T HE A RT OF T HE P OSSIBLE IN R ISK M ETRICS

1. THEARTOF THE POSSIBLE IN RISKMETRICS Feryal Erhun Management Science and Engineering Supply Chain Risk Leadership Council, London, February 22-23 2007

2. a short recap

3. potential questions to think about • What is the risk of potentially moving manufacturing facilities overseas? • What is the risk of not being able to fulfill a spike in consumer demand for our products? • What is the risk to your brand if an incident occurs at one of your suppliers or distributors? Source: Marsh Risk Consulting

4. still more questions … • What are the dependencies & weak links within your supply chain (SC)? • Do you understand the risk that has been inadvertently built into our SC? Where does risk reside in your supply network? • Have you identified the SC risks that you might be able to mitigate, eliminate, or pass on to another SC member? Have you considered alternative SC structures? • Have you fully integrated our business contingency plans and emergency response plans into your SCM initiatives? Source: Marsh Risk Consulting

5. effective risk management includes • Identifying and recognizing sources of uncertainty, i.e., risk events. • Measuring and assessing the frequency of occurrence and severity of impact of an event • Evaluating alternative approaches to mitigate or take advantage of the risk

6. an engineering approach to risk management • Goal • Risk analysis to improve the “performance” of supply chains • Approach • Using Probabilistic Risk Analysis (PRA) techniques • To provide a quantitative estimation of the overall vulnerability of supply chains to uncertain events (i.e., risk exposure) • To support rational decision making

7. Probability, P(V=v) Performance Measure, V how do we define risk Source: Tim Astley. Creating Value Through Resilience. SCRLC, February 2007.

8. obtaining a quantitative assessment Systems Modeling Develop a model of the system to obtain the distribution of the value to the firm, V, given each scenario. Deterministic or probabilistic V = F(Si) or Si→ FV|Si(v) Scenario Probability If Si is composed of events A, B, C p(Si) = p(A)p(B|A)p(C|A,B) where p(A) or p(B|A) are event probabilities Event Probability Statistical data, expert opinion, further decomposition Use Simulation to Simplify the Integration of Systems Modeling and Scenario Probability Computations

9. Analytical Process Customs Customs Customs Customs Definition of System and Performance Value Components Components Sub Sub - - assembly assembly Transportation Transportation Transportation Transportation to Sub to Sub - - assembly assembly to Assembly to Assembly Asia Asia North America North America Performance Measures Monitoring Reassessment Risk Identification Risk factors on Influence Diagram Risk Quantification Probability Distributions Risk Management risk analysis methodology Tools and Techniques Information Output Expert Opinion Expert Opinion; Statistics Influence Diagrams Expert Opinion; Statistics Simulation; Probabilistic Modeling Simulation; Box Plots; Risk Curves; Decision Analysis

10. Illustration step 1: defining the system

11. step 2: identifying risk factors (I/II)

12. Forecast Component Sourcing Illustration Variability Leadtime and Problems Cost EDI Outages CM Management Sourcing Qualifications Fiscal Quarter CM Capacity Tools and Process Leadtime and Changes Cost Sub - Assembly Yield Variability CM Bankruptcy Eng. Change Orders Leadtime Beginning of Carrier and Peak Season Rate Time of Cost Year per unit Monsoon Damage Rebuild Season and Loss Decision Import Clearance Time Leadtime and Dest. Cost Delay Hurricane Transportation Dest. Trucking Season Arrival Delay Origin Trucking Origin Delay Export Clearance Time Natural Disaster Leadtime Labor Strike Disaster Geo - political Instability step 2: identifying risk factors (II/II)

13. Illustration step 3: quantifying risk Distribution of Network Losses Conditional on Losses Being Positive

14. step 4: managing risk – mitigation strategies

15. Low Medium High 19 20 15 17 4 Impact 13 8 2 11 5 14 9 1 12 10 7 18 3 16 6 Low Medium High Likelihood 15 15 15 communicating risk (I/II)

16. 10 10 20 20 30 30 40 40 50 50 60 60 Less than Less than 0% 0% % % % % % % % % % % % % communicating risk (II/II) SeverityPr(Network Losses ≠ 0 Given Site Down During Time Horizon) FrequencyPr(Site Down during Time Horizon)

