Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Commodity Risk
From Supply Chain Strategies to Trading & Physical/Financial Hedging
Academic Year 2012-2013
Index
1.
Summary of the Research Objectives ............................................................................................... 2
2.
The Commodity Risk Management Continuum ................................................................................ 5
3.
Focus on Market Risks and material studied .................................................................................... 7
3.1 Case Study 1 - Commodity Arbitrage and Supply Flexibility ...................................................... 10
3.2 Case Study 2 - Commodity Indexation and Portfolio Management........................................... 12
4.
Focus on the Supply Chain Risks and material studied ................................................................... 14
4.1 Case Study 1 - Supply localization strategies and risks .............................................................. 16
4.2 Case Study 2 - Demand Risk and Advance Purchase Discount................................................... 18
5.
Research Next Steps ....................................................................................................................... 19
6.
Educational Activities ...................................................................................................................... 20
7.
Publications ..................................................................................................................................... 21
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
1. Summary of the Research Objectives
Over the past ten years, rapid economic development in emerging markets made resource inputs to
production increasingly scarce - companies need to develop a deep understanding of their exposure to
different commodities, by managing supply chain dependencies, regulatory risks as well as spot and
financial markets.
In a world with a greater correlation among resource prices, a more integrated approach will be a must,
with a coordination of raw-material strategies across business units. In the last ten years, the increase in
commodity prices reversed more than a century of steady and almost continuous price decline. The
2012 prices averaged nearly 70% more than 2002 base prices across nearly every key commodity.
Fuel and Non-Fuel Commodity Price/Volatility Indexes (source IMF, World Bank, World Economic Outlook, 2012)
Today’s price volatility lead many industrial companies to view commodity risk as a critical issue that
creates new challenges and opportunities due to higher complexity and uncertainty in commodity
markets, requiring a tight integration of risk plans into strategies to address access to feedstock,
geopolitical risk and changing patterns of global trades, new commodity trading regulations, price
volatility and the relations with financial markets
For risk mitigation purposes, the key is developing flexibility and integration across functions, to mitigate
raw materials price volatility by looking up and down the value chain. Traditional approaches often leave
decisions in the hands of a single function at each step in the value chain, resulting in ineffective
commodity management:

R&D determines the required feedstock and materials specifications to be used;

Procurement determines supply availability and negotiates with suppliers;

Finance provides hedging strategies and/or guidelines;

Manufacturing determines the production process and requirements.
In traditional approaches and academic research on commodity risk management, there is a significant
split between operations management and financial studies. An integrated approach is needed to
develop cutting-edge commodity strategies with the contribution of all the different expertise in a
company and the use of innovative methodologies to deal with commodity risks. This research tries to
bring together financial and operations perspective, with the application of some of the financial
methodologies (options and portfolio optimization) to some commodity business cases.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Real Options are one of the most innovative methodologies for the valuation of business cases affected
by uncertainty – often risk management methodologies focus on cost/benefit and probability of risks,
without considering how options (also in real cases) are vectors of “antifragility”, considering the
asymmetry of their pay-off that gives the opportunity to exploit the risk upside with no restraints.
Antifragility is a new concept introduced by Nassim Taleb in his last book (“Antifragile: things that gain
from disorder”), as property of systems that are not just immune to volatility (robust) but benefit from
it. With this concept, (real) options gain even more importance, as they allow benefiting from uncertain
events, potentially positive, with a limited and controlled investment.
A real option can be defined as the right, but not the obligation, to do something in the future at a fixed
predefined cost. Options give the holder the right (not the obligation) to buy (call option) or sell (put
option) a specific quantity of an underlying asset at a fixed price (exercise price), at or before the
expiration date of the option (maturity). The holder (buyer) pays a price for this right (option
premium).The real options analysis (ROA) considers strategic management as a process aimed at
actively reducing exposition to downside risk (to the price of the option), promoting exposition to upside
opportunities by responding appropriately to the events. Companies can use ROA to determine how
much they are willing to spend to create an option on a particular opportunity.
Real Options should be used by organizations as a “way of thinking” - this methodology is emerging as a
potentially powerful tool for executives combining a “real options state of mind” with developed
mathematical skills. In the world of real options, uncertainty has value and can be exploited, if managed
creating opportunities that require adequate decisions in order to materialize (flexibility). The sources of
uncertainty should be analyzed and the alternatives (real options) that promote exposition to favorable
outcomes should be identified and then created, sometimes through skilful negotiation, strategic
thinking and wise investment decisions.
ROA is a useful tool in risky environments, as building flexibility in contracts reveals the real value of
investment projects associated with uncertain market conditions and suggests the optimal investment
strategy. The technique is called “real options analysis” because it deals with investments in real
(tangible) assets, where sponsors have multiple options (decisions) to continue or abandon, with
applications mostly in industries characterized by large capital investments, uncertainty and flexibility.
Real options have their roots in the financial word, so the traditional methods used for the real option
valuation lie with closed-form solution equations, with some adjustments (e.g., the famous BlackScholes’ formula modified by Merton). Often it is difficult to use these methods in real word, as real
options’ value is often driven by multiple sources of uncertainty that challenge the foundation of the
(financial) option pricing theory – in order to overcome the limits of the traditional techniques, Monte
Carlo Simulations (MCS) can be used.
