1. Inoperability Models of
Risk in FLEXINET
Ali Niknejad, Prof. Dobrila Petrovic
2 July 2014 - The Futures Institute, Coventry University, Coventry
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2. 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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3. 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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4. Risk Factors (1)
2 July 2014 - The Futures Institute, Coventry University, Coventry
Risk Factor
Classification
Supply
Production
Information
andControl
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 ✓
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5. Risk Factors (2)
2 July 2014 - The Futures Institute, Coventry University, Coventry
Risk Factor
Classification
Supply
Production
Information
andControl
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 ✓
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6. Suppliers of Bottling
Products/Packaging
Suppliers of Sugar
Cider Fermentation
Plant
Bottling Plant Customers
Suppliers of Apples
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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7. 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
2 July 2014 - The Futures Institute, Coventry University, Coventry
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𝑐∗- Percentage vector of reduced final demand
𝐴∗
- Normalized interdependency square matrix
𝑞 - Inoperability vector
where

8. 2 July 2014 - The Futures Institute, Coventry University, Coventry
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
Dependency
Weight
Suppliers of Apples 0.65
Suppliers of Yeast 0.25
Suppliers of Sugar 0.37
Bottling Plant 0.8
0.8
0.25
Determining Dependencies
 Steps
 Rate the dependency criteria
 Very Low, Low, Medium, High, Very High
 Quantify
 Aggregate
(Wei, Dong and Sun, 2010)
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9. Dynamic Inoperability Models
 Consider dynamisms in risk propagation
 Extension to the normal inoperability models
 New features
 Time-varying perturbations
 Interdependent risk events
 Resilience
 Inventory
2 July 2014 - The Futures Institute, Coventry University, Coventry
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𝑐∗
𝐴∗
𝐾
𝑞
where
- Percentage vector of reduced final demand
- Normalized interdependency square matrix
- Industry Resilient Coefficient Matrix
- Inoperability vector

10. Dynamic Inoperability Models (Cont.)
2 July 2014 - The Futures Institute, Coventry University, Coventry
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Example: (Barker and Santos, 2010)

11. 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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12. Dynamic Fuzzy Inoperability
2 July 2014 - The Futures Institute, Coventry University, Coventry
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Inoperability Inoperability Inoperability Inoperability
MembershipDegreeInoperability
Time Time Time Time
Example: (Oliva, Panzieri, Setola, 2011)

13. 2 July 2014 - The Futures Institute, Coventry University, Coventry
Risk Propagation Prototype Demo
Perturbation
Inoperability
Dependency
Weights
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14. 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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15. 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.
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