1. Management Science Modeling of Risk in 21 st Century Supply Chains David L. Olson James & H.K. Stuart Chancellor’s Distinguished Chair University of Nebraska - Lincoln

2. Risk & Business Taking risk is fundamental to doing business – Insurance Lloyd’s of London – Hedging Risk exchange swaps Derivatives/options Catastrophe equity puts (cat-e-puts) – ERM seeks to rationally manage these risks Be a Risk Shaper 3-C Risk Forum 2011

3. Iceland volcano April 2010 European air cargo shut down for days South Carolina BMW plant slowed due to lack of leather seat covers from South Africa, & transmissions from Europe Tesco flower & produce deliveries from Kenya disrupted NYC flower district shipments from the Dutch disrupted Migros Swiss supermarkets missed asparagus from US, tuna from SE Asia Italian cheese & fruit producers lost $14 million/day RESPONSES – DHE & FedEx moved as much as possible through Spain, southern Europe – Those with business continuity plans fared better than their competitors

4. Japan including Fukushima nuclear plant Munic Re estimated $210 billion in disaster losses – Of 210 million, only 60 million insured – Sony/Ericsson had to redesign handsets, use components they could obtain New Zealand earthquakes in 2011 - $20 million US tornados in 2011 - $14.5 million Australian floods in 2011 – 7.3 million

5. 2011 Thai floods Oct 2011 worst in 50 years – 373 dead – Thai has been a manufacturing base for Japanese & American car companies & global technology firms – HONDA: postponed launch of Life minicar – TOYOTA: planned to cut output in North America – DIGI INTERNATIONAL: chip maker shut down facilities – LENOVO: constrained by lack of hard disk supply – FUJITSU IT services: disrupted by hard disk supply – NIPPON STEEL: lost 300,000 tons of lost production – AUTOLIV: airbags & seatbelts – cut sales forecasts – TESCO UK retailer: temporarily closed 30 stores in Thailand – CANON: cut forecasts – SONY, NIKON: forced to close plants

6. 2012 Thai floods Not as bad as 2011 – Economic growth only 0.1% – Government blamed for mismanagement 4 dead as of 12 September

7. Bangladesh clothing factory fire 25 Nov 2012 Dhaka 12 story building housed four factories Over 100 dead Served Wal-Mart, Sears

8. Supply Chain Risks & Outsourcing RISKElaborationImpact AccountingRisk of ruinHigh Asset investmentAsset utilizationIncrease risk to core Country riskMost innovative supplier may be in risky country Competitive riskNeed to differentiateOutsource products available to competitors Customer riskProduct obsolescence Low quality drives out customers; Outsourcing reduces risk of obsolescence Downside riskRisk of failureCan replace outsource vendors Financial riskFinancial market riskCore less threatened by outsourced vendor failure InteractionCommunication, coordination Outsourced vendors more independent; Can impose IT requirements FAIM 2008 Conference, University of Skövde

9. Continued RISKElaborationImpact Legal riskLitigation exposureRisk shifted to outsourcing vendor Product riskProduct technical complexity Core needs to assure outsourcing vendor competent Regulatory riskOutsourcing vendors assume local risk Reputation riskCustomer confidence Higher to core, as customers hold them responsible Shared riskOutsourcing allows access to market of vendors Supplier riskSmaller organizations have greater risk Supply disruption If outsourcing vendor fails, have alternatives FAIM 2008 Conference, University of Skövde

10. SUPPLY CHAIN REACTION Marsh Consulting Establish priorities for SKUs Alternate routing Additional storage (inventory) Collaborate with cargo carriers Alternative ground routes if air disrupted Communicate contingency plans within organization Review contracts Diversity source base

11. Contemporary Economics Harry Markowitz [1952] – RISK IS VARIANCE – Efficient frontier – tradeoff of risk, return – Correlations – diversify William Sharpe [1970] – Capital asset pricing model Evaluate investments in terms of risk & return relative to the market as a whole The riskier a stock, the greater profit potential Thus RISK IS OPPORTUNITY Eugene Fama [1965] – Efficient market theory market price incorporates perfect information Random walks in price around equilibrium value 3-C Risk Forum 2011