MCS-ROA methodologies are a powerful tool for decision making process in a risky and uncertain
environment because of the possibility to assign probability distributions to each uncertain or variable
input of the model. In addition, this method is able to simulate directly the decisional process without
writing complicated equations that describe the behaviour of the system. By overcoming the limits of
financial option pricing techniques, MCS allows considering theoretically infinite (as it is in the real world
problems) sources (also technical) of uncertainty, giving a “more realistic” (probabilistic) representation
of outcomes, with all the possible expected returns.
In this research, real options and their ability to handle multifarious uncertainties through montecarlo
simulation will support the tighter integration of market and supply chain risk implications, since in a
portfolio of commodities correlations play a significant role, both on the physical side (e.g. supply chain
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
disruptions linked to country risk or global sourcing implications i.e. long supply chains) and on the
market side (e.g. exposure to specific underlying assets in the petro-chemical industry or currency
fluctuations). Specific business cases will be assessed from a risk management standpoint.
Given the focus on the physical sourcing/trading of commodities, an integrated perspective on both
market and supply chain risks will be taken into account, to understand how real options can be
embedded into contracts to optimize the total exposure of a Company to commodity risks.
The portfolio approach will also be one of the key focus of this research – again with a technique coming
from the financial world, we will try to build a decision-making approach to optimize choices that touch
several assets (e.g. commodities to be sourced) and uncertainties. Given the aforementioned focus on
several uncertainties, the approach chosen for the portfolio optimization lies with genetic algorithm
(GA). GA is a search heuristic that mimics the process of natural selection. This heuristic is routinely used
to generate useful solutions to optimization and search problems. GA’s belong to the larger class of
evolutionary algorithms (EA), which generate solutions to optimization problems using techniques
inspired by natural evolution.
The objective of the research is to propose and empirically apply a new model of multi-objective GA that
handles constrained problems to approach the portfolio optimization, extending the Markowitz meanvariance model, to real business cases. Precedent research highlights that the genetic algorithms seem
to be able to minimize the negative consequences of the estimation errors, because these models,
unlike the quadratic programming methods, tend to generate highly diversified portfolios in case of lack
of confidence in the forecasts as in the case of commodity markets.
The application of the ROA and GA on commodity markets and supply chains will be linked in this
research to a specific commodity value chain, in the petro-chemical industry. Among the main risks
identified in an exploratory research with leading petro-chemical companies, there is in fact a strong
focus on the market misalignment between the indexes the oil derivatives are sold against and the
indexes used for their raw materials - companies want to optimize the expected margins and profits and
so align the action plans on the total portfolio, minimizing exposures in specific contracts. On the other
side, also supply chain risks are very important, as supply chain disruptions can have significant financial
consequences.
st
1 year PhD summary presentation:
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Relazione d'anno_I
D.Tauro
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
2. The Commodity Risk Management Continuum
As presented in the previous chapter, since the objective of this research is the integration of financial
and physical sides of commodities, with the application of advanced/quantitative methodologies to
solve a different range of problems decision makers face, the perspective on the decision maker
organizational set up is also needed. In particular, these methodologies can be more or less useful,
depending on the maturity of an organization - in this chapter we try to describe the different levels of
maturity of organizations in commodity risk.
In the commodity environments, when it comes to risk management, being this a recent discipline that
has gained high interest in modern times, we experience different maturity levels across different
sourcing organizations. In the chart below, an interesting perspective is presented, where different
maturity levels are plotted against two main axes:


risk management activities (amount of resources and sophistication of the methodologies);
trading activities (split between physical and paper trades of a given commodity or group).
Commodity Risk glide path (own construction - insights from McKinsey and AT Kearney working papers)
In general, also depending on the value proposition of a company (e.g. from manufacturing to banks),
organizations could fall into one of the main buckets identified above. The first four buckets can also be
considered as four consecutive steps organizations may take to move from simple purchasing and
supply chain management to a more advanced way of managing the entire portfolio of commodities
they deal with.
Given the high commodity and market pressure a lot of companies are undergoing, several change
programs are currently being deployed to improve the commodity management by decreasing the total
exposure and optimizing the risk profiles. In particular, let’s describe which are the main features and
differences across the level of maturity identified.
Supply management: companies (e.g. in the manufacturing business) manage their commodities in a
traditional way, with traditional purchasing methodologies (an annual volume at an indexed price with
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
time based variations, with orders that follow the demand of finished goods) and optimization of the
inventory levels (e.g. centralized war-housing).
Forward buying: companies (e.g. in the airline and beverage business, given the high exposure they
have against a single big commodity, being jet fuel and PET resins) manage their commodities in a more
proactive way, by deciding to source higher amounts in periods where commodity prices are low (or
expected to raise). This approach increases the risk management activities and sophistication, since
companies are trying to actively managing the price fluctuations with an ordering process de-coupled
(partly) from the demand needs, but linked to them in order to achieve also inventory optimization
objectives.
Position hedging: companies (e.g. in the airline and energy/utilities business) may decide to manage
their commodity risk exposure also via hedging some specific positions. Big step change versus the
previous two maturity levels, at this stage companies are able to manage commodities from a physical
(supply management, inventory optimization, forward buying) and financial perspective (with the use of
financial instruments as well). Main opportunities lie in the hedging with futures, options, and swaps
(for both publicly traded commodities and commodities without an OTC market, where specific proxies
should be used for hedging).
