Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
Sourcing & Supply Chain Risks – A Real Options Approach
Thesis Title:
“Real Options to optimize Risk Strategies and Financial Valuations in Sourcing and Supply Chains”
Academic Year 2013-2014
Index
1.
Real Options to define Advance Purchase Discount terms for Supply Chain Synchronization ........ 2
2.
Purchasing Consortium: A risk-based framework to support strategic and operational design ...... 4
3.
Mitigating Risks in the Procurement of Transportation Services: A real option-based approach ... 6
4.
Educational Activities ........................................................................................................................ 8
5.
Further work in progress with MIT - ZLC .......................................................................................... 9
The 3rd year of the PhD has been dedicated to paper writing – the first 3 chapters below present
abstracts of the 3 papers worked.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
1. Real Options to define Advance Purchase Discount terms for Supply
Chain Synchronization
The supply chain management (SCM) objective is to coordinate material, cash and information
flows along the supply chain (SC), with an End-to-End (E2E) perspective, from material
suppliers to consumers (Christopher, 1992; Tsay et al., 1999). In order to develop effective and
efficient supply chains (SCs), it is important to develop an integrated system to manage material,
cash and information flows, with suppliers, production, logistics and customer demand
uncertainties. As a general context, today’s SCM has to deal more and more with risks and
uncertainties, as new research streams demonstrated lately (Tang, 2006; Wagner and Bode,
2008; World Economic Forum, 2013). As the number of SC partners increases, global SCs can
become “longer” and “more complex” (Tang and Tomlin, 2008), making it difficult to realize
where vulnerabilities may lie (Sheffi, 2005). Considering the important role of SC disruptions in
SCM, managers have to understand the variety and interconnectedness of SC risks, to design the
right mitigation strategies. Among the others, one of the main risk categories identified is
“demand forecast”, driven by both inaccuracies (due to long lead times, seasonality, product
variety, short life cycles, small customer base) and the bullwhip effect or information distortions
(due to sales promotions-incentives, lack of SC visibility and exaggeration of demand in times of
product shortage), that can create high inefficiencies in cash flows (Chopra and Sodhi, 2004).
One of the mitigation strategies to reduce the impacts of demand forecast lies on increasing the
visibility of demand information (Lee et al., 2000; Chopra and Sodhi, 2004; Tang, 2006), hence
the importance of information sharing (Cachon and Fisher, 2000; Gavirneni et al., 1999).
Downstream SC actors may gather and share more accurate demand information that could be
used by upstream SC actors to be more efficient in relation to sourcing, production, inventory
and delivery (E2E perspective). Decision-making centralization may increase the value of such
information sharing that depends on internal factors such as coordination schemes (Anand and
Mendelson 1997; Aviv 2001, 2002) and production flexibility (Fisher and Raman 1996, Cachon
and Fisher 2000, Fisher et al. 2001, Milner and Kouvelis 2002). Also, a number of research
studies explain and demonstrate how the decentralization leads to sub-optimal SCs (Lee and
Billington, 1995; Lambert et al., 1998; Cachon and Zipkin, 1999, Frohlich and Westbrook, 2001;
De Kok et al., 2005; and others), since limited information sharing amplifies the bullwhip effect
(Chen and Lee, 2009).
As supply contracts can support proper information sharing (Cachon, 2003; Cachon and
Lariviere, 2005; Lee and Whang 1999), we focus on one of them, namely the Advance Purchase
Discount (APD) contract, that can improve SC costs by incentivizing accurate information flow,
from upstream to downstream SC actors. Focusing on the last echelon of the SC, we can say that
under an APD contract, the supplier can offer to its retailers the opportunity to place an order, in
advance, at a discounted price. APD contracts, thus, advance some of the demand to the supplier
and transfer some of the demand risks to the retailers (Cachon 2004), with an efficient
redistribution of risks in the SC. This is in line with the fundamental concept of risk management
of allocating risks to the party best able to manage them, as the party assuming the risk should be
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
able to best evaluate, control, bear the cost of, and benefit from it (Taleb, 2012; American
Society of Civil Engineers, 1990).
The APD program, besides also demand increase, allows the supplier to use the pre-committed
orders to develop more accurate forecasts and yield SC savings. From the supplier’s perspective,
the main question is: which is the right level of discount to offer to retailers in order to have an
overall positive impact on his/her profit? In order to answer this question, in this paper we model
the APD program as a Real (put) Option, where a real option can be defined as the right, not the
obligation, to do something in the future at a fixed predefined cost (Dixit and Pindyck, 1995).
