My name is Mikio Kubo from
Tokyo University of Marine
Science of Technology.
The title of this talk is
Trend in Supply Chain Optimization and
Humanitarian Logistics.
1
This is the agenda of my talk.
I will start with the definition of the
supply chain and logistics, and introduce
three decision levels of the SC,and then
show you the classification of inventory.
Next I’m going to talk about several
models in the SC; they are logistics
network design, inventory, production
planning, and vehicle routing.
And finally, I will talk about the SC
management and humanitarian logistics, if
time allows.
2
There are many definitions of the Supply
Chain. But my definition is simple.
IT (that means the information
technology)＋Logistics＝Supply Chain
To complete the definition, we need the
definition of logistics.
As shown in this figure, logistics optimizes
the flow of products between the point of
origin (here supply point) and the point of
consumption (here demand point) in order to
meet customers' requirements.
3
In general, the supply chain is composed of three systems:
One is the real system that includes real logistics objects such
as trucks, ships, plants, products, machines, etc.
Using the metaphor of human beings, the real system is compared to
muscle of the body.
Another is the transactional IT; for example, POS that means
Point-Of-Sales, you can see such a system in convenience stores or
supermarkets, ERP that means Enterprise Resource Planning that is
an extension of legacy MRP that means Material Requirement
Planning, DRP that means Distribution Requirement Planning, etc.
The transactional IT is compared to a nerve net of human beings
that just flows the pulse and executes automatic actions.
The other is the analytic IT composed of some models and
algorithms to solve them.
The analytic IT is compared to the brain of human beings.
That’s the main theme of this talk.
4
The decision support systems or analytic IT models can be
categorized into three levels.
The top level is the strategic level that deals with decisions having
a long-time effect that spans from a few years to 10 or more
years.
The tactical level includes decisions which are typically updated
between once every month or quarter, or once every year.
Finally, the operational level refers to day-to-day or real time
decisions.
Using some metaphors, the strategic level is compared to seeing
the forest, while the operational level is compared to seeing the
tree.
Such a change of the point of view is important for the decision
making using analytic IT models.
5
This figure represents an entire
supply chain; Procurement of parts
or raw materials from suppliers,
production at plants, stocking in
DCs or warehouses, and
distribution to retailers.These are
the decision levels; strategic,
tactical and operational that means
long-term, middle-term, and shortterm, respectively.
6
In the strategic level, we have an analytic
IT model named the Logistics Network
Design that encompasses the whole
logistics network and determines new
suppliers, a new flow pattern of products
through the network, a selection of
warehouse locations and capacities, the
production levels at each plant or the
production line in order to minimize total
production, inventory, and transportation
costs that means whole supply chain
costs.
7
In the tactical and operational levels, we have 3 types of
models; they are inventory, production planning and
transportation delivery.
The safety stock allocation model determines the positions of
safety stocks in the supply chain, and simultaneously
determines the pull-push boundary of each product.
The inventory policy optimization model decides at what point
to reorder and how much to order so as to minimize inventory
ordering and holding costs.
Production planning optimizes the acquisition of resources such
as machines or workers, the size of the production lots to be
manufactured or processed in a batch and the sequencing of
the production lots.
Finally, transportation delivery optimization involves the routes
and frequencies of vehicles such as ships or trucks. The most
important transportation delivery model is the vehicle routing
8
Inventory plays an important role in
the supply chain. Using the metaphor
of human body again, inventory is
compared to blood and it acts as glue
connecting several processes in the
SC. Inventory is spread throughout
the SC from raw materials, work-inprocess inventory, to finished
products in warehouses and
retailers.And such inventory varies
day by day or period to period due to
many reasons.
9
To understand the role of inventory in
the SC, we need to classify it into 5
categories by their motivations;
They are in-transit inventory, seasonal
inventory, cycle inventory, lot-size
inventory, and safety inventory.
We have to optimize the trade-offs
between inventory and some other factors.
10
In-transit inventory is inventory moving
through the arc or link in the SC. It can
be seen as a flow thorough a pipeline; so
it’s sometimes called pipeline inventory.
Such inventory exists ‘cause transportation
time is positive. In-transit inventory is
proportional to the flow volume and
transportation time. That means if the
speed of the flow is faster, in-transit
inventory becomes smaller. This inventory
will be treated and be optimized in the
logistics network design model.
11
The main role of inventory in the SC is to
fill the gap between supply and demand.
Seasonal inventory is inventory to counter
predictable seasonal demand under the
restriction of the limited production
resources.