Portfolio management: companies with exposure to several different commodities (e.g. oil & gas
companies, commodity trading companies) manage their commodity exposure via an integrated
approach where physical optimization of trades and contracts of multiple commodities is fully
integrated with the hedging strategies of those. In particular, the hedging strategies are also integrated
to optimize the risk exposure versus specific drivers (geographies, currencies, feedstock etc.). Important
to notice at this stage there is still a strong (and holistic) relationship between hedging/financial
activities and physical commodities managed.
Speculation: activity mostly performed by financial institutions (e.g. banking sector), there are trades
and contracts mainly at paper level, not backed by physical transactions.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
3. Focus on Market Risks and material studied
To explain what the market risk management problem is, we have to understand what the basis risk is.
Basis is defined as:
Basist,T = Spot pricet − F T (t)
and it is usually quoted as a premium or discount, the cash price as a premium or discount to the
Futures/Forward price. In general, basis risk exists when Futures/Forwards and spot prices do not
change by the same amount over time and, possibly, will not converge at maturity T.
Market participants analyze their risk in a mark-to-market perspective at date t (and not only at date T).
Consequently, basis risk is often defined as the variance of the basis:
σ2 (St − F T (t)) = σ2 (St ) + σ2 (F T (t)) − 2ρσ(St )σ (F T (t))
where ρ is the correlation coefficient between the futures and spot price series.
Basis risk is zero when variances between the Futures/Forwards and spot prices are identical and the
correlation coefficient between spot and Futures prices is equal to one. In practice, the second condition
is the most stringent one and the magnitude of basis risk depends mainly on the degree of correlation.
One of the purposes of market risk management is to “reduce” the basis risk.
For too long though, the definition of risk management have been shaped by risk hedgers, who see the
purpose of risk management as just removing or reducing risk exposures, without considering that with
risk management we can increase exposures to some specific risks, to increase our opportunities.
If the allure of risk is that it offers upside potential, risk management has to be more than risk hedging.
In fact, the most successful businesses of our time have all risen to the top by finding particular risks that
they are better at exploiting than their competitors. This more complete view of risk management
includes both risk hedging at one end and strategic risk taking on the other.
In the table below, there are some differences between risk hedging (which is focused on reducing or
eliminating risk) and risk management (where we have a far broader mission of reducing some risks,
ignoring other risks and seeking out still others).
Risk hedging
Risk management
View of risk
Risk is a danger
Risk is a danger and an opportunity
Objective
To protect against
downside of risk
Functional emphasis
Financial
Strategic, stretching across all functions
Process
Product oriented. Primarily
focused on the use of
derivatives and insurance to
hedge against risks
Process oriented. Identify key risk
dimensions and try to develop better
ways of handling and taking advantage
of these risks than the competition
Measure of success
Reduce volatility in earnings, Higher value
cash flows or value
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
the To exploit the upside created by
uncertainty
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Risk hedging and risk management are not mutually exclusive strategies. In fact, we consider risk
hedging to be part of broader risk management strategy where protecting against certain types of risk
and trying to exploit others go hand in hand.
Protecting against risk is not costless - sometimes, as is the case of buying insurances, the costs are
explicit. At other times, as with forward and Futures contracts, the costs are implicit. Explicit costs
reduce the earnings in the period in which the protection is acquired, whereas the implicit costs
manifest themselves only indirectly in future earnings. The effects of the hedging tool used will manifest
itself in subsequent periods with the latter reducing profitability in the event of upside risk not
exploited.
The motivations for hedging commodity market risk may vary across companies and are usually
different for companies that hedge against output price risk (like gold companies) as opposed to
companies that hedge against input price risk (such as airlines of FMCG) but the end result is the same.
The former are trying to reduce the volatility in their revenues and the latter are trying to do the same
with cost, but the net effect for both groups is more stable and predictable operating income, which
presumably allows these firms to have lower distress costs and borrow more.
Important to notice that companies that hedge against commodity market risk are looking for a
competitive advantage versus their competitors – the gain in absolute terms is relative, while it is more
important the differential gain generated versus competition. In this perspective, an active commodity
risk management, with the opportunity to exploit also the upside of risk can yield significant competitive
advantage for companies.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Material studied (selected books and papers):
[1]
Blanco C., Choi S., Soronow D., Energy price processes used for derivatives pricing & risk
management, Commodity now, 2001
[2]
Borovkova S., Geman H. Seasonal and stochastic effects in commodity forward curves, Reviews of
derivatives research, 2006
[3]
Davidson M, Portfolio optimization and linear programming, Journal of money, investment and
banking, 2011
[4]
Deaton A., Laroque G, On the behaviour of commodity prices. Review on economic studies, 1992
[5]
Geman H., Commodities and commodity derivatives: Modeling and pricing for agriculturas, metals
and energy, John Wiley & Sons, 2005
[6]
Geman H., Risk management in commodity markets: from shipping to agriculturals and energy,
John Wiley & Sons, 2008
[7]
Ingersoll J., Spiegel M., Goetzmann W., Portfolio performance manipulation and manipulationproof performance measures, Review of financial studies, 2007
[8]
Lin C., An effective decision-based genetic algorithm in multi-objective portfolio optimization
problem, Applied mathematical sciences, 2007
[9]
Longstaff F.A., Schwartz E.S., Valuing American Options by Simulation: A Simple Least-Squares
Approach, The Review of Financial Studies, vol. 14 no. 1, 2001
[10] Manikas A., Chang Y., Ferguson M., BlueLinx can benefit from innovative inventory management,
The international journal of management science. 2007
[11] Markowitz H., Portfolio selection, Journal of finance, 1952
[12] Pereira R., Genetic algorithm optimization for finance and investments, University library of
Munich, 2000
[13] Travers F.J. Investment manager analysis. s.l. : Wiley, 2004
[14] Triantis, A., Borison, A., Real Options: State of the Practice. (Bank of America), Journal of Applied
Corporate Finance, 14 (2), 2001
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
3.1 Case Study 1 - Commodity Arbitrage and Supply Flexibility
Over the last decade the legitimacy of supply chain contracting research has been established and many
research veins have been tapped. Unfortunately, theory has almost exclusively followed practice in this
domain i.e. practice has been used as a motivation for theoretical work, but theoretical work has not
found its way into practice, where coordination can be achieved with many different contractual forms.
