legal Requirements’ Infringement ✓

20. Future Regulation ✓

21. Major Technological Change ✓

22. Political Instability ✓

23. Price and Currency Risks/Inflation ✓

24. Environmental Pollutions ✓

25. Legal Uncertainty ✓

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Suppliers of Bottling Products/Packaging

Suppliers of Sugar

Cider Fermentation

Plant

Bottling Plant

Customers

Suppliers of Apples

Financial

Instability

Insolvency

Machine Issues

Suppliers of Yeast

Suppliers of Flavourings

Propagation of Risk

Full Operability

Low Inoperability

Medium Inoperability

High Inoperability

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Inoperability Models

Based on Leontief's economic Input / Output model

Propagation of risk

Initial perturbations

Measuring inoperability (i.e. normalised economic loss)

Interdependency between nodes

Considering multi-criteria (supply and demand relationships)

Economic loss of risk

- Percentage vector of reduced final demand

- Normalized interdependency square matrix

- Inoperability vector

where

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Suppliers of Bottling Products/Packaging

Suppliers of Sugar

Cider Fermentation Plant

Bottling Plant Customers

Suppliers of Apples

Suppliers of Yeast

Suppliers of Flavourings

Dependency of Cider Fermentation Plant

Trading Volume

Substitutability

Buffer Volume

Suppliers of Apples High High Low

Suppliers of Yeast Low Medium High

Suppliers of Sugar Medium High Medium

Bottling Plant Very High Very Low Medium

Dependency of Cider Fermentation Plant

Trading Volume

Substitutability

Buffer Volume

Suppliers of Apples 0.9 0.9 0.1

Suppliers of Yeast 0.1 0.5 0.9

Suppliers of Sugar 0.5 0.9 0.5

Bottling Plant 1 0 0.5

Dependency of Cider Fermentation Plant

DependencyWeight

Suppliers of Apples 0.65

Suppliers of Yeast 0.25

Suppliers of Sugar 0.37

Bottling Plant 0.8

0.8

0.37

0.25

0.65

Determining Dependencies

Steps

Rate the dependency criteria Very Low, Low, Medium, High, Very

High

Quantify

Aggregate

(Wei, Dong and Sun, 2010)

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Dynamic Inoperability Models

Consider dynamisms in risk propagation

Extension to the normal inoperability models

New features Time-varying perturbations

Interdependent risk events

Resilience

Inventory

𝑐∗

𝐴∗

𝐾𝑞

where

- Percentage vector of reduced final demand

- Normalized interdependency square matrix

- Industry Resilient Coefficient Matrix

- Inoperability vector

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Dynamic Inoperability Models (Cont.)

Example: (Barker and Santos, 2010)

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Fuzzy Inoperability Models

Uncertain and vague parameters

‘Around 2’, ‘Between 4 and 10 but most likely 8’, …

For example: Interdependency matrix and perturbations

Extension of inoperability models by using fuzzy numbers

Normal mathematical operations are possible (with certain considerations!)

Triangular or trapezoidal fuzzy numbers

Interval calculations

Advantages of fuzzy inoperability models

More reliable results

Analysis of the effects of uncertainty

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Dynamic Fuzzy Inoperability

Inoperability Inoperability Inoperability Inoperability

Mem

bersh

ip D

eg

ree

Inop

era

bility

Time Time Time Time

Example: (Oliva, Panzieri, Setola, 2011)

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Risk Propagation Prototype Demo

Perturbation Inoperabilit

y

Dependency Weights

2 July 2014 - The Futures Institute, Coventry University, Coventry

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Conclusions

Identified risk factors affecting GPNs

Inoperability models suitable to analyse propagation of disruptions

Interdependencies between nodes (supply or demand related)

Calculating dependencies in GPNs

Dynamic model can incorporate time-varying features

Resilience

Interdependent (co-occurring) disruptive events

Inventories

Fuzzy logic suitable to allow for uncertain information

2 July 2014 - The Futures Institute, Coventry University, Coventry

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References

Santos, J. R., & Haimes, Y. Y. (2004). Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures. Risk Analysis : An Official Publication of the Society for Risk Analysis, 24(6), 1437–51.

Lian, C., & Haimes, Y. (2006). Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model. Systems Engineering, 9(3), 241–258.

Wei, H., Dong, M., & Sun, S. (2010). Inoperability input-output modeling (IIM) of disruptions to supply chain networks. Systems Engineering, 13(4), 324–339.

Orsi, M., & Santos, J. R. (2010). Incorporating Time-Varying Perturbations Into the Dynamic Inoperability Input–Output Model. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(1), 100–106.

Barker, K., & Santos, J. R. (2010). Measuring the efficacy of inventory with a dynamic input–output model. International Journal of Production Economics, 126(1), 130–143.

Oliva, G., Panzieri, S., & Setola, R. (2011). Fuzzy dynamic input–output inoperability model. International Journal of Critical Infrastructure Protection, 4(3-4), 165–175.