The model supports the supplier in defining the most appropriate APD contract terms to be
offered to retailers that assures an overall positive impact on the supplier’s profit. With this main
aim, the model focuses on the supply chain benefits of APD, driven by manufacturing, inventory
and transportation cost savings. The paper is organized as follows. In Section 2, the theoretical
background of this study is introduced. The following section presents the research methodology
that has been adopted. The computational model developed for defining the optimal level of
discount that the supplier should offer to retailers to gain an overall positive impact on his/her
profit is addressed in Section 4. Section 5 applies the model to the real case of a Fortune 100
Multinational Company in the FMCG industry. Conclusions end the paper.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
2. Purchasing Consortium: A risk-based framework to support strategic
and operational design
Purchasing consortia are cooperative purchasing models involving two or more independent
organizations that join together, either formally or informally, with the aim combining their
individual requirements for purchased materials, services, and capital goods (Hendrick, 1997).
Examples of purchasing consortia are Covisint (automotive sector), Gas Intensive (energy
sourcing for SME’s in different sectors), Veira Medical Group (healthcare sector) and Prime
Advantage (raw material sourcing for OEM’s in different sectors).
While the partnership models in general have been extensively analyzed to date (Harbison and
Pekar, 1998; Hendrick, 1997; Hoffmann and Schlosser, 2001; Kogut, 1998; and Park and
Ungson ,1997), there is a lack of studies with specific focus on purchasing partnerships such as
consortia. Schotanus (2007) proposes a review of the different purchasing consortium types and
analyzes the advantages and disadvantages of cooperative purchasing. The theories applied to
identify the main benefits of purchasing consortia are the transaction cost theory, the experience
curve effect and the game theory to (Tella 2005) These benefits include price reduction, lower
management cost, increased flexibility of inventories, and lower logistics costs. Nevertheless,
there are only a few examples of successful purchasing consortia in the industrial sectors (Arnold
and Essig, 1997; Tella, 2005).
In this paper we offer an innovative contribution to the literature by developing a risk analysis
framework for Purchasing Consortia. This paper aims at supporting decision makers, to answer
two simple but important questions in the business world, when thinking of purchasing consortia:
1) From the perspective of the “initiators” i.e. the companies that are thinking whether
setting up a purchasing consortium, which is the optimal organizational set up? Which
are the strategic and operational options that would create the highest benefit?
2) From the perspective of the “followers” i.e. the companies that are thinking whether
joining or not an existing purchasing consortium, which are the risks linked to the
specific organizational and management set up as well as to the industry in scope?
As risks may represent a barrier for the implementation of complex partnerships, the objectives
of this paper are to:
1) identify which are the main risks involved in purchasing consortia, with the definition of
a detailed risk checklist, tailor-made for purchasing consortia and built starting from a
well-known risk framework from the construction industry, with the input of Purchasing
and Risk Experts;
2) support decision makers in defining the best governance structure (objectives,
management model, partner selection, purchasing activities and benefit sharing
approaches) and pursuing the most appropriate mitigation strategies. Each
strategic/operational choice linked to the governance structure will be assessed against a
tailor-made risk checklist (point 1), to understand how each choice may impact the
overall risk of the purchasing collaboration set-up.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
The outcome of the paper will be a matrix-based framework with strategic/operational design
choices and risks as the two axes, where the intersection would clarify the risk level for each
design choice. Such risk levels will be the outcome of semi-structured interviews to Purchasing
Experts in several companies and in several industries. To explain the application of the
framework, a detailed case study is provided concerning the design of a consortium buying for
the resins for a leading Fast Mover Consumer Good (FMCG) company. Conclusions and
managerial implications follow at the end.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
3. Mitigating Risks in the Procurement of Transportation Services: A real
option-based approach
Freight transportation carriers provide the physical connection between shippers and their
customers. Shippers are the beneficial owners of freight, for example, manufacturers,
distributors, and retailers. Carriers are transportation companies, such as trucklines, railroads,
airlines, and ocean transport providers. Standard and Poor (2001) estimated freight transportation
expenditures in 2001 in the US at $713 billion, or about eight percent of the US gross domestic
product.
Within this market, trucking represents over 83 percent of the freight transportation expenditure
in the US (Wilson, 2002), and in particular the truckload (TL) segment of this industry accounts
for over half of it. In TL operations, the truck is generally dedicated to a single shipment moving
between an origin and a destination, in contrast with less-than-truckload (LTL) and parcel
carriers, which consolidate many smaller shipments on a single truck.
Thus, a reduction in TL costs can have a large impact on total cost and therefore the profitability
of shippers’ operations. This justifies the growing attention reserved by researchers to the
procurement of transportation services.