12. Enterprise Risk Management Definition Systematic, integrated approach – Manage all risks facing organization External – Economic (market - price, demand change) – Financial (insurance, currency exchange) – Political/Legal – Technological – Demographic Internal – Human error – Fraud – Systems failure – Disrupted production Means to anticipate, measure, control risk

13. DIFFERENCES Traditional Risk MgmtERM Individual hazardsContext - business strategy Identification & assessmentRisk portfolio development Focus on discrete risksFocus on critical risks Risk mitigationRisk optimization Risk limitsRisk strategy No ownersDefined responsibilities Haphazard quantificationMonitor & measure “Not my job”“Everyone’s responsibility”

14. COSO Committee of Sponsoring Organizations Treadway Committee – 1990s Smiechewicz [2001] Assign responsibility – Board of directors Establish organization’s risk appetite establish audit & risk management policies – Executives assume ownership Policies express position on integrity, ethics Responsibilities for insurance, auditing, loan review, credit, legal compliance, quality, security Common language – Risk definitions specific to organization Value-adding framework

15. Risk Management Tools Olson & Wu Supply Chain Risk Management (2012) Multiple criteria analysis – Evaluative subjective Simulation – Evaluative Probabilistic; Can be subjective (system dynamics) Chance constrained programming – Optimization Probabilistic Data envelopment analysis – Optimization Objective, subjective, probabilistic

16. Long Term Capital Management Black-Scholes – model pricing derivatives LTCM formed to take advantage – Heavy cost to participate – Did fabulously well 1998 invested in Russian banks – Russian banks collapsed – LTCM bailed out by US Fed LTCM too big to allow to collapse 3-C Risk Forum 2011

17. Correlated Investments EMT assumes independence across investments – DIVERSIFY – invest in countercyclical products – LMX spiral blamed on assuming independence of risk probabilities – LTCM blamed on misunderstanding of investment independence 3-C Risk Forum 2011

18. Information Technology 1990s very hot profession Venture capital threw money at Internet ideas – Stock prices skyrocketed – IPOs made many very rich nerds – Most failed 2002 bubble burst – IT industry still in trouble ERP, outsourcing 3-C Risk Forum 2011

19. Real Estate Considered safest investment around – 1981 deregulation In some places (California) consistent high rates of price inflation – Banks eager to invest in mortgages – created tranches of mortgage portfolios 2008 – interest rates fell – Soon many risky mortgages cost more than houses worth – SUBPRIME MORTGAGE COLLAPSE – Risk avoidance system so interconnected that most banks at risk 3-C Risk Forum 2011

20. “All the Devils Are Here” Nocera & McLean, 2010 Circa 2005 – Financial industry urge to optimize – J.P. Morgan, other banks hired mathematicians, physicists, rocket scientists, to create complex risk models & products Credit default swap – derivatives based on Value at Risk models – One measure of market risk from one day to the next – MAX EXPOSURE at given probability 3-C Risk Forum 2011

21. Financial Risk Management Evaluate chance of loss – PLAN Hubbard [2009]: identification, assessment, prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events – WATCH, DO SOMETHING 3-C Risk Forum 2011

22. Value-at-Risk One of most widely used models in financial risk management (Gordon [2009]) Maximum expected loss over given time horizon at given confidence level – Typically how much would you expect to lose 99% of the time over the next day (typical trading horizon) Implication – will do worse (1-0.99) proportion of the time 3-C Risk Forum 2011

23. VaR = 0.64 expect to exceed 99% of time in 1 year Here loss = 10 – 0.64 = 9.36 3-C Risk Forum 2011

24. Use Basel Capital Accord – Banks encouraged to use internal models to measure VaR – Use to ensure capital adequacy (liquidity) – Compute daily at 99 th percentile Can use others – Minimum price shock equivalent to 10 trading days (holding period) – Historical observation period ≥1 year – Capital charge ≥ 3 x average daily VaR of last 60 business days 3-C Risk Forum 2011