For example, a can maker supplies tons of
cans to beverage makers such as beer
companies. For a high demand during the
summer season, the can maker builds up
inventory during low demand periods and
store it. This is seasonal inventory.
12
Assume that demand is constant and supply arrives
cyclically. In this case, inventory changes like
saw-teeth. Here, “saw” is a tool for cutting
woods whose teeth is like this figure. That’s
cycle inventory. Many activities in the SC have
economy of scale; that means large lots decrease
the cost. This is the motivation of cycle
inventory. If transportation activity has a nonnegative fixed cost, we have to have cyclic
inventory, which is optimized in the LND model.
If ordering activity has a non-negative fixed
cost, we have to have cyclic inventory, which is
optimized in the EQO model.
13
Lot-sizing inventory is a generalization
of cycle inventory when the speed of the
demand is not constant.
That means the demand changes over time,
period by period. As in cycle inventory,
lot-sizing inventory has the trade-off
for fixed costs, especially production
set-up costs and is optimized in lotsizing and multi-period logistics network
design models.
14
Safety inventory or safety stock is inventory to
protect against uncertainties of future events
such as customer demands.
So it has the trade-off for the customer service
level. If customers request 100% of service level
that means no stock out, retailers need a huge
amount of safety inventory. Service level is
determined by backorder or stock-out penalty
costs. So there’s a trade-off between safety
inventory costs and backorder costs. Many
analytic IT models include safety inventory. They
are safety stock allocation, logistics network
design, and inventory policy optimization models.
15
Inventory can be classified into these
5 types of inventory, in-transit,
seasonal, cycle or lot-size, and
safety inventory.
It’s not so easy to treat them
separately in practice. But we should
optimize them separately using some
analytic IT models.
I’ll talk about such models until my
lecture time is up.
16
The first analytic IT model is the logistics network
design which involves issues relating to plant,
warehouse, and retailer locations. These are strategic
decisions because they have a long-term effect on the
company. The objective is to design or reconfigure the
logistics network so as to minimize annual system wide
costs, including production and purchasing costs,
inventory holding costs, facility costs that include
storage, handling, and fixed costs, and transportation
costs.
There are many decisions optimized in this model. For
example, Where should we replenish parts? In which
plant or on which production line should we produce
products? Where and by which transportation-mode should
we transport products? Where should we construct (or
close) plants or new distribution centers?
17
There are many trade-offs in the model: for example,
increasing the number of warehouses typically yields:
-An improvement in service level due to the reduction in
average travel time to the customers.
-An increase in inventory costs due to increased safety
stocks required to protect each warehouse against
uncertainties in customer demands.
-An increase in overhead and setup costs.
-A reduction in outbound transportation costs:
transportation costs from the warehouses to the customers.
-An increase in inbound transportation costs:
transportation costs from the suppliers and/or
manufacturers to the warehouses.
18
Another trade-off in the LND model is between the
in-transit inventory cost and the transportation
cost. In-transit inventory is proportional to the
flow volume and transportation time. So if we
use a fast transportation mode such as a plane
that is expensive, in-transit inventory becomes
smaller and decrease the inventory cost.
Meanwhile, if we use a slow transportation mode
such as ships, in-transit inventory becomes
larger. That’s the trade-off and should be
optimized in the LND model that selects the
appropriate transportation mode for each link by
minimizing the sum of the transportation and intransit inventory costs.
19
We can extend the basic LND model to the multiperiod LND model. Let us consider situations
where demand changes over time because of seasonal
factors, or growing or shrinking markers.
It also allows for time-varying purchase,
production, transportation, and inventory costs.
The problem is to find the right balance between
the cost of holding seasonal inventory and the
other costs.
This model is in the tactical decision level and
can be seen as an extension of the master
production system (MPS) usually used in a plant
for determining the production level.
20
The multi-period LND model optimizes the trade-off
between seasonal inventory cost and overtime penalty cost.
Assume that demand has a seasonal peak, say in summer and
our plant has a limited resource. So we cannot supply the
peak demand by the production during the peak periods.
One strategy to cope with the peak demand is the so-called
constant production strategy using seasonal inventory.
That means the company builds up inventory during low
demand periods, say in spring, and stores it for the
summer, the peak season.
Another strategy is to vary the production level by hiring
new workers or by doing overwork; both require additional
costs. The multi-period LND model finds the best balance
of these strategies.