A plethora of studies focuses on the procurement of transportation services with different aims,
such as analyzing the advantages of combinatorial bidding or combinatorial procurement or
conditional bidding (i.e., the possibility that the carriers quote prices on groups or packages of
lanes, in addition to individual lanes) (Sheffi, 2004); focusing on the bid preparation step of the
transportation procurement process by applying generic purchasing strategies to transportation
applications (Gibson, Mundy, and Sink, 1995; Gibson, Sink, and Mundy, 1993); using
optimization techniques for the optimal assignment of carriers to lanes within the network
(Caplice and Sheffi, 2003).
Little attention is given in the literature to risk management in the procurement of transportation
services. Most shippers use annual auctions to procure transportation services, leading to annual
contracts and contract prices that are typically in effect for one to two years. They typically begin
by estimating the freight they will need to ship in the coming year, usually by compiling the prior
year’s movements (Sheffi, 2004). They transmit information to the carriers as a list of lanes on
which they expect the carriers to bid. Carriers then quote the prices at which they are willing to
haul the loads. The shipper evaluates the bids lane by lane, using a single criterion, usually price,
to select the winner.
The contract between parties is, therefore, linked to an annual volume of freight, usually
estimated accurately at annual level, but with a daily and/or weekly volatility that can be very
high.
This volatility can have two main consequences. When the shipper asks the carrier to haul an
additional load (compared to the one contracted), the carrier could not have the capacity to do
this, while the shipper may expect a price concession. However, to the shipper’s surprise, the
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
carrier may refuse to reduce its rate and may even set a higher price for the additional loads
(giving the carrier’s economies of scale).
To manage the situation, the shipper usually researches for at least another carrier in the market.
If there is any carrier available, it could haul the load with a (previously) unknown price and
unknown service level (usually measured in terms of on-time performance, equipment
availability, and extent of damage to goods hauled). Also, the search and selection of a new
carrier results in high transaction costs for the shipper. It is clear that this transport risk (daily
and/or weekly volatility of freight volume) could have a large impact on the total cost and
therefore erode the profitability of shippers’ operations.
The present paper focuses on this topic by investigating an alternative strategy to cope with this
problem and, therefore, mitigate this transport risk, namely the possibility to contract with an
alternative carrier, which would take care of extra/emergency shipments, at a fixed agreed price.
Then, the questions are: when is this strategy more valuable than the market? Does the
“possibility” to use an alternative carrier have a cost, also if the possibility is non used
(transaction costs)? Which is the fair price to be paid to this alternative carrier?
In order to answer this question, in this paper we use a real option-based approach. A real option
can be defined as the right, but not the obligation, to do something in the future at a fixed
predefined cost (Dixit and Pindyck, 1995). Hence, with the alternative carrier contract, the firm
has the possibility, against a higher (initial and ongoing) cost, to hedge some transport risks, such
as the unavailability of the traditional carrier to haul additional load or higher price required by
the carrier to haul this load. If the main carrier is unable to haul the extra shipments or ask for a
higher price, the shipper has the option to shift the extra/emergency shipments to the other carrier
without additional search and negotiation effort (typically incurred if the alternative is the
marker).
In order to take into account the multifarious uncertainties embedded in the problem, we adopt
the Monte Carlo simulation (Costantino and Pellegrino, 2010), with both historical data and
expert opinion used to define the different probability distributions of risk parameters.
We also apply the model to a numerical example, thus showing its value in supporting managers
in change of procuring the transportation services in the presence of risk and uncertainty.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino
Politecnico di Bari – PhD, 3rd year summary presentation
09.12.2014
4. 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, 3rd year summary presentation
09.12.2014
5. Further work in progress with MIT - ZLC
With MIT-ZLC (European Hub of MIT), we are working on an integrated framework to map the end-toend risks and exposures, linked to the decisions of different disciplines / functions across the value chain
of a company (purchases, logistics, manufacturing, engineering, research and development, network
design, sales, finance), with traceability on the specific impacts on the cost structure.
After proper prioritization, we will work on identifying, with multi-disciplinary teams, priority areas
where applying two main risk methodologies:
•
“Real Options”: how to build smart flexibility in contracts / supply chains, to limit the
exposure to the downside and maximize opportunities (exposure to the upside of risk)
•
“Cost-of-Risk”: how to optimize our cost structure, taking out all the unnecessary
buffers coming from a poor knowledge of risk
Finally, a set of clear choices should be presented to the decision makers, to identify vulnerabilities and
proper mitigation strategies, to impact cost structure as well as service levels to customers.
Below an example of the process:
A pilot of the approach will be run in the supply chain of a leading Fortune 100 multinational company in
the FMCG industry.
Dottorando: Danilo Tauro
Tutor: Prof. Nicola Costantino