Download 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 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
• 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 Skövde
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 Skövde
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”
“Everyone’s responsibility”
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
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 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
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 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
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
• WEREN’T: 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 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