RISK MANAGEMENT IN SUPPLY CHAINS by Sanjay Kumar APPROVED BY SUPERVISORY COMMITTEE: ___________________________________________ Kathryn E. Stecke, Chair ___________________________________________ Holly S. Lutze ___________________________________________ Divakar Rajamani ____________________________________________ Thomas G. Schmitt
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RISK MANAGEMENT IN SUPPLY CHAINS
by
Sanjay Kumar
APPROVED BY SUPERVISORY COMMITTEE:
___________________________________________Kathryn E. Stecke, Chair
___________________________________________Holly S. Lutze
1.1 Sources of Supply Chain Disruptions, Factors that Breed Vulnerability, and Mitigating Strategies .............................................................................2
1.2 Modeling and Analyzing Economic Consequences of Supply Chain Disruptions...................................................................................................3
1.3 Adaptive Search Methods for Ordering Decisions in Multi-stage Supply Chains.. ........................................................................................................3
CHAPTER 2 SOURCES OF SUPPLY CHAIN DISRUPTIONS, FACTORS THAT BREED VULNERABILITY, AND MITIGATING STRATEGIES...........5
3.4.1 Key Issue 1: A Reasonable Baseline for the Supply ChainStructure........ .................................................................................51
3.4.2 Key Issue 2: Inventory and Disruption Logic ................................55
3.4.4 Key Issue 4: System Performance with a Disruption.....................60
3.4.5 Key Issue 5: System Performance under Expediting .....................64
3.4.6 Key Issue 6: System Performance and Applicability of Analytics, Heuristics ......................................................................66
3.4.7 Key Issue 7: Genetic Search versus Line Search ...........................69
3.5 Conclusions and Future Directions............................................................71
CHAPTER 4 ADAPTIVE SEARCH METHODS FOR ORDERING DECISIONS IN MULTI-STAGE SUPPLY CHAINS.........................................................77
4.5.1 Implementation of Metaheuristics................................................103
4.5.2 Cost Comparison between Search Methods under Static Demand........................................................................................106
4.6 Comparison between Static and Dynamic Policies .................................110
4.6.1 Adaptive Search Methods under Seasonal Demand ....................111
4.6.2 Adaptive Search Methods under Disruptions...............................115
4.7 Extensions to a Global Supply Chain ......................................................118
Figure 2.1 Total number of attacks worldwide...............................................................10
Figure 2.2 Percentage attacks on various classes of targets from 1991 to 2003.............10
Figure 2.3 Percentage attacks on U.S. business interests to total attacks on U.S.Interests.........................................................................................................11
Figure 2.4 Average yearly accidents and the yearly damage caused..............................14
Figure 2.5 Average natural catastrophes reported and the yearly economic losses caused............................................................................................................15
Figure 2.6 Total natural disasters in the U.S...................................................................16
Figure 2.7 Total number of disasters in the U.S. with economic losses exceeding onebillion dollars.................................................................................................17
Figure 2.8 Components at various stages of a supply chain............................................21
Figure 4.4 Order-up-to levels for echelon 1 under seasonal demand........................... 115
Figure 4.5 Demand function with disruptions in final customer demand.....................117
Figure 4.6 Order-up-to levels for echelon 1 under a disruption demand......................118
Figure A.1 The N-ABLE enterprise agent....................................................................128
xiv
Figure A.2 Example of regional shortages caused by N-ABLE simulation of HurricaneKatrina.........................................................................................................130
Figure B.1 Case study supply chain structures .............................................................133
xv
LIST OF TABLES
Number Page
Table 2.1 International terrorist attacks by region ...........................................................11
Table 2.2 Supply chain practices and their effect on vulnerability causing factors.........18
Table 2.3 Severity and possibility of effects of a catastrophe..........................................23
Table 2.4 Interrelationship of strategies and catastrophe types .......................................34
Table 2.5 Interrelationship of strategies and vulnerability breeding factors ...................36
Table 3.1 Case summaries ...............................................................................................54
Table 3.2 Various costs at two levels each ......................................................................69
Table 3.3 Results of Line and Genetic search under various cost combinations.............71
Table 4.1 Cost comparison of various metaheuristics solution methods........................105
Health Hazard Epidemic, famine L L H L L L H M H M H H H L L LExtreme Weather Cold wave, extreme temperature H L M L L L M L M L H L L M L LNatural Fires Eruption, volcano, forest fires H L H L M L H L H L M L L L L L
Acc
i-de
nt
Industrial Accidents Gas leakage M H M L M L H L H L H H M H L L
Transport Accidents Train derailment, airplane crash H H M L L L H L H L M L L L L L
Non
-te
rror
ist
Strikes Workers strikes L L L L L L L L L L H H H L L LPolitical strikes H H H L H L H M H M H H H L L L
Environmental Changes in government spending, lifestyle, manufacturing technology.
L L H L M L H L H L L L H L M L
24
2.5 Catastrophe Mitigation
Considering potential losses from disruptions, mitigation strategies could be critical for
survival of a company. Despite the risks, at times, 95% of Fortune 500 companies are not
equipped to manage a disruption that the company has not experienced before (Mitroff and
Alpaslan 2003).
Planning for robustness requires identification of critical components that a company
is excessively dependent on, or those that, if disrupted, can have a severe impact on supply
chain performance. Intel performs “what if” drills to identify the components and activities
that are excessively vulnerable (Lund 2002). Monahan et al. (2003) identifies the following
five characteristics of a vulnerable component: a bottleneck that other processes depend on, a
high degree of concentration of information flow, single or scarcity of suppliers, limited
alternatives, association with high risk geographic areas, and insecure access to important
infrastructure. In this section, we identify mitigation strategies that can help make supply
chains robust. Sections 2.5.1 through 2.5.3 identify mitigating strategies. In Section 2.5.5, we
discuss cost-benefit tradeoffs in implementing mitigating strategies.
2.5.1 Proactive Strategies
Feasibility and cost permitting, an organization should choose strategies that make supply
chains unaffected by any or many catastrophe(s). This section identifies proactive strategies,
which are decisions/plans/actions that are aimed towards reducing the vulnerability and
probability of disruptions. These strategies help reduce the number of exposure points, as
defined in Section 4. They can also reduce the effects of a catastrophe on a supply chain.
Locate facilities at “safe” locations. The frequency and type of catastrophes vary across
geographical regions. Asia is more prone to earthquakes than Europe (Jones 1981). Most
25
hurricanes occur between the latitudes of 30 degrees North and South (Alexander 1993).
Terrorist attacks occur more in some countries than others. Risks of disruption can be
considerably reduced by choosing locations that are less susceptible to catastrophes. Besides
geographic location and country, other factors such as ownership, symbolic importance, type
of construction, neighbors, government, community, and economic situation affect the
probability of a catastrophe. After the 1993 World Trade Center bombings, Morgan Stanley
realized the risks and moved its offices to several locations outside the towers. The strategic
decision paid returns on September 11, 2001.
Choose robust suppliers and transportation media. A supplier that is well-prepared to
cope with catastrophes can reduce the vulnerability of the entire chain. Often transportation is
the most vulnerable part of a supply chain. Selecting a transport company that has the ability
to handle disruptions can provide stability during catastrophes.
Establish secure communication links. Reliable and robust communication links can help
control and coordinate operations of a dispersed supply chain. With decentralized and global
supply chains, the need and benefits of communication links are significant.
Enforce security. Security can help prevent some intentional man-made catastrophes.
Information security can prevent cyber attacks by hackers, computer viruses, and
unauthorized access to communication media.
Efficient human resource management. Understanding employees can have huge returns.
For example, background information can prevent the hiring of workers that have a criminal
background. Moreover, such information can be used for assigning critical and sensitive
responsibilities. Coutu (2002) emphasized worker resilience as a determining factor in their
26
performance during catastrophes. Finally, a close understanding and relationship with
workers can help avoid strikes and production stoppages.
2.5.2 Advance Warning Strategies
An advance warning or forecast of a catastrophe can provide a company valuable preparation
time to align its capabilities to minimize disruption effects. Besides better catastrophe
mitigation ability, foresight can provide strategic advantages. In 2000, by watching their
supplier processes, Nokia increased its market share by 4%. Philips, a chip manufacturer,
suffered a fire in its Mexico plant. Nokia, anticipating the potential disruption, responded fast
to contact Philips to use its alternate facilities to meet Nokia’s demand. Ericsson was late. All
available capacity of Philips was taken by Nokia (Chopra and Sodhi 2004).
Enhance visibility and coordination in a supply chain. Organizations in a supply chain are
vulnerable to catastrophes that can affect any stage of a supply chain. Vertical coordination
can help prevent a catastrophe from disrupting multiple stages. Sharing information can help
companies anticipate a brewing problem at a supplier or customer. Horizontal coordination
can also allow companies (even competitors) to forecast disruptions such as a change in law,
shifting customer preferences, and changes in technology. Vendors, such as I2, Manugistics,
ViewVelocity, and Celarix offer specialized software to enable extensive visibility across a
supply chain.
Increase transportation visibility. Prompt information about transportation disruptions can
allow managers to prepare supply chains in a better way, thereby avoiding disruptions at their
facilities. For example, transportation vehicles can be rerouted through alternate routes,
orders at other suppliers for which transportation routes are undisrupted can be increased, and
orders can be expedited.
27
Monitor weather forecasts. Toyota uses WeatherData Inc., a weather forecasting company,
to monitor weather conditions for all of its 330 suppliers and transportation routes. Because
of lack of forecasting and planning, in 1999 a snow storm disrupted production at Ford Motor
Company. Toyota’s plants were uninterrupted (Murphy 1999).
Act according to terrorist threat level. The homeland security advisory system announces
the terrorist threat level using five colors. For supply chains, an increase in threat level
increases delays at entry ports because of higher custom or border checks. It may also require
companies to follow strict rules, which can disrupt normal operations.
Monitor trends. Trends such as changes in customer preference, laws and regulations, and
technology can create disruptions. These disruptions can result in loss of market, increased
taxes, and increased competition. In most cases trends occur slowly, and provide time for
organizations to adjust. Other changes such as laws and regulation can happen suddenly.
2.5.3 Coping Strategies
Coping strategies provide a supply chain with the ability to mitigate the effects of a
disruption. These strategies are built on flexibility and redundancy in components, which
provide options that can allow a company to offset the losses in a part of a supply chain by
gains from available alternatives (options).
Maintain multiple manufacturing facilities with flexible and/or redundant resources.
Having multiple facilities in different geographical and political regions can reduce the
probability of simultaneous disruptions at multiple locations. Redundant or flexible resources
at these facilities can provide disruption mitigation ability. Volkswagen manufactures cars in
multiple countries, such as the U.S., Brazil, Mexico, and Germany. These are also its major
markets. These facilities have both the flexibility to produce different models as well as
28
excess capacity to meet demand fluctuations. Disruptions at a specific plant can be
compensated for by increasing production at other locations using resources that are
otherwise redundant (Simchi-Levi et al. 2001).
Jordan and Graves (1995) show the benefits of having multiple flexible plants.
