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Management Science Modeling of Risk in 21 st Century Supply Chains

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. Risk & Business. Taking risk is fundamental to doing business Insurance Lloyd’s of London Hedging Risk exchange swaps

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Management Science Modeling of Risk in 21 st Century Supply Chains

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  1. Management Science Modeling of Risk in 21st 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 volcanoApril 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. Japanincluding 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 fire25 Nov 2012 • Dhaka • 12 story building housed four factories • Over 100 dead • Served Wal-Mart, Sears

  8. Supply Chain Risks & Outsourcing FAIM 2008 Conference, University of Skövde

  9. Continued FAIM 2008 Conference, University of Skövde

  10. SUPPLY CHAIN REACTIONMarsh 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

  14. COSOCommittee of Sponsoring OrganizationsTreadway Committee – 1990sSmiechewicz [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 ToolsOlson & 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 = 0.64expect to exceed 99% of time in 1 yearHere 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 99th 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 ProblematicAPPROACHES 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 ModelsCooper [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 lightning17 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, AlyEltantawy [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 StrategiesTang [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 17th 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 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 • A tiger bit Siegfried or Roy – loss about $100 million • A contractor suffered in constructing a hotel annex, sued, lost – tried to dynamite casino • Casinos required to file with Internal Revenue Service – an employee failed to do that for years – Casino had to pay huge fine (risked license) • 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

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