25. Limits At 99% level, will exceed 3-4 times per year Distributions have fat tails Only considers probability of loss – not magnitude Conditional Value-At-Risk – Weighted average between VaR & losses exceeding VaR – Aim to reduce probability a portfolio will incur large losses 3-C Risk Forum 2011

26. Skewness & Assymetry Median vs. expectation – If distribution normal, the same NOT: Assume 90% of stocks made 10% gain; 10% lost 100% Median gained 10% Expectation = 0.9*[1.1]+0.1*[0] = 0.99 1% loss – MANY OUTCOMES NOT NORMALLY DISTRIBUTED Negative exponential – Cancer deaths; if survive a given period, likely to last Lognormal (financial ratios)

27. Fat Tails Investors tend to assume normal distribution – Real investment data bell shaped – Normal distribution well-developed, widely understood TALEB [2007] – BLACK SWANS – Humans tend to assume if they haven’t seen it, it’s impossible BUT REAL INVESTMENT DATA OFF AT EXTREMES – Rare events have higher probability of occurring than normal distribution would imply Power-Log distribution Student-t Logistic Normal 3-C Risk Forum 2011

28. Modeling Investments Problematic APPROACHES TO THE PROBLEM MAKE THE MODELS BETTER – The economic theoretical way – But human systems too complex to completely capture – Black-Scholes a good example PRACTICAL ALTERNATIVES – Buffett – Soros 3-C Risk Forum 2011

29. Better Models Cooper [2008] Efficient market hypothesis – Inaccurate description of real markets – disregards bubbles FAT TAILS Hyman Minsky [2008] – Financial instability hypothesis Markets can generate waves of credit expansion, asset inflation, reverse Positive feedback leads to wild swings Need central banking control Mandelbrot & Hudson [2004] – Fractal models Better description of real market swings 3-C Risk Forum 2011

30. Models are Flawed Soros got rich taking advantage of flaws in other peoples’ models Buffett is a contrarian investor – In that he buys what he views as underpriced in underlying long-run value (assets>price); holds until convinced otherwise – Avoids buying what he doesn’t understand (IT) 3-C Risk Forum 2011

31. Nassim Taleb Black Swans – Human fallability in cognitive understanding – Investors considered successful in bubble-forming period are headed for disaster BLOW-Ups There is no profit in joining the band-wagon – Seek investments where everyone else is wrong Seek High-payoff on these long shots – Lottery-investment approach Except the odds in your favor 3-C Risk Forum 2011

32. Supply Chain Perspective of ERM Historical vertical integration – Standard Oil, US Steel, Alcoa – Traditional military Control all aspects of the supply chain Contemporary – Cooperative effort Common standards High competition Specialization – Internet Service oriented architecture 3-C Risk Forum 2011

33. Supply Chain Problems Land Rover – Key supplier insolvent, laid off 1000 Dole 1998 – Hurricane Mitch hit banana plantations Ford – 9/11/2001 suspended air delivery, closed 5 plants 1997 Indonesian Rupiah devalued 50% – Blocked out of US supply chains – Jakarta public transport reduced operations, high repair parts – Li & Fung shifted production from Indonesia to other Asian sources 3-C Risk Forum 2011

34. More Problems Taiwan earthquake 1999 – Dell & Apple supply chains short components a few weeks Apple had shortages Dell avoided problems through price incentives on alternatives Philips semiconductor plant in New Mexico burnt 2000 – Ericsson lost sales revenue – Nokia had designed modular components, obtained alternative chips 3-C Risk Forum 2011

35. New Mexico microchip plant lightning 17 March 2000 Provided microchips to Nokia, Ericsson Ericsson – learned of fire 2 weeks later – Earnings dropped $400 million – Cut thousands of jobs – Merged with Sony on some product lines Nokia – Constantly monitored suppliers Learned from disruption in 1999 – Profit up 42% in 2000