Considering uncertain (normally distributed) product demand, they show that “limited
flexibility (i.e., each plant builds only a few products), configured in the right way, yields
most of the benefits of total flexibility (i.e., each plant builds all products).” Although the
paper limited the analysis and results to uncertain demand, it is intuitive that ‘limited’
flexibility should also be beneficial under various disruptions scenarios.
Carry extra inventory. Excess inventory can mitigate disruptions without affecting normal
supply chain operations. Lack of inventory at a supplier can result in a shortage of supplies at
organizations down the line in a supply chain. In addition to mitigating disruptions, extra
inventory provides the advantage of helping to meet day to day demand fluctuations.
Alternate sourcing arrangement. Nonavailability of an alternate supplier can considerably
increase the risk of disruption. During a disruption at a supplier, other un-disrupted suppliers
should have the capability and capacity to increase their output to meet production share
from the disrupted suppliers. Li and Fung, a Hong Kong-based garment manufacturer,
reserves manufacturing capacities at multiple suppliers. This strategy ensures the availability
of flexible capacity when their customers such as Gap, Disney, and Gymboree order various
different designs and quantities (Lee and Wolfe 2003).
Flexible transportation. Supply chains should have flexibility in using alternatives such as
air, ground, and sea transportation. Alternate transportation is more important for global
companies. Options such as alternative routes and expedited service are also important. After
29
September 11, Continental Teves, an auto industry supplier, avoided disruptions because of
air restrictions by using a contingency relationship with transport firms to supplement air
transport by land (Martha and Vratimos 2002).
Maintain redundant critical components. It is advantageous to have backup for critical
components that can be maintained with limited investment. Auto companies maintain power
generators that can run the plants. Intel maintains a redundant communication system.
Standardize various processes. A product with a standardized and well-documented
process can be easily processed at different facilities and by different workers. If production
at a certain plant (producing non-standard products) is disrupted, other non-disrupted plants
may not be able to substitute its production.
Redesign products for component and process commonality to pool risks. Inventory
pooling by designing products with common components across multiple products allows a
limited or the same set of facilities to satisfy demands for multiple products. Postponement,
mass customization, and centralized inventory management are other techniques that take
advantage of risk pooling by component commonality.
Influence customer choice. The ability of a company to motivate customers to buy what
they want to sell is important, both during normal operations and catastrophes. During the
Taiwan earthquake in 1999, Dell was able to steer customers to buy computer configurations
that Dell could make from the available components, by giving them either a free or cheap
upgrade.
Insure against various risks. Buying insurance for various components and types of
catastrophes is one option open to companies. Various supply chain components can be
30
insured against natural calamities, accidents, and theft. Some coverage includes loss of
assets, profits, extra costs, and expenses because of physical loss or damage to property.
2.5.4 Cost/Benefit Trade-offs of Mitigation Strategies
Investment in mitigation planning and implementation consumes resources. The challenge is
to find economically viable ways to reduce vulnerability. It is not clear as to which strategies
can reduce risks without hurting efficiency. During the quality movement of the 80s, it was
recognized that increased quality pays benefits (Lee and Wolfe 2003). Similarly, we argue
that improving robustness can also increase efficiency. In this section we identify the
potential benefits of mitigation strategies.
Reduction in lead time and lead time variability. Secure transport mediums can help
reduce the time needed at customs and checkpoints, while reliable suppliers can reduce the
need for inspection, accounting, and bookkeeping. Traceability of transport mediums can
also increase the predictability of delivery time, thus reducing the associated uncertainty and
variability.
Better inventory management. Increased visibility of supplier operations and transport
mediums can reduce the uncertainty in supplies. This along with reduction in lead time can
reduce the amount of safety stock needed. This can also help match demand and supply.
Efficient production planning and forecasting. Better inventory decisions and reliable
information about exact customer demand can increase the efficiency of production planning
and forecasting. Flexibility and redundancy can provide options for smoothing variations in
production because of demand fluctuations.
Reduction in the bullwhip effect. Increased information sharing and coordination can
reduce bullwhip effects (Lee et al. 1997). Reduction in lead times also reduces bullwhip
31
(Chen et al. 2000). A robust supplier can be expected to have higher reliability in fulfilling
orders at the right time and quantity. This implies a lesser probability of rationing demand
among customers, which in turn reduces bullwhip.
Increase in customer service. Companies can win customers by making products available
during catastrophes, when competitors may not be able to reach customers. A better
coordination between supplier, manufacturer, and retailer can also help in understanding and
meeting the expectations and choices of customers.
Better demand management. Coordination and visibility between supply chain
organizations can provide critical information for demand management. For example, price
and promotion decisions can be made based on availability of supplies and customer demand.
Companies such as Dell and Amazon dynamically change price depending on the supplies
and customer demand.
Modular products also help in demand management as such products use common
modules that assemble to form final products. Using modularity and postponement,
companies can reduce the number of components required to make a variety of final
products. Fewer components can reduce the effect of compounded service levels. Thus the
firm can achieve a high service level with limited inventory of components.
2.5.5 Selection of Mitigation Strategies
Mitigation planning requires identification of vulnerability causing practices and the
catastrophes that can affect a company and then choosing strategies to mitigate them. The
choice of appropriate mitigation strategies depends on various factors such as location,
market, culture, operations, suppliers, product and process characteristics, ownership, and
manufacturing type, to name a few. Such a complex dependence may make it difficult to find
32
the strategies that best suit a company. In this section we develop two tables that can be a
useful aid for managers in achieving this aim.
A specific mitigating strategy can help to address many catastrophes, while a
catastrophe can be mitigated by choosing from different strategies. Different companies
usually require a different set of strategies. In Table 2.4, we present the effectiveness
tendencies of various strategies against different catastrophes as high, medium, low, and very
low. Depending on the types of catastrophes a company faces, managers can use Table 2.4 to
help choose strategies that best fit their needs. For example, environmental catastrophes such
as changes in government regulations, customer preferences, and technological changes can
be addressed by using the strategies of building an ability to influence customer demand and
proactively monitoring trends and changes in the environment.
For the vulnerability-causing practices used by a company, managers can use Table
2.5 to choose suitable mitigating strategies. For example, a vulnerability resulting from
outsourcing can be managed by selecting suppliers located at safe locations, monitoring for
natural calamities, and using flexible transportation. There can be correlations between
vulnerability causes and the catastrophe types that affect companies. For example, many U.S.
companies are vulnerable because of outsourcing manufacturing to Asia (Section 2.3). We
also found that Asia has the fastest increasing rate of terrorist attacks (Section 2.2). Thus
companies outsourcing operations to Asia should choose mitigation strategies (from Tables
2.4 and 2.5), which are most effective for outsourcing and terrorist catastrophes.
Investment in mitigation planning needs justification by appropriate cost/benefit
analysis, which requires an estimation of losses and benefits in numerical quantities. In many
cases this is not easy to calculate.
33
Another difficulty is a lack of understanding of potential long-term effects. For
example, to mitigate the risk of disruptions, companies can increase inventory. This can be a
short-sighted strategy. Increased inventory may not be an economical alternative as it may
result in quality problems from waste of resources, higher rejection rates, and poor
management. Such a strategy may make a company worse off than a catastrophe. Therefore,
more research is needed in identifying the potential long term deleterious effects of various
strategies. Section 2.6 identifies such research issues.
2.6 Research Problems in Disruptions Management
This section describes important research areas on managing disruptions in supply chains.
We also provide references to existing research efforts in some of these areas.
Develop disruptions databases. A catastrophe database can aid in identifying the possibility
and severity of disruptions for a company located in a specific region. Complex operations
and dependencies between various businesses make it difficult to identify the catastrophes
that might affect a company most severely. The Inter-university Consortium for Political and
Social Research and the Center for Research on the Epidemiology of Disasters maintain
databases for natural catastrophes and accidents. The National Counterterrorism Center
maintains a database of all terrorist events worldwide. These databases do not explicitly
identify affected regions and industries. Information from such databases could be modeled
or used to provide useful results for industry risk mitigation.
34
Table 2.4. Interrelationship of strategies and catastrophe types.
Natural Accidents
Non-terrorist Terrorist
Cat
astr
oph
es
Infr
astr
uctu
re
Des
truc
tion
Tra
nspo
rtat
ion
Dis
rupt
ion
Hea
lth
Haz
ard
Ext
rem
e W
eath
er
Nat
ural
Fir
es
Indu
stri
al A
ccid
ents
Tra
nspo
rt A
ccid
ents
Str
ikes
Env
iron
men
tal
Att
ack
on
Infr
astr
uctu
re
Vio
lenc
e, M
ass
Kil
ling
,
Bio
logi
cal,
Che
mic
al,
Nuc
lear
Ter
rori
sm
Hoa
x or
Pro
paga
nda
Pol
itic
al A
ssas
sina
tion
Sab
otag
e of
T
rans
port
atio
n M
edia
Cyb
er T
erro
rism
War
Strategies
Exa
mp
les
Ear
thqu
akes
Hur
rica
nes,
flo
ods
Sto
rms,
win
ds,
land
slid
e, s
now
, ra
in, a
vala
nche
s
Epi
dem
ic, f
amin
eC
old
wav
e,
extr
eme
tem
pera
ture
Eru
ptio
n, v
olca
no,
fore
st f
ires
Gas
leak
age
Tra
in d
erai
lmen
t, ai
rpla
ne c
rash
Wor
kers
str
ikes
, po
liti
cal s
trik
esC
hang
es in
go
vern
men
t
spen
ding
, lif
esty
le,
te
chno
logy
Pow
er,
com
mun
icat
ion,
an
d pu
blic
ser
vice
s
Bom
bing
pub
lic
plac
es
Sar
in g
as, a
nthr
ax
Bom
bing
Air
plan
e bo
mbi
ng/h
ijac
king
, p
irat
es, t
rain
de
rail
men
t
Com
pute
r vi
ruse
s
Gul
f w
ar
Pro
acti
ve
Locate facilities at safe locations H H H L M H M L H L H M H L L L L H
Choose suppliers located at safe locations H H H L L M M L M VL H M H L L L L H
Choose robust suppliers H H H - H H H H L L H M L L L H L M
Choose robust transportation M M H - L M H H L - M M H L M H L L
Establish secure communication links H H - - - M L L - - M L H L L VL H -
Enforce security - - - - - - H M H - H H M H L H L -
Efficient human resource management - - - - - - H H H - H H M H - H H -
Ad
van
ce
War
nin
g
Enhance visibility and coordination M M H M M H H H M L L L H L M H L
Increase transportation visibility M M H L M H H L - H L L H L H H L
Monitor and react to weather forecasts - H H H H H - - - - - - - - - - - -
Act according to terrorist threat level - - - - - - - - - - - - - H - - - -
Monitor trends: customer preferences, regulations, and technology
- - - - - - - - - H - - - - - - - -
34
35
Table 2.4 continued. Interrelationship of strategies and catastrophe types.