For commodities, a key factor influencing the incentives of Sellers and the Buyer to sign contracts is the
existence of imperfect market access on the day, capturing possible access inefficiencies of the spot
market, including cost and quality differences between contract markets and the spot market. In
addition, in supply management for commodities, different grades and specifications for commodities
often require prior contracting and procurement relations. These alternative situations give rise to
various forms of commodity risk management.
In particular, for plastic resins and commodity chemicals, non-standard commodities are sourced, but
their prices are highly correlated with those of standard commodities. In this case, bilateral contracting
is used for all physical sourcing, with financial hedge instruments, defined on correlated standard
products, used as an overlay.
Contracted commodity prices follow specific public indexes, with a formula that usually modifies the
index itself (e.g. with a discount or a spread) or combines it with some other reference. Such indexes
(and so commodity prices) often fluctuate significantly from one period to the next - fluctuations, for
indexes tracking the same commodity in the same region (but sometimes also globally, in case the
commodity is globally traded), are aligned, so that the spread between different indexes tends to be
constant. These fluctuations may change instead across different regions, hence generating some
potential short term arbitrage opportunities.
Such global arbitrage opportunities are well known, so for the financial principle of the “no free lunch”
(informal synonym for the theory of no-arbitrage) the flexibility to source – in a contract – the material
from two different regions, with the buyer’s choice of the index (and so the region) based on the best
pricing in the market, is not negotiated for free.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Such sourcing flexibility has a value, so it would be important to understand its value before a
negotiation. The business case could be modeled as a real option, and the Black and Scholes (BS)
formula could be used for the quantification of the financial value of this contractual option.
Having the above mentioned purchasing flexibility option more than one uncertainty i.e. the two
indexes in the two different regions and the FX rate (eur/$), montecarlo simulations (MCS) could be
used as well to compare the results with the ones from BS. Furthermore, in the context of supply chain
contracting, a distinction is required in the types of options involved, between purely financial options
and those connected to physical delivery of a particular good at a particular time and place.
In the case of a Seller-Buyer relationship for physical transactions, given the stochastic lead time of
transportation, the basis risk should take into account also a third uncertainty, as fixing a deal in any
specific moment at another region’s index might not yield a positive value versus the index in the region
of use of the commodity that in the meanwhile (during the transportation) may go down, with a
significant opportunity loss versus competition, in case they are purchasing the commodity locally.
The results of the MCS and BS for the quantification of this real option will be compared.
The business case will consider a petro-chemical material that has become a global commodity and
several arbitrage and spot buying opportunities are present in the market, since there are different
regions (e.g. India, Korea, and Indonesia) that benefit from new Free Trade Agreements (2014).
The current situation of the market of this commodity, also linked to structurally low entry barriers
(limited capital investment required), presents an unbalanced supply-demand ratio, with overcapacity in
the industry. Furthermore, the increased volatility in the market and across the different regions could
make flexibility option described above very interesting.
Key contribution of this research
The main question here is – how much should a buyer be willing to pay for the flexibility to source
material from different regions (e.g. with a supplier that has production in different regions). This
purchasing flexibility (Commodity Global Arbitrage opportunities) would be modeled as an Australian
option, since there could be the options to switch region of supply on a monthly basis, for one year.
Risks considered would be European vs. Asian prices, FX EUR/USD and the transportation lead time. The
financial uncertainties will be modeled with well-known stochastic path-dependent processes (mean
reversion) while the transportation lead time will be estimated soliciting expert opinion to modify
historical data.
Main objectives of the research will be the valuation of the purchasing flexibility (real option analysis)
but also the quantification of the optimal exercise spread for the option, since due to the transportation
lead time, a positive price difference may turn into a loss in case price levels swap during such
transportation lead time.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
3.2 Case Study 2 - Commodity Indexation and Portfolio Management
As mentioned in the “risk management continuum” matrix, purchases organizations in their highest
maturity could leverage the multi-commodities they deal with by applying a “portfolio approach”. The
portfolio theory comes from the financial world, but in this specific case its application can be extended
to a “portfolio” of commodities priced with an indexation.