21
The LND model can be formulated as a multi-commodity network
flow problem. Supply chain or logistics network is modeled as
a network composed of nodes and arcs. The raw materials,
parts, intermediates, final products or items are modeled as
commodities or flow through the network. We have to model the
bill-of-material or recipe structure that represents the
relationship between the items. That means which product is
composed of which parts or raw materials.
We also incorporate the safety inventory cost into the model
that is a concave function of the flow volume through the arc
due to “statistical economy of scale” that we’ll discuss
later. Usually, the concave cost minimization problem is
difficult; of course, NP-hard. But by using approximation of
concave functions, the LND problem can be formulated as a
mixed integer programming problem and can be solved by
standard MIP solvers such as Gurobi or CPLEX.
22
The second model is the safety
stock allocation model in the
tactical level that optimizes
the trade-off between the
safety inventory and the
customer service level.
23
This model is based on a basic principle of
inventory called the statistical economy of scale;
that means gathering inventory together reduces
the total inventory volume and cost.
This principle is used in many modern supply chain
strategies such as risk pooling, delayed
differentiation, and design for logistics.
The safety stock allocation model answers how to
use these strategies and also answers the
following questions; where to keep safety stock?
which facility should produce to stock? and which
facility should produce to order?
24
The safety stock allocation model is based on the classical formula to
compute the safety inventory volume.
Assume that the demand follows the normal distribution with average
demand μ and the standard deviation σ.
Service level is the probability of no stocking out and is determined
by a decision maker.
Lead-time is the amount of time that elapses from the instant that an
order is placed until it arrives.
So the maximum inventory volume is computed by the formula
μ×Ｌ plus safety stock ratio×σ× square root of L..
Here, safety stock ratio can be computed from service level.
If the service level is 95%, then the safety stock ratio becomes
1.65.
Actually this formula is a special case of the classical newsboy
problem that I will talk later.
25
This figure represents the relationship
between the lead-time and the average,
safety and maximum inventory.
Horizontal axis is the lead time and the
vertical axis is the inventory volume;
average inventory (drawn in black line) is
a linear function, while maximum inventory
(drawn in pink) and safety inventory
(drawn in yellow) are both concave that
represents the statistical economy of
scale.
26
One characteristic of the safety stock allocation model is
to treat lead-time as a variable instead of a given
constant.
First we introduce the guaranteed lead-time that is a
committed time that the facility guarantees to deliver to
its customers.
In this example, this facility i guarantees to deliver to
its customers within 2 days represented by a yellow arrow
in the upper right corner. So the guaranteed lead time
denoted by L_i is 2.
This facility has another lead time called the entering LT
that is the GLT of his predecessor or upstream (supply)
facility.
In this example, the entering LT of facility i
represented by a yellow arrow in the lower left corner is
1 ‘cause its predecessor has GLT 1 day.
27
The facility has a given constant production time
that is the amount of time that elapses from the
instant that the item or product arrives until it
is ready to ship.
In this example it is represented by a green
rectangle in the lower right corner and it is 3
day. The net replenishment time is defined by
entering LT + production time –guaranteed LT.
In this example the net replenishment time
represented by the red arrow in the upper left
corner is 1+3-2=2.
The inventory of the facility is set to the
maximum demand volume during the net replenishment
28
time to satisfy the service level.
Next I’ll show you a mathematical programming approach for solving the
safety stock allocation problem in general network.
The main variable is x that represents the net replenishment time. The
objective function is nonlinear. Here D is the maximum demand
function. It is a general nonlinear function. D minus μx gives us the
safety inventory volume and by multiplying it by holding cost h_i, we
get the safety inventory cost. The objective function is the sum of
these nonlinear functions over all facilities. The first constraint
defines the net replenishment time is equal to entering LT LI_i (it is
a variable)+ processing time T_i (it is a constant) – guaranteed lead
time L_i (it is also a variable). The second constraint means that
the entering lead time of facility j is greater than or equal to the
guaranteed lead time of facility i if there exits an arc between i
and j, i.e., facility i is a supplier of facility j.
The third constraint restricts the lower and upper bounds of the
guaranteed LT, and finally, the forth constraint defines the nonnegativity of the net replenishment time.
This is a nonlinear programming problem. Generally it’s quite hard to
solve it (theoretically, it’s NP-hard problem) but by using some
piecewise linear approximations of non-linear functions, we can solve 29
In summary. The safety stock allocation problem
can be solved by several types of algorithms.
One is the mathematical programming approach that
relies on MIP solvers. It can handle middle-size
instances with general networks.