Cop
ing
Maintain multiple manufacturing facilities with flexible and/or redundant resources
M M H L H M H H M - H L M L M H M M
Carry extra inventory M M H - H H H H H - H H L H M H L L
Secure alternate suppliers H H H - M H H H H - H H H M H H L H
Choose flexible transportation options M M H - VL M H H L - M M H L M H - L
Maintain redundant critical components M M - - L L L L VL - H L L L L - M L
Standardize/simplify processes M M M H M M M H M M H H L M M L M
Product design for product commonality, controlled architecture, and postponement
H H H M H H H H M H H M L H H L H
Influence customer choice H H H H H H H H H H H H H H H H H H
Insurance against various risks H H H VL H H H H H - H L H - VL M M L
High (H), Medium (M), Low (L), or Very Low (VL).
35
36
Strategies
Vulnerability Breeding Practices
Globali-zation
Decentral-ization
Outsou-rcing
Reduction in Number of Suppliers
JIT Practices
Supply Chain
ComplexityLegisla
-tion
Pro
acti
ve
Locate facilities at safe locations M M M L H M H
Choose suppliers located at safe locations H H H H H M H
Choose robust suppliers H M M H M - -
Choose robust transportation M M M M H H H
Establish secure communication links L L L - VL - M
Enforce security - - - - - - L
Efficient human resource management - - - - - - M
Ad
van
ce
War
nin
g
Enhance visibility and coordination H H L M H L -
Increase transportation visibility M L M M H L H
Monitor and react to weather forecasts L M H H H H -
Act according to terrorist threat level L L L - M - H
Monitor trends: customer preferences, regulations, and technology - - - - - - H
Cop
ing
Maintain multiple manufacturing facilities with flexible and/or redundant resources M M M M L - -
Carry extra inventory M M M H H M L
Secure alternate suppliers M M M H H M H
Choose flexible transportation options H H H H H VL L
represents assembly of components A and B, and storage of the corresponding finished goods
awaiting demand from local and out-of-town customers, which might include OEM
manufacturers, distributors, retailers and ultimate consumers. At Echelon 2, Stages 2A and
2B represent transportation and storage of the respective components. In Stages 3A and 3B,
suppliers (often local) transport the parts, and either stores them as finished components, or
first adds value prior to storage. Stages 4A and 4B represent activities of two out-of town
distributors that order, purchase and store the parts for delivery to Echelon 3. The figure also
displays lead time parameters -- lead times (LT) under normal operating conditions and
expedited lead times (ELT). These times are based on observations in the case firms and
precedent in the literature. We discuss the model logic in the next section and details of the
parameters in Appendix B.
We cannot generalize beyond electronics, but this four echelon structure seems
reasonable as a baseline for a manufacturing supply chain. Among supporters of a minimum
of four echelons, Juneja and Rajamani (2003) cite an electronics supply chain with assembly
that includes Selectron (supplier), Matsushita (manufacturer), Panasonic (distributor), and
Best Buy (retail customer). Across industries, most aforementioned simulation research on
bullwhip effects covers four echelons. Additionally, Closs et al. (1998) applied a four
echelon assembly structure in simulation experiments to demonstrate that information
sharing contributes to better customer service. To motivate the use of agent based modeling,
54
Swaminathan et al. (1997) propose multiple echelons and assembly. We felt comfortable as
well including assembly of at least two components, e.g., when observing objects within
sight anywhere, whether at home, in an office, in a vehicle or in a factory, one would likely
see products that involve assembly of at least two components.
INT ABC XYZ
Model
Supply chain span
Electronic Co. Finished Product
60 models with over 2500 configurations
200 different finished product types
Three product groups, each that includes a lot of customization
Electronic Co. Primary customers
Manufacturers, distributors, and retailers
Automotive, utility, military, and aerospace industries
Aerospace OEMs
Electronic Co. Sole Sourced Components
Approx. 10% Approx. 20% Approx. 20%
Operation Type Assemble-to-order Make-to-order Primarily make-to-order
Electronic Co. Operations Model
Lean JIT system using visual controls and Kanban for internal processes. MRP ordering for externally sourced components.
Toyota style JIT for internal processes. MRP ordering for externally sourced components.
MRP ordering internally and externally.
Management issues
High product variety, high inventory and overhead costs, and delivery problems
Expectations for high customer service
Product cost and delivery performance
Risk causes
Recent disruptions
Power failure, transportation accident, sole source disruption
West coast port lockoutTexas port closure, Land-line service failure
Notable consequences of disruptions
Electric power vulnerability. Loss of sole-source supplier may introduce delays of up-to two years.
Better equipped than the other two because of lesser number of components and SKUs. No telecom backups or contingency plans.
Vulnerable to power and telecommunications failure. No backup generator or contingency plans
Mitigation planning
Buffering. Expediting. Basic preventive measures such as fencing, guards, and lighting.
Buffering. Expediting. Alternate sources and routing. Backup generators.
Buffering. Expediting. Alternate sources for most components. Geographically dispersed locations.
Telecommunication/power failure, transportation accidents, and sole sourcing
Risk
man
agem
ent
Table 1 -- Case Summaries
Ope
ratin
g Po
licie
s
Global spread with overseas transport, and distribution centers located elsewhere in the US, suppliers primarily local, local electronics manufacturer, customers ranging from local to overseas.
A 4-echelon supply chain with assembly is sufficiently representative.
Supp
ly C
hain
Table 3.1. Case summaries.
55
3.4.2 Key Issue 2: Inventory and Disruption Logic
We addressed one aspect of Sandia’s simulation model, the multi-echelon supply-chain
ordering system, which regulates the goods flows and inventories. While there are many
aspects of the Sandia model not covered, the ordering system serves as a fundamental means
of linking activities between companies in a supply chain.
Personal interviews revealed that operating managers of the case firms and their
suppliers do not share point-of-sale data within the respective supply chains. With buffering,
frequent rescheduling and repeated expediting, the managers believed their processes
Figure 3.1. Prototypical supply chain.
56
encountered the full effects of demand amplification. Most were familiar with features of the
Beer Game. There was a strong consensus among these managers that the most realistic way
to reflect the effects of disruptions is through local planning, i.e., deriving echelon-by-
echelon forecasts and buffer parameters without the benefit of shared point-of-sale data. We
recognize however, the existence of progressive supply chains that plan more holistically
(e.g., Brown, Schmitt, Schonberger, and Dennis, 2004; Ferdows, Lewis, and Machuca, 2004;
Li, Shaw, Sikora, Tan, and Yang, 2006).
We assume stationary, autocorrelated demand at Echelon 1 (see Figure 3.1). Each
echelon/stage, including Echelon 1, observes only the demand it receives from its immediate
stage customer without knowledge of the underlying demand distribution of final customers.
Using historical demand observed from the immediate customer, we forecast at every stage
the mean and variance of demand using single exponential smoothing (Snyder, Koehler,
Hyndman, and Ord, 2004). Exponential smoothing is a popular method used by many
companies (Gardner, 1985; Makridakis and Hibon, 2000).
The demand and variance estimates are applied, along with a service-level parameter,
in a single-stage order-up-to formula to calculate the replenishment order quantity.
Replenishment orders at one echelon/stage shown in Figure 3.1 become the demand at the
preceding stage. Inventory balance equations link each stage in a periodic (daily) review
system. Each stage follows the FIFO logic each day:
i. Launch a replenishment order if necessary using an order-up-to system
ii. Withdraw this day’s demand from available inventory to initiate shipment to the
customer
iii. Receive goods from the previous stage into inventory
iv. Update the inventory or backorder quantity (where backorders are permitted).
57
If demand at the succeeding stage is more than the available inventory, we assume a partial
shipment. The rest is lost at Echelon 1, and backordered at Echelons 2, 3, and 4. Other
approaches to shortages are applied as well in practice (Sodhi, 2005.) At Echelon 2, the
assembly stage, inventory of the two component types is maintained and orders for each are
placed if warranted. The quantity of an assembly order cannot exceed the minimum available
inventory of the two components.
The parameters we use in demand generation, forecasting and inventory control are
presented and justified in Appendix B. The order logic assumes periodic review with no
setup or order cost, infinite production rates, fixed lead times, and i.i.d. demands (Zipkin,
2004 and Nahmias, 1993). While optimally is by no means guaranteed with echelon by
echelon order-up-to policies in our system of stationary demand and lost sales at Echelon 1,
this logic is the most robust and suitable of those available. Such a policy is common in
practice and has been studied extensively in the literature. Nahmias (1993) and Axsater
(2000) provide details about order-up-to systems, which afford optimality for base-stock
policies under certain assumptions about the supply chain structure, shortages, order cost,
and demand distributions. Recent papers have found base-stock policies optimal over a
variety of unimodal risk-neutral and risk-averse objectives in single-item, single stage
systems with multi-period finite horizons and no order cost (e.g., Marinez-de-Albeniz and
Simchi-Levi, 2006; Chen et al., 2007; Huh et al., 2009).
Sandia wished to isolate the effects of a supply chain disruption on regional
economics. Consequently, we chose to induce a generic shock (time delay) to represent many
types of disruptions that occur in practice. After an initialization period of 1000 days, we
induce a 20-day disruption, and compare performance thereafter with the base case (without
58
expediting and disruptions). We consider disruptions at each end of our supply chain:
Echelon 1 (the electronics firm’s storage prior to shipment to customers), and Echelon
4/Stage A (supply by a parts distributor or component fabricator). During each disruption, the
facility at the affected echelon receives shipments or orders in-route before the start of the
disruption, but stops all other operations. It cannot place orders, produce orders, or make
shipments, and it turns away all demand during the disruption.
Management within all three case supply chains indicated that “time of a disruption”
becomes the critical factor, and expressed concerns about response and recovery. Prior
disruptions included: fire, weather disasters, worker strikes, raw material availability, power
failures, telecommunication failures, supply shortages, transportation breakdowns, and
machine breakdowns. Loss of a sole-source supplier introduced delays of up to two years to
find and procure alternate materials (as in the case of INT – Table 3.1). A disruption in
transportation of commodity components, with replenishment by sea, might involve as much
as a 90-day delay (as for Supplier 5 in case companies – Appendix D). Loss of electric power
or telecommunications would quickly stop activities in every firm in the area during the
length of the disruption, although essential telecommunication transactions might be handled
by cellular telephone, if the networks are not overloaded. These expressed concerns reflect
perceptions of the “new reality” as well as prior experience with disruptions such as port
lockouts, power outages, and loss of telecommunications. Management of the case firms
agreed that basic preventative measures such as fencing, guards, lighting and planning
against prior disruptions are insufficient, and that contingency planning deserves to be
elevated in importance within their organizations.
59
Firms within all three case supply chains frequently use expediting to avoid
shortages. Depending on the echelon, the percentage of orders expedited ranges from 5-20%,
with a premium cost per unit ranging from 10-50%. Bradley (1997) provides additional
motivation for expediting within the electronics industry. Expediting is typically
accomplished with faster transportation or production adjustments such as overtime,
additional shifts, part-time help, alternate routing, and responsive outsourcing. Following
prior practice and research, we apply two triggers to expedite lead times as orders are
launched, and experimented with a range of parameters for each type of trigger to achieve an
expediting frequency we observed in the case studies. The first is activated when the quantity
throughout the stage pipeline (on-order plus on-hand inventory) falls below the expected lead
time demand (Fakuda, 1960; Whitmore and Saunders, 1977; Groenevelt and Rudi, 2003;
Veeraraghavan and Wolf, 2008). The second trigger is activated when the on-hand inventory
falls below a demand-based target (Appendix B). As an example, Beyer and Ward (2002)
observed that Hewlett Packard’s supply network applies this second type of trigger to
expedite via air transport.