A portfolio is a collection of assets, with specific risk and return - to find the optimal allocation of assets,
namely the portfolio that performs better according to specific criteria, the Modern portfolio theory
states that a rational investor should either maximize his expected return for a given level of risk, or
minimize his risk for a given expected return. More specifically, the term portfolio refers to any
collection of assets that, in the case of the financial world, may be held by individual investors and/or
managed by financial professionals, hedge funds, banks and other financial institutions. It is a generally
accepted principle that a portfolio is designed according to the investor's risk tolerance, time frame and
investment objectives, since the monetary value of each asset may influence the risk/reward ratio of the
portfolio and is referred to as the asset allocation of the portfolio.
Portfolio optimization is the process of choosing assets and their specific proportions, to make the
portfolio better than any other according to some specific criteria. The criteria will combine, directly or
indirectly, considerations of the expected value of the portfolio's rate of return as well as of the return's
dispersion and possibly other measures of financial risk. To choose among different investment
opportunities, we should be able to compare investments and rank them according to preferences. The
first approach to this problem was developed by Harry Markowitz in 1952. He is considered the father of
Modern portfolio theory, which basically states that risk can be reduced by combining different assets,
also following the popular saying of “don’t put all your eggs in one basket”. This theory, also called
“Mean-Variance” approach, states that a rational investor should either maximize his expected return
for a given level of risk, or minimize his risk for a given expected return [32]. The approach considers
important the interrelationship between assets and measures them with correlations (see chap 5.1.2).
Markowitz formalized what is known as diversification, the concept that one can reduce risk by
combining two uncorrelated assets.
In reality, there are an infinitive number of portfolios available for the investment, but the investor does
not need to evaluate all these portfolios on return and risk basis because Markowitz portfolio theory
declares the efficient set theorem: an investor will choose his optimal portfolio from the set of the
portfolios that offer maximum expected return for varying level of risk, and offer minimum risk for
varying levels of expected return. Moreover, an important concept of the Modern portfolio theory is the
correlation among assets, assessed through the correlation Value at Risk and possibly estimated using
Monte Carlo simulations, where the biggest disadvantage is the high number of risk factors and the
consequent computational effort required. Principal component analysis is a widely used technique in
portfolio risk management that allows reducing the number of risk factors driving portfolio value
changes. The main risk factors to assess the risk-return profile of a portfolio were presented – worth
mentioning the Value at Risk, the Expected Shortfall and the Sharp ratio.
For the specific portfolio business case of interest of this research on commodity risk management,
since specific assumptions on normal expected returns and constant variance and asset correlation over
time do not hold, we introduce the Genetic algorithm as a methodology to overcome the stated issues
and optimize asset selection and allocation based on specific criteria. In this research, since we want to
assess the optimal strategies to manage and optimize contracts for a basket of different commodities, in
order to develop robust and risk-conscious portfolio optimization methods, we will use Genetic
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
algorithms for the portfolio optimization and leverage a tool created by the Palisade Corporation –
RISKOptimizer - that is still an add-in to Microsoft Excel, where users can set up a model and have some
parameters optimized to achieve a global optimization of a final output.
The big advantage of Genetic algorithms is that we don't have to specify all the problem details in
advance. Potential solutions are evaluated by a fitness function representing the problem we want to
solve. By an evolution procedure Genetic algorithm produces new candidate solutions. The idea is that
combining good solutions (solutions that score high on the fitness scale) should lead to better solutions.
By adding some noise (mutating the candidate), we hope to find better solutions. Part of the evolution
process consists in choosing the members which will form the next generation of solutions.
Evolution has produced systems with amazing capabilities through relatively simple, self-replicating
building blocks that follow a few simple rules:


Nature tends to make more copies of chromosomes which produce a more “fit” organism. If an
organism survives long enough, and is healthy, its genes are more likely to be passed along to a
new generation of organisms through reproduction. This principle is often referred to as
“survival of the fittest”. Remember that “fittest” is a relative term; an organism only needs to be
fit in comparison to others in the current population to be “successful”.
Diversity must be maintained in the population. Random changes are frequent in nature and
require organisms to adapt to them for their survival. With a wider spectrum of possible
combinations, a population is also less susceptible to a common weakness that could destroy
them all (virus, etc.) and is more robust (as a whole) to random nature/context changes.
Once we break down evolution into these fundamental building blocks, it becomes easier to apply these
techniques to the computational world, and truly begin to move towards more fluid, more naturally
behaving machines.
Key contribution of this research
The main objective is to work with a basket of different commodities and optimize sourcing choices with
a portfolio approach, by taking into consideration the specific opportunities and risk metrics.
The logics and methodologies are inherited from the portfolio management theories, with a key
difference – in the Modern Portfolio Theory, the main problems are asset selection and allocation, while
in the sourcing problem of a basket of commodities, asset selection (which commodities should be
bought) and asset allocation (quantities to be bought of each commodity) are already fixed by the
sourcing needs, while the main uncertainties are linked to (1) formula/index based pricing to use for the
sourcing of each commodity and (2) financial products (e.g. forwards, futures and options) that can be
used to hedge some specific risks and impact the final distribution of the portfolio of commodities.
The objective is to finalize a paper on this innovative sourcing methodology for commodities
(application to a purchasing business case of another theory that has its routes into the financial world).