Another is based on dynamic programming. We can
extend the DP algorithm we discussed to a more
general tree network case. But, unfortunately, we
cannot extend the DP approach to general
networks, .
Other approaches are metaheuristics such as local
search, iterated LS, or tabu search, etc. They
give approximate solutions instead of the exact
solutions but it’s usually faster and more robust.30
Here is a real example reported in the book written by
David Simchi-Levi of MIT.
Final demand (part 1 in this figure) is sold in Dallas
that has a normal distribution with average 100 and
standard deviation 10 and customer’s guaranteed LT is 30
days. Many parts are required to produce the final product
and they are produced in many facilities.
By optimizing the allocation of safety inventory, they
could save more than 43 thousand dollars. It’s about 40 %
cost down from the baseline model.
The safety stock allocation model can be used in so-called
“what if” analysis, too. For example, what if we change the
guaranteed lead-time to the customer from 30 days to 15
days that means an improved customer service. The model
answers the question. The cost increases to 51 thousand
dollars.
31
Next we will turn our attention to another inventory
model called inventory policy optimization.
Inventory policy optimization is in the operational or
tactical decision level.
We first introduce 2 classical models; newsboy model and
economic ordering quantity model.
The newsboy model introduced by Scarf in 1960 is a
framework where we optimize the trade-off between lost
sales and safety inventory costs.
The EOQ model introduced by Harris in 1915 (not 50, it’s a
very old model) is a framework where we optimize the
trade-off between fixed ordering and cycle inventory
costs.
32
Newsboy model can be extended to multi period
model in which the ending inventory at a period
becomes the starting inventory of the next
period.
In this case we have to monitor the inventory
position that is defined as the sum of in-hand or
local inventory + inventory on order (that means
the amount of orders that has not been arrived
yet) – backorder (that means demand not satisfied
now but carried over to future).
We determine the ordering quantity so that the
inventory position becomes a pre-determined value
called the base stock level; that is the base
33
stock policy
If the fixed ordering cost is positive, the base
stock policy is no more optimal. In such cases,
we need modified base stock policies, namely
(Q,R) policy and (s,S) policy.
In (Q,R) policy, we monitor the inventory
position and if it reaches a re-order point R, we
order a fixed quantity Q. Here R and Q are system
parameters. We may use EOQ model to compute the
ordering quantity Q.
In (s,S) policy, we again monitor the inventory
position and if it is below a re-ordering point
small s, we order the amount so that the
inventory position becomes an order-up-to level
capital S.
34
This figure shows the change of inventory levels of (Q,R)
and (s,S) policies. Here is a re-order point R (or small
s) and here is the order-up-to level Q+R or capital S.
When the inventory position becomes R, both policies order
the amount Q and the inventory position becomes R+Q or S.
After the lead time, the order arrives and the in-hand
inventory increases and coincides with the inventory
position.
If the bulk demand occurs and the system orders below the
re-order point, the (Q,R) policy orders Q while the (s,S)
policy orders more so that the inventory position becomes
capital S.
Under some reasonable assumptions, (s,S) policy is proved
to be optimal.
But I believe (Q,R) policy is more practical in Japan
‘cause the ordering lot-size is fixed in many companies.
35
In the base stock or (Q,R) or (s,S) policies, we treat the time
as continuous variable. But in many situations the timing of
decisions is restricted to a discrete time. In such cases,
discrete time model is more appropriate.
We now turn our attention to the discrete time inventory
model, namely the periodic ordering policy.
In this policy, we check the inventory periodically, say once
a day. If the inventory position is below the base stock level,
we order the amount so that it recovers the base stock level.
We assume the demand occurs during the day, and the
ordering quantity is determined at the end of the day, and
the order arrives at the beginning of the day after the lead
time.
36
We have talked about the inventory policy
optimization. For the continuous time models, we
get the optimal base stock via dynamic
programming and we can get similar algorithms
for (Q,R) and (s,S) policies.
For the discrete time model, called the periodic
order policy model, we used a simulation based
optimization algorithm. Such an approach is called
the infinitesimal perturbation analysis. I believe
this approach will be one of the candidates for
solving real inventory problems.
37
Next we consider a model in production
planning. The first model is the lot-size
optimization model.
This model supports the decision maker in
the tactical level and optimizes the
trade-off between the set-up cost and lotsize inventory.
This model can be seen as an extension of
the EOQ model in which the customer demand
is not necessarily constant.
38
To solve the real lot-sizing problems
is very difficult.
One approach is to use the MIP solver
using strong formulation.
Another approach is to construct
heuristics or meta-heuristics.