3.4.3 Key Issue 3: Performance Metrics
Three operational metrics drive important marginal economic effects in the case supply
chains. The first is the service level experienced by customers of electronics firms at the final
supply chain echelon. Firms at intermediate echelons typically have long-term relationships
with customers that call for backordering any items shorted (Sodhi, 2005). At the final
echelon, customers such as retail consumers and some manufacturers may have choices, and
shortages may be lost to the firm (INT and ABC). Others, e.g., some OEM manufacturers,
60
may have contractual arrangements that might instead permit backorders with the electronics
firm (INT and XYZ).
Shortages are undesirable anywhere in the supply chain, but countermeasures such
inventory positioning and expediting enable some orders to catch up. If shortages reach the
final supply chain echelon, however, the financial stakes may be exceedingly high. For
example, late delivery of avionics to Boeing Commercial, an OEM customer, may in turn
cause late delivery of an aircraft. This would result in loss of interest on delayed revenue
receipts of hundreds of millions of dollars, diminished revenue arising from contractual
penalties, and loss of goodwill with the airlines. In any event, the opportunity costs of
shortages at the final echelon can be severe in cases of either lost sales or backorders, with
loss of future business at stake.
System expediting, the second metric, offers obvious advantages in preventing
shortages. Nevertheless, expediting introduces significant premiums for transportation and
production that drains profit margins throughout the supply chain.
The third metric, system inventory, also drains profit margins in the supply chain.
Well-positioned inventory, however, provides a safety net against disruptions, and can
decrease expediting and shortages. All three case firms used ERP, but none had visibility of
total supply chain inventory. We chose total supply chain inventory as a metric because
without such visibility, important system inventory costs might be overlooked.
3.4.4 Key Issue 4: System Performance with a Disruption
To further guide experimentation by Sandia and other researchers under more rigorous
operating circumstances, we conducted simulation experiments using the aforementioned
model structure, and adapted the experimental design accordingly to address Key Issues 4-7.
61
We observe the performance on a day-by-day basis over 100-day time blocks,
replicate the experiments 100 times, and compare performance between the disrupted and
base cases. A period of 100 days covers five months in a 240-working day year.
It is important to ensure that our initiation period is long enough to remove transient
effects, and the run length effectively captures steady-state performance. Each simulation
was run for 2000 days. The first 1000 establish steady state conditions, and we observe
performance over the remaining time. Pilot experiments suggested that our choice of
initiation period satisfies Welch’s conditions outlined in Law and Kelton (2000).
We distinguish issues of independence of a performance measure within and across
replications by drawing on Chapter 9 of Law and Kelton (2000). We do not claim or expect
independence within replications, either in demand or supply. Demand is by definition
autocorrelated over time, and inventory levels and replenishment orders are clearly linked
from one period to the next. Indeed, we wish to induce bullwhip effects over time within
each replication to reflect the effects observed in practice. Across replications, we applied
terminating sampling and chose independent random number seeds to initiate each sample
replication.
Cost structures varied substantially by firm and item in our case studies. We
addressed this complication by analyzing one set of weights in Key Issue 6 to demonstrate an
example that counters conventional research assumptions. For more generality in 7, we
consider a design with a range of costs. With 4 and 5, we apply MANOVA across
performance metrics, and focus on statistical inferences consistent among them as reported
over replications.
62
For Key Issue 4), our MANOVA design has two fixed factors: time block and
location of disruption. We do not enable the expediting option as mitigation, and observe the
service-level and system-inventory metrics as dependent variables. We found that values
from each metric are drawn from different distributions, according to Pillai's Trace, Wilks'
Lambda, Hotelling's Trace, and Roy's Largest Root statistics at the .01 level (Arbuckle,
2005). Results for the two metrics are summarized in Figure 3.2. Each point in the graphs
depicts the mean value over an indicated five-month time block.
Waller-Duncan multiple-range post-hoc tests disclose that for both disrupted
locations, service level is significantly different between disrupted and base-case states over
the first three time blocks (15 months). In the final eight time blocks, there is no significant
difference among the means and zero. All of these results hold at the .01 and .05 levels.
System inventory is significantly different over the first six time blocks (30 months).
In the final five time blocks, there is no significant difference among the means and zero.
Clearly, the effects of disruptions on the two performance metrics last for a long time. There
are severe decreases in service level for more than a year. Additional system inventory over
the base case exceeds ten weeks of demand for more than two years after a disruption at
Echelon 1.
In analyses comparing disruptions at Echelons 1 and 4, the MANOVA statistics at the
.01 level indicate significant deterioration in service level over the first five months after a
disruption at Echelon 1, as well as increased system inventory (more than twice the amount)
in months six through 30.
63
a. Each point represents a mean value over a time block. Waller-Duncan multiple range tests indicate that points not circled are significantly different from one another; those circled indicate no significant difference between one another and zero. These results hold at the .01 and .05 levels.
Figure 3.2. Performance effects of a disruption.a
6363
64
We observe a strong bullwhip influence, especially for a disruption at Echelon 1,
where order variability is amplified backwards in the supply chain. With the disruption at
Echelon 4 rather than 1, final assembly orders have a chance to recover over the longer
cumulative lead times; the derived order-up-to levels were more consistent; and the
associated system inventory levels were lower on average. This is in contrast to findings by
Wu and Chen (2009) who found that regardless of source in a two stage system, larger
fluctuations occur closer to the disruption and dampen when propagating away. This can be
attributed to the differences in our model of more stages, the presence of lead times, the lack
of perfect information flows, and the corresponding presence of bullwhip effects.
Our experimentation sheds light on the relevance of Key Question 4), and highlights
the importance of studying the effects of disruptions at different stages in the supply chain.
This led us to recommend that Sandia consider a variety of disruptions at various stages and
between stages.
3.4.5 Key Issue 5: System Performance under Expediting
We compare performance results of the base case (without expediting and disruptions) with
an expediting case (no disruptions). If the expediting option is enabled, we permit expediting
at all echelons, with order crossover a possibility. Figure 3.1 shows values of the LTs and
ELTs we chose in the experiments with details in Appendix B.
To compare performance between the base case and expediting, we use all three
performance metrics (final-echelon service level, system inventory, and system expediting)
as dependent variables using MANOVA. Values from the three metrics are drawn from
different distributions, according to the aforementioned statistics at the .01 level.
65
We also observe significant performance differences at the .01 level between the base
and expediting cases for each metric. While significant, however, the relative performance
differences raise issues about the value of expediting. Shortages improve with expediting by
only 0.18 units per day on average demand of 111 units, while total system inventory
increases by an average of 787 units per day (almost eight days of average demand). System
inventory increases so much because expediting increases the variability in order quantity
and frequency, and this variability is amplified downstream in the supply chain.
Expediting, while considered necessary (Bradley, 1997; Cohen et al., 2003), is
usually expensive (Arslan, Ayhan, and Olsen, 2001; Groenevelt and Rudi, 2003). Beyer and
Ward (2002) observed that expediting by air in HP’s supply chain costs as much as five times
more than standard shipment by sea. In the case firms, we found that expediting was quite
expensive as well.
According to the personnel we interviewed, expediting offers a first line of defense
against variability deemed irregular as well as that considered part of normal business
conditions. Our experimental findings raise questions about the value of this practice as a
mitigation approach of choice. We are not the first to raise the issue. Even before bullwhip
effects were understood, the practice of expediting was challenged because of nervousness it
may induce in MRP systems (e.g., citations in Schmitt, 1984). Regardless of the evidence, we
do not expect firms to discontinue the practice of expediting. It would be difficult to convince
managers of an electronics firm not to expedite one component when the remaining 700
needed for assembly and sale of a finished product are available. However, practitioner
behavior might be influenced by future research that finds merit in the application of certain
types of expediting at specific supply chain stages, or between stages.
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3.4.6 Key Issue 6: System Performance and Applicability of Analytics, Heuristics
Traditionally, researchers offering analytics or heuristics have been careful in claiming utility
only within their system assumptions. Base-stock policies have been shown optimal for
Without showing optimality, some have used order-up-to and other well known policies in
simple supply chains in the presence of supply or demand disruptions (Chao, 1987; Gupta,
1996; Arreola-Risa and DeCroix, 1998; Snyder, 2006, Lewis et al., 2008). Yet even in single-
stage stationary systems, researchers have not found tractability when shortages are lost (e.g.,
Karlin and Scarf, 1958; Janakiraman and Roundy, 2004; Janakiraman, Seshadri, and
Shantihikumar, 2006), and others have proposed heuristic solutions (Nahmias, 1979;
Donselaar et al. 1996; Metters 1997; Ketzenberg et al. 2000).
In this section, we observe non-unimodal total cost behavior within a range of stage
order-up-to levels under conditions of no expediting or disruption. To make a case, we
choose a holding cost of $1/unit/day, a backorder cost of $2/unit/day (at Echelons 2, 3, and
4), a lost-sales cost of $3/unit (at Echelon 1), and a stage production/transportation cost of
$1/unit. We present results for this case, and in the next section, report summary statistics
over a variety of costs.
Figure 3.3 shows plots of the simulation results of total cost over various stage order-
up-to levels only for the first of the replications. The two graphs to the left focus solely on
behavior at Echelon 1, while the ones to the right show interactions between Echelon 1 and
Echelon 2.
The graphs on the left show total cost values for discrete order-up-to levels at Echelon
1, while allowing the other three echelons to derive order-up-to levels from stage demand
67
forecasts. The top left graph displays total cost performance versus Echelon 1 order-up-to
levels over the range [1, 2000]. Clearly, the local optima vary considerably in value, e.g., one
yields a total cost 6.95 times larger than the lowest observed total cost value, given the
starting point.
The bottom left graph depicts a finer grain relationship over the range [1000, 1530]. It
highlights the striking volatility of the cost function.
The graphs on the right of Figure 3.3 show the interactive behavior between the first
two echelons, depicting total supply chain cost while varying Echelon 1 order-up-to levels
for prescribed Echelon 2 levels. In this set of experiments, Echelons 3 and 4 derive order-up-
to levels from stage demand forecasts. The course and fine grain representations at the top
and bottom right, respectively, generalize the previous observations solely about Echelon 1.
Clearly, computationally-efficient iterative line searches or analytic searches (based on
assumptions of well-behaved first and second order functions) would derive very poor
solutions in this operating scenario, depending on the starting point and method efficiency.