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
4. Focus on the Supply Chain Risks and material studied
International trade is vital to the world economy and businesses that trade internationally are supported
by interlinked global supply chains. A number of recent events highlighted that these dynamic, complex
systems are vulnerable to numerous risks, since their connections amplify even small-local events that
can escalate rapidly and cause significant disruptions. It is important to explore today’s supply chain risk
landscape and presente multi-stakeholder frameworks for improving supply chain resilience.
Higher Supply Chain Risks
Supply chain disruptions are more common today, with increasing impacts on financial performances key drivers are both some external & internal factors. Amongst external factors, there are globalization
and complexity, with multi-stakeholder supplier-customer governance dynamics with also severe
reputation implications. In particular, the integration of current systems and networks (also thanks to
new technologies) increases their interconnectedness and allows widespread domino effects (i.e.
butterfly effect), even amplified by the growing importance of black swan risks. Amongst the internal
factors, there are longer and leaner supply chains, where the strong research of efficiency and
optimization goes against robustness and makes supply chains more fragile and vulnerable to risk
events, as usually mitigation strategies are not aligned with supply chain goals.
Organizational perspectives
The alignment of risk management and supply chain objectives is crucial, especially within E2E supply
chains, where suppliers, customers and product supply organizations will have to work with aligned
interests that can be enabled also through solid incentive systems built into contractual agreements.
The total flexibility to react to fast changing conditions goes together with the empowerment of
employees (through decision making decentralization and quicker approval process) and the
effectiveness of Enterprise Risk Management (ERM) programs (more focused on mindset changes, from
compliance to business perspectives). In particular, both risk-conscious designs & crisis management
processes should be in place, to leverage different responsibilities & expertise, in order to cope
effectively and efficiently with unexpected scenarios.
Risk Management Inputs
Given the higher financial implications of supply chain disruptions, companies should invest in building
resilience into their supply chain designs and in developing solid business continuity processes (to be
structured and tested upfront) to help in crisis-management situations. The main dilemma will involve
agility vs. robustness/redundancy configurations, since multiple/related comparative (dis-)advantages
are difficult to capture together. Multidisciplinary approaches and methodologies from the banking
sector (bottom-up aggregation, mitigation strategies, montecarlo and what-if scenarios) can serve this
purpose - all suppliers (across echelons, geographies and tiers) could be mapped, to support holistic
assessments. Apart from quantitative frameworks, the importance of strong relationships as well as
communication and information sharing are the base for any resilient organization (e.g. social/media big
data can help managing our increasing complexity). Leveraging higher levels of education will also be
vital for future modern organizations.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
Material studied (selected books and papers):
[1]
Blanchard B., Supply Chain Management Best Practices, John Wiley & Sons, 2007
[2]
Chopra S., Sodhi M., Managing Risk to Avoid Supply Chain Breakdown, MIT Sloan Management
Review, 2004
[3]
Christopher, Mena, C., Approaches to managing global sourcing risk, Supply Chain Management:
An International Journal, 2011
[4]
Christopher, Towill, D., An integrated model for the design of agile supply chains. International
Journal of Physical Distribution and Logistcs Management, 2001
[5]
De Xia, Bo Chen, A comprehensive decision-making model for risk management of supply chain,
Expert Systems with Applications, 2011
[6]
Gérard P. Cachon, Choi S., In Search of the Bullwhip Effect, Manufacturing & Service Operations
Management, 2007
[7]
Hau L. Lee, The Triple A Supply Chain, Harvard Business Review, 2004
[8]
HSU A., Zeng D., Effective Leadtime-Cost Tradeoff in Supply Chain Management, International
Journal of Intelligent Control and Systems, 2005
[9]
Lee, H.L., Padmanabhan, V. & Whang, S., The Bullwhip Effect in Supply Chains. Sloan Management
Review, 1997
[10] Sheffi Y., Building a Resilient Supply Chain, Harvard Business Review, 2005
[11] Sheffi Y., The value of CPFR, RIRL Conference Proceedings, 2002
[12] Tang W., Girotra K., Synchronizing Global Supply Chains: Advance Purchase Discounts, Insead
Faculty & Research Working Paper, 2010
[13] Tang C., Perspectives in supply chain risk management, International Journal of Production
Economics, 2006
[14] Tang C., Tomlin B., The power of flexibility for mitigating supply chain risks, International Journal
of Production Economics, 2008
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
4.1 Case Study 1 - Supply localization strategies and risks
Localization is the process of selecting the place for specific socio-economic activities - each place offers
certain resources and each economic activity is characterized by certain needs. Many manufacturing
companies over the years have developed the trend of moving to low cost regions. Manufacturing
companies mostly source from low cost regions in order to improve their productivity. Not only do they
source from low-cost regions but also produce goods in those countries. In this context, localization is
defined as the placement of the physical facilities of a company and/or its supply chains in specific
locations. Companies’ costs are highly affected by their localization strategies that depend on internal
and external factors.