17. remarks • This is not an optimization; the goal of the PRA is to bring relevant information to the decision maker • The risk is captured by the variability of chosen supply chain performance measures

18. insights from the case study • Managers should be aware of the hidden costs and account for them in their analysis, by carefully choosing an adequate performance measure • A team of actors with different responsibilities should be in charge of the analysis from the identification of risk factors to probability assessments • Risks may be specific to product families and supply chain structures • From the managers’ perspective, the output of a risk assessment process should be to know wherethe risk resides within the supply chain and its order of magnitude • Mitigation strategies should be appraised within a global context

19. summary • Supply chains are vulnerable to uncertainties, they should be managed as such • Managers have levers to improve the robustness and reliability of supply chains • Risk analysis can help supply chain managers choose a better, if not best, alternative through • Identification of the weak points of the system • Quantification of risk exposure to serve as a support for their decisions

20. Kleindorfer and Saad’s framework (I/II) • One has to put one’s own house in order first before expecting or requiring others in the extended supply chain to do so. • Make use of, and extend the main premise of portfolio theory, namely: diversification reduces risk. For disruption risk management, such diversification should be extended to include facility locations, sourcing options, logistics, and operational modes. • Robustness to disruption risks in a supply chain is determined by the weakest link in the chain, especially with respect to the actions of purposeful agents attempting to disrupt supply operations. • Preventionis better than cure, i.e., loss avoidance and preemption are better than mitigation of losses after the fact. • Extreme leanness and efficiency may result in increasing the level of vulnerability, at both the individual firm level and across the supply chain. • As a corollary to principle 5, establishing backup systems, contingency plans, and maintaining reasonable slack, can increase the level of readiness in managing risk.

21. Kleindorfer and Saad’s framework (II/II) • Collaborative sharing of information and best practicesamong supply chain partners is essential in identifying vulnerabilities and in preparing for and executing effective crisis management. • Good crisis management is not enough; linking risk assessment and quantification with risk management options ex ante is of fundamental importance in understanding the potential for ultimate harm to the organization from supply chain disruptions and for evaluating and undertaking prudent mitigation. • Modularity of process and product designs, and other key elements of agility and flexibility for lean supply chain design, can also provide leverage for risk reduction, especially for interruptions involving discontinuities in raw material availability and component supply. • Applying TQM principles, e.g., the Six-Sigma Approach, provides leverage in achieving higher supply chain security and reduction of disruptive risks faced while reducing operating costs.

22. references • Deleris, L.A., and F. Erhun. “Risk Management In A Supply Network: A Case Study Based On Engineering Risk Analysis Concepts,” in Handbook of Production Planning. Edited by K. Kempf, P. Keskinocak, and R. Uzsoy, Kluwer International Series in Operations Research and Management Science, Kluwer Academic Publishers (To appear). • Deleris, L.A. and F. Erhun, 2005. Risk Management in Supply Networks Using Monte-Carlo Simulation, in Proceedings of the 2005 Winter Simulation Conference. Edited by M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines. • Kleindorfer, P.R. and G.H. Saad. Managing Disruption Risks in Supply Chains. Production and Operations Management, Vol. 14, No. 1, pp. 53-68. Spring 2005. • Lee, H. L., S. Whang. 2003. Higher Supply Chain Security with Lower Cost: Lessons from Total Quality Management. Research Paper No. 1824, Stanford University, Stanford, CA. • Lee, H. L., M. Wolf. 2003. Supply chain security without tears. Supply Chain Management Review 7(1) 12–20.

23. potential discussion questions • In your company: • How do you define supply chain risk? How do you perceive risk? • What supply chain performance metrics do you use? How should these metrics be adjusted when risk is considered as well? • Have you identified - and do you monitor - key risk indicators of upstream or downstream activities that might result in a disruption in the supply chain? How do you measure them? How do you communicate them? • Do you incorporate the element of risk when making strategic or tactical decisions about your supply chain? Have you fully captured your enterprise-wide risk profile? Do you believe this can be done? • What are the additional performance metrics for risk? • Who do you see as the leader in supply chain risk management in your industry? In general? Which industry is the leader when it comes to risk management? • Additional questions that the group wants to discuss ….