There are multiple ways to demonstrate our concerns about ill-structured performance
behavior and inappropriateness of exact approaches, which include deriving violations of
Kuhn-Tucker conditions and finding derivatives with the wrong sign. We chose another way,
the presentation of this counter example, one where exact approaches would yield a total cost
almost seven times larger than optimum. Existence of such a counter example, of any
magnitude, is sufficient to obviate generality of exact analytical approaches. In the next Key
Question, we consider the efficacy of a unimodal search method over a wide variety of cost
structures.
Figure 3.3. Shape of total cost function.b,c
Total Supply Chain Cost vs Echelon 1 Order-up-to level over the Quantity Range [1, 2000]
0
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Total Supply Chain Cost vs Echelon 1 Order-up-to levelover the Quantity Range [1000, 1530]
9500105001150012500135001450015500165001750018500
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Total Supply Chain Cost vs Echelon 1 Order-up-to level over the Quantity Range [1, 3000], at various Echleon 2 Order-up-to Levels
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Total Supply Chain Cost vs Echelon 1 Order-up-to level over the Quantity Range [940, 1357], at various Echleon 2 Order-up-to Levels
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Order-up-to at Echelon 1
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Ech. 2 Level = 2500
Ech. 2 Level = 3000
Ech. 2 Level = 3500
Ech. 2 Level = 4000
Ech. 2 Level = 2500
Ech. 2 Level = 3000
Ech. 2 Level = 3500
Ech. 2 Level = 4000
b. Total Supply Chain Cost while Varying Echelon 1 Order-up-to Level; Three Other Echelons DeriveOrder-up-to Levels using Demand Forecasts
c. Total Supply Chain Cost while Varying Echelon 1 Order-up-to Level at prescribed Order-up-to Levelsat Echelon 2; Other Two Echelons Derive Order-up-to Levels using Demand Forecasts
68
69
3.4.7 Key Issue 7: Genetic Search versus Line Search
We next examine the modality of the objective function surface under a variety of cost
settings. Sandia programmed a general genetic search (GA) function to “jump over” local
optima within a wide range of parameter values. We conducted experiments to examine
Sandia’s GA approach to search for cost-effective order-up-to quantities at Echelon 1, while
allowing demand forecasts to guide decision making at other Echelons. The GA utility
represents candidate solutions as binary numbers. Crossover and mutation operators are used
to overcome local minima. For details, see Goldberg (1989).
We consider line search (LS) as a basis for procedural contrast and to examine
performance modality. LS ensures optimality only when the objective function is unimodal.
In our problem setting, a LS approach that increments upwards from an order quantity of 0 to
find a local optima may result in a significantly higher than optimal cost. To facilitate a
reasonably fair comparison with GA, our adaptation of LS begins with an order-up-to
quantity equal to the mean lead time demand at Echelon 1 and searches each way in the
neighborhood (upward first) until we encounter local optima. In pilot experiments this
adaptation resulted in solutions much better than starting at 0.
Table 3.2. Various costs at two levels each.
Back Ordering
Cost
Cost of Lost Sales
Carrying Cost
Expediting Cost
Low 4 6 2 3High 8 12 4 5
70
To represent a wide variety of operating conditions, we selected a range of cost
parameters as presented in Table 3.2. The parameters are based on findings in literature and
case studies. Realistic costs depend on the type of product, industry, and supply chain, among
other factors. Cohen et al. (2003) observed that with short life cycles and obsolescence in the
semiconductor industry, the cost of losing a sale is about twice the cost of backlogging, while
the holding cost is three times the cost of backlogging. We follow these ratios for setting the
cost parameters. By contrast, Faaland et al. (2004) experimented with lost-sales costs in a
single-echelon ranging from 62 to 500 times the periodic inventory holding cost, yielding
shortage values much higher than the ones we chose. Their parameters were based on a
cross-industry survey by Boer and Jeter (1993). Our choice of relatively low shortage costs
reported here is intended to explore benefits of GA over LS under modest conditions. We
observed in pilot experiments that larger shortage costs relative to inventory carrying costs
further exaggerated differences between GA and LS.
Table 3.3 shows the cost savings under various cost scenarios. The percentage
improvements represent averages over 100 replications (observed over periods 1000-2000)
for each of the 16 cost combinations. The superiority of GA over LS at Echelon 1 holds at
the 0.005 level using student-T tests. GA as compared with LS yields overall cost savings
greater than 16%, and in one of the cost setting by more than 30%.
Genetic Search yielded significantly better costs at the expense of computation time.
On a laptop, the time per run for GA was about 14 hours, and our adaptation of LS averaged
about 10 minutes. While the computation time for GA may be acceptable for Sandia with its
fast complex of computers, we recognize an opportunity in future work to adapt standard GA
utilities to special properties of this problem setting.
71
Table 3.3. Results for Line and Genetic Search under various cost combinations.
Back Ordering
Cost
Cost of Lost Sales
Carrying Cost
Expediting Cost
Line Search
LS
Genetic Search
GA
MeanPercentage
Cost Savings
Low Low Low Low 551.01 472.38 14.27Low Low Low High 606.78 493.61 18.65Low Low High Low 808.55 685.79 15.18Low High Low Low 621.22 496.91 20.01High Low Low Low 690.61 516.98 25.14High High Low Low 764.22 544.26 28.78High Low High Low 996.62 922.66 7.42High Low Low High 742.80 524.13 29.44High High High Low 1028.71 927.69 9.82High High Low High 797.62 551.08 30.91High Low High High 994.61 922.56 7.24Low High High High 974.21 896.36 7.99Low High High Low 873.29 812.41 6.97Low High Low High 668.32 510.28 23.65Low Low High High 852.02 833.00 2.23High High High High 1071.11 941.23 12.13
3.5 Conclusions and Future Directions
Disruptions can have many sources covering the gamut from natural to accidental to
intentional. Regardless of cause, disruptions can have long-lasting, widespread, and costly
effects on supply chains. We describe aspects of a stream of research to assess the economic
impact of supply chain disruptions. The overarching research mission of our sponsor, Sandia
National Laboratories, is to develop a large-scale simulation model that depicts regional and
national economic behavior to a disruptive event. Simulation is intended to augment existing
analytical and statistical models whose utility may depend on the validity of inherent
simplifying assumptions. Such optimization models routinely assume aggregation of demand
and supply data, substitutability of supply options, independent and steady-state behavior of
72
underlying stochastic distributions, optimization over well-behaved objective functions, and
simple supply chain structures (Chen, Sim, Simchi-Levi, and Sun, 2007 and citations).
Sandia’s concern was that with such high stakes, entities within the U.S. government and
private sectors cannot afford to wait for researchers to overcome the substantial challenges of
overcoming simplifying assumptions in the optimization models.
Sandia’s agent-based simulation has the capability to incorporate supply chain
networks with a million or more firms and supporting infrastructure. The first project for the
system has been to address the Pacific Northwest region. We concentrated on one stage of
this effort on how to model business activity of small and medium sized firms within supply
chains. It is anticipated that these insights will be useful in other research as well.
We conducted case studies of three electronics firms in the region, and drew from the
cases to offer a fundamental set of design requirements, performance drivers, and important
research questions. Sandia may not need to represent entire supply chains, but if electronics
assembly is representative, their model should include at least:
(i) Four echelons per industry,
(ii) An echelon with assembly,
(iii) Bullwhip effects facilitated by a multi-echelon, multi-stage inventory
system with local planning,
(iv) Shortages along the supply chain in the form of backorders and lost sales,
(v) Capability to expedite at all stages,
(vi) Service level at the final stage, system expediting and system inventory as
performance metrics, and
(vii) Disruptions in the form of time delays at various stages in the supply
chain.
To guide Sandia’s experimentation, we followed with research questions, whose answers
were driven by simulation results. One important finding was that the system cost function
73
can be quite ill-behaved in a four-echelon supply chain, even in the absence of disruptions
and expediting. We reveal a weakness of analytical optimization approaches in the present
setting by providing one example where these methods failed to obtain satisfactory solutions,
along with supportive statistical evidence over a wide range of operating conditions.
Analytical methods that assume unimodal behavior may be inappropriate for real-world
supply chains.
Another finding confirms that disruptions may have long-lasting, rippling, and costly
consequences within the supply chain structure presently considered. Another is that standard
industry practices of setting order parameters locally and using expediting as mitigation seem
to exacerbate these undesirable effects. In addition, our cases, experiments and subsequent
observations by Sandia (Appendix A) support Craighead et al. (2007), who suggest
simulation analysis and field study as means to verify propositions that the severity of a
disruption is related to time, its location, the structure of the supply chain, and types of
mitigation. We believe that additional field study across industries is warranted.
We address disruptions at each end of a four echelon supply chain, but we suggest
that an expanded study may find value in investigating disruptions elsewhere. In support of
this, Sandia’s model has capabilities to explore more fully the hypothesis that mitigation
efforts should focus on specific areas of network criticality, i.e., to explore disruptions at any
supply chain stage as well as in infrastructure shared by many firms within a region, such as
transportation hubs, electric power, and telecommunications. Their model also offers more
structural flexibility, e.g., parts distributors may have multiple customers, which may enable
demand aggregation and dampening of the demand amplification elsewhere in the supply
chain.
74
Our experimental results suggest caution, however, regarding mitigation efforts and
resiliency. Information sharing offers clear advantages in a supply chain, and authors have
addressed this issue (e.g., Milgrom and Roberts, 1988; Lee et al., 2000; Chatfield, Kim,
Harrison, and Hayya, 2004; Sodhi, 2005). As a caveat, our experiments disclose that
information must be discounted considerably to control bullwhip effects in moving from the
source of uncertainty in a supply chain. For instance, we derived a very low forecasting
weight of 0.01 on the most recent local information for the firms in the last echelon, Echelon
4. While we support the notion that increased information and flexibility to react are
generally desirable, we caution that it is possible to overreact. A disruptive event creates a
critical watershed. The issue is how much weight to place on the news of such an event and
what to do about it.
Furthermore, with the irregular cost objective surface documented by our results, we
believe future efforts should be directed towards developing efficient and effective search
methods to find order parameters yielding local optima close to global cost values. Further
research is needed to explore the efficacy of hybrid GA approaches as well as other
metaheuristics (Corne et al., 1999; Kimbrough et al., 2002; Glover and Kochenberger, 2003;
Rego, 2005). A brute force approach would be to increase the generation count of GA to
further improve solution quality, but this would likely exacerbate the formidable computation
problem. We think a better alternative would be to take advantage of problem structure and
domain knowledge (Holland, 1992; Gen and Cheng, 2000). One approach would be to assign
the lead time demand as one of the starting solutions for GA, since the operators of crossover
and mutation in standard utilities do not allow a ‘directed’ intelligent search. This might help
solution effectiveness and efficiency as it did with LS in our experiments.
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Our findings suggest that consideration of lost sales, multiple echelons, and assembly
perturb stationary behavior that might otherwise be found in less complex systems, and that
disruptions and expediting exacerbate the problem. Further research is warranted on dynamic
order-up-to policies with parameters updated periodically, whether the adjustments are made
using adaptive search over demand history, or cost-based search directly on order-up-to
parameters. This premise is further supported by simulation experiments conducted by Ross
et al. (2008) who found a time-varying order-up-to policy more effective than a static policy
in terms of the total costs of holding, ordering and lost sales. They considered a single
product at a single stage in the presence of cyclical demand and recurrent disruptions. From a
practical perspective, case firms such as those we studied, which employ periodic review
time-phased order point systems, may be able to incorporate such dynamic ordering policies.