Internal factors:

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Supply Chain set up (implications on manufacturing, purchasing and logistics)
Competitive strategy (“Operational excellence”, “Customer intimacy”, “Product leadership”)
Industry and tier (demands, standards and production processes)
Product size (cost of shipping is dependent on weight and geometry)
Size of the company (economies of scale levels to be met)
EBIT (earnings before income tax - used for benchmarking)
External factors:


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External Market Factors (market growth and shift, customers delivery requirements, duties)
External Risk Factors (natural disasters , geopolitical risk, currency, supplier performance)
External Demography (wage arbitrage, productivity arbitrage, competence availability)
Other External Factors (ease of establishing, local Knowledge, IT & integration, sustainability)
In particular, a survey shows how external factors are ranked in term of importance for companies:
Rank External market factors
1 Speed & flexibility
2 Market shift
3 Market growth
4 Trade regions
5
External risk factors
Supplier problems
Transportation prices
Currency fluctuation
Political risk
Natural disasters
External demography factors
Wage arbitrage
Productivity arbitrage
Competence availability
Other external factors
Ease of establishment
Local Knowledge
New IT solutions
CSR
Green laws
In terms of ease of localization, context conditions are important - below the diamond model by Porter.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
The last decades of the twentieth century witnessed a considerable expansion of supply chains into
international locations. This growth in globalization, and the additional management challenges it
brings, has motivated both practitioner and academic interest in global supply chain management.
A supply chain design problem comprises the decisions regarding the number and location of production
facilities, the amount of capacity at each facility, the assignment of each market region to one or more
locations, and supplier selection for sub-assemblies, components and materials. On top, there are
decisions to be made for the Procurement and R&D organizations, today more and more integrated in
the end-to-end supply chain, as well as customers and suppliers.
In parallel, Economic and environmental systems are more and more under stress worldwide, and this is
testing resilience at the global and national levels. Economic difficulties worldwide are continuing to
make greater demands on political attention and financial resources. Meanwhile, the impact of climate
change is more evident as temperature rises and more frequent extreme weather events loom on the
horizon. The economic and environmental challenges require both structural changes and sound choices
and strategic investments - Countries and their communities are on the frontline when it comes to
systemic shocks and catastrophic events. In an increasingly interdependent and hyper-connected world,
one nation’s failure to address a global risk can have a ripple effect on others.
Resilience to global risks – incorporating the ability to withstand, adapt and recover from shocks – is,
therefore, becoming more critical. Risk management has to be embedded within an organization from
top to bottom and has to include a consistent set of key performance indicators. Several business
innovations and trends of recent decades have succeeded in reducing higher probability, profit-sapping
risks:




Lean supply chains, by design, lay bare the causes of frequent failures, forcing organizations to
learn and design reliability into their processes
Globalization provides opportunities for diversification of supply
Specialized production and scale accelerate learning and the ironing-out of risks
IT-enabled visibility gives advance warning of problems and enables decentralized solutions
These advances sometimes help to manage the less likely major systemic upsets too. However, in some
cases they can amplify risks. Despite these challenges, a blueprint for resilient supply chains can assist in
aligning and organizing priorities to address the most problematic global supply chain risks.
Key contribution or this research
In order to assess the opportunity of localizing (and which supply localization strategies to pursue) or
integrate upstream to hedge volatility in commodity markets, a strategic decision framework is needed,
to support decision makers (Supply Chain Executives) to decide the best configuration of their supply
chains given the external and internal challenges, that are increasing in our VUCA world (volatile,
uncertain, complex, ambiguous).
Such strategic framework would support decision makers in their localization choices – main question
would be which supply localization strategies and supply chain set ups (which parts to localize, how,
with which transition) would be more suitable in base of the specific risks and uncertainties on the
external factors (market risks, disruption risks, country risks, demography risks etc.).
Also here, the framework of real options (less quantitative and more as a way of thinking in the strategic
planning process) could be useful to assess which alternatives a company has and which choices should
be preferred in order to leverage and exploit uncertainty and volatility.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
4.2 Case Study 2 - Demand Risk and Advance Purchase Discount
One of the most common supply chain breakdowns is linked to the forecast risk category – an unreliable
forecast increases the total costs of the supply chain, with significant losses generated by the bullwhip
effect - information distortion due to sales promotions, incentives and lack of supply chain visibility. In
this specific business case, we do not consider the market price uncertainty, but the market demand
volatility and its impacts on the total supply chain. This problem, more relevant for a complex
manufacturing company, will be analyzed outside of the commodity space, for the sake of relevance.
Supply chain management (SCM) aims at coordinating material, cash and information flows along the
supply chain (SC), from material suppliers to consumers (End-to-End perspective). In order to develop
effective and efficient supply chains, it is important to develop an integrated system to manage
material, cash and information flows, with suppliers, production, logistics and customer demand
uncertainties. One of the incentives, presented in the literature of contracts and developed to entice SC
partners to coordinate their decisions and enable information sharing, is represented by the Advance
Purchase Discount (APD). Essentially, these contracts enable accurate, timely, and self-enforcing
information sharing, which reduces the demand-supply mismatches, and improves the profitability of
each of the agents in the SC.
Under such a scheme, the firm offers its downstream retailers an opportunity to place an order, in
advance, at a discounted price. The APD program advances some of the retailer’s demand and, besides
demand increase, this allows the firm to use the pre-committed orders to develop more accurate
forecasts and yield supply chain savings. From the firm’s perspective, the main question is: which is the
right level of discount to offer to downstream retailers in order to have an overall positive impact on the
firm’s supply chain?