36. Supply Chain Risk Sources Giunipero, Aly Eltantawy [2004] – Political events – Product availability – Distance from source – Industry capacity – Demand fluctuation – Technology change – Labor market change – Financial instability – Management turnover 3-C Risk Forum 2011

37. Robust Strategies Tang [2006] Postponement – standardization, commonality, modular design Strategic stock – safety stock for strategic items only Flexible supply base – avoid sole sourcing Economic supply incentives – subsidize key items, such as flu vaccine Flexible transportation – multi-carrier systems, alliances Dynamic pricing & promotion – yield management Dynamic assortment planning – influence demand Silent product rollover – slow product introduction - Zara 3-C Risk Forum 2011

38. Practical View: Warren Buffett Conservative investment view – There is an underlying worth (value) to each firm – Stock market prices vary from that worth – BUY UNDERPRICED FIRMS – HOLD At least until your confidence is shaken – ONLY INVEST IN THINGS YOU UNDERSTAND NOT INCOMPATIBLE WITH EMT 3-C Risk Forum 2011

39. Empirical BUBBLES – Dutch tulip mania – early 17 th Century – South Sea Company – 1711-1720 – Mississippi Company – 1719-1720 Isaac Newton got burned: “I can calculate the motion of heavenly bodies but not the madness of people.” 3-C Risk Forum 2011

40. Modern Bubbles London Market Exchange (LMX) spiral – 1983 excess-of-loss reinsurance popular – Syndicates ended up paying themselves to insure themselves against ruin – Viewed risks as independent WEREN’T: hedging cycle among same pool of insurers – Hurricane Alicia in 1983 stretched the system 3-C Risk Forum 2011

41. Practical View: George Soros Humans fallable Bubbles examples reflexivity – Human decisions affect data they analyze for future decisions – Human nature to join the band-wagon – Causes bubble – Some shock brings down prices JUMP ON INITIAL BUBBLE-FORMING INVESTMENT OPPORTUNITIES – Help the bubble along – WHEN NEAR BURSTING, BAIL OUT 3-C Risk Forum 2011

42. Views of Bubbles Cohen [1997] Chaos viewSoros [2008] TriggerInception INVEST ExpansionAcceleration INVEST MORE Rising pricesReinforcement (pass challenges) Overtrading Mass trading Twilight period GET OUT DoubtReversal point OPTIMAL GET OUT Selling floodAccelerated decline TOO LATE CollapseCrisis 3-C Risk Forum 2011

43. Taleb Statistical View Mathematics – Fair coin flips have a 50/50 probability of heads or tails – If you observe 99 heads in succession, probability of heads on next toss = 0.5 CASINO VIEW – If you observe 99 heads in succession, probably the flipper is crooked MAKE SURE STATISTICS ARE APPROPRIATE TO DECISION 3-C Risk Forum 2011

44. CASINO RISK Have game outcomes down to a science ACTUAL DISASTERS 1.A tiger bit Siegfried or Roy – loss about $100 million 2.A contractor suffered in constructing a hotel annex, sued, lost – tried to dynamite casino 3.Casinos required to file with Internal Revenue Service – an employee failed to do that for years – Casino had to pay huge fine (risked license) 4.Casino owner’s daughter kidnapped – he violated gambling laws to use casino money to raise ransom 3-C Risk Forum 2011

45. DEALING WITH RISK Management responsible for ALL risks facing an organization CANNOT POSSIBLY EXPECT TO ANTICIPATE ALL AVOID SEEKING OPTIMAL PROFIT THROUGH ARBITRAGE FOCUS ON CONTINGENCY PLANNING – CONSIDER MULTIPLE CRITERIA – MISTRUST MODELS 3-C Risk Forum 2011

46. Conclusions Risk management of growing importance – Including supply chains – opportunities with risks Models can help – Fast, dynamic situations – Large quantities of data Economic models require complex, accurate data – More than can be expected Practical – ACCEPT THE RISKS YOU CAN COPE WITH The things you are professionally good at – HEDGE (INSURE, whatever) the others But it costs