Additional insights may also result from future simulation research that relaxes some
of our simplifying assumptions. Sandia has already extended our model to include
price/demand elasticity functions and a diverse customer base for agent firms. A broader
spectrum of costs and revenues can be advantageously investigated within an experimental
design to identify the conditions under which mitigation approaches are effective against
certain disruptions. Our experiments embraced both normal and expedited activity lead
times, but did not consider capacity interactions that might result from finite replenishment,
setups and specific capacity adjustments such as overtime, additional shifts, part-time help,
alternate routing, and sub-contracting. Non-stationary demand, stochastic lead times,
stochastic failure times, and lead time/demand elasticity represent other realistic extensions.
However, prior work as well as ours suggests that variability in quantities and lead times,
whether from normal operations, disruptions, or expediting, is amplified in supply chains. It
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is possible, although unlikely, that future extensions such as those we suggest would
contribute to improved problem tractability. The practical value of our contribution is
affirmed in the following feedback from Sandia [2007]: “[This work] demonstrated the
importance of careful design in modeling the realities of supply chain behavior, and provided
strong motivation for further simulation development and experimentation.”
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CHAPTER 4
ADAPTIVE SEARCH METHODS FOR ORDERING DECISIONS IN MULTI-
STAGE SUPPLY CHAINS
4.1 Introduction
To remain competitive, today’s marketplace requires that supply chain managers make
efficient decisions. These decisions can be made using sophisticated mathematical modeling
and solution techniques. Advancements have been made in mathematical modeling; however,
some have not had the desired impact on decision-making in supply chains (Lourenco, 2005).
Moreover, many analytically tractable models suffer from restrictive assumptions (Kochel
and Nielander, 2005). The difficulty is partly due to the complexity of real-world supply
chain decisions. Other factors such as supply chain disruptions also add to the difficulties in
decision-making.
Supply chain management decisions include strategic, operational, and tactical.
Ganeshan et al. (1999) further classify operational decisions into inventory management and
control; production, planning, and scheduling; information sharing, coordination and
monitoring; and operations. Our work deals with inventory management, which is a critical
part of supply chain management as it impacts all business functions and can account for 20-
40% of the total item cost (Schroeder, 2008; Ballou, 1992). We further focus on periodic
review order-up-to ordering policies. These policies are popular in practice and are shown to
be optimal for single-echelon, independent items, and stationary stochastic demand (Axsater,
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2004). Despite their popularity, the optimality of these policies under relatively simple but
practical situations such as lost sales and expediting is not known (Karlin and Scarf, 1958;
Janakiraman, Seshadri, and Shanthikumar, 2006; Veeraraghavan and Wolf, 2008).
Lack of optimal policies has motivated use of simulation and heuristics in research
and practice. Simulation has been useful because of its ability to handle complex problems
involving stochastic variables and non-linear functions (Amodeo et al. 2009). Simulation is
primarily used for evaluation of inventory policies and is not an optimization tool (Daniel
and Rajendran, 2005). We embed metaheuristic optimization toots in our simulations to
search for efficient solutions.
We propose and test practically implementable adaptive search methods in a multi-
stage supply chain that includes assembly, lost sales, and expediting. The metaheuristic
methods proposed can accommodate a wide range of real-life supply chain constraints. To
our knowledge, this is the first study that uses adaptive search methods for supply chain
ordering decisions. We also consider the possibility of lost sales, which increases the
practical relevance of our model. Besides order-up-to levels, our decision variables include
percentage of orders expedited and two expediting triggers. We believe ours is the first such
effort in using metaheuristics for expediting decision variables.
We propose a novel objective function, for both local and global supply chain
planning. Our objective function involves holding costs, backorder costs, lost sales costs, and
expediting costs, and resembles the exponential smoothing method. Depending on the
volatility of demand and possible disruptions, the ‘smoothing parameters’ can be adjusted,
making the objective function well-suited for steady as well as disrupted supply chains.
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Many inventory models, including the pioneering work of Clark and Scarf (1960),
assume stationary demand and find a static order-up-to policy; however, the stationary
assumptions may not be valid in practice. Market trends; seasonal demand and production;
variable material, labor, and equipment availability; and disruptions are some of many causes
that can violate the stationary assumption. Therefore, a dynamic inventory policy may be
more appropriate. Our design of search methods and the objective function allow us to
‘optimize’ under a dynamic situation; i.e., decision variables are time variant and are
determined each period.
Metaheuristic methods have been shown to perform better when designed with
problem-specific properties (Holland, 1992; Chen et al., 2004; Gen and Cheng, 2000). We
reveal useful properties of objective function and feasible solution space. These properties
are then used to make several problem-specific adaptations to increase search efficiency.
Specifically, candidate list strategy, intensification, reference set, and a flexible aspiration
criterion are introduced to increase the solution quality and efficiency of adaptive search.
Some aspects of scatter search are also incorporated. We also compare the performance of
adaptive search with genetic search and Fibonacci search. Using cost parameters from
Schmitt et al. (2009) we show the superiority of adaptive search over other methods.
Both stationary and non stationary demand scenarios are considered. We effectively
use Adaptive search to find dynamic ordering policies under a seasonal and disrupted
demand. The results are shown to be superior to best static policies obtained assuming
complete future demand information.
The remainder of the chapter is organized as follows: We review related literature in
Section 4.3; we describe the supply chain model and parameters in Section 4.4; Section 4.5
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includes a description of metaheuristic methods; in Section 4.6, we present the results;
Section 4.7 lists extensions, and Section 4.8 concludes the chapter.
4.2 Literature Review
Since the pioneering work of Clark and Scarf (1960), inventory management in supply chains
has received much attention. See Axsater (1993) or Federgruen (1993) for a thorough
literature review. Clark and Scarf modeled a periodic review serial supply chain with fixed
lead time between echelons and a cost function that includes inventory holding, backorder,
and per item ordering cost. Assuming complete backorder, they showed that an order-up-to
policy is optimal. Federgruen and Zipkin (1984) extend the results to infinite horizon and
show that calculating order-up-to values is simpler in infinite horizon. Chen and Zheng
(1994) provide optimality results for continuous review policies. Chen and Song (2001)
consider a state-dependent demand function and show that a state-dependent order-up-to
policy is optimal for long run average cost function. Muharremoglu and Tsitsiklis (2001)
simplify the optimality proofs of order-up-to policies and establish the optimality in
stochastic lead time and state-dependent demand. Parker and Kapuschiski (2004) add
capacity constraints. They show that a modified order-up-to policy is optimal when a lower
echelon has a smaller capacity.
Clark and Scarf (1962) generalize the problem by including a fixed ordering cost.
They show that the optimal policy is complex, and even if the optimal policy is identified it
would be difficult to implement, making it unattractive in practice. Moreover, under general
conditions, a simple base stock policy may not be optimal or computation of parameters may
become challenging (Swaminathan and Tayur, 2003). Optimal policy under lost sales, even
with deterministic demands, is an open problem (Swaminathan and Tayur, 2003). Optimal
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policies under generalized expediting are also not known (Veeraraghavan and Wolf, 2008).
Most analytical research uses dynamic programming with recursive algorithms to find
optimal policies. However, developing such algorithms is mathematically and
computationally difficult (Clark, 1960; Shang and Song, 2003). Many supply chain inventory
decisions are based not on algorithms but rule of thumb (Lourenco, 2005).
Difficulties with analytics have motivated a part of research to focus on heuristics or
approximate solution procedures for finding order-up-to values (Chen, 1999). To overcome
difficulties with order-up-to policies, Chen (1999) and Cachon (1995) implemented (R, NQ)
policies, where R is the reorder point and Q is the base order quantity. N is the minimum
integer required to increase inventory beyond R. This policy is proposed as an approximation
to (s, S) policy. By converting an N-stage serial system to a 2N-stage problem, Shang and
Song (2003) provide an easily implementable myopic heuristic for determining order-up-to
quantities. Under restricted assumptions on demand, horizon, and cost function, they show
their heuristic to be close to optimal. Under capacity constraints, de Kok (1989) proposed a
modified base stock policy by using the inventory position at the start of a period which
equals S − X, where X is the waiting time in a D/G/1 queue. He also provided a heuristic for
computing order-up-to quantity. Bollapragada and Morton (1999) consider an ordering
policy problem with setup cost and provide an effective myopic heuristic by approximating
the non-stationary problem with a stationary problem and solving for the stationary problem.
Using an application of the Hewlett-Packard supply chain, Lee and Billington (1993) propose
a simple search heuristic that looks for service level targets. Other heuristic methods can be
found in Hausman and Peterson (1972), Heath and Jackson (1994), Donselaar et al. (1996),
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Graves et al. (1998), Eynan and Kropp (1998), Rajagopalan and Swaminathan (2001),
Roundy and Muckstadt (2000), Lu et al. (2006), Hurley et al. (2006), and Levi et al. (2008).
Simulation, because of its ability to manage real-world complexity, has been used as
a powerful alternative to analytics. In the domain of supply chain inventory management,
simulation has been used to study the performance under various inventory policies (Towill
et al. 1992), supply chain configurations (Souza et al. 2000), Web-based operations (Beamon
and Chen, 2001), operating policies (Holweg and Bicheno, 2002), demand uncertainty (Closs
et al, 1998), information sharing (Strader et al. 1998; Li et al. 2005), and market conditions
(Ye and Farley, 2005). Chatfield et al (2004) study the effect of lead time and information on
bullwhip effect.
Simulation is not an optimization tool and is primarily used for performance
evaluation and not problem solving. To address this shortcoming, simulation has been
hybridized with search methods creating simulation-based optimization tools. These tools
take advantage of the flexibility and versatility of simulation and the optimization power of
search heuristics (Gosavi, 2003). Glasserman and Tayur (1994, 1995) find the base stock
using a simulation-based optimization procedure, where infinitesimal perturbation analysis
was used to develop an efficient solution methodology. Rao et al. (2000) analyzed various
supply chain configurations. Besides dynamic programming, they used simulation-based
optimization to find inventory levels for various configurations in Caterpillar’s construction
equipment supply chain. Ettl et al. (2000) used a conjugate gradient method as a search
routine to find base-stock levels for each echelon with the objective of minimizing the supply
chain inventory while maintaining required service levels.
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Metaheuristics has been used in many supply chain applications; however, their use
for ordering decisions is relatively new. Daniel and Rajendran (2005) provide a Genetic
Algorithm (GA) based simulation method to find periodic review order-up-to values in a
four-echelon serial supply chain. The cost objective consists of holding and shortage costs
across all echelons. The performance of GA is compared with solutions from random search
and complete enumeration. The solutions from GA are shown to be near optimal. Both
stochastic and static lead times are modeled. Final customer demand at echelon 1 is drawn
from a uniform distribution. While assuming all shortages to be backlogged, they test six
different cost combinations of shortages and holding. GA is used to search through a time
horizon of 1200 periods, repeated 30 times. Daniel and Rajendran (2006) extend the model to
include advanced Genetic Algorithm techniques of gen-wise crossover and random keys
presentation of chromosomes. Furthermore, they consider separate objectives for holding
costs and shortage costs and develop a GA to find non-dominated Pareto solutions.
