Management Science Modeling of Risk in 21st Century Supply Chains

Management Science Modeling of Risk in 21st Century Supply Chains

Management Science Modeling of Risk in 21st Century Supply Chains David L. Olson James & H.K. Stuart Chancellors Distinguished Chair University of Nebraska - Lincoln Risk & Business Taking risk is fundamental to doing business Insurance Lloyds 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 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

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 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 2012 Thai floods Not as bad as 2011 Economic growth only 0.1% Government blamed for mismanagement 4 dead as of 12 September Bangladesh clothing factory fire

25 Nov 2012 Dhaka 12 story building housed four factories Over 100 dead Served Wal-Mart, Sears Supply Chain Risks & Outsourcing RISK Elaboration Impact

Accounting Risk of ruin High Asset investment Asset utilization Increase risk to core Country risk Most innovative supplier may be in risky country Competitive risk

Need to differentiate Outsource products available to competitors Customer risk Product obsolescence Low quality drives out customers; Outsourcing reduces risk of obsolescence Downside risk Risk of failure Can replace outsource vendors

Financial risk Financial market risk Core less threatened by outsourced vendor failure Interaction Communication, coordination Outsourced vendors more independent; Can impose IT requirements FAIM 2008 Conference, University of Skvde Continued

RISK Elaboration Legal risk Litigation exposure Risk shifted to outsourcing vendor Product risk Product technical complexity Regulatory risk Reputation risk Impact Core needs to assure outsourcing vendor

competent Outsourcing vendors assume local risk Customer confidence Higher to core, as customers hold them responsible Shared risk Outsourcing allows access to market of vendors Supplier risk Smaller organizations have greater risk

Supply disruption If outsourcing vendor fails, have alternatives FAIM 2008 Conference, University of Skvde 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 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 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 DIFFERENCES Traditional Risk Mgmt ERM Individual hazards Context - business strategy Identification & assessment Risk portfolio development

Focus on discrete risks Focus on critical risks Risk mitigation Risk optimization Risk limits Risk strategy No owners Defined responsibilities Haphazard quantification

Monitor & measure Not my job Everyones responsibility COSO Committee of Sponsoring Organizations Treadway Committee 1990s Smiechewicz [2001] Assign responsibility Board of directors Establish organizations 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 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 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 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 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 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 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 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 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 VaR = 0.64

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

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 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) 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 havent seen it, its 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 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 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 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 doesnt understand (IT) 3-C Risk Forum 2011 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 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 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 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 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 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 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 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 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 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 WERENT: hedging cycle among same pool of insurers Hurricane Alicia in 1983 stretched the system 3-C Risk Forum 2011 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 Views of Bubbles Cohen [1997] Chaos view Soros [2008] Trigger Inception

INVEST Expansion Acceleration INVEST MORE Rising prices Reinforcement (pass challenges) Overtrading Mass trading Twilight period

Doubt Reversal point Selling flood Accelerated decline Collapse Crisis 3-C Risk Forum 2011 GET OUT OPTIMAL GET OUT TOO LATE

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 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 owners daughter kidnapped he violated gambling laws to use casino money to raise ransom 3-C Risk Forum 2011 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 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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