The other approach is the MIP-based
meta-heuristics. The example of such
approaches are: The relax and fix,
capacity scaling, and MIP based
neighborhood local search.
39
Next we consider the second model in production
planning. That is the scheduling optimization
model.
This model supports the decision maker in the
operational level and optimizes the allocation of
activities over time under the constraints of
finite resources.
Here activities mean jobs or tasks or operations
to be executed (represented by rectangles in this
figure).
In this example, we have 3 machines as finite
resources.
40
The key concepts of the scheduling optimization
are the activities and resources.
There are many resources in production lines;
machines, workers, raw materials, or money can be
seen as scare resources.
The scheduling must satisfy some precedence
constraints between activities. In this example
the red activities have to be
executed in this order. The red activity on
machine 2 cannot start unless the red activity on
machine 3 finishes, and also
the red activity on machine 1 cannot start before
the finish time of the red activity on machine 2. 41
The history of the scheduling theory is
long; so many researchers have been proposed
a number of algorithms.
The first category of algorithms is myopic
heuristics such as active scheduling scheme,
non-day scheduling scheme, or dispatching
rules. Such heuristic algorithms are heavily
used in practice.
Theoretically constraint programming and
metaheuristics and their hybrid are wellstudied recently.
42
Finally, I’ll show the model for transportation and
delivery, called the vehicle routing problem.
The vehicle routing occurs at
the distribution from the DCs
retailers or customers. A set
served by a fleet of vehicles
the final part of the SC;
or warehouse or depots to
of customers has to be
with limited capacities.
The vehicles are initially located at a given depot.
The objective is to find a set of routes for the
customers and returns to the depot without violating
the capacity constraint. In many distribution systems,
each customer specifies, in addition to the load volume
to be delivered to it, a period of time, called a time
window that specifies the earliest and latest time to
start the service.
43
As in the scheduling problem, many
algorithms have been proposed for the
vehicle routing problem. The classical
approaches are:
the saving method by Clarke and Wright,
the sweep method by Gillet and Miller,
Insertion method, and local search.
Recently many mataheuristic algorithms are
used for solving difficult real problems.
44
This figure shows the family tree of the vehicle routing
algorithms. In 1970 or before, many ad hoc methods have
been proposed such as saving, insertion, sweep, and local
search.Local search is brushed up to metaheuristics such
as tabu search, simulated annealing, and then more general
adaptive memory programming that also includes genetic
algorithm as a special case.
Sweep method that can be seen as a cluster-first routesecond approach is refined to generalized assignment
heuristics, location based heuristics that is proved to be
asymptotically optimal under some assumptions.
Saving and insertion methods that can be sees as
construction algorithms have a descendant named GRASP that
is a construction type metaheuristics. And we have another
branch of exact methods such as set partitioning approach,
cutting plane, etc.Recently these algorithms are unified
to the hierarchical building block method proposed by me. 45
Recently, we Japanese had a large disruption caused by
an earthquake, and then Tsunami, and finally an
explosion of nuclear plants in Fukushima.
Many supply chains are stopped; so we recognized supply
chains should be not only efficient
but also robust with respect to such disruptions.
Supply Chain Risk Management (SCRM) is a new area of SCM
to copy with the supply chain disruptions.
This figure represents the change of the performance of
a supply chain before and after a disruption event.
The action before the disruption is called “proactive” ,
while the action after the disruption is called
“response”. Our aim is to use the supply chain
optimization models so that the supply chain becomes
robust w.r.t the disruption and recovers quickly after
the disruption.
46
The SCRM has been becoming important recently. There are
many reasons. The first one is the increase of disasters.
These 10 years, we –human beings- encountered many
disasters.
Examples of natural disasters are:
Non-natural that means man-made disasters are:
SARS (Severe Acute Respiratory Syndrome), BSE (Bovine
Spongiform Encephalopathy),
CBRNE (Chemical Biological, Radiological, Nuclear,
Explosive) The other reasons are the trend in SCM such as
lean SC and globalization of SC.
The lean system makes the inventory low, so increases
vulnerability of the SC, while
globalization makes the lead time longer and the trend of
outsourcing makes the supply un-stable.
47
Two communities of business management and consulting are
closely related to SCRM.
One is the risk management that is defied as the
identification, assessment, and prioritization of risks.
Another is the Business Continuity Planning whose
objective is to write a thick manual.
But, unfortunately, a series of disasters in Japan proved
that both did not work well, or useless.
More important related area is the humanitarian
logistics.
48
Humanitarian Logistics can be defied as a branch of
logistics which specializes in organizing the delivery
and warehousing of supplies during natural disasters to
the affected area and people.
It is different from the usual (commercial) logistics
with respect to the following points.
It is decentralized and there are many players
(government, self defense force, NGOs).
And No SCM unit
nor trained staffs
Everything is ad hoc
No performance measure (fairness, speed, …)
No information & communication technology
49
To cope with risks, we have to classify them.
The first approach named risk mapping classifies risks into 2dimensional space;
That is usually used in risk management.
The horizontal axes is the impact of risks, while the vertical axis
represents the frequency of risks.
This figure shows a risk mapping of an imaginary company.
For example. this company categorized typhoon and earthquake into the
area that has large impact but rare.
This red area represents that the impact is large and high frequency.
That includes strike and the fluctuation of exchange rate.
This yellow area represents that the impact is small but frequency is
high.
That includes line stops in the plant and supply delay.
This white zone is that both the impact and frequency is small.
50
Risks can be classified into
supply, internal and demand risks.
Environmental risk is outside of
the corresponding SC.
51
Another classification.
Disaster risk that is caused by natural
and man-made disasters
Political risk that is caused by
contracts, laws, regulations
Social risk such as child labor / abuse
Intellectual property risk related to
patents, trademarks, copyrights
Example of other types of risks are:
Financial risk, employment risk,
reputation risk, …
52
Strategies to copy with risks are:
1) Accept the risk and do nothing.
2) Avoid the risk factor, if possible,
3) Transfer the risk using insurance or
option
4) Alignment that means share risk with
other SC partners by contract,
5) Or, finally, strengthen the supply
chain, by adding desirable properties to
the SC.
53
Such desirable properties to strengthen the SC
are:robustness, resiliency,
redundancy,flexibility, compatibility for
proactive strategies,and agility and visibility
for response strategies.
Such technical terms are not well-defied and
sometimes called BUZZWORDS.
We propose the robustness is defied as the depth
of the valley.
The time resiliency is the duration between the
disruption and recovery.
The performance resiliency is the percentage of
the performance recovery.
54
An example of redundancy is the
strategic inventory that is the
inventory for preparing the
disruption.
Also it is shard by many SC
partners. Remark that it must be
considered separately to the safety
stock that is for the demand
uncertainty.
55
A concrete example of flexibility
the multiple sourcing strategy
is
that procures from two or more
suppliers. Make-and-buy strategy is
another kind of multiple sourcing.
Remark that these two suppliers
should not be located in the area.
Otherwise, both suppliers may be
down.
56
This is another concrete example of flexibility
called the process flexibility.
If each plant can produce exactly one products,
it is called 1-flex.
If each plant can produce 2 products like this,
it is called 2-flex.
Graves and Tomlin at MIT showed that 2-flex is
the similar performance as Full-flexibility
that means each plant can produce all the
products.
But recent simulation study showed that it is not
true when the supply disrupts.
57
The other types of flexibility is
transportation flexibility.
That is multi-mode (that means using
ship and air) , multi-carrier (that
means using DHL and UPS) and also
multi-route (that means using ship
for the south coast (and then using
ground transportation) and via
Panama channel).
58
An example of compatibility is the risk
pooling strategy that is very common in
modern SC.
Before the Earthquake in Japan, we Japanese
had too many types of caps of the bottles.
After the Earthquake, caps became a
bottleneck to produce bottles. So Japanese
beverage makes recognized that
the compatibility is important and switched
to produce the white one only.
59
Using the strategies to copy
with risks, we can avoid,
transfer and reduce the impact
and probability of the risks.
60
We can also use optimization techniques to the SCRM.
Basically we can apply all the SC optimization models
using what if analysis.
For proactive decisions, strategic and tactical models
are useful.
For response decisions, we use operational models such
as scheduling, transportation and vehicle routing
models.
If you want to model both decisions, we need to use the
framework of stochastic programming
in which decisions before the disruption is modeled as
here and now variables, while
decisions after the disruption is modeled as recourse
variables.
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We have developed many SC optimization systems; we
are now extending them for the SCRM.
For example, LND is extended using stochastic and
robust optimization framework.
Safety stock allocation model is extended by
incorporating sourcing decision.
We are also developing quick and dirty solution system
for the vehicle routing problem without using ITs.
We wish such a non-IT system can be used in
humanitarian logistics for last-mile delivery.
Anyway, much remains to be done in this are. Thanks
for your attention.
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