Kochel and Nielander (2005) modeled a continuous review, base stock, five-echelon
serial supply chain with the option of transshipment between the echelons. The modeled
parameters include cost of holding, backorder, and shipment. Various combinations of
transshipment costs were studied. They also discuss the possibility of using the method for
non-serial supply chains. The tests include a comparison of centralized and decentralized
supply chains. GA was used as the metaheuristic method. Amodeco et al. (2009) model a
periodic review, two-stage supply chain that includes three suppliers and one manufacturer.
The manufacturer assembles three components from the suppliers to produce one final
product. A dual objective of customer service level and inventory cost is considered. The
lead times and costs are fixed, while demand at the manufacturer is exponentially distributed.
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All unmet demand is backlogged. They report the usefulness and success of GA in finding
the base stock levels.
We contribute to research in simulation-based optimization for ordering decisions and
in general in inventory management. Our work differs from earlier research in, i) model; ii)
Note: A single variable search involves searching order up-to values. Multiple variable searches are performed over four decision variables as discussed in this chapter.
initial solution’s neighborhood is searched for 100 iterations. The results shown in this
chapter are the average of 100 independent runs.
4.5.2 Cost Comparison between Search Methods under Static Demand
Table 4.1 shows the average costs for 100 independent runs under the three metaheuristics
methods used in this chapter. Each run covers 2000 periods. The first 1000 periods are used
to reach a steady state. Data is collected from period 1001 through period 2000. Variance
reduction is achieved through the use of 50 pairs of antithetic variables (Law and Kelton,
2000). The method of antithetic sampling helps in reducing sampling errors. The percentage
of cost savings by using various methods is presented in Table 4.2. For comparison purposes,
the tables also include the costs obtained in Chapter 3, where only order-up-to quantity was a
decision variable.
The costs in Table 4.1 clearly illustrate the importance of searching over multiple
variables. Table 4.2 summarizes the cost information in Table 4.1. The results show the
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importance of optimizing expediting variables. When searched over four decision variables,
Genetic search resulted in an average savings of 4.89% when compared to search over only
order-up-to quantity. Even simple local search method of Fibonacci resulted in a cost savings
of over 2%. In the multi-variable optimization, Tabu search provides an added improvement
of 3.10% over Genetic search.
To test the consistency of methods we compared the variability of solutions. Table
4.3 presents the average cost variability for 100 runs.
We computed cost variability in 100 independent runs in each cost combinations.
Table 4.3 presents the average for 8 cost combinations. Besides lower cost, Tabu search also
reaches the best solutions consistently. A low variability from Tabu search shows
consistency of the method over Genetic search, which implies that Tabu search should be
expected to reach the best solutions in less number or runs. Also, if constrained by
computation power and time, Tabu search should be preferred.
The Product Group’s suppliers also represent over $60 million per year in purchases, and are
located in the United States, Canada, Mexico, China, Japan, and Malaysia. The four largest
suppliers account for 43% of the total of this supply, and are located in New York, Arizona,
Canada, and China. Supplies to these four are, in turn, covered by 60% domestic vendors
located in NY and Arizona, 20% in Canada, and 20% in China. Inbound shipments for the
domestic suppliers arrive from U.S. seaports, Mexico, and Canada. Overall, the transport mix
is 30% air, 30% ship, and 40% truck.
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Twenty percent of INT’s supply base is sole-sourced. An example of a critical sole-
sourced material includes proprietary custom component features assembled in California.
They, in turn, obtain silicon from the Far East and component systems from California. The
sole-source supplier then transports the custom features through distribution partners with a
stocking agreement. The distributors provide forecasts of demand and the sole-source
supplier manufactures to forecast with a buffer. If demand spikes from INT and other
customers, the sole-source supplier expedites the channel to satisfy the critical need and refill
the stock and buffer.
ABC’s Disruptions and Contingency Plans
When asked to discuss a disruption that had occurred in their business, ABC mentioned the
West Coast port lockout. As the lockout approached, ABC prepared with contingency plans
to use alternative sources and routing. They also established larger inventory buffers to cover
demand over longer lead times. Increased inventory, holding, and expediting costs were
incurred during the lockout, but were considered negligible relative to costs of possible
shortages.
ABC is highly dependent on electricity and telecommunications. They have backup
generators to run their mission critical IT system, including ERP, for one week should a
power disruption occur. They do not have telecommunications backup or contingency plans.
They believe these contingency plans must be more fully developed to decrease disruption
risks in critical infrastructure inherent in the post-911 business world.
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XYZ CORPORATION
XYZ designs, develops, and manufactures systems for use by OEM aerospace
manufacturers. XYZ focuses on three product groups for use on 42 aircraft types, which
serve 450 customers in 49 countries. XYZ is more vertically integrated than most of its
competition. This enables rapid prototyping of new products, but presents challenges to keep
product costs down and maintain service levels.
Echelon 1 – Delivery to XYZ’s Customers
XYZ’s six largest customers comprise 51% of its business, with the largest two representing
one third of total business. XYZ quotes lead times of four to 20 weeks, and takes two to four
weeks to assemble its products. XYZ averages 200-250 orders from customers per week,
which translates into 355 shipments per week. 25% of the orders are exported, all via
airfreight. Domestically, nearly all deliveries are either customer pickup or ground delivery.
Customers determine the delivery service with typical options of UPS surface, BAX, and
FedEx ground/economy.
The price of XYZ’s products is insignificant relative to the value of its customers’
products. XYZ’s customers accept, but do not appreciate, backorders. Repeated poor delivery
performance would result in temporary or permanent loss of a customer in XYZ’s small
customer base.
Echelon 2 – XYZ’s Assembly of the Electronic Products
XYZ’s end products contain between 200 and 700 component SKUs. Of the assembly orders,
65% are made-to-order, and the rest forecasted spares. This mix results in varied finished
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goods inventory levels: less than one week for make-to-order products, three to four weeks
on expensive spares, and up to three months on less expensive spares. XYZ operates with a
direct labor percentage of 11-13%, with 15-20% touch time of the total lead time. Assembly
orders are typically delayed until sufficient backordered components arrive to fully satisfy
demand requirements, although orders may be split for customer orders deemed critical
Echelon 3 – Delivery of Components to XYZ
XYZ draws from over 300 international and domestic component suppliers and distributors,
receiving approximately 55 material shipments per day with an average of two SKUs per
shipment. 20% of XYZ’s components are sole-sourced, and consequently represent a
significant supply risk. With 90% of their shipments coming via diverse ground delivery and
only 10% expedited by air, XYZ is reasonably buffered against risks in the transportation
system. Only seven to 10% of their component shipments are direct imports, though it is
unclear what percentage of this represents critical inventory. XYZ experiences a purchasing
lead time between six and eight weeks, including one to seven days of transportation. XYZ
averages 10 weeks of inventory to cover the lag between ordering and delivery.
Echelon 4 – Delivery to Suppliers of XYZ
Of the more than 300 international and domestic suppliers of XYZ, we chose three (Supplier
6, Supplier 7, and a common Supplier 5) for Echelon 4. Supplier 6 has a distribution hub in
Boston and Supplier 7 in Fort Worth. These distributors employ diversification strategies,
multiple transportation modes, and inventory holding policies to help ensure minimal risk to
their customers.
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Supplier 6 procures materials from source companies in Israel, Japan, Europe, and
U.S., and makes its components in the Philippines, Malaysia, Mexico, and various other
foreign countries. Multiple source companies ensure redundancy and limit supplier risk at
this echelon. Once fabricated, the electronic components are shipped to Supplier 6’s central
U.S. distribution hub in Boston. The mode of shipment depends on the origin of the foreign
country and the nature shipments, but 85-90% of Supplier 6’s components are shipped to
Boston via air/truck combination, and 10-15% via seaport/truck combination. All domestic
shipments are transported via truck. Supplier 6 incurs a 20-50% premium for rush orders
Supplier 7 operates similarly to 6. A distribution hub in Fort Worth, Texas receives
components from abroad and maintains inventory that turns an average of six times per year.
Representatives at Supplier 7 estimated that in the event of a complete halt of deliveries, they
would run out of components for key customers within six weeks. To keep the likelihood of a
complete shutdown at a minimum, Supplier 7 geographically dispersed its own supply
companies by operating in Europe, Asia, the Middle East, South America, and North
America. Supplier 7 also diversifies its modes of shipment receipt; 25% of all foreign
shipments arrive via air (Dallas/Ft. Worth airport), 50% via ports in Texas and New Orleans,
Louisiana, and 25% via ground transport (usually shipments from Central America and
Mexico). All domestic shipments are handled by trucking shippers.
Despite this diversification strategy, Supplier 7 encountered problems with a Texas
port closure. This left representatives with a feeling that despite an ability to reroute, an
unannounced port closure of greater than one hundred days would be problematic
economically. Additionally, Supplier 7 represents a potential source of vulnerability for
XYZ’s critical inventory. A crisis would result in Supplier 7 emptying pipeline inventory of
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critical parts within six weeks. With only two additional weeks of inventory on XYZ’s
shelves, XYZ would find itself in a materials shortage within two months.
XYZ’s Disruptions and Contingency Plans
Besides the economic risks born by the nature of the business, supply chain structure and
transportation dependencies, XYZ representatives also sense exposure in manufacturing and
testing to electrical and telecommunications disruptions. The company does not operate with
a backup generator, and has no contingency plans in the event of a prolonged blackout.
Additionally, nearly all customer and order records are stored electronically. Although a
blackout poses no real risk of data loss, the company admitted that data access could hamper
significantly the firm’s operations were a blackout to last longer than a few days.
Regarding telecommunications risk, the company is dependent upon phone, email, and
internet connections for order receipt and fulfillment, customer service, intra-organizational
communications, business research, and other important functions. Recently, XYZ
experienced loss of land-phone service for just under a week. During this time,
representatives were forced to use cellular phones, an issue that caused significant customer
service friction. Quoting company representatives “Were both electric power and
telecommunications to go down simultaneously, this company would stop dead in its tracks”.
COMMON SUPPLIER 5
Supplier 5 is a large U.S.-based components distributor that supplies electronics firms
worldwide. The following is from the perspective of Supplier 5, versus that of its customers
INT, ABC, and XYZ. Supplier 5 services 40,000 North American customers, 30,000 of
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which are found in their Components Division. Supplier 5’s customer sales and outbound
transportation activities are voluminous and diverse, maintaining in the aggregate a relatively
stable delivery schedule of 100,000 shipments per week. 65% of shipments to major
customers are made via UPS, FedEx, and LTL ground, and 23% are shipped via two to three
day air (UPS, FedEx, and DHL). EMERY two to three day air is used to deliver shipments to
Canada. 12% of deliveries are made using FedEx overnight service. Currently, 25-30% of
shipments are expedited over their normal delivery time, which is similar to their pre-9/11
levels in the peak of 2000.
Supplier 5 has roughly 280 suppliers and receives 3500 shipments per week (2500
shipments/week for the Components Division – 71% of total in North America alone). 25%
of Supplier 5’s materials are imports, purchased from American multinational companies
with overseas manufacturing plants, rather than from foreign companies. The majority of
Supplier 5’s material shipments arrive via two to three day air, with the remainder arriving
via a geographically favorable California seaport. Supplier 5 does not choose its mode of
shipping. In its terms of procurement, ownership of goods is not taken until they reach the
U.S. port. Part delivery to Supplier 5’s manufacturing headquarters is usually one to two days
via ground transportation. Overall, Supplier 5 operates with a total order/transportation lead
time of two to three weeks for semiconductors, three to 12 weeks for non-commodity items,
and seven-week average for all others.
The characteristics of Supplier 5’s supply chain render it reasonably well protected
against transportation, supplier, and infrastructure risk. In the event that a mode of domestic
or international outbound shipment was removed, Supplier 5 would simply switch to another
ground carrier. Outbound transportation could be shifted to other carriers, while realizing
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only a two to three day delay in lead time and a 10% increase in shipping costs. Supplier 5
maintains a system for managing significant disasters, where major customers would receive
first priority, other customers who provide stable product forecasts second priority, and so
forth.
With respect to its supply chain, less than 20% of the purchased SKUs are sole
sourced, and contingency plans are in place to handle disruptions for all but 8% of their
supplier’s goods. Since Supplier 5 employs engineers on staff, a planned contingency for a
loss of a sole-source supplier would be to redesign the part and outsource it. The current
expectation is that a disruption to Supplier 5’s supply chain would extend delivery lead times
by only two to three days, but result in a 30% increase in freight costs (non-contract
shipments would need to be auctioned). Supplier 5 maintains a disaster recovery team to
minimize the effects of a major disruption in their supply chain. In the event that a major
mode of shipment was removed, Supplier 5 is confident that it would be able to switch
modes quickly (for example, seaport to airport), relying on relationships with multiple
carriers (UPS, EMERY, DHL, FedEx). These carriers are anxious to service essential
customers such as Supplier 5 and would be able to absorb extra demand. For example, when
UPS workers threatened to strike, Supplier 5 began the process of coordinating alternative
carriers. Supplier 5’s disaster planning team has also developed contingency plans to deal
with commercial disruptions caused by the SARS virus.
Supplier 5 is reasonably well insulated against disruptions to critical civic
infrastructure such as electric power or telecommunications networks. Supplier 5 asserts that
their status as a global company protects them against major disruptions since their many
plants around the world can accommodate the loss of one plant. That being the case, Supplier
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5 acknowledges their dependence on electrical power, and maintains backup capabilities to
counter a grid disruption. Its power company supplies electricity through two separate
electrical grids. Thus, a backup grid is in place to sustain operations.
Supplier 5 has a central IT site that handles all networking for each location
worldwide. They have an on-site diesel generator that can feed their IT systems for up to four
days. These systems are tested weekly to ensure properly working condition. Additionally,
they have a one-hour battery backup, and maintain backup data facilities off-site.
Similarly, all telecommunications lines have dual backup to avoid disruptions to their
data communications systems. If the phone lines are down, Supplier 5’s email system will
still be operational. Additionally, managers are instructed to use cell phones 100% of the
time internally, which is effective given the mobile nature of managers’ work. Otherwise,
Supplier 5 does not maintain a telecommunications contingency plan.
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APPENDIX C
CASE INTERVIEW PROTOCOL
Electronics Firm
Customer Base/Distribution
1. Who are your major customers?
2. Approximately what proportion of your company’s revenue does each major customer
provide?
3. What are the major products your company makes for these customers?
If you know, what are the SIC/NAIC codes associated with these products?
4. How are products transported to each of these major customers?
What are the modes, format, and proportion of shipments (e.g., Express mail,
DHL, two day air, overnight delivery; 80% of shipments to major customer
x)?
If delivery time is not guaranteed, what is the average and range of delivery
times for each mode?
Approximately how many shipments are made per day (week or month)?
Approximately how many product types are in a shipment?
What percentage of your shipments are exports? What modes of transportation
do you use for these exports?
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If a mode of domestic or international shipment mode were suddenly
removed:
- How would you then ship goods to each of these customers?
- What additional delay would be incurred?
- What additional shipment cost (as a percentage over preferred method)
would be incurred?
- If faced with the problem of rationing a shortage of components, what
factors would you consider in deciding which customers would receive
priority?
Production
1. Would you classify your business as make to stock, make to order, assemble to order,
other?
2. For the products sold to the major customers identified earlier, what is the approximate
average and range of:
Quoted lead times for these products?
Manufacturing lead times for these products?
- What percentage of this lead time would you guess is actual direct labor
touch time?
- Purchased lead times for materials used to make these products?
3. Approximately how much finished goods inventory (in days – average and range) do you
maintain for these products?
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4. Approximately how many production orders are launched per day (week or month) into
your factory?
5. What is the approximate average and range of direct labor percentage in a product's
selling price; raw material percentage; profit before taxes?
6. What is the approximate average and range of number of SKU’s for purchased material
for any given product?
Approximately what is the percentage of purchased SKUs that are shared in
common between saleable products?
Supply Base
1. Approximately what percentage of the purchased SKUs is sole sourced?
2. Approximately how many suppliers of materials do you have?
3. Who are your major suppliers?
Approximately what is the proportion of business for this supplier to total
supplier business?
4. Choose two critical purchased materials (e.g., ones that tend to be in short supply, are
indispensable, are sole sourced, have long lead times, etc.).
What are they?
Who supplies them?
How are they transported?
From the time you place a purchase order, what is the approximate average
and range of lead times for materials delivered by each of these major and
critical suppliers?
151
Approximately how much material inventory (in days – average and range) do
you keep on hand for materials provided by each of these major and critical
suppliers?
If you know, what is the SIC or NAIC code of each supplier or commodity
they supply?
How do these suppliers transport materials to you?
What are the modes, format, and proportion of shipments?
If delivery time is not guaranteed, what is the average and range of delivery
times for each mode?
5. Approximately how many material shipments do you receive per day (week or month)?
Approximately how many material SKUs are in a typical shipment?
What percentage of your shipments are direct imports?
What modes of transportation do you use for these imports?
If a mode of shipment (domestic or international) were suddenly removed,
how would each supplier then ship goods to your site?
If faced with the problem of losing one of these suppliers, what alternatives
would you have, which alternative would you choose, and why?
Who would you guess are major suppliers of materials that your supplier uses
to make or store the materials shipped to you?
How would you guess these materials are delivered to your supplier’s
supplier?
152
More on Possible Disruptions
1. Do you recall some disruption (even a small one) in your business?
What happened?
What were the short-term consequences, e.g., delays, additional costs?
How was the problem resolved?
2. Electric Power: In what general ways is your firm dependent on electric power?
What if there is a disruption in power; how would you respond?
Do you have emergency back up?
- Over what duration?
- Which critical items would be supplied with emergency power and which
would not?
3. Telecommunications: In what general ways is your firm dependent on
telecommunications?
What if there is a disruption in production due to telecommunications; how
would you respond?
Do you have emergency back up?
- Over what duration?
- Which critical items would be supplied with emergency power and which
would not?
4. Have we missed any important vulnerabilities with respect to critical infrastructure?
153
Component Supplier
Customer Base/Distribution
1. Approximately how many customers do you have?
Who are your major customers?
Approximately what proportion of your company’s revenue does each major
customer provide?
2. Approximately how much material inventory (in days – average and range) do you keep
on hand for a typical SKU?
3. Shipments
Approximately how many shipments do you make per day (week or month)?
- Approximately how many SKU’s are in a shipment?
In general, how are shipments made to these major customers?
- What are the modes, format, and proportion of shipments?
- What percentage of the shipments to customers is expedited over normal
delivery at this time?
- What was the percentage when business was at its peak; when was the
peak?
4. What percentage of your shipments is exported?
What modes of transportation do you use for these exports?
5. If a mode of domestic or international shipment mode were suddenly removed,
How would you then ship goods to each of these customers?
- What additional delay would be incurred?
154
- How would lead times be affected?
- What additional shipment cost (as a percentage over preferred method)
would be incurred?
6. If faced with the problem of rationing a shortage of components, what factors would you
consider in deciding which customers would receive priority?
Supply Base
7. Approximately how many suppliers of materials do you have?
Who are your major suppliers?
Approximately what is the proportion of business for this supplier to total
supplier business?
Choose two critical purchased materials (e.g., ones that tend to be in short
supply, are indispensable, are sole sourced, have long lead times, etc.).
- What are they?
- Who supplies them?
- How are they transported?
8. Approximately how many material shipments do you receive per day (week or month)?
Approximately how many material SKUs are in a typical shipment?
What percentage of the material shipments is expedited over normal delivery
at this time?
- What was the percentage when business was at its peak; when was the
peak?
What percentage of your shipments is directly imported?
155
- What modes of transportation do you use for these imports?
Approximately what percentage of the purchased SKUs is sole sourced?
If faced with the problem of losing one of these suppliers, what alternatives
would you have, which alternative would you choose, and why?
Choose one major overseas supplier that delivers components to you through
a seaport.
- How does this supplier transport materials to you? What are the modes,
format, and proportion of shipments (e.g., truck from China manufacturer
to Chinese port, carrier to the Port of Long Beach, from the Port of Long
Beach -- 80% of shipments via Express mail, and 20% of shipments
overnight delivery)?
- From the time you place a purchase order, what is the approximate
average and range of lead times for components delivered by this
supplier?
- If a mode of shipment (domestic or international) were suddenly
removed, how would the supplier then ship goods to your site?
How would lead times be affected?
What additional shipment cost (as a percentage over preferred method) would be incurred?
More on Possible Disruptions
1. Do you recall some disruption (even a small one) in your business?
What happened?
What were the short-term consequences, e.g., delays, additional costs?
156
How was the problem resolved?
2. Electric Power
In what general ways is your firm dependent on electric power?
What if there is a disruption in power; how would you respond?
Do you have emergency back up?
- Over what duration?
- Which critical items would be supplied with emergency power
and which would not?
3. Telecommunications
In what general ways is your firm dependent on telecommunications?
What if there is a disruption in production due to telecommunications; how
would you respond?
Do you have emergency back up?
- Over what duration?
- Which critical items would be supplied with emergency power and which
would not?
Have we missed any important vulnerabilities with respect to critical infrastructure?
157
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VITA
Sanjay Kumar was born to Lakshman Singh and Lakshmi Devi in Tezpur, in the Indian state
of Assam. After completing high school, Sanjay pursued education in Production and
Industrial Engineering. He developed an interst in teaching and research and after completing
a Masters in Industrial and Management Engineering. He then joined the University of Texas
at Dallas for a Ph.D. in Management Science with a concentration in Operations