Management Science Modeling of Risk in 21st Century Supply Chains

David L. OlsonJames & H.K. Stuart Chancellor’s Distinguished

ChairUniversity of Nebraska - Lincoln

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

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

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

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 fire25 Nov 2012

• Dhaka• 12 story building housed four factories• Over 100 dead• Served Wal-Mart, Sears

Supply Chain Risks & OutsourcingRISK 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 Skövde

ContinuedRISK Elaboration Impact

Legal risk Litigation exposure Risk shifted to outsourcing vendor

Product risk Product technical complexity

Core needs to assure outsourcing vendor competent

Regulatory risk Outsourcing vendors assume local risk

Reputation 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 Skövde

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

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

DIFFERENCESTraditional Risk Mgmt ERMIndividual 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” “Everyone’s responsibility”

COSOCommittee of Sponsoring Organizations

Treadway 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

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

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

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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

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VaR = 0.64expect to exceed 99% of time in 1 year

Here loss = 10 – 0.64 = 9.36

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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 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

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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

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

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

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

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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 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

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 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

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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

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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.”

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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

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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

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Views of BubblesCohen [1997] Chaos view Soros [2008]

Trigger Inception INVEST

Expansion Acceleration INVEST MORE

Rising prices Reinforcement (pass challenges)

OvertradingMass trading

Twilight period GET OUT

Doubt Reversal point OPTIMAL GET OUT

Selling flood Accelerated decline TOO LATE

Collapse Crisis

3-C Risk Forum 2011

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 million2. A contractor suffered in constructing a hotel annex,

sued, lost – tried to dynamite casino3. 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

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