In order to answer this question, we can model the APD program as a Real (put) Option, namely the firm
has the right, without the obligation, to sell its products at a discounted price in order to receive
improved trade terms and so enable SC savings. The firm would pay some "revenue loss" (selling
products at a discounted price) to gain "SC savings". The APD program would be launched, should the
expected SC benefits (exercise price of the put option) be higher than the revenue loss (stock price).
Key contribution of this research
In our model, the expected benefits of the APD program, represented by the SC savings obtained
through increased visibility, are calculated by analyzing and estimating savings in three main areas: (i)
Manufacturing & Operating Expenses (MOE); (ii) Transportation & Warehousing (T&W); (iii) Inventory
Cost (IC).
In order to take into account the multifarious uncertainties embedded in the problem, we adopt the
Monte Carlo simulation, with both historical data and expert opinion used to define the different
probability distributions of risk parameters. We also apply the model to the case of a multinational
company, thus showing its value in supporting the firm in the terms definition of the APD program.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
5. Research Next Steps
First next step of this research lies in the validation of some models that have been built (e.g. Advance
Purchase Discount as a Real Option model) with real case data (from a FMCG multinational company, in
their end-to-end supply chain). Furthermore, models already built and validated with real case data will
be formalized into an academic paper.
Step two would lie in the finalization and fine tuning of a framework (risks vs. strategies) useful for
supply localization projects, as often these interventions are linked o objective of upstream integration
into commodity markets to hedge volatility. In parallel, for a portfolio of commodities, the theory of
financial portfolio optimization (with special reference to genetic algorithm) will be applied to verify as
the integration of financial and sourcing approaches can lead to positive results.
As a follow up, as per the core objective of this research – defining “an integrated approach to develop
cutting-edge commodity strategies with the contribution of all the different expertise in a company and
the use of innovative methodologies to deal with commodity risks” – the next steps lie in the
identification of new business cases where real options would yield significant competitive advantage
for companies with exposure to commodity risks, from both a market and supply chain perspective.
In particular, the two areas that will be investigated relate to and build on top of the previous research:
1. Commodity Contracts, Indexation and Portfolio Management
2. Supply Chain Risks and related Supply Chain Design for Resilience
The methodological focus will be still on Real Options Valuation with MonteCarlo Simulations and
Portfolio Optimization with Genetic Algorithm.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
6. Educational Activities
Course typology
Scope of the course
Institution
Financial Analysis @Risk
Procter & Gamble
8
1
Financial Analysis - Supply Chain Analysis
Procter & Gamble
8
1
Sourcing College (Industry analysis, Supply Chain Design,
Economic Analysis)
Procter & Gamble
12
1
International Trade
Berkeley
25
1
Thriving in the Midst of the Storm: Making strategic decisions
in difficult economic times
Stanford
1
1
How Strategic Risk Management Helped a US Manufacturer
Avoid Bankruptcy
Stanford
1
1
Strategic Decision and Risk Management
Stanford
1
1
Technology in a Dangerous World
M.I.T.
1
1
Grand Challenges in Energy - Engagement Strategies
The Complexity of Analyzing Vulnerabilities and Failures in
Complex Systems
M.I.T.
1
1
ETH Zurich
1
2
Managing Security of Supply in a highly Interlinked System
A new concept to deal with Complexity, Uncertainty and
Ambiguity
ETH Zurich
1
2
ETH Zurich
1
2
Financial and Insurance Applications of Markov Chains
Uni of Barcelona
10
Before
Cluster Competitiveness (Strategy, Industry analysis)
IESE Business School
40
Before
Cluster Management (Cluster organization, Cluster initiatives)
IESE Business School
40
Before
Master post-lauream
Week on Project Management and Innovation Management
Luiss Business School
40
Before
Conference
Vulnerability and Resilience of Supply Chains
ETH Zurich
16
2
Job
Scope of the experience
Investment and Risk Valuations - probabilistic NPV & scenario
analysis, airlines industry
Operational Risk Management - definition of a quantitative
framework, Oil&Gas industry
Supply Chain Risk - backward integration in commodity
markets
Commodity Market Risk – integration of the different
commodities with a portfolio approach
Definition of the localization strategies of several supply chains
in a developing region
Company
Business Accademy
Web-based Seminar
Summer School
Consulting
Consulting
Internal Consulting
Internal Consulting
External Consulting
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Hours
Year
Months
Year
Bip. - Booz&Co.
3
Before
Bip. - Arthur D. Little
4
Before
Procter & Gamble
6
1
Procter & Gamble
3
2
Procter & Gamble
3
2
Politecnico di Bari – PhD, 2nd year summary presentation
20.12.2013
7. Publications
The four business cases presented between chapters 3 (Market Risks) and 4 (Supply Chain Risks) are
intended to be finalized into four papers, as applications of Real Options Analysis with MonteCarlo
Simulations and Portfolio Optimization to real business cases:
1.
2.
3.
4.
Commodity Arbitrage and Supply Flexibility
Commodity Indexation and Portfolio Management
Supply localization strategies and risks
Demand Risk and Advance Purchase Discount
In particular, the fourth business case has already been submitted, as an extended abstract, to the 23rd
IPSERA conference (South Africa, 2014):
Costantino N., Pellegrino R., Tauro D., “Defining the terms of Advance Purchase Discount
programs: A Real Option model”, submitted December 16th 2013
The paper will be finalized and fully submitted by February 2014.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino