Cours du master de recherche Génie Industriel
Supply Chain Logistics and Operations Management
Xiaolan XIE, Professeur
Centre Ingénierie et Santé
CNRS URM 6158 LIMOS – Equipe ROGI
Ecole Nationale Supérieure des Mines
xie@emse.fr
Supply Chain Logistics and Operations Management
Xiaolan XIE, Professeur
Centre Ingénierie & Santé
CNRS UMR 6158 LIMOS-Equipe ROGI
Ecole Nationale Supérieure des Mines
xie@emse.fr
polycopie sur http://www.emse.fr/~xie/master
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Plan
References
Chapter 1. Introduction
Chapter 2. Supply chain design
Chapter 3. Managing economies of scale in a supply chain
Chapter 4. Managing uncertainty in a supply chain
Chapter 5. Value of information
Chapter 6. Distribution strategies & strategic alliance
Chapitre 7. Outils informatiques en SCM
• D. Simchi-Levi, P. Kaminsky, E. Simchi-Levi,
«Designing and managing the supply chain»,
Irwin MsGraw-Hill
• S. Chopra, P. Meindl, « Supply chain
management: strategy, planning and
operation »
• G. Cachon, Terwiesch, « Matching supply with
demand »
• H. Stadtler C. Kilger : "Supply chain
management and advanced planning"
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1
What is a Supply Chain?
Chapter 1. Introduction to Supply chain management
1.
2.
3.
4.
• A supply chain consists of all parties involved,
directly or indirectly, in fulfilling customer requests
What is SCM?
Different views of supply chains
Drivers and objectives of supply chains
Supply chain decisions
• The entire process from point of origin (raw materials) to
point of consumption (final products bought by customers)
• A network (interdependent system) of facilities including
• materials supply from suppliers
• transformation of materials to (inventories of)
semi-finished and finished products
• distribution of finished products to customers
•Supply network or supply web.
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6
What is a Supply Chain?
Supply Chain Examples
• SCM is a set of approaches utilized to efficiently integrate
suppliers, manufacturers, warehouses, and stores, so that
merchandise is produced and distributed at the right
quantities, to the right locations, and at the right time, in
orders to minimize systemwide costs while satisfying service
level requirements.
Example 1: Wal-Mart
Procter & Gamble
Da-Fa Clothing, Inc. (China)
Wal-Mart
or third-party
distribution
centers
Wal-Mart
Stores
Customers Request:
Buying detergent,
clothes, TV, …...
SONY Factory (Malaysia)
1: SCM takes into account every facility that has an impact
on cost and plays a role in product making and distribution.
Plastic Producer
2: The objective of SCM is to be efficient and cost-effective
across the entire system.
Chemical Producer
Fabric Producer
Electronics Components Producer
Zipper Producer
Plastic Producer
Thread Producer
SC of detergent : (i) custmer need for detergent, (ii) the Wal-Mart retail store he visits, (iii)
FGI warehouse or DC supplying the store using trucks by a 3rd party; (iv) DC is stocked by
the manufacturer P&G, (v) P&G plants receives raw materials fro suppliers, who may have
been supplied by lower-tier suppliers (eg packaging from Tenneco while Tenneco receives
raw materials from other suppliers.
3: Because SCM revolves around efficient integration of all
suppliers-manufacturers-warehouses-stores, it
encompasses the firm’s activities at many levels, from
strategic to tactical and operational levels.
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Pioneer of cross-docking system.
2
Example 3: Dell
Example 2: Hewlett & Packard (HP)
Suppliers
Suppliers
Suppliers
IC Mfg
US DCs
Retailer
PC Board
Europe
DCs
Retailer
Consumers
Far East
DCs
Retailer
Consumers
Consumers
Monitors by SONY (Mexico)
FAT
Subassembly
Keyboards by Acer (Taiwan)
CPU by Intel (USA)
Dell
Assembly
Plant
Customers order
computers on-line
Other components
Suppliers
SC : customer, Dell’s Web site, Dell assembly and all Dell’s
suppliers and their suppliers
FAT = Final assembly & test
IC Mfg = Integrated circuit manufacturing
PC Board = Printed circuit board
Known for its direct sell and build-to-order system.
Pioneer of Postponement or delayed differentiation
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Supplier
Supplier
manufacturer
Distribution
system
Customer
A Flow View of SC
A Typical Supply Chain
Flows in both directions
Dominant flow of products and services: Flow of
products and semi-finished products, rework, recycling,
etc
Supplier
Suppliers
Manufacturing
Plants
Regional
Warehouses
(Distribution
Centers)
Field
Warehouses
(Distribution
Centers)
Dominant flow of demand and design information:
Procurement order, demand data, inventory information,
product information, prices, etc.
Retail
Stores
Customers
Financial flow: Payment, cash receivable, refund,
consignment contracts, etc.
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A Flow View of SC
An Organization View of SC
Example 1: Wal-Mart
Procter & Gamble
Da-Fa Clothing, Inc. (China)
SONY Factory (Malaysia)
Wal-Mart
or third-party
distribution
centers
Wal-Mart
Stores
Customers Request:
Buying detergent,
clothes, TV, …...
Fabric Producer
Electronics Components Producer
Zipper Producer
Plastic Producer
• A supplier or a customer can be internal or belong to different
companies
• The supplier and customer may have different objectives and
make decisions independently
• The best performance can only be achieved when all members
of the SC work for the same goal, in some way
Plastic Producer
• Two SC visions :
Chemical Producer
Thread Producer
– Intra-organizational Supply Chain : cooperations
• Wal-Mart provides the product, pricing and availability, info to customer.
• Customer transfers funds to Wal-Mart.
of all facilities of a large company
• Wal-Mart conveys point-of-sales data and replenishment orders to warehouses or DC,
who transfer the replenishment orders via trucks back to the store.
– Inter-organizational Supply Chain : network of
• Wal-Mart transfers funds to DC after the replenishment.
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• DC provides pricing info and sends delivery schedules to Wal-Mart
• Wal-Mart may send back packaging material to be recycled.
enterprises that work together toward a commun goal
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An Organization View of SC
An Organization View of SC
Focus company
Intra SC
Procter & Gamble
Da-Fa Clothing, Inc. (China)
Wal-Mart
or third-party
distribution
centers
Wal-Mart
Stores
supplier
Customers Request:
Buying detergent,
clothes, TV, …...
Cust.
CF
Assem
DC
supplier
SONY Factory (Malaysia)
Cust.
CF
Assem
Cust.
Fabric Producer
Electronics Components Producer
Plastic Producer
Zipper Producer
supplier
CF
Assem
DC
Plastic Producer
Cust.
Chemical Producer
out-bound logistics
In-bound logistics
Thread Producer
Sourcing
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Making
Delivery,
sales, service
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4
An Organization View of SC
An Organization View of SC
Single-location entreprise
Multiple-location entreprise
Factory 1
Factory 3
Factory 2
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An Organization View of SC
An Organization View of SC
Supply chain
Supply network
Entreprise A –Factory 1
Stores
Entreprise A –Factory 3
Entreprise A –Factory 2
Warehouse
Tier 2
suppliers
Tier 1
suppliers
Procurement
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Processes Involved in a Supply Chain
Processes Involved in a Supply Chain
A supply chain is a seqence of processes & flows that take
places within and between different SC stages and
combine to fill a customer need (To be analysed with
SCOR).
___________________
1.
2.
3.
4.
Suppliers
Manufacturers
Cycle view:
Processes in a SC
are divided into a
series of cycles,
each performed at
interface between
two successive
stages
___________________
1.
2.
3.
4.
Distributors
___________________
1. Procurement
2. Manufacturing
3. Customer order filling
4. Delivery
Retailers
Consumers
___________________
1.
2.
3.
4.
Not every SC will have
all four cycles clearly
separrated.
Customers
Cust. Order cycle
Retailers
Replenishment
cycle
Distributors
Manufacturing
cycle
Manufacturers
Procurement
cycle
Suppliers
Dell
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Push/Pull view of SC processes
Traditional Push/Pull view of intra-organisational supply chains
Cycle view:
Each cycle consists of six subprocesses.
Each party works to improve the efficiency of each subprocess.
Demand information changes between different cycles
The scale of orders grows as we move farther from the customer
Buyer stage
places order
Stages
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Processes Involved in a Supply Chain
Supplier stage
markets product
Cycles
Delivery lead time
Design
Purchase
manufacture
Ship
manufacture
Assemble
Engineering to
order
Make to order
Ship
PULL
PUSH
Buyer stage
receives supply
Assemble
Delivery lead time
Inventory
Buyer returns
reverse flows to
supplier or 3rd party
manufacture
Delivery lead time
Inventory
Assemble
Ship
assemble to
order
Delivery lead time
Supplier stage
receives order
Supplier stage
supplies order
manufacture
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Assemble
Inventory
Ship
Make to stock
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Push/Pull view of SC processes
Push/Pull view of SC processes
Push/Pull view:
Push / Pull boundary : point at which customer orders arrive
Processes divided into 2 categories depending on whether they
are executed in response to a customer order or in anticipation of
customer orders.
L.L. Bean : a mail-order company
• Pull processes are initiated by a customer order (demand
known with certainty)
DELL : direct sell
Customers
PULL
• Push processes are initiated in anticipation of customer order
(demand unknown and must be forecast)
Cust. Order &
Replenishment
cycle
Customers
PULL
Cust. Order &
manufacturing
L.L. Bean
cycle
Replenishmnt
&Manufacturing
Manufacturer
cycle
PUSH
Procurement
cycle
Manufacturing
(DELL)
Suppliers
PUSH
Procurement
cycle
Suppliers
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Push/Pull view of SC processes
Push / Pull boundary : point at which customer orders arrive
• Raw material such as fabric purchased 6-9
months before demand arrival
• Mfg begins 3-6 months before point of sale
Only inventory of components
Push/Pull view of SC processes
Key point :
Paint industry : production of the base, mixing colors, and
packing.
A cycle view clearly defines the processes involved
and owners of each process. Useful when
considering operation decisions because it specifies
roles & responsibilities of each member & the desired
outcome of each process
Till 1980, all processes done in large factories and paint cans
shipped to stores.
Key point:
Now,
• base preparation and packing of cans in push phase and
• color mixing at retail stores.
Push / Pull view very useful when considering
strategic decision as it is relating to supply chain
design.
Another example of gains from suitably adjusting the push/pull
boundary:
Result?
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Make-To-Stock or Make-To-Order
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Push-Pull Supply Chains
Push-Pull Strategies
The Supply Chain Time Line
Customers
Suppliers
PUSH STRATEGY
Low Uncertainty
PULL STRATEGY
• The push-pull system takes advantage of the rules of
forecasting:
– Forecasts are always wrong
– The longer the forecast horizon the worst is the forecast
– Aggregate forecasts are more accurate
• The Risk Pooling Concept
• Delayed differentiation is another example
– Consider Benetton sweater production
High Uncertainty
Push-Pull Boundary
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Objective of Supply Chain Management
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Managing a Supply Chain is not Easy
• SCM is concerned with the efficient management of
a supply chain so as to maximize supply chain profitability
across the entire supply chain
1. Uncertain _demand__
Supply chain profitability or surplus = total revenue - total cost
2. Conflicting _objectives_ across the supply chain
• Manage globally, not locally
(Companies do not compete, their supply chains do)
• Consider both _cost_ and _customer service level__
(Optimal tradeoff between _cost_and _responsiveness_)
• Match _supply_ and _demand_ dynamically (in real time)
• infrastructure (design), operations, integration
Manufacturers
Distributors
Large production batches
Low inventory
Few DCs
Retailers
Few stores
Low inventory
Little variety
Close to DCs
Consumers
Convenience
Short lead time
Large variety of
products
Large shipments
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Conflicting Objectives of SC functions
Function
Objectives
Implication
Marketing
• High revenue
• High
• High product availability
•Low
Supply Chain Performance Drivers
• Logistic drivers: Facilities, inventories, transportation
Customer
service
• Cross-functional drivers: information, sourcing, pricing
• While logistics remains a major part, SCM is increasingly
becoming focused in cross-functional drivers.
•Low production cost
Production
•High level production
•Long production runs
Finance
• Low investment & cost
•Fewer fixed costs
•Many
•Few
• High
Disruption
to
production
Inventories
• The goal is to strike the balance between responsiveness and
efficiency
•Low
•Low inventories
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Supply Chain Performance Driver: Inventory
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Supply Chain Performance Driver: Transportation
• Inventory is the major source of cost in a supply chain and
changing inventory policies dramatically alters SC’s
efficiency and responsiveness.
• High inventory level will be more responsive to market
demand, however, at a higher cost
• A lower inventory level will be less responsive to customer
demand, however, lower inventory carrying and
obsolescence cost
• Nordstrom targets upper-end customers with high responsive
requirement. It stocks a large variety and quantity of
inventory than other department stores. It charges a premium
by providing a higher service level to those customer who
can afford it
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• Transportation can take the form of many combinations of
modes and routes.
• Air shipment versus sea or land shipment
• Full truckload or less-than-full truckload
• Transportation choices have large impact on SC responsiveness
and efficiency
• Responsiveness versus cost
• Nabisco, Inc. pioneered a collaborative logistics effort with
other firms to share trucks and warehouses to save logistics cost
• Home Depot moves 85% of its merchandise directly from
suppliers to stores in full truckload, save in transportation and
warehousing costs
• Laura Ashley favors responsiveness for its mail-order businees
and works with FedEx to allow next day delivers.
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Supply Chain Performance Driver: Facilities
• Facilities are physical locations where product is
stored, assembled or fabricated.
• Two major types : production and storage sites.
• Decisions: role, location, capacity, flexibility
• Centralized facility provides economy of scale, is
more efficient
• Decentralized facilities can be located close to the
market, is more responsive
• Toyota has a policy of having manufacturing
facilities in every major market it served to be more
responsive to its customers (side benefits: protection
from currency fluctuations, trade barriers)
Supply Chain Performance Driver: Information
• Potentially the biggest driver as it directly affects all other
drivers.
• Information serves as the connection between the different
stages of a supply chain.
• High quality and timely information provides the most
important ingredient in making high quality decisions to
achieve both efficiency and responsiveness
• Andersen Windows built an information system that can give
customers immediate price quotes and automatically send an
order to the factory. This allowed the company to get
customized product to market rapidly
• Information about the demand pattern, shipping options can
help improve SC
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Supply Chain Performance Driver: Sourcing
Supply Chain Performance Driver: pricing
• Sourcing is the choice of who will perform a particular SC
activitity (prod, storage, transp, info management).
• At the strategic level, these decisions determine what functions
a firm outsources and what a firm performs.
• Sourcing affects both efficiency and responsiveness.
• Pricing affects the behaviour of the buyer of the good or
service, and hence the SC performance.
• It affects the customer segments that choose to buy the product,
as well as the customers’ expectations.
• It affects the SC in terms of responsiveness required as well as
demand profile.
• Pricing is also a lever for matching supply and demand through
short-term discount.
• After Motorola outsourced much of its production to contract
manufacturers in China, its efficiency improves but its
responsiveness suffers because of the long distances.
• To remedy, it started flying in some of its cell phones from
China even though this increased the transportation cost.
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• Costco, a membership-based wholesaler in the US, has a policy
that prices are kept steady but low. Customers expect low prices
but are comfortable with lower level of product availability.
The Costo SC aims to be very efficient, at the expense of some
responsiveness.
• Amazon : free shipping (7-14 days), standard shipping at $4.98
(3-5 days), 2-days shipping at $11.47, 1-day shipping at $20.47.40
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Supply Chain Key Performance Indices: KPI
Supply Chain Key Performance Indices: KPI
Customer oriented KPI
Facility-related Metrics
Inventory-related Metrics
• Delivery performance (Time to full delivery, on time delivery)
• Capacity
• Average inventory
• Order fulfilment performance (order fill rate, order leadtime)
• Utilisation
• Perfect order fulfilment
• Products with more than N number
of days of inventory
• Supply chain responsiveness (supply chain response time)
• Theorectical flow/cycle time of
production
• Production flexibility (vol. flexibility, product-mix flex.)
• Actual average flow/cycle time
• Average safety inventory
• Flow time efficiency = ratio of above
two
• Seasonal inventory
Supply chain oriented KPI
• Total logistics (SC) management cost
• Product variety
• Value-added productivity per employee
• Processing/setup/down/idle time
• Warranty cost
• Average replenishment batch size
• Fill rate
• Fraction of time out of stock
• Average production batch size
• Cash-to-cash cycle time
• Production service level
• Inventory days of supply
• Volume contribution of top 20% SKUs
and customers
• Asset turns
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Supply Chain Key Performance Indices: KPI
Transportation-related Metrics
• Average inbound transportation cost
• Average incoming shipment size
• Average inbound transportation cost
per shipment
• Average outbound transportation cost
• Average outbound shipment size
• Average outbound transportation cost
per shipment
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Supply Chain Key Performance Indices: KPI
Sourcing-related Metrics
Information-related Metrics
Pricing-related Metrics
• Forecasting horizon
• Days payable outstanding
• Profit margin
• Frequency of update
• Average purchase price
• Days sakes outstanding
• Forecast errors
• Range of purchase price
• Incremental fixed cost per order
• Seasonal factors
• Average purchase quantity
• Incremental variable cost per unit
• Variance from plan
• Fraction on-time deliveries
• Average sale price
• Ratio of demand variability to order
variability (to identify bullwhip
effect)
• Supply quality
• Average order size
• Supply lead time
• Range of sale price
• Range of periodic sales
• Fraction transportation by mode
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Quick summary of important factors of SC
Supply chain analysis
• The SC includes all activities and processes to supply a
product or service to a final customer.
• Based on Supply Chain Operations Reference (SCOR) model
for modelling and analysis of a Supply Chain.
• Developped by the Supply Chain Council
• Any number of companies can be linked in the SC.
– http://supply-chain.org/
• A customer can be a supplier to another customer so the total
chain can have a number of supplier/customer relationships.
• With 5 generic process types: plan, source, make, deliver,
return
• Objective : model the supply chain management processes
• While the distribution system can be direct from supplier to
customer, depending on the products and markets, it can
contain a number of intermediaries (distributors) such as
wholesalers, warehouses, and retailers.
Plan
• Product or services usually flow from supplier to customer and
design and demand information usually flows from customer
to supplier. Rarely is this not so.
Deliver
Suppliers’
Supplier
Source
Make
Source
Deliver
Make
Deliver
Your Company
Supplier
Internal or External
Source
Make
Deliver
Customer
Internal or External
Source
Customer’
s
Customer
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Why Study SCM?
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Why Study SCM?
• Nike: BW “The Swoosh Stumbles” (2000)
Delivery efficiency
Inventory reduction
Time to full delivery
Quality of demand forecast
Overall productivity
SC cost reduction
Service level
Capacity increase
– trouble with i2 implementation leads to major inventory problems.
16% – 28% Improvement
25% – 60% Improvement
30% – 50% Improvement
25% – 80% Improvement
10% – 16% Improvement
25% – 50% Improvement
20% – 30% Improvement
10% – 20% Improvement
• Sony Play Station 2
– 2000 launching: shipped 500k units as opposed to 1,000k desired.
• Amazon.com 2001 Q4 profits attributed to
“improved operating efficiencies”
Source: 1997 PRTM ISC Benchmark Study
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– Sorting orders
Fewer items were put in the wrong locations in its distribution centers, and
shipped 35% more units with the same number of workers as a year ago,
which helped cut fulfillment costs by $22 million last quarter, even with
sales increase of 15%.
– Anticipating
Using software to more accurately forecast purchasing patterns by region,
slashed inventory levels by $31 million, or 18%, in the 4th quarter
– Opening a marketplace
Allowing other sellers to offer used books and other merchandise on
Amazon.com helped boost sales in the holiday season by 23%, to 38
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million.
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Why Study SCM? (examples from text)
Why Study SCM?
• In 1998, American companies spent $898 billion (GDP 10.6%)
in supply-related activities.
– Transportation 58%
– Inventory 38%
– Management 4%
• Wal-Mart vs. K-Mart:
 In 10 years, Wal-Mart transformed itself by changing its
logistics system (own fleet of trucks); over 80% shipped to
stores from its own 27 DCs, rest directly from suppliers;
usually received within 48 hours. It has the highest sales
per square foot, inventory turnover and operating profit of
any discount retailer.
(crossdocking, Everyday Low Price, innovative IS/IT)
• Third party logistics services grew in 1998 by 15%
to nearly $40 billion.
• It is estimated that the grocery industry could save $30 billion
(10% of operating cost) by using effective logistics strategies.
 K-Mart, on the other hand, ships less than 50% on own,
– A typical box of cereal spends more than three months getting from
factory to supermarket.
and suffers from supply chain inefficiency
(inability to respond quickly to customer demand)
– A typical new car spends 15 days traveling from the factory to the
dealership, although actual travel time is 5 days.
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Why Study SCM?
Logistics vs SCM
• Effective supply chain management is a top priority in
many companies, e.g.,
Both are concerned with efficient management of
physical flows, and matching supply & demand
 Boeing: Streamlining its procurement process using Exostar
(internet exchange) -- to eliminate paper trail, facilitate
information exchange across the supply chain
 General Motors: Reengineering its distribution operations, partnering
with Vector SCM (a thrid-party service provider) -- to reduce in-transit
inventory & achieve fast delivery from factories to dealers
Logistics
Supply Chain Management
Scope: Within a firm
Scope: Entire supply chain
Objective: minimizing logistics cost
Objective: minimizing cost &
maximizing customer services
Tactical – ignored by top mgmt
Strategic -- top mgmt attention
 ExxonMobil: Redesigned its logistics network after the merge of
Exxon and Mobil -- to drive down cost and complexity
 P&G, Dell, Cisco, Amazon, Bristol-Myers Squibb, McDonald’s, etc.
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Why is SCM hot now?
Supply Chain Decisions
TIME FRAME
TYPE
IT as a key enabler
years
Strategic
1) Communications: Internet
ability to share data through SC
2) Computing: PC speed
ability to solve/analyze complicated quant
models
3 mo.- 1year
1 + 2 => DECISIONS
daily
Also, transportation flexibility (multi-modal)
Tactical
Operational
TYPICAL DECISIONS
•Supply chain strategies (Sell direct or through
retailers? Outsource or in-house? Focus on cost or
customer service?)
•Supply chain network design (How many plants?
Location and capacities of plants and warehouses?)
•Product mix at each plant
•Workforce & Production planning
•Inventory policies (safety stock level)
•Which locations supply which markets
•Transportation strategies
•Production scheduling
•Distribution scheduling and routing
•Place inventory replenishment orders
•Lead time quotations
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Some Strategic Supply Chain Questions
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Some Strategic Supply Chain Questions
Q1: Should my supply chain focus more on cost or customer service?
Supply chain strategy should fit the company’s competitive
strategy.
Q2: Do I need retailers in my supply chain?
The competitive strategy defines, relative to its competitors, the set of
customer needs that its seeks to satisfy through its products and
services.
Q3: Do I need warehouses/DCs in my supply chain?
Q4: Perform all the functions in-house or outsource some?
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Q1: Should my supply chain focus more on cost or
customer service?
Some Strategic Supply Chain Questions
Achieving strategic fit by
Competitive strategy targets one or more market segments:
1. Understanding the customer and SC uncertainty
Wal-Mart : Provide high availability of a variety of products of
reasonable quality at low prices.
2. Understanding the SC capabilities
McMaster-Carr (sells MRO products, Offers 400000+ products thru a
catalog and a web site) : Provide the customer with convenience,
availability and responsiveness.
3. Achieving strategic fit
Dell (build to order): Customization and variety at a reasonable cost,
with customer having to wait about one week.
Gateway (selling eMachine PCs thru retailers): Low price, fast
response time, help in product selection but with limited variety
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Q1: Should my supply chain focus more on cost or
customer service?
Q1: Should my supply chain focus more on cost or
customer service?
Impact of customer needs on implied demand uncertainty
1. Understanding the customer and SC uncertainty
Identify the needs of customer segments targeted by competitive
strategy
• Quantity of prod needed in each lot,
• response time the customers are willing to tolerate
• The variety of products needed
• The service level required
• The price of the product
• The desired rate of innovation in the product
All can be translated into a single metric: implied demand
uncertainty.
Implied demand uncertainty is the uncertainty of the demand the SC
is targeting.
Customer tolerated response time greatly impacts the implied
demand uncertainty.
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customer need
Causes implied demand uncertainty to range of quantity required increases
increases bcs it implies greater variance in demand
lead time decreases
increases bcs there is less time to react
variety of products required increases
increases bcs demand /prod becomes more disagregate and lack of scale
Nb of distribution channels increases
increases bcs demand disagregated over more channels
rate of innovation increases
increases bcs new prod tend to be more uncertain
required service level increases
increases bcs the firm has to handle unusual demand surges
Products with high implied uncertainty are :
• less mature, high product margin,
• but more difficult to forecast, more difficult to match demand and
supply, more season-end marked down
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Q1: Should my supply chain focus more on cost or
customer service?
Q1: Should my supply chain focus more on cost or
customer service?
•
Along with demand uncertainty, it is important to consider the
uncertainty resulting from the capability of the supply chain.
•
Supply uncertainty is also strongly affected by the life-cycle position
of the product. New produscs have higher supply uncertainty bcs
designs and production processes are still evolving.
Locating your demand and supply on the uncertainty
spectrum
Predictable supply & uncertain demand
Predictable
supply and
demand
Impact of supply source capability on Supply uncertainty
Supply source capability
increases
Unpredictable and low yields
increases
Poor quality
increases
Limited supply capacity
increases
Inflexible supply capacity
increases
Evolving production processes
increases
or somewhat uncertain supply & demand
An existing auto
model
Commonplace
goods: gasoline,
salt
Causes implied supply uncertainty to Frequent breakdowns
or Predictable demand & uncertain supply
High uncertainty
Entirely new
products
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Q1: Should my supply chain focus more on cost or
customer service?
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Q1: Should my supply chain focus more on cost or
customer service?
2. Understanding the SC capability
Consider and categorize the characteristics of the SC
responsiveness
SC responsiveness
• Respond to wide ranges of quantities demanded
• Meet short lead times
• Handle a large variety of products
• Build highly innovative products
• Meet a high service level
• Handle supply uncertainty
High
Cost-responsiveness efficient
frontier
low
Responsiveness comes at a cost.
High
SC efficiency : the inverse of the cost of making and delivering a
product to the customer.
low
cost
63
64
16
Q1: Should my supply chain focus more on cost or
customer service?
Q1: Should my supply chain focus more on cost or
customer service?
What is the Right Supply Chain for Your Product?
Locating your SC on the responsiveness spectrum
Responsive
supply chain
High
somewhat
Somewhat
efficicient
efficicient
responsive
Dell, Sport Obermeyer
Highly
responsive
GM, HP
Integrated steel
mills scheduled
weeks or months
in advance
Little flexibility
Hanes apparel:
A traditional
make-to-stock
with production
leadtime =
weeks
Automotive
production:
Delivery variety
of products in
couple of weeks
DELL:
Cost-effective
supply chain
Customer made
PC in a few days
P&G
Low
(functional products)
Demand
uncertainty
High
(innovative products)
65
Q1: Should my supply chain focus more on cost or
customer service?
66
Q1: Should my supply chain focus more on cost or
customer service?
Two “Extreme” Types of Supply Chains
Understanding Your Product
Cost effective supply chain
Market-responsive SC
Primary purpose
Supply predictable demand efficiently at
the lowest possible cost
Respond quickly to unpredictable
demand in order to minimize
stockouts, and obsolete inventory
Manufacturing focus
(capacity utilization)
Lowest cost via high utilisation
Maintain capacity flexibility for
unexpected demand
Inventory strategy
Minimum inventory to lower cost
Maintain buffer inventory for
unexpected demand
Transportation
strategy
Mainly rely on low cost modes
Use responsive modes
Approach to
choosing suppliers
Selection based on cost & quality
On speed, flexibility & quality
Lead-time focus
Reduce but not at the expense of cost
Aggressively reduce even if the
costs are significant
Two “Extreme” Types of Products -(a) Functional products -- predictable demand & long life cycle
-- Easy to meet customer demand (revenue predictable)
-- Competition based on price offered to customers
-- Strategy: Supply chain design should focus on minimizing total cost
(b) Innovative products -- unpredictable demand & short life cycle
-- Difficult to forecast demand
-- Difficult meeting customer demand (oversupply or stockout)
-- Competition based on customer service (product variety, degree
of customization, lead time)
-- Strategy: Supply chain design should focus on customer service
67
68
17
Q1: Should my supply chain focus more on cost or
customer service?
Q1: Should my supply chain focus more on cost or
customer service?
Product Life Cycle
Market
Volume
Life Cycle Differences
Ramp-up and
growth
Ramp-up and
growth
Maturity
Maturity
End-of-life
Demand
uncertainty
End-of-life
Competition
Basis
Role of
inventory
Cost of
shortage
Cost of
overage
Time
Product Type
69
Q1: Should my supply chain focus more on cost or
customer service?
70
Q2: Do I need retailers in my supply chain?
Key:
To achieve strategic fit, a firm must tailor its supply
chain to best meet the needs of different customer
segments
Dell
(Direct sell to customers)
HP-Compaq
(Sell through retailers)
Customers
Customers
Dell
Suppliers
Pull
Push
Pull
Retail stores
Compaq
Push
Suppliers
To retain strategic fit, SC strategy must be
adjusted over the life cycle of a product and as te
competitive landscape changes.
Pros: 1. Customization (larger variety of computers)
2. Elimination of retailers (lower facility/inventory cost)
3. Price flexibility
Cons: 1. Customers have to wait to get the computer
2. Higher transportation costs (2~3% relative to price of a PC)
3. Lack of interaction between customers and sales personnel
71
72
18
Q3: Do I need warehouses/DCs in my supply chain?
Q4: Perform all the functions in-house or outsource some?
Why Use Warehouses/DCs?
Functions in a Supply Chain
Suppliers
Manufacturers
Distributors
Retailers
Consumers
Three major functions:
Manufacturing Plants
DCs/Warehouses
1. Manufacturing
Suppliers -- parts, subassemblies
Manufacturers -- final products
Retailers
2. Distribution and Warehousing
Suppliers to manufactures
Manufacturers to distribution centers
3. Retailing
Retailers to customers
73
Q4: Perform all the functions in-house or outsource some?
Past vs Now
74
Q4: Perform all the functions in-house or outsource some?
What Functions to Perform?
Past: Perform all stages of manufacturing, distribution & warehousing
Based on your core competencies
Example 1: Wal-Mart
Core competencies: Retailing & Logistics
-- Most efficient retail stores in the world
-- Manage its own DCs and Warehouses
-- Products provided by hundreds of suppliers
Now: Perform key stages of manufacturing
Example 2: General Motor’s Saturn Division
Core competencies: Manufacturing
-- Focus on manufacturing
-- Logistics and transportation taken care by Ryder Logistics
The anwser depends to a large extent on the competitive strategy.
75
76
19
Review of supply chain concepts
• What is meant by the push-pull boundary?
• make to stock vs. build to order
• primary source of difficulties in SCM
• SC processes
• SC focus
• product life-cycle considerations
• two “extreme” type of products
77
20
Major Network Design Decisions
• Facility role
number of warehouses, centralised / decentralised,
flexibility
Chapter 2. Supply chain design
• Facility location
location of a warehouse, a plant
• Capacity allocation
size of a warehouse, space for products
• Market and Supply allocation
Determining which products customers will receive from each
warehouse
1
Discussion: Factors that Influence Facility Location?
2
Discussion: Factors that Influence Facility Location?
• Stratgic factors depending on the competitive strategy
• Technological factors : high fixed cost (semiconductor, bottling plants of
Cocacola)? flexibility of production
offshore facility: low-cost facility for export
• Macroeconomic factors: tariffs, tax inventives
source facility: lower-cost facility for global production
• Exchange rate and demand risk
server facility: regional production facility
• Political factors
contributor facility: regional production facility with development skill
• Infrastructure factors
outpost facility: regional facility built to gain local skills
• Competitive factors : positive externalities between firms – locating to
split the market
lead facility: facility that leads in development & process technology
• Customer response time and local presence
• Logistics and facility costs
3
4
1
Objective of Network Design
Discussion: Impact of Increasing # of Facilities?
balance service level against
• Impact of increasing number of retail stores?
(market share, ...)
• annual system-wide costs, including production,
purchasing, inventory, facility costs, transportation
costs
• Impact of increasing number of warehouses?
(service level, inventory costs, overhead & setup costs, outbound
transportation costs, inbound transportation costs)
Goal: find a minimal-annual-cost configuration of
the distribution network that satisfies product
demands at specified customer service levels.
5
A framework for network design
competitive strategy
internal constraints
capital, growth strategy
existing network
Production tech., cost,
scale/scope impact
support required, flexibility
phase I
SC strategy
competitive environment
production methods
skill needs, response time
A framework for network design
Phase I: define a supply chain strategy/design
• Define the firm’s broad SC design: SC stages, in-house or
outsourced SC functions
• Starts with the definition of the competitive strategy with the
set of customer needs, the SC capabilities.
• Forecast the evolution of the global competition & whether
competitors in each market local or global
• Identify constraints on available capital, whether growth by
acquisition/building new facilities/partnering.
Global competition
tariff & tax incentives
phase II
Regional facility
configuration
regional demand, size,
growth homogeneity
local specifications
political, exchange rate
demand rate
phase III
Desirable sites
6
available infrastructure
competitive strategy
Factors : costs, labor,
materials, site specific
phase IV
Location choices
logistics costs, transport
inventory coordination
internal constraints
capital, growth strategy
existing network
7
phase I
SC strategy
Global competition
8
2
A framework for network design
A framework for network design
Phase II: define the regional facility configuration
Phase III: Select a set of desirable potential sites
• Select a set of desirable potential sites within each region
• Based on an analysis of the infrastructure availability to
support the production technologies
• Hard infrastructure requirements: availability of suppliers,
transportation services, communication, utilities,
warehousing
• Soft infrastructure requirements: availability of skilled
workforce, workforce turnover, community receptivity
• Identify regions where facilities will be located, their roles, their
approximate capacity
• Starts with the forecast of the demand by country, measure the size of the
demand, homogeneity of demand across different countries
• Identify whether economies of scale or scope can play a significant role in
reducing cost, given available production technologies.
• Identify demand risk, exchange risk, political risk , regional tarrifs, local
production quota, tax incentives, export/import restrictions of different
regional markets
• Identify competitors in each region and make a case for whether to locate
close to or far from competitors
tariff & tax incentives
Production tech., cost,
scale/scope impact
support required, flexibility
competitive environment
phase II
Regional facility
configuration
regional demand, size,
growth homogeneity
local specifications
political, exchange rate
demand rate
production methods
skill needs, response time
10
Data for Network Design
Phase IV: Location choices
• Select a precise location and capacity allocation for each
facility
• Restricted to sites selected in Phase III
• Designed to maximize total profits
phase IV
Location choices
available infrastructure
9
A framework for network design
Factors : costs, labor,
materials, site specific
phase III
Desirable sites
1. All products including vol. & transp. mode
2. Location of customers, existing warehouses, DCs, plants, and
suppliers
3. Demand for each product by customer location
4. Facility, labor, material costs by site
5. Transportation rates by mode
6. Inventory costs by site and as a function of quantity
7. Sale price of product in different regions
8. Taxes and tarrifs
9. Customer service goals
10. Shipment sizes by product and frequencies of customer
delivery
logistics costs, transport
inventory coordination
11
12
3
Customers
Impact of Aggregating Customers
• Customers located in close proximity are aggregated
using a grid network or clustering techniques. All
customers within a single cell or a single cluster are
replaced by a single customer located at the centroid of
the cell or cluster (referred to as a customer zone).
• The customer zone balances
1. Loss of accuracy due to over aggregation
2. Needless complexity
• What affects the efficiency of the aggregation?
Baltimore metro
Western MD
1. The number of aggregated points, that is the
number of different zones
Eastern Shore
DC metro
2. The distribution of customers in each zone.
Southern MD
Before clustering
After clustering
13
Why Aggregate?
14
Recommended Approach for Aggregation
• Use at least 150-200 aggregated points.
• Make sure each zone has an equal amount of total
demand.
• Place the aggregated point at the center of the zone.
In this case, the error is typically no more than 1%.
One solution : aggregate according to Zip-code
15
16
4
Product Grouping
Transportation rates
• Companies may have hundreds to thousands of individual items
in their production line
• Rates are almost linear with distance but not
with volume.
• Collecting all data and analyzing it is impractical for so many
products
• Internal or external transportation fleet.
• In practice, items are aggregated into a reasonable number of
product groups, based on
• For internal fleet, cost per mile per SKU (Stock
Keeping Unit) can be easily estimated from the
annual costs per truck, annual mileage per truck,
annual amount delivered and truck’s effective
capacity.
1. Distribution pattern
2. Product type
3. Shipment size
4. Transport class of merchandise
• It is common to use no more than 20 product groups.
17
18
Mileage estimation
Transportation rates
• For external fleet, two modes : TL (truckload)
and LTL (less than truckload).
• Step 1: estimation of distance (in miles)
Dab  69
• For TL, cost/mile is available from a zone-tozone cost table.
 lona  lonb 
2
  lata  latb  (straightline, good for short distance)
2
2
2
 lat  latb 
 lon  lonb 
Dab  2(69) sin 1 sin  a
  cos(lat a ) cos(latb ) sin  a
 (for long distance)
2
2




• Step 2: Taking into account a circuity factor , i.e. Dab with  =
1.3 for urban area and  = 1.14 otherwise.
• For LTL, three types of freight rates : class
(classification tariff), exception; commodity
• Example, Chicago (-87.65, 41.85) and Boston (-71.06, 42.36).
Distance can also be checked with internet mapping systems.
19
20
5
Warehouse cost
Warehouse cost
• Storage costs: inventory holding costs which are
proportional to average inventory level.
• Handling cost : labor and utility costs which are
proportional to annual flow
• Fixed costs: depending on the warehouse capacity in a
non linear way
• Estimation of inventory level by inventory turnover ratio
(taux de rotation des stocks)
$1500000
inventory turnover ratio =
$1200000
annual sales
average inventory level
$800000
20000
40000
60000
80000
Warehouse
capacity (sq. ft)
21
Warehouse capacities
22
Potential warehouse locations
• Step 1: according to the EOQ model,
Potential warehouse locations must satisfy a variety of conditions:
Warehouse capacity = 2 average inventory level
• Geographical and infrastructure conditions
• Step 2: taking into account empty space needed for access
and handling
• Natural resources and labor availability
Warehouse capacity = 2 average inventory level)
• Local industry and tax regulation
Where  > 1 is typically chosen to be 3.
• Public interest
On-hand
Inventory
Demand Rate
Average Cycle
Inventory
Time
23
24
6
Service level requirements
Network Design Solution Approaches
• Maximum distance between each customer and the
warehouse serving it
• Heuristics
• Percentage of population whose distance to their
assigned warehouse is within a given distance.
-- rules of thumb
-- sub-optimal & usually no guarantee of performance
• Exact Algorithms
-- Guarantee optimal solutions for the problem
-- Optimization techniques:
Linear Programming (LP), Integer Programming (IP)
25
26
Optimization Approach
Two Network Design Problems
• Problem 1: Given facility locations (plants, warehouses),
find the best distribution strategy from plants to warehouses
to markets.
• Problem 2: Given a set of candidate locations, find the
best locations for warehouses and best distribution strategy
from plants to warehouses to markets.
Step 2
Step 1
Analyze
intangible
aspects
List of
potential
sites
Optimization:
•Selection of one
or more sites
•Allocation of
demand to sites
Our focus here
Possible Approaches: Heuristics and Exact Algorithms
27
Fall 2007 SCM
28
7
Phase II: network optimization model
•
Phase II: network optimization model
To decide regions in which facilities are to be located based on
regional demand, tarriffs, economies of scale, aggregate factor
costs
The capacity plant location model
Example:
To optimize the supply chain of the SunOil to meet its demand
from five regions.
Cost and demand data for SunOil are as follows:
Demand region production and transportation cost per 1000000 units
Fixed
low
N. S. supply region America America Europe Asia Africa cost capacity
N. America
81
82
101
130
115
6000
10
S. America
117
77
108
98
100
4500
10
Europe
102
105
95
119
111
6500
10
Asia
115
125
90
59
74
4100
10
Africa
142
100
103
105
71
4000
10
Demand
12
8
14
16
7
Fixed
•
N = nb of potential plant/location/capacity
•
M = nb of markets or demand points
•
Dj = annual demand from market j
•
Ki = potential capacity of plant i
•
Fi = annualized fixed cost of opening plant i
•
Cij = cost of producing & shipping one unit from plant i to market j
(production, inventory, transportation, tarriffs)
high
Decision variables
cost capacity
9000
20
6750
20
9750
20
6150
20
6000
20
29
Phase III: Gravity center model
•
Yi = 1 if plant i is open, 0 otherwise
•
Xij = quantity shipped from plant i to market j
To be continued.
30
Phase III: Gravity center model
Identify the geographic location of potential sites
Example:
Locate a Distribution Center (X, Y)
Locate a new assembly plant supplied by three plants and serving
5 markets.
to serve N markets located at (Xn, Yn) with demand Dn and unit
shipping cost Fn per unit of product and per kilometer.
Algorithm:
Step 1. Select any location (X, Y) of the DC
Sources/markets
supply sources
buffalo
memphis
St Louis
Markets
atlanta
boston
jacksonville
philadelphia
new york
Step 2. For each market n, evaluate its distance from DC
 X  X n   Y  Yn 
Step 3. Obtain a new location (X’, Y’)
dn 
2
2
Dn Fn X n
dn
, Y '
X '
DF
 dn n
n

Dn FnYn
dn
DF
 dn n
n

Step 4. Replace (X, Y) by (X’, Y’) and repeat 2-3 till convergence.
31
transp cost
quantity in $/ton.mile (Fn) Tons (Dn)
Xn
Yn
0,9
0,95
0,85
500
300
700
700
250
225
1200
600
825
1,5
1,5
1,5
1,5
1,5
225
150
250
175
300
600
1050
800
925
1000
500
1200
300
975
1080
32
8
Phase IV: Network optimization models
Allocating demand to production facilities
•
Similar to regional configuration-Phase II but with facility
locations given
Locating plants: the capacity plant location model
•
Similar to regional configuration model but with location-specific
costs and duties
Locating plants: the capacity plant location model with single
sourcing
•
Similar to the previous one but with each customer point sourced
by one plant
Locating plants and warehouse simultaneously
9
Case study I: Distribution network redesign for automotive
industry
Two scenarios: centralised / distributed distribution
Facility location models
Boat
Plant 1
Boat
Train
HB1
HB2
Train
Customer 1
DC 1
Truck
Train
Train
Train
Train
Plant 2
TP1
Train
Customer 2
Truck
Train
Train
Train
Truck
Train
Truck
DC 3
Xiaolan Xie
Customer ...
Customer 16
Truck
DC 4
Customer 1
Boat
Plant 1
Customer ...
DC 2
Plant 3
Boat
Train
HB1
HB2
Train
Truck
DC 1
Customer 2
Truck
Train
Train
Plant 2
Truck
TP1
Train
Customer ...
Truck
Truck
Customer ...
Plant 3
Customer 16
1
Case study I: Distribution network redesign for automotive
industry
Requirements:
 Scenario analysis
 Complex operation processes (operating rules)
 Performance evaluation considering stochastic aspects
 Trade-off between costs and customer service level
 Leadtime balance between MTS and MTO products
2
Case study II: supplier selection in textile industry
Enterprise
@Europe
AS-IS
Boat +truck
Delivery
Supplier 1
Far East
Distributor
Customer
Boat +truck
Plane + truck
Boat + planet + truck
Supplier 1
Far East
Enterprise
Boat +truck
TO-BE
Plane + truck
Delivery
Supplier 2
Far East
Boat + planet + truck
Distributor
Customer
Truck
 Simulation is the only possible tool for faithful
evaluation of the performances
Truck
Supplier 3
Europe
Supplier 4
Europe
3
4
1
Case study II: supplier selection in textile industry
Requirements:



Strategic + operational decisions
 Supply chain network design
 Order assignment (split) ratio
 Replenishment level
 Huge nb of alternatives
Dynamic in nature
 Demand seasonality
 Unstable transportation time
Multiple criteria
 Total costs, Backlog ratio,
service levels
Existing models
Simulation
+
optimisation
5
Models for location decision

The p-Median Problem

Set covering location model





6
The p-Median Problem
warehouses
SS-Capacitated Facility Location Problem
Capacitated Facility Location Problem
Multi-Commodity Capacitated Facility Location
Problem
Multi-Commodity Tri-Echelon Model
Multi-Commodity Network Design
retailers
Problem:
Locate p warehouses from m potential locations to serve n
retailers.
Assumptions:
1. No fixed cost for opening a warehouse
2. No capacity constraint on the warehouses
7
8
2
The p-Median Problem
Min
n
m
i 1
j 1
 
C ij X
m
s .t .

X
X
ij
 Y
m

Y
j 1
j
: total
transport
ij
cost
Solution techniques:
Lagrangian relaxation heuristics (Beasley93, Bramel& Simchi-Levi99)
Heuristic rules: ADD (Kuehn&Hamburger, 63), DROP (Feldman et al.,
66), SHIFT, ALA, VSM
j
Performances:
 P
Y j  0 , 1 : warehouse
X
ation
 1
ij
j 1
ij
The p-Median Problem
Duality gap < 2,5 % on large pb with m = n = 200, p = 10
j opening
 0 , 1 : warehouse
j serving
Poor LP-bounds  Commercial package isn't efficient
decision
retailer
i
Possible extensions: Handling fee of products, Distance limit
of links, Consideration of existing facilities.
9
10
SS-Capacitated Facilty Location
SS-Capacitated Facilty Location
SS-Location/Allocation
Min
transport
n
m
i1
j 1
 
Single-Source Capacitated Facility Location Problem:
C
m
Select warehouses from m potential locations to serve n
retailers.
warehouses
total
s .t .

X
ij
j1
retailers
diX
i1
Y
Assumptions:
X
j

ij
X
ij

cost

j 1
f jY
ij
 q jY
costs
:
j
j
0 , 1 : warehouse
0 , 1 : warehouse

 fixed
m
 1
n

ij
ation
j opening
j serving
decision
retailer
i
1. Fixed cost for opening a warehouse
2. Maximal capacity of each warehouse
11
12
3
SS-Capacitated Facilty Location
Capacitated Facilty Location
Location/Allocation Problem
Solution techniques:
Lagrangian relaxation heuristics (Barcelo&Casanovas 84, Klincewicz&
Luus 86 , Sridharan 93, Beasley 93, Bramel & Simchi-Levi, 99)
Mutli-Source Capacitated Facility Location Problem:
Branch & Bound (Neebe&Rao 83), LP+heuristics (Daskin&Jones 93)
Select warehouses from m potential locations to serve n
retailers.
Performances:
Related to the transportation problem
Duality gap < 1,2 % on large pb with m = n = 100
warehouses
retailers
Poor LP-bounds  Commercial package isn't efficient
Assumptions:
Possible extensions: Handling fee of products, Distance limit
of links, Consideration of existing facilities.
1. Fixed cost for opening a warehouse
13
Capacitated Facilty Location
Min
total
n
transport
ation
m
 
cost
 fixed
costs
C ij X ij 

14
Capacitated Facilty Location
Solution techniques:
:
m
i 1 j 1
2. Maximal capacity of each warehouse
Branch&Bound based on omega/delta rules (Akinc&Khumawala 77)
f jY j
j 1
Heuristic rules: ADD, DROP, SHIFT, ALA, VSM (Jacobsen 83)
m
s .t .

X ij  d i
Lagrangian relaxation heuristics (Beasly 93)
j 1
Extension to Multi-commodity CFLP problem:
n

X ij  q j Y j
Benders decomposition (Geoffrion&Graves 74)
i 1
Y j  0 , 1  : warehouse
opening
X ij  0 : demande
allocation
decision
Cross decomposition (Lee, 93)
Lagrangian relaxation (Mazzola&Neebe 98)
15
16
4
Multi-Commodity Tri-Echelon Model
Multi-Commodity Tri-Echelon Model
(Pirkul & Jayaraman 96)
Plants
warehouses
MIN
Plant-warehouse shipping costs
+ Warehouse-Retailer transportation costs
+ Fixed plant openning costs
retailers
+ Fixed warehouse openning costs
s.t.
- Each retailer supplies from 1 warehouse for each prod.
- Warehouse capacity of any site is not exceeded
- Plant supply capacity for each prod. is not exceeded
Mutli-commodity Capacitated Facility Location Problem:
- Material balanving at each warehouse
Select warehouses and plants from a list of potential
locations to serve n retailers with p products.
- Nb of openned warehouses
- Nb of openned plants
17
18
Multi-Commodity Tri-Echelon Mode
Multi-Commodity Network Design
Solution techniques:
Commodity 1:
Origin
Lagrangian relaxation heuristics
20
Performances:
Commodity 2:
Origin
10
15
20
16
Duality gap < 2,7% for large problems (100 retailers, 20
warehouses, 10 plants, 3 prod.)
Destination 2
Destination 1
Very poor LP-bounds
Multi-Commodity Network Design Problem:
Possible extensions:
•
warehouse handling fees, distance limit of any link,
selection different warehouse types (small, medium, large)
Select the set of capacitated arcs to open to minimize the
fixed costs and flow transportation costs
•
Include many location models as special cases
•
Possibility to take into acount multi-modal logistics
19
20
5
Multi-Commodity Network Design
Multi-Commodity Network Design
Solution techniques:
MIN
  C ijk X ijk  
k (i , j )
st
Dual-ascent method for uncapacitated case (Magnanti et al 89, 94)
f ij Y ij
Lagrangian relaxation (Holmberg&Yuan 00)
(i, j )
d k ,


k
k
X ij 
X ji    d k ,
0,
j
j


 X ijk

 U ij Y ij :
if i  o ( k )
Benders decomposition (Geoffrion & Graves 74, Magnanti&Wong84)
if i  d ( k ), : Flow conservati on at node i
otherwise
Branch & Bound (Crainic et al 93, Melkote & Daskin 01)
Performances:
Arc capacity
Flow of commodity k
Good LP-bounds for a disagregating formulation of the
uncapacitated case
Arc openning decision of arc (i, j)
Very poor LP-bounds for capacitated case
k
X ijk  0 :
Y ij  0 , 1 :
21
22
Fixed Charge Facility Location
Uncapacitated Fixed Charge Facility
Location Problems
Select warehouses from n potential locations to serve n retailers
in order to minimise opening costs and transportation costs
similar to
p-median
SS-Capacitated Facilty Location
23
24
6
Fixed Charge Facility Location
€100
15
€200
10
15
€150
18
€130
12
B
€225
5
25
E
€210
16
15
G
24
12
30
H
s .t .
€175
24
€165
13
12
X
F
€230
22
22
25
19
K
J
€215
20
Y
22
transport
n
n
 

i1 j 1
n
s .t .

j1
ation
h i d ij X ij 
cost
 fixed
X ij 

j 1
serving
j
j
0 , 1 : warehouse
 0 , 1 : warehouse

ij
f jY
 1
i opening
decision
j serving
retailer
 = unit transportation cost
i
26
costs
:
Locate:At site that minimises
sum of fixed and routing costs
f jY j
opening

j 1
n

0 , 1 : warehouse
ij
:
Heuristic algorithm: ADD
X ij  1
X ij  Y j
Y j  0 , 1 : warehouse
j
 Y
X
costs
dij = distance from node i to warehouse j
21
Fixed Charge Facility Location
total
ij
ij
ij
 fixed
cost
n
hi = demand of node i
25
Min
X
hid
ation
fj = fixed cost of opening a warehouse at site j
L
19
j1

X
19
i1
j 1
I
€125
19
n
n
12
20
transport
n
 
16
18
€190
11
total

D
C
24
Min
18
22
A
Fixed Charge Facility Location
Assign:Demand nodes to
Find:Facility site that reduces
nearest facilities
total cost the most
decision
retailer
yes
j
Locate:At cost reducing site
Case where the warehouses are given, i.e. Y is given:
Cost reducing
site found?
serve each node i from the closest warehouse, i.e. the warehouse j
with minimal dij among all warehouse j such at Yj = 1.
No
STOP
27
28
7
Heuristic algorithm: ADD
Locate the first warehouse
A
A
B
C
D
E
F
G
H
I
J
K
L
total
fixl
fix+0,35total
0
150
444
990
120
1440
198
528
624
880
1102
1340
7816
100
2836
B
C
D
E
225 555 825 360
0 220 400 380
264
0 216 192
720 324
0 612
190
80 170
0
1248 720 288 864
363 451 649 275
768 448 736 192
546 260 312 312
1210 1276 1364 1034
1159 741 817 703
1220 780 680 860
7913 5855 6457 5784
200 130 150 225
2970 2179 2410 2249
F
900
520
360
216
180
0
627
672
156
1100
589
440
5760
175
2191
Heuristic algorithm: ADD
dij*hi
hi * min{dij, diI}
H
J
K
L
I
270 495 720 600 870 1005
330 480 420 550 610 610
492 336 240 696 468 468
1062 828 432 1116 774 612
125
60 120 235 185 215
1368 1008 288 1200 744 528
0 165 495 242 440 671
240
0 480 592 400 736
585 390
0 494 247 247
484 814 836
0 418 880
760 475 361 361
0 399
1220 920 380 800 420
0
6936 5971 4772 6886 5576 6371
190 210 165 230 125 215
2618 2300 1835 2640 2077 2445
Locate the second warehouse
G
A
B
C
D
E
F
H
J
K
L
G
I
A
0 225 555 720 360 720 270 495 720 600 720 720
B
150
0 220 400 380 420 330 420 420 420 420 420
C
240 240
0 216 192 240 240 240 240 240 240 240
D
432 432 324
0 432 216 432 432 432 432 432 432
E
120 120
80 120
0 120 120
60 120 120 120 120
F
288 288 288 288 288
0 288 288 288 288 288 288
G
198 363 451 495 275 495
0 165 495 242 440 495
H
480 480 448 480 192 480 240
0 480 480 400 480
I
0
0
0
0
0
0
0
0
0
0
0
0
J
836 836 836 836 836 836 484 814 836
0 418 836
K
361 361 361 361 361 361 361 361 361 361
0 361
L
380 380 380 380 380 380 380 380 380 380 380
0
total
3485 3725 3943 4296 3696 4268 3145 3655 4772 3563 3858 4392
fix
265 365 295 315 390 340 355 375 165 395 290 380
fix+0,35total 1485 1669 1675 1819 1684 1834 1456 1654 1835 1642 1640 1917
fi + fI
29
Heuristic algorithm: ADD
hi * min{dij, diI , diG}
30
Heuristic algorithm: ADD
Locate the third warehouse
hi * min{dij, diI , diG , diA}
B
C
D
E
F
H
J
K
L
A
G
I
A
0 225 270 270 270 270 270 270 720 270 270 270
B
0 220 330 330 330 330 330 420 330 330 330
150
C
0 216 192 240 240 240 240 240 240 240
240 240
D
0 432 216 432 432 432 432 432 432
432 432 324
E
80 120
0 120 120
60 120 120 120 120
120 120
F
0 288 288 288 288 288 288
288 288 288 288 288
G
0
0
0
0
0
0
0 495
0
0
0
0
H
0 480 240 240 240
240 240 240 240 192 240 240
I
0
0
0
0
0
0
0
0
0
0
0
0
J
0 418 484
484 484 484 484 484 484 484 484 836
K
0 361
361 361 361 361 361 361 361 361 361 361
L
0
380 380 380 380 380 380 380 380 380 380 380
total
2695 2770 2647 2689 2929 2641 3145 2845 4772 2661 2718 2765
fix
455 555 485 505 580 530 355 565 165 585 480 570
fix+0,35total 1398 1525 1411 1446 1605 1454 1456 1561 1835 1516 1431 1538
Locate the fourth warehouse
B
C
D
E
F
H
J
L
A
G
I
K
A
0
0
0
0
0 270
0 720
0
0
0
0
B
0 150 150 150 150 330 150 420 150 150 150
150
C
0 216 192 240 240 240 240 240 240 240
240 240
D
0 432 216 432 432 432 432 432 432
432 432 324
E
80 120
0 120 120
60 120 120 120 120
120 120
F
0 288 288 288 288 288 288
288 288 288 288 288
G
0
0
0
0
0
0
0 495
0
0
0
0
H
0 480 240 240 240
240 240 240 240 192 240 240
I
0
0
0
0
0
0
0
0
0
0
0
0
J
0 418 484
484 484 484 484 484 484 484 484 836
K
361 361 361 361 361 361 361 361 361 361
0 361
L
0
380 380 380 380 380 380 380 380 380 380 380
total
2695 2545 2307 2239 2479 2191 3145 2395 4772 2211 2268 2315
fix
455 655 585 605 680 630 355 665 165 685 580 670
fix+0,35total 1398 1546 1392 1389 1548 1397 1456 1503 1835 1459 1374 1480
31
32
8
Heuristic algorithm: ADD
Heuristic algorithm: ADD
hi * min{dij, diI , diG , diA , diK} Locate the fifth warehouse
Locate the sixth warehouse (STOP)
hi * min{dij, diI , diG , diA , diK , diD}
B
C
E
F
H
J
L
A
D
G
I
K
A
0
0
0
0 270
0 720
0
0
0
0
0
B
0 150 150 150 150 330 150 420 150 150 150
150
C
0 216 192 216 240 216 240 216 240 216
240 216
D
0
0
0
0 432
0 432
0 432
0
432
0
E
80 120
0 120 120
60 120 120 120 120
120 120
F
0 288 288 288 288 288 288
288 288 288 288 288
G
0
0
0
0
0
0 495
0
0
0
0
0
H
0 480 240 240 240
240 240 240 240 192 240 240
0
0
0
0
I
0
0
0
0
0
0
0
0
J
0 418 418
484 418 418 418 418 418 484 418 836
K
0
0
0
0 361
0 361
0
0
361
0
0
L
0
380 380 380 380 380 380 380 380 380 380 380
total
2695 1662 1556 1812 1620 1524 3145 1512 4772 1394 2268 1432
fix
455 930 860 730 955 905 355 940 165 960 580 945
fix+0,35total 1398 1512 1405 1364 1522 1438 1456 1469 1835 1448 1374 1446
B
C
E
F
H
J
L
A
D
G
I
K
A
0
0
0
0 270
0 720
0
0
0
0
0
B
0 150 150 150 150 330 150 420 150 150 150
150
C
0 216 192 240 240 240 240 240 240 240
240 240
D
432 432 324
0 432 216 432 432 432 432 432 432
E
80 120
0 120 120
60 120 120 120 120
120 120
F
0 288 288 288 288 288 288
288 288 288 288 288
G
0
0
0
0
0
0 495
0
0
0
0
0
H
0 480 240 240 240
240 240 240 240 192 240 240
I
0
0
0
0
0
0
0
0
0
0
0
0
J
0 418 418
484 418 418 418 418 418 484 418 836
K
0
0
0
0 361
0 361
0
0
361
0
0
L
380
380
380
380
380
380
0
380
380
380
380
380
total
2695 2118 1880 1812 2052 1764 3145 1968 4772 1850 2268 1888
fix
455 780 710 730 805 755 355 790 165 810 580 795
fix+0,35total 1398 1521 1368 1364 1523 1372 1456 1479 1835 1458 1374 1456
33
Heuristic algorithm: DROP
34
A Lagrangian Relaxation Approach
Initial problem
Locate:At all candidate facility
sites
L*
Find:Facility site whose removal
Assign:Demand nodes to

M in
s .t .

X
j1
X
yes
Remove:Facility from
cost reducing site
n
n
i1
j1
 
n
reduces total cost the most
nearest facilities

Y
Cost reducing
X
ij
j
ij
X
ij

n

j 1
f jY
j
 1
j
0 , 1  : w a r e h o u s e o p e n i n g
 0 , 1  : w a r e h o u s e s e r v i n g

ij
ij
 Y
hid
d e c isio n
re ta ile r i
site found?
No
STOP
35
36
9
A Lagrangian Relaxation Approach
A Lagrangian Relaxation Approach
Relaxed problem
L 

 M in 
n
n
i 1
j 1

h i d ij X
ij

n

j1
Solving the relaxed problem
f jY j 
n

i 1

i 1 

n

X
j 1
ij



L 

s .t .
X
ij
X
ij

 Yj
Yj 
0 , 1 : w a r e h o u s e

X
s e rv in g re ta ile r i
X
Constraint relaxed:
n
j1
ij
s .t .

Min    h i d ij   i X ij  f j

j1
 i 1
X ij  Y j , Y j  0 , 1 , X ij  0 , 1 

i 1
i
d ij   i
d ij   i
X
X
ij
ij

 f j Y j 


 f j Y j 

n

i
i 1
n

i 1
i
 Yj
ij
0 , 1 : w a r e h o u s e o p e n i n g
0 , 1 : w a r e h o u s e s e r v i n g

d e c is io n
r e t a i le r i
38
A Lagrangian Relaxation Approach
Solving the relaxed problem
L   
  h
n
i
37
A Lagrangian Relaxation Approach
n
n
i 1
X ij  1
L()  L*
n
n
j1
s .t .
Yj 


    h

M in 

n
j1
0 , 1 : w a r e h o u s e o p e n i n g d e c i s i o n

 M in


Dual Problem

Y j    i

i 1

L  *  
n
L 
MAX


s .t .
L    Min

n
n
 
i 1 j 1
For each j,
X ij  Y j
Y j  0 , 1 : warehouse
Vj = fj + i min(0, hidij – i)
X ij  0 , 1 : warehouse
Yj = 1, if Vj < 0 and Yj = 0, if not.
h i d ij X ij 
opening
serving
n

j 1
f jY j 
n

i 1

i 1 



X ij 

j 1

n

decision
retailer
j
Xij = 1, if hidij – i < 0 and Xij = 0, if not.
L() = j min(0, Vj ) + i i
39
40
10
A Lagrangian Relaxation Approach
Dual gradient
 L 
i
1
Gradient method for solving dual problem
Step 1: Initialisation: i = 0.
n

j 1
X ij
Step 2: Solving the relaxed problem L()
as
L    Min

n
n
 
i 1 j 1
A Lagrangian Relaxation Approach
h i d ij X ij 
n

j 1
f jY j 
X ij  Y j , Y j  0 , 1 , X ij  0 , 1 
n

i 1

i 1 



X ij 

j 1

n

Step 3: Derive a feasible solution from solution of L() and save the bestso-far solution
Step 4: Derive gradients L()/ i.
Step 5: Update : i  i + s . L()/ i with
s 
C L *  L 

 L 
 i
n 

i 1 

2
 


Step 6: If not converge, i.e. iL()/ i , go to Step 2.
41
A Lagrangian Relaxation Approach
Derive a feasible solution from solution of L()
42
A Lagrangian Relaxation Approach
Derive a feasible solution from solution of L()
Let Xij, Yj be the solution of the relaxed problem L().
Let Xij, Yj be the solution of the relaxed problem L().
If no warehouse is opened, i.e. Y = 0, open any warehouse and assign all
demans to it
If no warehouse is opened, i.e. Y = 0, open any warehouse and assign all
demans to it
If some warehouses are opened, i.e. Yj = 1 for some j, then assign each
demand site to its nearest warehouse such that Yj = 1 .
If some warehouses are opened, i.e. Yj = 1 for some j, then assign each
demand site to its nearest warehouse such that Yj = 1 .
Can also be solved by column generation.
Can also be solved by column generation.
43
44
11
Hodder & Dincer (1986)

The state of the art of
mathematical models for
Global Supply chain network
configuration




Cohen and Lee (1989)




A deterministic non-linear MIP model, based on
EOQ techniques, to develop a global resource
deployment policy.
Maximises the total after-tax costs for the
manufacturing facilities and distribution centres.
Subject to "managerial constraints" (resource and
production constraints) and "logical consistency
constraints" (feasibility, availability, demand limits).
Determines product flow among vendors, plants,
DCs, markets and transportation channels.
International plant location with financial
capacities.
Include exchange rate fluctuations, market prices,
international interest rates, and fixed costs.
Maximise Expected after-tax profit - Risk
aversion factor  Variance of profit.
Constraints: plant capacity, an upper bound on the
market demands, financial constraints, and bounds
on decision variables.
A large-scale non-linear MIP solved by heuristics.
Cohen and Moon (1990)




Extend Cohen and Lee (1989) by developing a
constraint optimisation model, called PILOT.
Investigate the effects of parameters on supply
chain cost.
Consider the location problem of manufacturing
facilities and distribution centres.
Conclude that there are a number of factors that
may dominate supply chain costs under a variety
of situations, and that transportation costs play a
significant role in the overall costs of supply chains
operations.
12
Vidal and Goetschalckx (00)




Kouvelis and Rosenblatt (1997)
First attempt to include supplier reliability in a
strategic production-distribution model.
Consider a zero-echelon system.
Include deterministic exchange rates, material flow
linkage constraints, and a set of linearized
suppliers' reliability constraints.
Probability of being on time of all suppliers
shipping to each plant is at least a specified target
value.



A mixed integer programming model to prescribe
an optimal design for an international logistic
network.
Focus on policies that individual countries adopt to
attract international trade, including taxation,
subsidised financing and local content rules.
Demonstrate the sensitivity of the global network
design to even small changes in the policies of just
one country.
GSCM model of Arntzen et al (1995)





Develop a MIP model, called global supply chain
model (GSCM) with numerous experiences at DEC
and later become JD Edwards.
Can accommodate multiple products, facilities,
stages, time periods, and transportation modes.
Minimises a composite function of: activity days
AND total cost.
Input: bills of materials, demand volumes, costs
and taxes, and activity day requirement
Output: Nb and location of DCs, customer-DC
assignment, Nb of stages, the product-plant
assignment.
GSCM model of Arntzen et al (1995)


Use Global Bill Of Materials (GBOM) to represent
possible material flows across the supply chain.
May include Sourcing options depending on prod.,
location, and stage.
Disk
PC Box
UK, Canada, Taiwan
Media
Motherboard
CPU
Chip
Customer
Region 1
Monitor
Taiwan, Spain, Mexico
Head
Disk
Array
Memory
Printer
Japan, Italy, USA
Software
Germany, USA, HK
System
UK
USA
Taiwan
Canada
Parent
Customer
Region 2

Customer
Region n
Children
13
GSCM model of Arntzen et al (1995)
Minimise
[facility production & MH costs + transportation
costs
+ Duty + Taxe
+ facility fixed charges + product line fixed costs
+ fixed costs associated with mfg methods
- duty drawback – duty avoidance]
GSCM model of Arntzen et al (1995)
Production/inventory/shipping constraints

+




(1-)[Processing activity days for all product-facilities
+ Transit activity days for all prod. at all links for
all modes]

GSCM model of Arntzen et al (1995)
Offset trade and local content constraints


Local value added in nation n >= nNational
sale values.
Local value added in nation n >=
nWorldwide sale values (USA, EU).
Demand for each product-period-customer
region.
Balance of inventory, production and
shipping for each product-period-facility.
Total weight of products through a facility is
limited
Production at each facility-method is limited
Lower bound <= Production, inventory and
shipping at each facility-period-product <=
Upper bound
GSCM model of Arntzen et al (1995)
Duty drawback and duty relief constraints


Duty credit to total export of each product
out of a nation-group.
Duty credit to total export of each product
into a nation-group
14
GSCM model of Arntzen et al (1995)
GSCM model of Arntzen et al (1995)
Solution method based on:





Special structure of the problem.
Elastic penalties to distinguish hard and soft
constraints.
Row factorisation for computation of
cascaded material balance constraints
(Brown & Olson 94)
Constraint-branching enumeration.
Integrality gap < 0.0005 percent (?).
Unsolved issues of GSCM models
System configuration constraints




Limit on Nb of facilities making a product.
Limit on Nb of facilities for each type.
Limit on Nb of facilities using a method.
Given opened facilities, product-facility
decisions, and facility-method decisions.
Thèse de l’Université de Metz
Une approche d’optimisation basée sur la simulation
The following factors are particularly important
in designing global supply chains:




Impact of uncertainties,
Good estimation of network operation
performance measures (KPI),
Realist model for transportation
facilities,
Country related costs.
pour la conception des chaînes logistiques :
Applications dans les industries automobile et textile
présentée par
Hongwei DING
sous la direction de
Mrs. L. BENYOUCEF et X. XIE
MACSI
15
Contexte et motivations (1)
Contexte et motivations (2)
De plus en plus,

Sous l’effet de la globalisation
Marchés instables (offre-demande, …)
Systèmes manufacturiers, informatiques, … complexes
Cycle de vie des produits/technologies très réduits

…



Conception et pilotage des chaînes logistiques
Besoins industriels

Chaînes logistiques / Supply chains



Dans un futur proche, la
concurrence ne sera pas entre
entreprises mais entre chaînes
logistiques.


Outils d’aide à la décision
Prise en compte des
interactions entre les différents
niveaux décisionnels
Prise en compte des aspects
aléatoires et dynamiques
Prise en compte du niveau de
service client
Manques des méthodes
existantes



Modèles trop simplifiés et
données trop agrégées
Analyses peu réalistes (prise en
compte insuffisante des impacts
des aléas et des stratégies
opérationnelles)
Monocritère orientées coût
[Aikens 85], [Verter et Dincer 92], [Geoffrion et Powers 95],
[Slats et al. 95], [Vidal et Geotschalckx 97], [Beamon 98],
[Schmidt et Wilhelm 00], [Snyder 04], etc.
- Christopher 1992
61
62
Contexte et motivations (3)
Contexte et motivations (4)


Projet Européen GROWTH - ONE

Optimization methodology for Networked Enterprises
Période : 02/2001 – 02/2004

Partenaires industriels :

Partenaires académiques :


Nos contributions dans le projet ONE




Développement d’une approche d’optimisation basée sur la simulation
Définition des règles de pilotage pour la simulation des chaînes logistiques
Implémentation et intégration de l’approche dans l’outil ONE
Application de l’approche à deux cas d’étude
Reconfiguration d’un réseau
de production et de distribution
Objectif: Développer des approches pour la conception
et le pilotage des chaînes logistiques, s’appuyant sur des
modèles réalistes, avec la prise en compte :

des coûts, des délais, des taux de service

des impacts sociaux et environnementaux
Choix de fournisseurs
63
64
16
Plan de la présentation
Problème de conception et pilotage d’une chaîne

Conception



Conception et pilotage des chaînes logistiques

L’approche d’optimisation basée sur la simulation

Application dans l’industrie automobile

Application dans l’industrie textile



Conclusions et perspectives



Choix de fournisseurs
Localisation des sites
Choix des technologies utilisées
…
Pilotage


Planification de l’approvisionnement
Planification de la production
Gestion de stock
…
Décisions
stratégiques
Décisions
tactiques /
opérationnelles
Indicateurs de performances (KPIs)


Financiers : Coût d’investissement/désinvestissement, coût de production,
coût de transport, coût de stockage, etc.
Logistiques : Taux de demande satisfaite par le stock,
pourcentage des commandes livrés dans les délais souhaités, etc.
65
66
Etat de l’art (1)
Etat de l’art (2)
Choix de fournisseurs

Conception des réseaux de production et de distribution
Critères de choix


Prix, délai de livraison, qualité, capacité de production, etc.


AHP (Analytical Hierarchy Process)

Programmation mathématique
[Gaballa 74], [Weber et Current 93], [Weber et Ellram 93], etc.

Analyse par simulation


Modèles généraux de simulation des chaînes logistiques
[Jain et al. 01], [Rossetti et Chen 03], [Herrmann et al. 03], [Biswas et Narahari 04]
[Bagchi et al. 98], [Schriber et Brunner 03], [Kilgore 03], etc.
Manques

Localisation des sites et gestion de stock
[Erlebacher et Meller 00], [Nozick et Turnquist 01], [Daskin et al. 02], [Shen et al. 03], etc
[Narasimhan 83], [Dyer et Forman 92], [Korpela et Tuominen 96], etc.

Localisation des sites
[Geoffrion et Graves 74], [Cohen et Lee 85, 89], [Arntzen et al. 95], [Verter et Dincer 95],
[Bel et al. 96], [Canel et Khumawala 97], [Jayaraman et Pirkul 01], etc.
Méthodes existantes

Modèles de programmation mathématique

[Dickson 66], [Ellram 90], [Weber et al. 91], [Barbarosoglu et Yazgac 97], etc.
[AHP] Les attributs importants associés à chacun des
fournisseurs sont connus avec certitude.
=> Absence des incertitudes et de la dynamique de la chaîne
Seuls les aspects relatifs aux fournisseurs sont considérés
=> Non prise en compte des aspects liés au transport, à la gestion
de stock, etc.

Analyses spécifiques
[Towill et al. 92], [Petrovic et al. 99], [Bhaskaran 98], etc.

Manques des modèles d’optimisation



67
[Vidal97] Peu de
modèles considèrent les
aspects stochastiques,
e.g. les délais, etc.
Non prise en compte des aléas.
Aspects tactiques et opérationnels souvent ignorés lors de la conception.
Monocritère orientés coût.
68
17
Objectif de la thèse
Plan de la présentation
Développement d’une approche de conception fondée
sur un modèle réaliste avec la prise en compte des :

Interaction entre les décisions à différents niveaux




Incertitudes tout au long de la chaîne




Décisions stratégiques
Décisions tactiques
Décisions opérationnelles
Demande aléatoire
Délai de transport aléatoire
Fournisseur non-fiable, …
Multicritères

Dans le but de concevoir une
chaîne qui est
opérationnellement efficace
Coûts d’investissement et opérationnels

Conception et pilotage des chaînes logistiques

L’approche d’optimisation basée sur la simulation

Application dans l’industrie automobile

Application dans l’industrie textile

Conclusions et perspectives
Coûts d’investissement/désinvestissement, coût d’approvisionnement,
coût de production, coût de transport, coût de stockage, etc.

Niveau de service client

…
Pourcentage des produits livrés dans les délais souhaités, etc.
69
70
Approche proposée (SIM-OPT)
Module de simulation
Algorithmes
Génétiques
Ce module a pour objectif d’évaluer les performances
Module d’optimisation
configuration
+
règles de pilotage
Indicateurs de
performance

Une configuration de la
chaîne étudiée

Module de simulation


71
Fournisseur, usine, centre de
distribution, client
Connexion de transport
Liaison d’information,
entreprise

Le système de pilotage
associé


Gestion de stock, planification
de la production, gestion des
ordres de production, etc.
Affectation des ordres
d’approvisionnement,
répartition des produits
transportés, etc.
72
18
Caractéristiques des entités

Fournisseur

Usine

Centre de distribution
Prix, délai d’approvisionnement, quantité minimale acceptée par ordre, etc.

Capacité de production, délai de production, coût de production, etc.

Affectation des
ordres de production
Capacité de stockage, coût de stockage unitaire, etc.


Exemple de modélisation
Client
Gestion
de stock
Demande moyenne, fréquence des demandes, type de comportement, etc.


Connexion de transport

Liaison d’information

Entreprise
Capacité de transport, délai de transport, coût de transport unitaire, etc.

Expéditeur des ordres, récepteur des ordres, etc.

Ordonnancement
des ordres
Pour rôle la gestion des flux d’information
=> information centralisée et partagée

Planification
de la production
Chargement des
moyens de transport
73
Règles de pilotage d’une chaîne


74
Caractéristiques des règles
Règles locales
DIFFICULTE !!!

Ordonnancement des ordres de fabrication et livraison

Politique de gestion de stock

Règles de chargement des moyens de transport

Règles de départ des moyens de transport

…
Règles globales

Planification du service client ‘qui sert qui’

Affectation des approvisionnement internes

Affectation des approvisionnement externes

Choix des connexions de transport utilisées

…
Création automatique des
modèles de simulation
pour différentes
configurations.
Module
’optimisation
dd’optimisation
Moduled’



Départ régulier
Prêt à partir
Départ suivant
un planning
configuration
+
règles de pilotage
Indicateurs de
performance
Module
Modulede
desimulation
simulation
SOLUTIONS
Règles génériques et
flexibles capables de
s’adapter à différentes
configurations.
75
76
19
Exemple d’une règle de pilotage (1)
Exemple d’une règle de pilotage (2)




Le plus proche
Le plus rapide
Optimisation
statique
…
Configuration 1:
Configuration 2:
DC1 + DC3
DC1 + DC2 + DC3
Règle choisie:
Le plus proche
C1

Décisions : Fermeture/ouverture des centres de distribution (DC)

Hypothèse : Chaque client est servi par exactement un DC

Question : Pour chaque client, quel DC assurera son service ?
Règle choisie:
Le plus rapide
C2
DC1
C3
DC2
C4
DC3
Cn
77
Implémentation


Module d’optimisation
Exigences d’optimisation de l’approche SIM-OPT
Un environnement pour la simulation par événements discrets

Ordonnanceur des événements, moteur de simulation, etc.
Un cadre de simulation des chaînes logistiques

78
Entités principales, règles de pilotage, etc.
CFacility
CTransportationLink
2
1..*
C++
CTransportationNetwork
CSupplier

Optimisation combinatoire avec variables qualitatives et quantitatives

Bruits importants des résultats de la simulation

Capable d’apprendre des expériences de simulation

Multicritères
CInformationLink
#id : int
#name : string
#location : string
#existing : bool
#closable : bool
#active : bool
#associate_cost : float
#predecessor_list : list<int>
#successor_list : list<int>
#incoming_link_list : list<int>
#outgoing_link_list : list<int>
2
Pourquoi les algorithmes génétiques ?
1..*

Algorithme de recherche [Goldberg 89]

De nature stochastique et itérative
CEnterprise

CManufacturer
CDistributer
CCustomer

79
Fitness = Qualité
Besoin uniquement de la fitness pour guider la recherche des
solutions optimales
Cherche les solutions d’une population à une autre
80
20
Algorithmes génétiques multicritères (MOGA)

Codage adopté
Décisions stratégiques
Non-dominated Sorting GA-II (NSGA-II)

Un des meilleurs MOGAs jusqu’à présent

Optimalité au sens Pareto

Sélection élitiste

Méthode de classement efficace

[Deb et al. 02]
Décisions tactiques/opérationnelles
Configuration de la chaîne
Choix de règles
‘1’ : ouverture du site
Paramètres associés
‘0’ : fermeture du site
‘1’ : choix de la règle numéro 1
‘2’ : choix de la règle numéro 2‘R’ : point de commande
‘Q’ : quantité commandée
81
Pseudo algorithme (1)
82
Pseudo algorithme (2)
Étape1: Création d’une population initiale
1 chromosome = 1 solution candidate (configuration + règles)
Étape3: Classement et sélection des chromosomes pour
l’opération de croisement
Étape2: Évaluations de toutes les solutions candidates
Étape4: Opérations de croisement et mutation pour la
reproduction d’une nouvelle population
f1 = ∑(différents coûts); f2 = Niveau de service; f3 = …; etc.
0 1 1 0
Étape5: Lancement de la procédure de vérification et de
réparation des chromosomes
Fournisseur 1
Fournisseur 2
0 0 1 1
0 1 0 0
0 0 1 1
Fournisseur 3
Centre de
distribution
Fournisseur 2
KPI
Fournisseur 3
1 0 0 0
Mutation
Croisement
Client
Faisabilité ?
Fournisseur 4
Centre de
distribution
Client
 Coût d’approvisionnement
 Coût de transport
 Coût de stockage
 Taux de demandes satisfaites
…
83
0 0 1 1
0 0 1 0
0 1 1 0
0 1 1 1
0 0 0 0
Réparation
1 0 0 0
84
21
Pseudo algorithme (3)
Plan de la présentation
Étape1: Création d’une population initiale
1 chromosome = 1 solution candidate (configuration + règles)
Étape2: Évaluations de toutes les solutions candidates

Conception et pilotage des chaînes logistiques

L’approche d’optimisation basée sur la simulation

Application dans l’industrie automobile

Application dans l’industrie textile

Conclusions et perspectives
f1 = ∑(différents coûts); f2 = Niveau de service; f3 = …; etc.
Étape3: Classement et sélection des chromosomes pour
l’opération de croisement
Étape4: Opérations de croisement et mutation pour la
reproduction d’une nouvelle population
Étape5: Lancement de la procédure de vérification et de
réparation des chromosomes
Aller à Étape2
85
86
Cas d’étude

Stratégies de production et de distribution
Italie
Reconfiguration d’un réseau de production et de distribution

Ouverture/fermeture de sites

Gestion de stock

Usine1
train
HB1
HB2
Client1
train
camion
camion
camion
train
train
train
train
train
Client2
RDC1
train
CDC
Usine2
Affectation des ordres de production
Allemagne
bateau
train
camion
camion
camion
camion
Usine3

Véhicules standards




87
MTS
MTO
Make-to-Stock (MTS)
Volume important
Délai de commande court
Coût de stockage important
Client...
RDC2
Client...
RDC3
camion
RDC4

Client16
Véhicules haut de gamme



Make-to-Order (MTO)
Délai de commande long
Coût de stockage faible
88
22
Modélisation de la chaîne
Analyse par scénarios
Seules les décisions de fermeture des RDCs (2,3,4)
Toutes les règles de pilotages sont fixées à l’avance
Performances évaluées par simulation

Production
Gestion de stock
multi-niveaux

Affectation des
clients aux DCs

Configuration
décentralisée
actuelle
Délais de transport
aléatoires
Configuration
centralisée
suggérée
Demandes aléatoires
Indicateurs de performances
Coûts: désinvestissement, production, transport, stockage
 Temps de réponse moyen aux demandes clients
 Pourcentage des véhicules livrés dans les délais promis

La configuration centralisée est préférée


Espace de
solutions limité !
Réduction du coût de stockage (économie d’échelle)
Réduction du temps de réponse aux demandes clients
89
Optimisation basée sur la simulation
Affectation
par ratios
90
Expériences numériques et analyses (1)
(R, Q) ou (s, S)

Paramètres GAs

Le plus proche






Simulation



Décisions à prendre




Fermeture/ouverture des usines et
des RDCs (2, 3, 4)
Politiques de gestion de stock dans
CDC et RDC1
Affectation des ordres de production
aux usines ouvertes
91

NSGA-II adapté
Nombre de générations : 2000
Taille de la population : 100 individus
Sélection: Tournoi binaire
Croisement: Un point et deux points, Pcross = 0.1
Mutation: Uniforme, Pmut = 0.9
Horizon de simulation : 3 ans
Période de réchauffement : 3 mois
5 simulations pour chaque solution candidate
Paramètres
déterminés selon des
tests effectués
Indicateurs


CPU Pentium IV 1.5 GHz, 256 Mb de mémoire
Temps de calcul : 17.2 heures
92
23
Expériences numériques et analyses (2)

Min. du coût total moyen



Désinvestissement, production, transport, stockage
Demandes générées
Min. du temps de réponse moyen aux demandes clients

Temps écoulé entre l’instant de la réception d’une commande et la
livraison de cette commande.
5700
5600
Coût total moyen (€)
g1
g2
5662
8.7
Politique de
stockage
Configuration
CDC
5500
5400
10
12
14
Temps de réponse moyen (jour)
16
Poids
d'affectation
R
Q
s/R
S/Q
P1
P2
P3
1648
1200
1936
3099
4
6
4
5638
8.8
P1+P2+CDC+RDC1 (R,Q )
(s,S )
8.9
P1+P2+CDC+RDC1 (R,Q )
(R,Q )
1648
1200
1997
1998
4
6
7
9
P1+P2+CDC+RDC1 (R,Q )
(s,S )
1648
1200
1997
3154
4
6
8
5349
9.1
(s,S )
1934
309
1964
3134
8
8
4
9.3
9.4
P1+CDC+RDC1
P1+CDC+RDC1
P1+CDC+RDC1
(R,Q )
(R,Q )
(R,Q )
(s,S )
(s,S )
1755
1973
1973
1201
329
322
1996
1953
1980
3166
3190
3195
4
3
4
6
6
7
4
7
9.5
P1+CDC+RDC1
(R,Q )
(s,S )
1922
322
1962
3187
2
7
4
9.6
P1+CDC+RDC1
(R,Q )
(s,S )
1816
320
1856
3026
2
7
4
10.2

Conception et pilotage des chaînes logistiques

L’approche d’optimisation basée sur la simulation

Application dans l’industrie automobile

Application dans l’industrie textile

Conclusions et perspectives
4
5295
5227
5218
5100
RDC1
(s,S )
RDC1
5573
5303
5200
P1+P2+CDC+RDC1 (R,Q )
CDC
5615
5340
Frontière Pareto
5300
8
Plan de la présentation
P1+CDC+RDC1
(R,Q )
(R,Q )
1642
322
1987
1218
5
6
3
5216
10.4
P1+CDC+RDC1
(R,Q )
(R,Q )
1487
352
1990
1617
2
8
8
5211
10.5
P1+CDC+RDC1
(R,Q )
(R,Q )
1685
351
1679
1672
7
2
6
5202
10.6
P1+CDC+RDC1
(R,Q )
(R,Q )
1394
314
1965
1680
2
8
7
5193
11.1
P1+CDC+RDC1
(R,Q )
(R,Q )
1188
388
1996
1669
1
8
3
5191
11.2
P1+CDC+RDC1
(R,Q )
(R,Q )
1395
302
1965
1680
1
8
7
5181
11.4
P1+CDC+RDC1
(R,Q )
(R,Q )
1395
341
1755
1652
1
6
6
5176
11.9
P1+CDC+RDC1
(R,Q )
(R,Q )
1177
348
1965
1230
7
5
5170
12
P1+CDC+RDC1
(R,Q )
(R,Q )
970
1305
1648
1454
8
5
5167
12.9
P1+CDC+RDC1
(R,Q )
(R,Q )
664
335
1990
1675
5
8
4
5162
13
P1+CDC+RDC1
(R,Q )
(s,S )
545
1305
1755
2963
8
7
2
5160
13.8
P1+CDC+RDC1
(R,Q )
(R,Q )
638
339
1990
1669
5
2
4
5142
14.1
P1+CDC+RDC1
(R,Q )
(R,Q )
613
328
1933
1652
3
8
7
3
3
93
Situation actuelle
94
Présentation du problème
Choix de fournisseurs
Actuel
Moins cher
Moyen

Fournisseur



Transport


Plus rapide
Délai de disponibilité pour le transport assez long
Prix non compétitif par rapport à de nouveaux fournisseurs

Délai de transport très long et aléatoire
Client

Demande saisonnière
95
Décisions à prendre

Choix parmi les 4 fournisseurs candidats

Paramètres de la politique de gestion de stock du DC1

Affectation des ordres d’achat (DC1 -> Fournisseurs sélectionnés)

Répartition des produits sur les différentes connexions de transport
96
24
Modélisation
Expériences numériques et analyses (1)
Gestion de
stock (R,Q)
Paramètres GAs

Demandes
aléatoires
Affectation
par ratios
NSGA-II adapté
Nombre de générations : 2000
Taille de la population : 100 individus
Sélection: Tournoi binaire
Croisement: Un point et deux points, Pcross = 0.1
Mutation: Uniforme, Pmut = 0.9




Délais de transport
aléatoires
Répartition
par ratios


Règles de pilotage utilisées




Simulation

Gestion de stock par point de commande et quantité économique (R, Q)
Règle d’affectation des ordres d’achat basée sur les ratios
Règle de répartition des produits transportés basée sur les ratios
Horizon de simulation : 3 ans
Période de réchauffement : 3 mois
5 simulations pour chaque solution candidate



Indicateurs

CPU Pentium IV 1.5 GHz, 256 Mb de mémoire
Temps de calcul : 15.6 heures
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
97
98
Expériences numériques et analyses (2)
Min. du coût total moyen



Engagement, approvisionnement, transport, stockage

Max. du pourcentage des demandes client satisfaites
par le DC1 sans délai
Optimisation locales
Seule optimisation des paramètres quantitatifs
en fixant le choix de fournisseurs à l’avance.


> 97%
95%
90%
85%
< 97%
80%
23
25
27
29 31 33 35
Coût unitaire (€/paire)
37
39
100%
f1(€)
f2 (%)
Fournisseurs
choisis
Poids d’affectation
R
Q
40,26
100
S2 + S3
L2(16, 23, 16) + L3(31)
6351
1652
39,51
99,99
S2 + S3
L2(23, 10, 23) + L3(31)
6465
1590
…
…
…
…
…
…
28,71
97,19
S2 + S3
L2(16, 23, 21) + L3(31)
5363
1681
28,60
97,17
S2 + S3
L2(16, 19, 24) + L3(31)
5309
1625
28,56
96,90
S2
L2(24, 21, 22)
7196
1187
28,39
96,76
S2
L2(19, 30, 24)
6780
1216
…
…
…
…
…
…
23,67
82,08
S2
L2(29, 24, 24)
6033
1005
23.66
81.86
S2
L2(25, 27, 17)
5760
1001
Pourcentage des demandes client satisfaites
sans attente
Pourcentage des demandes clients satisfaites
sans attente
100%
Fournisseur S2
Fournisseurs S2 + S3
> 97%
99%
GOpt
98%
LOpt_S2
LOpt_S2+S3
97%
28
41
30
32
34
36
38
Coût unitaire (€/paire)
99
Pourcentage des demandes client satisfaites
sans attente

Vérifications (1)
40
42
44
97%
96%
95%
94%
93%
92%
91%
90%
89%
88%
87%
86%
85%
84%
83%
82%
81%
80%
< 97%
GOpt
LOpt_S2
LOpt_S2+S3
23
24
25
26
27
28
Coût unitaire (€/paire)
29
30
100
25
Vérifications (2)

Plan de la présentation
Recherche exhaustive



Un problème simplifié avec seulement deux fournisseurs
Frontière Pareto réelle obtenue par la recherche exhaustive
Solutions obtenues en appliquant l’approche proposée
Pourcentage des demandes client satisfaites
sans attente
100%
95%
90%

Conception et pilotage des chaînes logistiques

L’approche d’optimisation basée sur la simulation

Application dans l’industrie automobile

Application dans l’industrie textile

Conclusions et perspectives
Frontière Pareto réelle
85%
Resultats obtenus par
optimisation
80%
17 19 21 23 25 27 29 31 33 35 37 39
Coût unitaire (€/paire)
101
102
Conclusions

Innovations et originalités de l’approche


Perspectives

Évaluation fiable des performances de la chaîne sous des
conditions réalistes

Incertitudes bien couvertes

Optimisation combinatoire et multicritères pilotée par GA

Validation de l’approche sur deux cas d’étude
Développement d’un module de simulation plus
générique et complet



Développement d’un logiciel d’optimisation/simulation
et intégration dans l’outil ONE
103
Conception et implémentation de mécanismes de pilotage
pour une simulation plus réaliste de la chaîne
Développement d’autres techniques d’optimisation
Comparaison avec des méthodes analytiques
existantes sur des exemples numériques
104
26
Chapter 3. Managing economies of scale in a supply
chain: cycle inventory
Learning objectives:
1. Balance the appropriate costs to choose the optimal amount of
cycle inventory in a supply chain
2. Understand the impact of quantity discount on lot size and
cycle inventory
3. Devise appropriate discounting schemes for a supply chain
4. Understand the impact of trade promotions on lot size and
cycle inventory
5. Identify managerial levers that reduce lot size and cycle
inventory in a supply chain without increasing cost
Role of cycle inventory
1
The role of cycle inventory in a
supply chain
Why do companies hold inventory?
Why might they avoid doing so?
• WHY?
• A lot or batch size is the quantity that a stage of a SC
either produces or purchases at a time.
• The lot size is usually larger than the quantities
demanded by the customer.
• Cycle inventory is the average inventory in a SC due to
this difference.
– To take advantage of economic purchase order size :
economy of scale (cycle inventory)
– To meet anticipated customer demand
– To account for differences in production timing
(smoothing)
– To protect against uncertainty (demand surge, price
increase, lead time slippage)
– To maintain independence of operations (buffering)
• WHY NOT?
– Requires additional space
– Opportunity cost of capital
– Spoilage / obsolescence
2
Key point : Cycle inventory exists in a SC bcs different stages
exploit the economies of scale to lower total cost. The costs
considered include: material cost, fixed ordering cost, and
holding cost.
3
4
1
The role of cycle inventory in a
supply chain
Two Decisions in
Inventory Management
On-hand
Inventory
• Example: Consider a computer store selling an average of D = 4
printers a day but ordering Q = 80 printers from the
manufacturer each time.
• Cycle inventory = lot size/2 = Q/2 = 40
• Average flow time = cycle inventory/demand rate = 40/4 = 10
days (inventory holding time)
• Inventory turnover (taux de rotation), inventory coverage (taux
de couverture)
Q
Q/2
• When is it time to reorder?
• If it is time to reorder, how much?
Demand
Rate, D
Average Cycle
Inventory, Q/2
Time
5
6
Economies of scale to exploit fixed costs:
Economic Order Quantity Model
Time Between Orders
On-hand Inventory
(Cycle Time) T = Q/D
Economies of scale to exploit fixed costs:
Economic Order Quantity Model
Q
Demand
Rate, D
Average Cycle
Inventory, Q/2
Q/2
Reorder
Point, R
Place
Order
Receive
order
Time
Lead
Time,
L
7
8
2
Economic Order Quantity Cost Model:
Constant Demand, No Shortages
Basic EOQ Assumptions
Constant Demand Rate
Constant Lead Time
Orders received in full after lead-time.
Constant Unit Price (no discounts)
=
=
=
=
=
=
total annual inventory cost
annual demand (units / year)
order quantity (units)
cost of placing an order or setup cost ($)
cost per unit
annual interest rate
Total Annual
Inventory
=
Cost
TC
=
Annual
Ordering
Cost
Annual
+ Holding
Cost
(D / Q) K + (Q / 2) Ic
9
10
(Constant Demand, No Shortages)
Many orders,
low inventory
level
Total
Cost
Carrying
Cost
On-hand Inventory
Trade-off in EOQ Model:
Inventory Level vs. Number of Orders
Cost Relationships for Basic EOQ
Q
Time
Q
Few orders,
high inventory
level
Ordering
Cost
Q*
Order Quantity (how much)
EOQ balances carrying
costs and ordering
costs in this model.
On-hand Inventory
•
•
•
•
TC
D
Q
K
c
I
Time
11
12
3
EOQ Results (How Much to Order)
Determining When to Reorder
(Constant Demand, No Shortages)
Economic Order Quantity = Q* =
• Quantity to order (how much…) determined by EOQ
• Reorder point (when…)determined by finding the
inventory level that is adequate to protect the
company from running out during delivery lead time
• With constant demand and constant lead time,
(EOQ assumptions), the reorder point is exactly the
amount that will be sold during the lead time.
2DK
Ic
Number of Orders per year = D / Q*
Length of order cycle T = Q* / D
Example:
Total cost = TC = (D / Q*) K + (Q* / 2) Ic
13
14
Exercise
EOQ Example
Question: What if the company can only order in multiples
of 12? (That is, order either 0 or 12 or 24 or 36, etc…)?
D = 1,000 units per year
BE CAREFUL!
S = $20 per order
IC = $8.33 per unit per month
HOW MUCH TO ORDER?
WHEN TO ORDER?
Number of orders per year =
Length of order cycle = T =
Total cost =
15
16
4
Robustness of EOQ model
Example: EOQ Robustness
• Suppose that in the last problem, you have mis-specified the
order costs by 100% and the holding costs by 50%. That is,
Very Flat Curve - Good!!
– S used in the computation = $40/order (actual cost = $20 / order)
– IC used in computation = $150 / unit / year (actual = $ 100)
– Then, using these wrong costs, you would have gotten
Total
Cost
TC
Q*-Q
Q*
Q*+Q
Q' 
Order Quantity
Would have to mis-specify Q* by quite a bit
before total annual inventory costs would
change significantly.
2(1,000)40
 23.1
150
Your actual TC (computed substituting Q’ into TC, using correct costs of S = $20, and h = $100:
TC 
1,000
23
20  100  $2,019
23
2
Only 1% above minimum TC!
17
18
Key points
KP1 : Total ordering and holding costs are relatively stable around the
economic order quantity. A firm is often better served by ordering a
convenient lot size close to the EOQ rather than the precise EOQ
(robustness).
KP2 : If the demand increases by a factor of k, the optimal lot size
increases by a factor of k0,5 . The number of orders placed per year
increases by a factor of k0,5. Flow time due to cycle inventory decreases
by a factor of k0,5.
KP3 : To reduce the optimal lot size by a factor of k, the fixed cost K must
be reduced by a factor of k2.
KP4 : Aggregating replenishment across products, retailers, or suppliers in
a single order allow for a reduction of lot size of individual products bcs
the fixed costs are now spread across differents aggregated entities. 19
Lot sizing with multiple products or
customers
20
5
Assumptions :
• In general, the ordering, transportation, and
receiving cost of an order grows with the
variety of products or pickup points.
• A portion of the fixed cost of an order can be related to
transportation (this depends only on the load but not
on the product variety)
• A portion of the fixed cost is related to loarding and
receiving (this cost increases with variety on the truck)
21
Three approaches :
• similar to EOQ model except the followings.
• Di : annual demand for product i
• S: order cost incurred each time an order is placed,
independent of the variety of products included
• si: additional order cost incurred if product i is
included in the order.
22
Example:
• Best Buy sells 3 models of computers, the Litepro, the
Medpro, the Heavypro.
• The annual demands are DL = 12000, DM = 1200, DH =
120.
• Each model costs Best Buy 500$.
• A fixed transportation cost of 4000$ is incurred each
time an order is delivered. For each model ordered and
delivered on the same truck, an additional fixed cost of
1000$ is incurred for receiving and storage.
• Best Buy has an annual holding cost of 20%.
1. Each product manager orders his model
independently (highest cost)
2. The product managers jointly order every product in
each lot (easy to administer and implement, but not
selective enough and expensive joint ordering if
product specific order cost high)
3. Product managers order jointly but not every order
contains every product, i.e. each lot contains a
selected subset of products.
23
24
6
Approach 1 : Independent ordering
• QL = 1095, QM = 346, QH = 110.
• Oder frequencies : 11/year, 3,5/year, 1.1/year.
• Total inventory cost = 155140 $
• Other measures of interest : cycle inventory, annual
holding cost/prod, annual ordering cost, flow time.
Approach 2 : Lots ordered and delivered for all
• Combined fixed order cost/order : K = S +  si
• The optimal order frequency is (to explain, express
total cost in T):
k
n* 
D I c
i 1
i i i
2K
• Example : n* = 9.75, annual inventory cost = 136528$,
i.e. a reduction of 13% over approach 1.
25
Approach 3 : Lots ordered and delivered jointly for a
selected subset of products
Step 1. Identify most frequently ordered product
assuming each being ordered independently.
n  max ni
i
ni 
Di I i ci
2  S  si 
26
Step 2. Identify the frequency with which other products
are included.
• Calculate the order frequency as a multiple of n
• As the most frequently ordered product is in each
order, the inclusion of a product i incurs an additional
product specific fixed order cost of si.
• Product i is included once every mi orders
mi   n ni 
The most frequently order products i* is included each
time an order is placed
27
ni 
Di I i ci
2si
28
7
Step 3. Recalculate the order frequency of the most
frequently order product n.
n
Step 4. For each product, evaluate the order frequency ni
= n/mi and the total cost of such an ordering policy.
D I c m
2S   s m 
i i i
i
i
Example :
n = 11.47, mL = 1, mM = 2, mH = 5,
annual total inventory cost = 130767$, a reduction of
4% over approach 2.
i
Why? (order cycle T for n, order cycle miT for i)
29
30
Key point:
• A key to reducing cycle inventory is the reduction of
lot size.
• A key to reducing lot size without increasing costs is
to reduce the fixed cost associated with each lot.
• This may be achieved by reducing the fixed cost itself
or by aggregating lots across products, customers,
suppliers.
• When aggregating, tailored aggregation is best,
especially if product specific costs are large.
Economies of scale to exploit quantity
discounts
31
32
8
Introduction
Two basic questions
• Pricing schedule often displays economies of scale, with prices
decreasing as lot size increases.
• A discount is lot size based if the pricing schedule offers
discounts based on the quantity ordered in a single lot.
• A discount is volume based if the discount is based on the total
quantity purchased over a given period.
• Two commonly used lot size based discount schemes : all unit
quantity discounts, marginal unit quantity discount or
multiblock tarriffs
• Given a pricing schedule with quantity discount, what is the
optimal purchasing decision for a buyer? How does this affect
the SC in terms of lot size, cycle inventories, flow times?
• Under what conditions should a supplier offer quantity
discounts? What are appropriate pricing schedules that a
supplier should offer?
33
34
EOQ with all quantity discount
Example
• Pricing schedule :
The unit purchase cost is Ci if the order quantity is at least qi
with q0 = 0 < q1 < q2 < … < qr = ∞ and c0 > c1 > c2 > …
• Drug Online (DO) is an online retailer of prescription drugs.
Demand for vitamins is 10000 bottles per month. DO incurs a
fixed order cost of 100$ each time an occurs is placed with the
manufacturer. DO has an annual holding cost of 20%.
• The pricing schedule of the manufacturer is the all unit discount
schedule:
• The retailer’s objective is to maximise its profit, i.e. minimise
the sum of material, order, and holding costs.
Order quantity
35
Unit Price ($)
0‐5000
3
5000‐10000
2,96
10000 or more
2,92
36
9
Solution
Example (draw TC(Q))
Step 1. Determine the EOQ Qi for each price Ci
Step 1: Q0 = 6324, Q1 = 6367, Q2 = 6410
Step 2:
Order quantity
0‐5000
Q0 >= 5000, TC0 ignore
5000‐10000
5000 < Q1 < 10000, TC1 = 358969 $
10000 or more
Q2 < 10000, TC2 = 354520$
Step 3: Optimal order size = q2 = 10000, TC = TC2.
Qi 
2 DK
Ici
Step 2. Determine the total annual cost TCi for each price range
Case 1: Qi >= qi+1, ignored as it is considered for Qi+1
Case 2: Qi < qi,
D
q
TCi    K  i Ici  Dci
2
 qi 
Remarks :
• Presence of quantity discount leads to Larger order size of
10000 units than the normal EOQ = 6324
• If S = 4$, order size under all unit discount schedule is still
10000 units and is 8 times the normal EOQ = 1265.
Case 3: qi <= Qi < qi+1,
D
Q
TCi    K  i Ici  Dci
2
 Qi 
Step 3. Determine the optimal order quantity.
Unit Price ($)
3
2,96
2,92
37
38
EOQ with marginal quantity discount
Solution
• Pricing schedule :
The pricing schedule contains specified break points q0 = 0 <
q1 < q2 < … < qr = ∞. The marginal cost of a unit decreases at
the break points to ci if the order quantity is at least qi with c0
> c1 > c2 > …
Step 1. Determine the EOQ Qi for each price range Ci (why?)
• The purchasing cost Vi of qi units is determined as follows:
V0 = 0, Vi+1 = Vi + ci (q i+1 – qi), for i = 0, 1, …
• Purchasing cost of an order of Q such that qi <= Q < qi+1 is:
C(q) = Vi + ci(Q-qi)
Qi 
Ici
Step 2. Determine the total annual cost TCi for each price range
Case 1: Qi < qi, Qi* = qi
Case 2: Qi > qi+1, Qi* = qi+1
Case 3: qi <= Qi < qi+1, Qi* = Qi
TCi  Q  

39
2 D  K  Vi  qi ci 
Vi   Q  qi  ci
1  V   Q  qi  ci 
K
 I i
Q 
Q D 2 
Q
Q D

V   Q  qi  ci
1
K
 I Vi   Q  qi  ci   i
Q D 2 
Q D
Step 3. Determine the optimal order quantity.
40
10
Example (draw TC(Q))
V0 = 0, V1 = 15000, V2 = 29800
Step 1: Q0 = 6324, Q1 = 11028, Q2 = 16961
Step 2:
Order quantity
0‐5000
Q0 >= 5000, TC0 = 363900$
5000‐10000
Q1 > 10000, TC1 = 361780 $
10000 or more
10000 < Q2, TC2 = 360365$
Step 3: Optimal order size = Q2 = 16961, TC = TC3.
Key point
Unit Price ($)
3
2,96
2,92
Remarks :
• Much larger order size of 16961 units than the normal EOQ =
6324
• If S = 4$, order size 15755 is 12,5 times the normal EOQ =
1265.
• There can be significant increase of order size and
cycle inventory in the absence of fixed order costs as
long as quantity discounts are offered.
• Quantity discounts lead to a significant buildup of
cycle inventory in a supply chain.
• In many SC, quantity discounts contribute more to
cycle inventory than fixed ordering cost.
• Value of quantity discount in a supply chain?
41
42
Coordination to increase total SC profits
Quantity discount for commodity products
• For commodity products, a competitive market exists,
the market sets the price, the firm’s objective is the
lower costs.
Why quantity discount?
• For the retailer DO (Drug Online), its lot sizing
decision is based on costs it faces.
43
44
11
Coordination to increase total SC profits
Coordination to increase total SC profits
Quantity discount for commodity products
Quantity discount for commodity products
DO : D = 10000 bottles vitamins/month, Kr = 100$, I = 20%, cr
DO : TC_inv = D/Q*100 +0,2*3*Q/2
Manufacturer :
= 3$, EOQ = 6324, TC_inv = 3795 $.
Manufacturer : processing, packing & shipping DO orders
•
•
•
•
•
•
•
•
•
•
•
A line packing bottles at a steady rate matching the demand.
Fixed setup cost Km = 250$ / order
Production cost cm = 2$/bottle
Holding cost = 20%
Annual setup cost = 120000/6324*250 = 4744$
Annual holding cost = 6324/2*0,2*2 = 1265$
Total manufacturer setup & holding cost = 6009$
Total SC cost = 6009 + 3795 = 9804$
Fixed setup cost Km = 250$ / order
Production cost cm = 2$/bottle
Holding cost = 20%
Total setup & holding cost = D/Q*250+0,2*2*Q/2
Total SC cost = D/Q*350 + (0,2*3+0,2*2)*Q/2
SC lot size Q = [2*D*350/ (0,2*3+0,2*2)]0,5=9165
Opt SC cost = 9165 $, gain = 9804 – 9165 = 638$
45
46
Coordination to increase total SC profits
Coordination to increase total SC profits
Quantity discount for commodity products
Quantity discount for commodity products
Pricing scheme for achieving opt SC profit:
• C = 3$/bottle if Q < 9165, C = 2.9978$ otherwise.
DO :
• has an incentive to order Q = 9165,
• material cost reduction just enough to offset the
increase of ordering & holding cost
Total SC cost = opt SC cost = 9165 $
In practice, the manufacturer may have to share the
increase of SC profit of 638$.
Key point
• For commodity products for which price is set by the
market, manufacturer with large fixed costs per lot can
use lot-size quantity discounts to maximise total SC
profits.
• Lot size-based discounts, however, increase cycle
inventory in the SC.
• The benefit of quantity discount decreases as the setup
cost of the manufacturer decreases. (Importance of
coordination between marketing & production)
47
48
12
Coordination to increase total SC profits
Coordination to increase total SC profits
Quantity discount for products for which the firm has market
power
Quantity discount for products for which the firm has market
power
When decisions are coordinated:
• SC profil :
Prof_SC = (p – Cs)(360000 – 60000p)
• Consider the scenario in which the manufacturer has
invented a new vitamin pill, vitaherb, for which few
competitors exist.
• The price p at which DO sells vitaherb influence
demand.
• Assume that: D = 360000 – 60000p.
• Production cost Cs = 2$/bottle
• The manufacturer decides the price Cr to charge DO
Results:
• p = 3 + Cs/2 = 4
• D = 120000,
• Prof_SC = 240000$
49
50
Coordination to increase total SC profits
Coordination to increase total SC profits
Quantity discount for products for which the firm has market
power
When decisions are made independently:
• Manufacturer :
MAXCr Prof_m = (Cr – Cs)(360000 – 60000p)
• DO :
MAXp Prof_r = (p – Cr)(360000 – 60000p)
Results:
• p = 3 + Cr/2, Cr = 3 + Cs/2 = 4, p = 5
• D = 60000,
• Prof_m = 120000$, Prof_r = 60000$, SC profil = 180000
• Loss of 60000$ due to independent price setting, phenomenon
known as double marginalization
Quantity discount for products for which the firm has market
power
Key point
• The supply chain profit is lower if each stage of the
supply chain makes its pricing decisions
independently, with the objective of maximizing its
own profit.
• A coordinated solution results in higher profit.
51
52
13
Coordination to increase total SC profits
Coordination to increase total SC profits
Quantity discount for products for which the firm has market
power
Quantity discount for products for which the firm has market
power
Pricing schemes to achieve the coordinated solution
Pricing schemes to achieve the coordinated solution
Two-part tariff :
• The manufacturer charges its entire profit as an up-front
franchise fee and then sells to the retailer at cost.
• It is then optimal for the retailer to price as though the two
stages are coordinated.
Example :
• Opt Prof_SC = 240000 $, Prof_DO = 60000$ (when no
coordination)
• Pricing scheme: charge the DO of the franchise fee of 180000$
and material cost of Cr = 2$/bottle.
• DO maximises its profit if it sets p = 4$.
Volume-based quantity discount:
• The two-tariff is a volume-based quantity discount as the
average material cost of DO declines as the purchase increases.
• Design discount scheme to encourage DO the purchase the opt
quantity 120000.
• Pricing scheme : Cr = 4$ if the purchase < 120000, and Cr =
3.5$ otherwise.
• DO optimal solution: p = 4, Prof_DO = 60000$, D = 120000,
Prof_SC = 240000$.
53
54
Coordination to increase total SC profits
Quantity discount for products for which the firm has market
power
Key point
• For products for which the firm has market power, two-part tariffs or
volume-based discounts can be used to achieve SC coordination and
maximize SC profits.
• Lot size-based discounts are not optimal even in the presence of
inventory costs. In such as setting, either two-part tariff or a volumebased discount, with the supplier passing on some of its fixed cost to
the retailer, is needed for the SC to be coordinated.
• Lot size based discount tends to raise the cycle inventory. In contrast,
volume based discounts are compatible with small lots. Use lot size
based discount only when the supplier has high fixed cost.
• Volume-based discounts suffer from orders peak toward the end of
financial horizon. Volume discount based on a rolling horizon could
55
help.
Short-term discounting: trade
promotions
56
14
Introduction
Introduction
• Manufacturers use trade promotions to offer a discounted price
and a time period over which the discount is effective.
Key goals (from the manufacturer perspective)
• Induce retailers to use price discount, displays or advertising to
spur sales
• Shift inventory from the manufacturer to the retailer and the
customer
• Defend a brand against competition
• Ex: 10% off for any purchase from 12/15 to 01/25.
• The goal is to influence retailers to act in a way that helps the
manufacturer achieve its objectives.
Need to understand the impact of trade promotion on the
behaviour of a retailer and SC performances.
57
Introduction
58
Forward buy
Retailer’s options facing a trade promotion
1. Pass through some or all of the promotion to customers to spur
sales (increase the sales of the whole SC)
2. Pass through very little of the promotion to customer but
purchase in greater quantity during promotion period to exploit
the temporary reduction in price (forward buy and no increase
of sales)
59
• d$: discount per product offered
• Q* : EOQ at normal price
• Qd : lot size ordered at discounted price
Assumptions:
• Discount is offered once
• Retailer takes no action to influence demand
• Qd is an integer multiple of Q*.
60
15
Forward buy
Qd 
dD
c  d  I

cQ *
cd
Forward buy = Qd – Q*
Qd
• Why? Profit maximization (gain in fix cost, gain in purchase,
loss of inventory cost).
Q*
Time
61
62
Forward buy
Forward buy
• Let T = Q/D be the period covered by short-term promotion
• Cost during T without promotion
 Q Q * K  Qc  0.5  Ic  Q * T
Example:
• DO is a retailer selling vitaherb. Demand is 120000 bottles/year.
The manufacturer currently charges 3$/bottle and DO has an
annual holding cost of 20%. Fixed order cost K = 1000 $. What
is the current lot size Q* of DO, cycle time, average flow time?
• The manufacturer has offered a discount of 0.15$ for all bottles
purchased by the retailer over the coming month. How many
bottles should DO order given the promotion?
• Cost during T with promotion
K  Q  c  d   0.5  I  c  d   QT
• Cost Gain during T
  Q    Q Q *  1 K  Qd  0.5  I  c  d  Q  IcQ * T
• The optimal Q is obtained from
  Q  Q  0
KD Q *  0.5  Ic  Q * (optimal EOQ)
63
Answer:
• Q* = 6324, Qd = 38236, Forward buy = 31912
Remark:
• 5% discount causes the lot size to increase by 500+%.
64
16
Forward buy
Impact on the demand
Key point :
• Trade promotions lead to a significant increase in lot size and
cycle inventory because of forward buying by retailer.
• This generally results in reduced SC profits unless the trade
promotion reduces demand fluctuations.
Example:
• Assume DO selling at price p faces a demand of D = 300000 –
60000p. The normal price charged by the manufacturer is Cr =
3$/bottle. Ignoring the inventory related costs, evaluate the
optimal response of DO to a discount of 0.15$ per bottle.
Answer:
• Without discount and Cr = 3$, p = 4$, D = 60000
• With discount of 0.15$ and Cr = 2.85$, p = 3.925$, D = 64500.
• 7.5% increase in demand, DO pass only half of the trade
promotion discount to Customers.
65
66
Impact on the demand
Key point
• Faced with a short term discount, it is optimal for retailers to
pass through only a fraction of the discount to the customer,
keeping the rest for themselves.
• Simultaneously, it is optimal for retailers to increase the
purchase lot size and forward bur for future periods.
• Thus, trade promotions often lead to an increase of cycle
inventory in a SC without a significant increase in customer
demand.
• Trade promotion should be designed so that retailers limit their
forward buying and pass along more of the discount to end
customers.
Managing multiechelon cycle inventory
67
68
17
A mutliechelon distribution supply chain
One manufacturer supplying one retailer
(Instantaneuous production, lotsize Q)
No synchronization : production right after delivery, average INV = 3Q/2
mfg
inventory
retailer
inventory
stage
1
stage
2
stage
3
shipping
production
stage
4
A multiechelon supply chain has multiple stages and possibly many
players et each stage.
Goal: decrease the total costs by coordinating orders across the SC
Synchronization : production after before delivery, average INV = Q/2
69
70
distributor replenshment order arrives
Simple multiechelon with one player at each stage
Distributer replenishes every two weeks
Integer replenishment policy:
• lot size at each stage = integer multiple of the lot size of its
immediate customer
• Coordination of ordering across stages allows for a portion of
the delivery to a stage to be cross-docked on to the next stage
• Extent of cross-docking depends on the ratio of fixed ordering
cost S and holding cost H at each stage. The closer the ratio, the
higher the optimal percentage of cross-docked product.
retailer shipment is cross-docked
Retailer replenishes every week
retailer shipment is from inventory
Retailer replenishes every two weeks
retailer shipment is cross-docked
Shown to be quite close to optimal.
retailer shipment is cross-docked
Retailer replenishes every four weeks
71
18
One distributor supplies multiple retailers
Integer replenishment policies
Integer replenishment policy:
• Distinguish retailers with high demand from those with low
demand
• Group retailers such that all retailers in one group order together
• fr = n*fd or fd = n*fr, for each retailer r where n is an integer
and fr and fd are retailer and distributor order frequencies
• Each player orders periodically with reorder interval equal to an
integer multiple of some base period
• Divide all parties within a stage into groups such that all parties of a
group order from the same supplier and have the same reorder interval
• Set reorder intervals across stages such that the receipt of a
replenishment order at any stage is synchronized with the shipment of a
replenishment order to at least one of its customers. The synchronized
portion can be cross-docked.
• For customers with a longer reorder interval than the supplier, make the
customer reorder interval an integer multiple of the suppliers' interval
and synchronize their replenishment to facilitate cross-docking
• For customers with a shorter reorder interval, make the supplier's
reorder interval an integer multiple of the customer's interval and
synchronize the replenishment
• The relative frequency of reordering depends on the setup cost, holding
cost and demand at different parties.
Shown to be near optimal by Roundy.
73
Key points
74
Integer replenishment policies
• Integer replenishment policies can be synchronized in multiechelon
supply chains to keep cycle inventory and order costs low.
• Under such policies, the reorder interval at any stage is an integer
multiple of a base reorder interval.
• Synchronized integer replenishment policies facilitate a high level of
cross-docking.
• Whereas the integer policies synchronize replenishment and decrease
cycle inventories, they increase safety inventories because of the lack of
flexibility with the timing of a reorder
• These policies make the most sense for supply chains in which cycle
inventories are large and demand is relatively predictable.
75
• Divide all parties within a stage into groups such that all parties of a
group order from the same supplier and have the same reorder interval
• Set reorder intervals across stages such that the receipt of a
replenishment order at any stage is synchronized with the shipment of a
replenishment order to at least one of its customers. The synchronized
portion can be cross-docked.
• For customers with a longer reorder interval than the supplier, make the
customer reorder interval an integer multiple of the suppliers' interval
and synchronize their replenishment to facilitate cross-docking
• For customers with a shorter reorder interval, make the supplier's
reorder interval an integer multiple of the customer's interval and
synchronize the replenishment
• The relative frequency of reordering depends on the setup cost, holding
cost and demand at different parties.
76
19
Echelon inventory
• Ordering policies based on echelon inventory (s, S), (r, Q)
• Problems: where to locate the inventory, how to allocate the inventory
warehouse echelon inventory
supplier
warehouse
warehouse echelon lead time
77
20
Chapter 4. Managing uncertainty in a supply chain:
safety inventory
Learning objectives:
• Understand the role of safety stock in a SC
• Identify factors that influence the required level of
safety stock
• Utilise managerial levers to lower safety stock and
improve product availability
Role of safety inventory in a supply
chain
1
Two Decisions in
Inventory Management
2
EOQ Model when there is no uncertainty
Time Between Orders
On-hand Inventory
(Cycle Time) T = Q/D
• When is it time to reorder?
• If it is time to reorder, how much?
Q
Demand
Rate, D
Average Cycle
Inventory, Q/2
Q/2
Reorder
Point, R
Place
Order
Receive
order
Time
Lead
Time,
L
How many to order : Q = EOQ
When to order: reorder point R = L.D
3
4
1
Effects of Demand / Lead Time Variability
on Reorder Point (When)
Role of satefy inventory
On-hand Inventory
• Inventory carried to satisfy demand that exceeds the forecasted demand.
• Needed because of uncertain demand and uncertain product supply.
Variable demand
Expected demand at
average demand rate d
Q
QUESTION: How
much inventory is
needed during lead
time L?
s
Q/2
Safety Stock level
Cycle
Inventory
Place
order
safety
Inventory
Receive
order
L
KEY POINT: s is larger
when there is uncertainty
about demand or L
Time
5
6
Safety Stock
Safety Stock
• Stock carried to provide a level of protection against
costly stockouts due to uncertainty of demand during
lead time
• Safety Stock Criterion:
– stockouts occur when
demand during lead time (DL) …
– service level (1 -  )100%.
• Stock outs occur when
– Demand over the lead time is larger than expected
Inventory
Level
s= ROP
• DL is a random variable.
What kind of probability distribution?
Expected
demand
Time
7
8
2
Computing s …
Assumption: Demand over lead-time is normally distributed
1-
Probability {Demand over lead-time < s} =
Determining appropriate level of safety inventory
and choosing an inventory control policy
Service Level
Probability distribution
of demand over L
1-

s
9
10
Computation of Variance for Demand over Lead Time:
Variability Comes From Two Sources
Computing s: Normal Distribution
Probability distribution
of demand over L:
1: Suppose only demand di in day i is variable; lead time is constant at AVGL
DL  d1  d 2  ...  d AVGL
Mean = ; Std Dev = 
1-
AVGL times


Var {DL }  Var {d1  d 2  ...  d AVGL }  Var {d1 }  Var {d 2 }  ...  Var {d AVGL }  AVGL  STD 2
s
di’s are independent
di’s are identically distributed
2: Now, suppose only lead time is variable; daily demand is constant at AVG
1-
.90
.95
.98
.99
.999
z
1.28
1.65
2.05
2.33
3.09
z
1-
s

 s    z
3: Adding the two terms, we get to our result

From normal table
or, in Excel, use:
=normsinv (0.90)
0
DL  L  AVG
Var {DL }  Var {L  AVG }  AVG 2  Var {L}  AVG 2  STDL2
Var {DL }  AVGL  STD 2  AVG 2  STDL2
z
11
12
3
More specifically….
Mean demand
over LT
Safety
factor (std
normal
table)
Standard
deviation of
demand over LT
Example:
Safety stock SS
s  AVG  AVGL  z  STDL2  AVG 2  STD 2  AVGL
• Consider inventory management for a certain SKU at Home Depot.
Supply lead time is variable (since it depends on order consolidation
with other stores) and has a mean of 5 days and std deviation of 2 days.
Daily demand for the item is variable with a mean of 30 units and c.v.
of 20%. Find the reorder point for 95% service level.
AVG  30;
Note:
•If lead time is constant, STDL  0
• If demand is constant, STD  0
STD  0.2(30)  6
AVGL  5;
STDL  2
95% service level  z = 1.64
s  AVG  AVGL  z  STDL2  AVG 2  STD 2  AVGL
Note: This is a very good approximation even when demand is not normally distributed.
 30  5  1.64 22  302  62  5  150  100.8  251
13
Inventory
The (s,S) Policy:
Fixed Ordering Costs
The (s,S) Policy: Fixed Ordering
Costs
• Order when: inventory position (IP) drops below s
• Order how much: bring IP to S
S
sR
• Compute s exactly as in the base-stock model:
Average demand
during lead time
s  AVG  AVGL  z  STDL2  AVG 2  STD 2  AVGL
Safety Stock
L
Order
placed
14
Order
arrives
• Compute Q using the EOQ formula, using mean
demand D = AVG (be careful about units…):
Time
Q
s should be set to cover the lead time demand and together
with a safety stock that insures the stock out probability is
within the specific limit (When to reorder).
S depends on the fixed order cost – EOQ (How much)
2  K  AVG
h
• Set S = s + Q
15
16
4
Example: (s,S) Model
Summary of Inventory Models
Use EOQ
•How much: EOQ formula
•When: inventory level drops to d*L
• Consider previous Home Depot example, however, there are fixed
ordering costs, which are estimated at $50. Assume that holding costs
are 15% of the product cost ($80) per year. Also, assume that the store
is open 360 days a year.
s  251
yes
(from previous calculations)
h  (.15)80 / 360  0.0333;
K  50;
2  AVG  K
2(30)50
Q

 300
h
0.0333
Is demand rate and
lead time constant?
AVG  30
S  s  Q  251  300  551
Are there fixed
ordering costs?
yes
no
no
Use (s, S) policy
•How much: Q = S – s (Q is from
EOQ formula)
•When: IP drops below s (basestock policy formula)
Use base stock (s) policy
•When: IP drops below s
•How much: necessary to
bring IP back to s
17
18
Periodic review
Periodic review
Review interval: T, i.e. order every T time units.
Review interval: T, i.e. order every T time units.
Use modified EOQ
•How much: EOQ covering N periods
•When: inventory level drops to d*min(L, T)
R  ( L  T )d  z T  L
yes
Is demand rate
and lead time
constant?
Reorder point R = Stock level to
cover demand DT+L
Are there fixed
ordering costs?
no
yes
Use (s, S) policy
•How much: Q = S – s (Q is from
EOQ formula)
•When: IP drops below s (basestock policy formula)
Safety stock
SS  z T  L
T
no
Use base stock (s) policy
•When: IP drops below s
•How much: necessary to bring IP
back to s
L
Point de commande R ou s = stock nécessaire sans
commander à l'instant 0 pour couvrir la demande DT+L jusqu'à
l'arrivée de la prochaine commande à la date T+L
19
S - s = quantité économique
20
5
Risk Pooling
• (safety) stock based on standard deviation
– square root law: stock for combined demands
usually less than the combined stocks
(depends on what?)
Risk pooling or impact of aggregation
• Example: independent demand
 X2 Y   X2   Y2
 X Y   X2   Y2   X   Y
21
22
HP Example:
Benefits of a Universal Product
Risk Pooling
• (safety) stock based on standard deviation
Because of a different power supplies, HP had two laser printers, one for
Europe and one for N. America. A universal product (with a universal
power supply) has been proposed, but costs $30 extra. Is it worthwhile?
N. America
Europe
N(200,60)
N(150,50)
– square root law: stock for combined demands
usually less than the combined stocks (depends on what?)
• Centralizing inventory control reduces safety stock, hence
average inventory level for the same service level.
(This phenomenon is called risk pooling)
• works best for
Consider z = 2 (98% of service level)
– negatively correlated demand. Why?
– high coefficient of variation,
which increases required safety stock.
What is the difference is safety stocks required?
assume independent demand
seen by HP (NA and Europe)
• other kinds of risk pooling:
across markets, products, time
23
24
6
Example
Risk Pooling Example
• Consider two systems:
Warehouse 1
Market 1
Warehouse 2
Market 2
Decentralized System:
Two warehouses,
each serving
one customer
Supplier
Market 1
Supplier
Centralized System:
One warehouse,
serving both
customers
Warehouse
Market 2
Questions:
Q1: For the same service level, which system will require more inventory?
Q2: For the same total inventory level, which system will have better service?
AVG STD
SS
s
Q
S
Average
Inventory
Warehouse 1
39.3
13.2
25.08
65
132
197
91
Warehouse 2
38.6
12.0
22.8
62
131
193
88
Centralized
Warehouse
77.9
20.7
39.35 118
186
304
132
Safety Stock SS = z ·STD · L
Reorder Point s = AVG·L + SS
Order Quantity Q = sqrt(2K·AVG/h)
Order-up-to-level S = s + Q
Average Inventory  SS + Q/2
Decentralized system:
total SS = 47.88
total avg. invent. = 179
25
Centralized vs. Decentralized
Calculating demand variability of centralized system
Warehouse 1
Warehouse 2
Market 1
Market 2
Market 1
Warehouse
Market 2
d1: (1, 12)
d2: (2, 22)
: correlation coefficient of d1, d2
2
= 1 22 + 212,
where -1    1
d1+d2: (, 2)
 = 1 + 2
 = ??
Conclusions:
1. Stdev of aggregated demand is
less than the sum of stdev of individual
demands
2. If demands are independent or
negatively correlated, the std of
aggregated demand is much less
26
2+
Inbound transportation cost
(from factories to warehouses)
1. If d1, d2 positively correlated,  > 0
2. If d1, d2 are independent,  = 0
3. If d1, d2 negatively correlated,  < 0

1+2
 12   22
N.C.
Decentralized
Facility/Labor cost
   1+  2
-1
Centralized
Outbound transportation cost
(from warehouses to retailers)
Inventory cost
Responsiveness to customers
(lead time)
Safety stock, overhead, economy of scale, lead time, service,
transportation costs.
0
Ind.
P.C.
1

27
Hybrid policy possible depending on the products (low demand
product centralised and high demand products at local warehouse)
28
7
Design for Supply Chain
• Also called: design for logistics -- DFL
Take supply chain costs into account when designing
product and manufacturing processes
Design for Logistics
• Approaches
1. Economic Packaging and transportation (obvious,
design products that can be packed compactly and
efficiently, IKEA)
Modularity & Component Commonality
Postponement
2. Modularity & Component Commonality
3. Postponement
Benefits: Reduced inventory and transportation
costs in the supply chain
29
Approach 2: Modularity/Component Commonality
• Modularity allows components of the product be
decoupled and manufactured in parallel.
30
Approach 2: Modularity/Component Commonality
• Some modules are common across different products
(example?)
Serial processing
PC board
Europe
Printer
Customer (Europe)
1
Asia
printer housing
(motor,
printhead,
housing)
2
Without component commonality
Parallel processing
Europe
Asia
2
With component commonality
• Benefits:
Customer (Europe)
PC board
1
-- Modularity allows parallel processing  reduced lead times
-- Risk pooling  reduced inventory cost
-- Fewer components  reduced inventory handling
and procurement costs
Printer
Housing
31
32
8
Modular vs. Integral Design
Why is Modular Design Preferred,
from a Supply Chain Standpoint?
Modular design
One-to-one mapping between functional elements and components
Interfaces between components not coupled
• Example:
Consider Chrysler. It needs to renew its Durango and Cherokee lines. Currently, each car
has very little component commonality with the other, since both use integral designs.
Chrysler is considering a modular platform design, in which 60% of the components, in
terms of dollar value (chassis, transmission, underbody, etc.) are common to the new
Cherokee and Durango. Suppose the monthly demand for the Cherokee, in 000s,
N(50,202), whereas the demand for the Durango is N(40,202). Compute the monthly
holding cost savings regarding inventory safety stock, if the modular design is used.
Assume that each car costs Chrysler US$15,000 to manufacture, and that lead-time
across components is constant at one month (for simplicity). Consider annual holding
cost of a component to be 12% of the component value. Assume a 95% service level.
Integral design
Complex mapping from functional elements to components
Interfaces between components are coupled
integral
modular
33
Illustration of Chrysler Product Strategies
34
Solution to the Chrysler Example
AVGC  50, STDC  20, AVGD  40, STDD  20, L  1, z  1.64
Integral Design:
Current:
Integral
Designs
Safety stock C = z  STDC2  L  1.64 202 (1)  33
Safety stock D  z  STDD2  L  1.64 202 (1)  33
Total safety stock: 33 + 33 = 66
Modular Design: AVG  50  40  90, STD  2  202  28.3
Safety stock = z  STD 2  L  1.64 28.32 (1)  46.4
Proposed:
Modular
Design
Monthly inventory holding cost savings = (66-46.4)*15,000*0.60*(0.12/12) =1,764 (in 000s),
or US$ 21 million per year!
35
36
9
Approach 3: Postponement
Approach 3: Postponement
• Design product and manufacturing processes so that
decisions about specific products can be delayed as
late as possible
• Concepts for delayed differentiation:
-- For example, first manufacture a generic product, then differentiate
it to make specific products
-- Aka. Delayed product differentiation -- DPD
Without postponement
Raw materials
components
–
–
–
–
Resequencing product manufacturing steps
Commonality
Modularity
Standardisation
With postponement
Raw materials
components
Generic
product
Different products
Different products
• Benefits:
– Reduced demand uncertainty (why?)  Higher service level
or/and reduced inventory cost
37
38
Benetton Manufacturing Process
Postponement
Example: Benetton
• A world leader in knitwear
Old Sequence
Knitting
Wool Plant in Castrette, near Treviso. Knitting
division. Computerized knitting loom capable of
automatically producing the most complex product
designs
New Sequence
Dyeing
Purchase Yarn
Purchase Yarn
Dye Yarn
Knit Garment Parts
Finish Yarn
Join Parts
Knit Garment Parts
Dye Garment
Join Parts
Finish Garment
This process
is postponed
Dyeing vats for the finished knitted product.
39
40
10
Benetton Example: Evaluating the Value
of Dyeing Postponement
Process Redesign for Supply Chain:
Postponement at Benetton
Dye yarn only after the season’s fashion preferences become more
established (knit lead-time much longer than dyeing lead-time).
Consider the previous example (say, a sweater). Suppose demand for each of the 4
different sweater colors has a mean of 10000. Dyeing time is 1 month; knitting time
is 6 months. Due to the short season and long production lead times, there is only one
production run before the season. The standard deviation of demand (forecast error),
however, depends on how long the forecast is done before the season starts. If seven
months before the season, the standard deviation is 6000. If done one month before
the season (color preferences are well known by then), the standard deviation is 1000.
Compute approximate inventory holding cost savings in WIP as a result of dyeing
postponement. Assume that each sweater costs $30, annual holding cost is 12% of the
product value, and 95% service level.
Example: single product; four colors
knit
Dyeing
operations
postponed
dye
dye
knit
Outcome: Reduces demand uncertainty & inventory
41
Benetton Example Solution
Fall 2005
42
HP Printers Cases
Quantities for yarn are based on the scheduled production for the final
product, which is shown below:
Case 1
1) What were the problems (crises) facing HP?
What were the causes?
2) Resolving the crises –
What alternatives did HP consider
for solving the problems?
For each color, STD = 6,000, z =1.65 (consider dyeing only)
SS = z*STD = 9840, total SS = 4(9,900) = 39,600
Case 2
Aggregate demand (pooling), STD = 4(1000)2 = 2,000, z =1.65
SS = z*STD = 3300
savings in monthly holding cost: 36300*$30*(0.12/12) = $11K,
for 100 SKUs, annual savings exceed $10M !
43
44
11
Inventory-Service Crisis
HP DeskJet Printer Supply Chain
What is the crisis?
US DC
Customer
Europe
DC
Customer
Far East
DC
Customer
Vancouver
Plant
Suppliers
Excess inventory in some products
Shortages in other products
What caused it?
Poor demand forecasting
Large product variety due to many markets
Long shipping lead time from plant to DC
Inventory levels set incorrectly
HP
45
Setting the Right Level of Inventory
46
Drivers of Safety Stock
How are current inventory levels set at each DC?
SS  z•STD•
Rule of thumb
How should they be set?
-- Use (s, S) policy
-- Reorder point: s = AVG · L + z · STD ·
Safety stock
Drivers of safety stock:
L
-- Use EOQ model to determine optimal order quantity
–
Q*
L
1.
Service level Z
2.
Demand uncertainty STD
3.
Order lead time L
2 · K·AVG
=
h
– Order-up-to level: S = Q* + s
47
48
12
Resolving the Crisis -Options Discussed in the Case
Options
Postponement: DC Localization Strategy
Pros/Cons
• Plant in Europe
•
Shorten lead time; May not have
enough volume; Lose economy of
scale in manufacturing
• Air shipments
•
Shorten lead time; May be too
expensive
• Better forecasts
•
Would be great! But unclear how
to do that …
• Hold more inventory
(higher z)
•
Better service; but already a
problem
• Factory-Localization Strategy: (customization performed at the factory)
HP
DC
customers
(manufacturer)
• DC-Localization Strategy: (customization performed at the DCs)
HP
DC
customers
(manufacturer)
Postponement: Delaying the point of differentiation
Postponement?
49
50
Risk pooling strategies
Existing strategies for coping with uncertainties
Risk pooling strategies
Location pooling
Product pooling
Lead Time Pooling
Capacity pooling
•
Collecting data to ensure best demand forecast
•
Make-to-order production
•
Reactive capacity
Risk pooling strategies :
to redesign the SC, the production process or the
product to either reduce the uncertainty the firm faces
or to hedge uncertainty so that the firm is in a better
position to mitigate the consequence.
51
52
13
Location pooling
Risk pooling strategies
Q: How many different locations should the firm store
inventory?
Q: To keep one stockpile inventory per sales
representative or to serve demand from multiple
territories from a single location?
Q: 1 DC or N DC?
Risk-pooling strategies to reduce and hedge uncertainty
•
Location pooling
•
Product pooling
•
Lead Time Pooling
•
Capacity pooling
Concepts of location pooling :
• pooled territory & pooled inventory
• individual inventory & individual territory
Important issues :
Supply chain responsiveness: response time to orders
53
54
Product pooling
Location pooling
• Concepts: universal design to serve demand with
fewer products
CV: Coefficient
of Variation
expected
inventory in
days of demand
CV
• Pooling of 2 demands with mean  and variance 2.
E  pooled demand   2 
 pool  2 1  coefficient of correlation   
average
inventory
CV pool 

1
1  coefficient of correlation  
2

Drawbacks of a universal design
# of territories
pooled
•
•
•
# of DC
55
May not provide the needed functionality to consumers with special needs
May be more expensive or cheaper to produce than focused products
May eliminate some brand / price segmentation opportunities
product line rationalisation
56
14
Lead Time Pooling
Lead Time Pooling
Concept 2: Delayed differentiation / Postponement.
• To remedy the drawbacks of previous strategies:
– location pooling creates distance
– product pooling degrades product functionality
Good when:
•
customers demand many version and variety is important
Concept 1: Consolidated distribution
•
Less uncertainty wrt total demand than wrt individual versions
Keep inventory close to customer while avoiding location imbalance
•
variety is created late in the production process
8 week LT
store 1
1 wk LT
store 1
•
variety can be added quickly and cheaply
•
components needed to create variety are inexpensive relative to
the generic component.
Without postponement
Supplier
Supplier
store 100
8 wk LT
With postponement
retail
DC
store 100
Raw materials
components
Generic
product
Different products
57
Capacity pooling with flexible manufacturing
Raw materials
components
Different products
58
Capacity pooling with flexible manufacturing
10 links : no flexibility
plants
vehicles
1
1
• Mainly used in automotive assembly lines for
different car models
• Traditionally, assembly lines are dedicated to
one model
• The future trend tends to flexible assembly
lines capable of assemblying different models
(modèles mixtes)
plants
1
vehicles
1
2
2
2
2
3
3
3
3
10
10
10
10
20 links
59
11 links
Total flexibility
plants
1
vehicles
1
plants
1
vehicles
1
2
2
2
2
3
3
3
3
10
10
10
10
60
15
Capacity pooling with flexible manufacturing
Capacity pooling with flexible manufacturing
Impact of adding flexibility
Chaining : groups of plants & groups of vehicles
expected
sale
20
plants
total
flexibility
1
11
12
links
no
flexibility
80
100% capacity utilization
A configuration of 20 links has approximately equal
capability to respond to demand uncertainty than totally
flexible configuration
61
vehicles
1
2
2
3
3
4
4
9
9
10
10
62
16
01/10/2013
Base Stock Policy under Periodic Review
Supply Chain Logistics and Operations Management
• Assumptions
Chapter 5 Value of Information
qt
Dt
– Review period is 1
– Lead time is L
– No need to consider fixed cost
• Bullwhip Effect
• Causes of Bullwhip
• Means to Counter Bullwhip
• Policy
– At review point, if inventory position IP is lower
than the base stock level, place an order to bring
the inventory position back to the base stock level
– Base stock level = Lμ + zβ σ L1/2
1
Moving Average Forecast
Sequence of Events at the Beginning of a Period:
• Demand forecast updated
• Base stock level computed
St = Lμt^ + zβ σt^ L1/2
• Order is placed if needed
qt = St – St-1 + Dt-1
• Order, if any for the period, is delivered
• Demand is realized
• Inventory level is recorded
• Inventory position is recorded
• In period t, we update the demand estimate using the
realized demands in the last p periods, Dt-1, …, Dt-p
• The base stock level for each period is computed with
the updated mean and variance
• The updated estimates of mean and variance for
period t
ˆ t 
D t  p    Dt 1
p
 ( D t  p  ˆ t ) 2    ( Dt 1  ˆ t ) 2
, ˆ t  

p 1

2
1/ 2



3
4
1
01/10/2013
t
1
2
3
4
5
6
7
8
9
3-Period Moving Average Example (1)
3-Period Moving Average Example (1)
L =3, β=0.90, zβ=1.285
L =3, β=0.90, zβ=1.285
D
120
101
115
102
113
112
120
92
110
μ^
110
108
112
σ^
10
11
11
112
106
110
109
115
108
10
8
7
6
4
14
IP
Q
μQ^
σQ^
t
1
2
3
4
5
6
7
8
9
D
120
101
115
102
113
112
120
92
110
μ^
110
108
112
σ^
10
11
11
112
106
110
109
115
108
10
8
7
6
4
14
IP
Q
352
348
360
358
335
346
341
355
356
80
116
113
112
79
123
107
134
93
μQ^
σQ^
5
t
1
2
3
4
5
6
7
8
9
6
3-Period Moving Average Example (1)
3-Period Moving Average Example (1)
L =3, β=0.90, zβ=1.285
L =6, β=0.90, zβ=1.285
D
120
101
115
102
113
112
120
92
110
μ^
110
108
112
σ^
10
11
11
112
106
110
109
115
108
10
8
7
6
4
14
IP
Q
352
348
360
358
335
346
341
355
356
80
116
113
112
79
123
107
134
93
μQ^
103
114
102
105
103
121
σQ^
t
20
2
19
23
22
14
1
2
3
4
5
6
7
8
9
7
D
120
101
115
102
113
112
120
92
110
μ^
110
108
112
σ^
10
11
11
112
106
110
109
115
108
10
8
7
6
4
14
IP
Q
μQ^
σQ^
691
683
707
703
661
682
673
704
693
80
111
125
111
60
134
103
151
82
105
116
99
102
99
129
23
8
35
38
38
24
8
2
01/10/2013
The Phenomenon Observed
Bullwhip Effect
if there is no collaboration at all
Order Size
The bullwhip effect is a phenomenon observed in
supply chains wherein the demand variability
increases as one moves upstream from retailers to
distributors to manufacturers
Customer
Demand
Distributor Orders
Retailer Orders
Production Plan
Retailers
Warehouses/
Distributors
Time
production
distributor
retailer
customer
Manufacturers
9
10
Bullwhip Effect
Example 2: Campbell Soup
Bullwhip Effect
Example 1: P&G Diapers
11
12
3
01/10/2013
Conclusions
Is the Bullwhip Effect Good or Bad? Why?
• Good or bad?
-- Bad. It distorts the order information & amplifies order variability.
• Order variability is amplified up the
supply chain: bullwhip effect
• Upstream echelons face higher
variability
• Multiple causes and can be complex
• Impact of Bullwhip Effect:
-- Inventory: More safety stock needed
-- Transportation: Lower utilization of transportation
-- Warehousing: More warehouse capacity needed
Higher costs
-- Manufacturing: Lower capacity utilization
-- Customer Service: Lower service level, more likely to cause
stockouts and lost sales
13
14
Cause 1: Demand Forecast Updating
(Demand Signal Processing)
Causes of Bullwhip Effect
• Root Causes:
1. Demand forecast updating
Order Qt goes to upstream
2. Order batching
upstream
3. Price fluctuation
4. Rationing and shortage gaming
Orders from downstream
in the past p time periods
Dt-p, Dt-p+1, …, Dt-1
Lead time L
downstream
• Moving average to forecast demand
at period t based on Dt-p, Dt-p+1, …, Dt-1
• Use base-stock policy
to determine order Qt
Var(Q)
1+
Var(D)
15
2L2
2L
+ 2
p
p
Demand variability
gets amplified from
downstream to upstream!
16
4
01/10/2013
Cause 1: Demand Forecast Updating
(Demand Signal Processing)
Q3
Q2
Stage 3
L3
Stage 2
L2
No collaboration
stage i+1 at period t based on Qit-p, …, Qit-1
• Use base-stock policy for each stage
to determine order Qit
Var  D 
Dt
• Demand forecasting
– No visibility of end demand
– Forecast base on orders not the end demand
– Long lead time increases forecast inaccuracy
Stage 1
L1
Information sharing
• Moving average to forecast demand of
Var  QI 
Q1
Cause 1: Demand Forecast Updating
(Demand Signal Processing)
I 
2 L 2 L2 
  1  i  2i 

p
p 
i 1 
• Moving average to forecast demand of
stage i+1 at period t based on Dt-p, …, Dt-1
• Use local or echelon base-stock policy
for each stage to determine order Qit
Var  QI 
Var  D 
 1
2  L1  ...  LI 
p

2  L1  ...  LI 
2
p2
Information sharing reduces but cannot cancel Bullwhip effect
17
18
Cause 2: Order Batching
Example of Order Batching
Order Q* > Dt to upstream supplier
Company waits
several periods
before placing an
order for Q* units
Order from downstream
per time period Dt
Demand/order
Price incentives from
upstream supplier
(even if K = 0):
•Quantity discounts
•Promotions
Orders to manufacturers
(once every 4 weeks)
Fixed ordering
costs K
Q* 
2DK
h
Orders from retailers
(once a week)
4
19
8
12
16
weeks
20
5
01/10/2013
Example of Order Batching
Cause 3: Price Fluctuation
• Reasons for batching
–
–
–
–
• Estimates indicate that 80 percent of the transactions
between distributors and manufacturers in the grocery
industry are made in a forward buy arrangement.
(Kurt Salmon Associates)
High ordering cost
Full truckload economies
Quantity discounts
Push ordering, salespersons need to fill sales
quotas
• A forward buy is one in which items are bought in advance
of requirements, usually because of a manufacturer’s
attractive price offer.
• With price fluctuations, customers buy in quantities that
do not reflect their immediate needs:
-- They buy in larger quantities and stock up when price is low
-- They postpone purchases when price is regular or high
21
22
Examples of Initiatives to
Counteract the Bullwhip
Cause 4: Rationing and Shortage Gaming
• When product demand exceeds supply, a manufacturer often rations
its product to customers. Example:
Car Manufacturer
Available = 200
Dealer 1
Order = 100
Received = 67
Dealer 2
Order = 200
Received = 133
Only 2/3 of the order can be fulfilled
• Knowing the manufacturer policy, customers exaggerate their real
needs when they order (game the system). Example:
Car Manufacturer
Available = 500
Dealer 1
Need = 120
Order = 180
Received = 180
Dealer 2
Need = 180
Order = 270
Received = 270
Order more than needed so that if only 2/3 of the
order is filled you still get what you actually need
• As a result, customers’ orders give the supplier little information on a
product’s real demand, a particularly vexing problem for new products
23
Cause of
Bullwhip
Initiative(s)
1. Demand Signal
Processing
•Use of point-of-sale (POS) data
•Electronic data interchange (EDI)
•Vendor-managed inventory (Barilla Case)
•Lead-time reduction
2. Order Batching
•Use of EDI (to reduce ordering costs)
•Logistics outsourcing
3. Price Fluctuations
•Every day low price (EDLP)
4. Shortage Gaming
•Sharing sales and inventory data
•Allocation based on past sales
24
6
01/10/2013
Information for Effective Forecasting
Information for Coordination
• Questions
• Pricing, promotion, new products
– Who will optimize?
– How will savings be split?
– Different parties have this information
– Retailers may set pricing or promotion without
telling distributor
– Distributor/Manufacturer might have new
product or availability information
• Information needed
–
–
–
–
–
• Collaborative Forecasting addresses these
issues.
Production status and costs
Transportation availability and costs
Inventory information
Capacity information
Demand information
25
Lead-Time Reduction
Information to Address Conflicts
• Lot Size – Inventory
– Advanced manufacturing
systems
– POS data for advance
warnings
• Inventory-Transportation
– Lead time reduction for
batching
– Information systems for
combining shipments
– Cross docking
– Advanced DSS
• Why?
–
–
–
–
26
Customer orders are filled quickly
Bullwhip effect is reduced
Forecasts are more accurate
Inventory levels are reduced
• How?
– EDI
– POS data leading to anticipating incoming orders.
27
• Lead Time-Transportation
– Lower transportation
costs
– Improved forecasting
– Lower order lead times
• Product Variety-Inventory
– Delayed differentiation
28
7
01/10/2013
Background Quick Response at Benetton
Strategy
Quick Response at Benetton
• Benetton, the Italian sportswear manufacturer,
was founded in 1964. In 1975 Benetton had
200 stores across Italy.
• Ten years later, the company expanded to the
U.S., Japan and Eastern Europe. Sales in 1991
reached 2 trillion (lira).
• Many attribute Benetton’s success to the
successful use of communication and
information technologies.
• Benetton uses an effective strategy, referred to as
Quick Response, in which manufacturing,
warehousing, sales and retailers are linked
together. In this strategy a Benetton retailer
reorders a product through a direct link with
Benetton’s mainframe computer in Italy.
• Using this strategy, Benetton is capable of
shipping a new order in only four weeks, several
week earlier than most of its competitors.
29
Coping with
Bullwhip Effect at Benetton
30
Managerial Insights
• Bullwhip effect exists, in part, due to the retailer’s
need to estimate the mean and variance of
demand.
• The increase in variability is an increasing
function of the lead time.
• The more complicated the demand models and the
forecasting techniques, the greater the increase.
• Centralized demand information can significantly
reduce the bullwhip effect, but will not eliminate it
• Information has value and cannot be shared freely
• Integrated Information Systems
– Global EDI network that links agents with production
and inventory information
– EDI order transmission to HQ
– EDI linkage with air carriers
– Data linked to manufacturing
• Coordinated Planning
– Frequent review allows fast reaction
– Integrated distribution strategy
Benetton
Benetton
31
32
8
Distribution Strategies
Chapter 6. Distribution strategies & strategic alliance
•
• Direct shipping
Distribution strategies
- direct shipping
- shipiping via warehouses
- shipping via cross-docks
– Examples: JCPenney
• Shipping via warehouses
– Examples:
•
Strategic alliance
- Third Party Logistics (3PL)
- Retailer-Supplier Partnerships (RSP)
• Shipping via cross docks
– Cross docks serve as _inventory coordination_ points
– Products spend _very little time_ at cross docks
– Examples: Wal-Mart
• allowing transshipments (often at the retailer level)
1
Strategy 1: Direct Shipping
manufacturer
retailer
manufacturer
retailer
manufacturer
retailer
manufacturer
2
Strategy 2: Shipping via Warehouse
retailer
manufacturers
retailers
warehouse
Type 1. Single origin
single destination
Type 2. Single origin
multiple destinations
Type 3. Multiple origins
single destination
Type 4. Multiple origins
multiple destinations
Type 1. Without milk runs
manufacturers
retailers
warehouse
Type 2. With milk runs
Direct shipping with milk runs
Common when the retail store requires fully loaded trucks
Mandated by powerful retailers or in situation when lead time is critical
3
4
1
Strategy 3: Shipping via Cross Docks
Role of Warehouses
• Warehouses:
• Warehouses play important roles in the supply chain
Receiving, Sorting, Storing, Order Picking, Shipping
– Position _products_ __close_ to customer
– _coordination_ function
• Cross Docks = Warehouses without inventory
• _inbound_ shipments from multiple suppliers
• _outbound___ shipments to multiple customers
Receiving, Sorting, Shipping (from incoming trucks to outgoing trucks in < 12h)
Shipping
Receiving
– Even if firms sell products directly to customers (no retailers),
they may still use warehouses
Example:
Sorting
Inbound shipments
Requires coordination & IT support
& fast and responsive transport
& Forecast critical, needs of info. Sharing
& Good for large distribution systems
5
Comparison of the Three Strategies
Facility
Direct Shipping
Shipping
via warehouses
Shipping via
cross docks
no
facility
needed
wareho
use
cross
docks
Inventory
Transportation costs
increased
transport
cost
reduced
inbound
costs
reduced
inbound
costs
high
holding
costs
lower
holding
costs
No
holding
costs
Outbound shipments
6
Pro & cons of different transportation networks
Lead Time
from mfg to
retailer
reduced lead
times
long
reduced
SS proportional to what factors?
7
network structure
Pros
Cons
Direct shipping
No intermediate warehourse
Simple to coordinate
High Inv (large lot sizes)
Significant receiving expense
Direct shipping wt milk Lower transp. for small lots
runs
Lower inventories
Increased coordination
complexity
Via central DC with
inventory
Lower inbound transp. by
consolidation
Increased inventory cost
Increased handling at DC
Via central DC with
cross-dock
Lower transp cost through
consolidation
Increased coordination
complexity
Shipping via DC using
milk runs
Lower outbound transp. for
small costs
Further increase in coordination
complexity
Tailored network
Transp choice best matches
needs of individual product
and store
Highest coordination
complexity
8
2
Types of Strategic Alliances
Why direct shipping has higher transportation or/and higher inventory cost?
• Example:
Retailer’s weekly demand = 1/2 truckload
Shipping cost from manufacturer to retailer = $100
• Third Party Logistics (3PL)
• Retailer-Supplier Partnerships (RSP)
Direct Shipping
– Quick response (QR)
– Continuous replenishment (CR)
– Vendor managed inventory (VMI)
Shipping via Warehouse
1/4 truckload
per 1/2 wk
Full truckloads
per 1/2 wk
1/2 truckload
per wk
per 1/2 wk
Full truckloads
1/2 truckload
per wk
Full truckloads
1 truckload
per two wks
per wk
1 truckload
per two wks
1/4 truckload
per 1/2 wk
per wk
per 2 wks
per 2 wks
9
3PL
10
Why 3PL can achieve economy of scale & provide better service?
• 3PL = Some or all of a firm’s logistics functions is taken over by an
independent logistics service provider (LSP)
• Involve long term commitments and multiple function instead of
traditional transaction-based, single function logistic supplier
relationships.
• Consolidation is the key!
Example: 2 independent firms, 2 independent supply chains
Consider 2 scenarios
• Example: Ryder Integrated Logistics
Scenario 1: The firms performs their own logistics functions
– Annual revenues around US$ 1.5 billion
– Offers everything from transportation to network design and consulting
– A five year agreement to design, manage and operate all of Whirlpool’s
inbound logistics.
Firm 1
2 warehouses
2 separate distribution networks
Firm 2
• Advantages of 3PL:
– focus on core strengths, providing technological flexibility (IT & equipment),
providing other flexibilities (warehousing, small retailers, ...)
Scenario 2: A 3PL takes care of both firms’ logistics functions
Firm 1
• Issues with 3PL:
– know your own cost, customer orientation of the 3PL, specialization of the
3PL, asset-owning vs non asset-owning 3PL
11
Firm 2
3PL
1 warehouse
1 distribution network
12
3
Major 3PLs
Company
3PL in Practice
Revenues
($ million)
Ryder Integrated Logistics
$1,300
Penske Logistics
959
Schneider
875
Tibbet & Britten Group
659
Americold
650
North American Logistics
650
Fritz Companies
578
UPS Logistics
488
APL Logistics
420
Federal Express
• “3PL Study: Results and Findings of 2001 Annual Study”
by Cap Gemini Ernst & Young
– 93 companies
– Covering automotive, chemical, computer, consumer products, &
electronics
– Most prevalent among large companies, 52% with sales revenues
over $1B, 10% between $500M to $1B
360
Source: Logistics Magazine (07/00)
13
How many companies use 3PL?
From “3PL Study: Results and Findings of 2001 Annual Study”
by Cap Gemini Ernst & Young
14
What 3PL functions do companies use?
15
From “3PL Study: Results and Findings of 2001 Annual Study”
by Cap Gemini Ernst & Young
16
4
Retailer-Supplier Partnerships (RSP)
Benefits of 3PL
• Quick Response (QR)
– Supplier receives POS data from retailers
– Supplier use it to improve its own forecasting and
production scheduling
– but retailer still prepares its own orders
• Continuous Replenishment (CR)
– Supplier replenishes retailers
– Supplier receives POS data and use it to
prepare shipments at previously agreed upon intervals
to maintain specific levels of inventory
Increasing
trust level
• Vendor Managed Inventory (VMI)
– Supplier replenishes retailers
– Suppliers have the total control over
replenishment decisions
From “3PL Study: Results and Findings of 2001 Annual Study”
by Cap Gemini Ernst & Young
17
Information
sharing
Continuous
replenishment
Joint
forecasting
& planning
Supplier
control
of inventory
decision
Advantages
• Improved _forecast_
• Decreased _cost_
VMI
VMI with
supplier
inventory
ownership
– Lower _inventory__
– Lower _stockout_
increasing _trust_ level
_local_
inventory
control
18
RSP Issues and Advantages
Continuum of RSP Relationships
Quick Response
Inventory
decision-making
increasingly
global
Issues
_inventory_ ownership
IT (heavy investment)
Mutual _trust_
Suppliers have more
responsibility
• Sharing benefits
• Confidentiality
•
•
•
•
_global_
inventory
control
19
20
5
Example of RSP Success
Why does RSP have those advantages?
• VF Corporation’s Market Response System:
Without RSP: sequential, myopic optimization
Supplier
Retailer
Information
flow
– The VF Corporation, which has many well known brand
names (including Wrangler, Lee, Girbaud, and many others),
began its VMI program in 1989.
Retailer optimizes its operations first.
Then supplier optimizes its operations
subject to the constraints imposed by the
retailer.
– Currently, about 40 percent of its production is handled using
some type of automatic replenishment scheme.
– This is particularly notable because the program
encompasses 350 different retailers, 40,000 store locations,
and more than 15 million replenishment levels.
With RSP (particularly, VMI): Joint optimization
Supplier
Customer
Supplier optimizes its operations and the
retailer’s. This is system-wide.
– VF’s program is considered one of the most successful in the
apparel industry.
Information
flow
21
22
Other Kinds of Partnerships:
Third Party e-Fulfillment (3eF)
Example of RSP Failure
Spartan Stores (grocery chain)
• 3eF = the outsourcing of the back-end logistics of
e-business including: the integration with front-end Internet
operations, order capture and processing, fulfillment of
individual orders, and return logistics.
– Shut down its VMI effort about one year after its
inception
– Buyers were not spending any less time on reorders
than they did before
• Differences between 3PL & 3eF?
– Issue: buyers didn’t trust suppliers -- continued to
carefully monitor inventories and deliveries and to
intervene at hint of trouble.
S
M
W
R
Logistics: physical flow from suppliers to manufacturers, or/and from
manufacturers to retailers
– Suppliers did little to allay these fears; suppliers did
not deal well with promotions -- delivery levels were
often unacceptably low during these periods of peak
demand.
M
W
R
C
E-Logistics: “last mile”, i.e. logistics of order fulfillment of e-businesses
23
24
6
Why is 3eF different from 3PL?
3eF Examples
1. Fingerhut Business Services
Traditional Supply Chain
–
–
e-Supply Chain
Supply Chain Strategy
Push
Push-Pull
Shipment Type
Bulk
Parcel
Information Flow
Unidirectional
Bi-directional
Reverse Logistics
Simple
Highly Complex
2. OrderTrust
–
Destination
A major provider of e-fulfillment service
Wal-Mart’s cyberstore is managed by Fingerhut
Manages SkyMall.com’s order fulfillment
Small Number of Stores Highly Dispersed Customers
Lead Times
Depends
Short
25
26
3eF: Why Need for Reverse Logistics?
• Return Percentage in the Offline World
(Online world has much higher percentages)
Industry
Magazine Publishing
(50%)
Book Publishers
(20-30%)
Book Distributors
(10-20%)
Greeting Cards
(20-30%)
Catalog Retailers
(18-35%)
Computer Manufacturers
(10-20%)
CD-ROMs
(18-25%)
Consumer Electronics
(4-5%)
Source: Rogers and Tibben-Lembke
27
7
01/10/2013
1
2
Les fonctions de gestion à couvrir
Long terme, gestion intégrée
Gestion du réseau logistique
mois
Planif des
Planif
Planification distribution
appros production
jour
Temps
réel
Gestion
Ordo
Ordo
des appros Production des stocks
Gestion
transports
suivi des conduite Conduite
achats production entrepôts
Conduite
transports
CLIENTS
semestre
Vendre
Prévision des ventes
Chapitre 7.
Les outils informatiques
du supply chain management
Acheter Fabriquer Stocker
Livrer
Dimensionnement du réseau logistique
Administration des
ventes
FOURNISSEURS
ans
Allocation
stocks,
Gestion
commandes
Court terme, gestion localisée
Le domaine « classique » ERP et APS
Acheter
Fabriquer
Stocker
Livrer
FOURNISSEURS
trimestres
Plan industriel et commercial
Planif
appros
mois
Ordo
des appros
Prévision des
ventes
Planif
production Planification distribution
Administration
GPAO
ERP
des ventes
Gestion
Gestion
Ordo
production des stocks transports
MES
Temps
réel
APS
conduite Conduite
production entrepôts
SCE
Conduite
transports
Gestion
commandes
4
Objectifs :
CLIENTS
Planification du réseau logistique
L’approche ERP classique : objectifs
 Résulte du mélange de logiciels de GPAO, de
comptabilité, de prévision, de finance, de distribution
Vendre
Simulation du réseau logistique
ans
jour
3
Transactionnel : réaliser l’échange d’information et gérer les
informations => base de donnée unique
Financier : permettre l’évaluation financière de toutes les
activités : production, distribution, administratif, commercial
Méthodes de calcul issues des logiciels de support
PIC, MRP, DRP, gestion de stocks, ...
1
01/10/2013
Acheter
Fabriquer
Stocker
Livrer
5
Vendre
ans
Plan Industriel et Commercial multi-site
Prévision de
marchés
Planif
appros
MRP II
DRP
mois
jour
Ordo
appros
Temps
réel
Ordo
Gestion
production des stocks
Suivi
Suivi
production entrepôts
Prévision de vente
Administration
des ventes
Gestion
transports
Conduite
transports
CLIENTS
FOURNISSEURS
trimestres
DRP <=> Calcul des besoins de MRP
La nomenclature DRP est le réseau de sites à traverser pour distribuer une
demande
 Besoins Brut
 Besoins Brut
 Stock, Attendu, Besoin Net
 Stock, Attendu, Besoin Net
 Loi de gestion
 Loi de gestion
 taille de lot de production
 taille de lot d ’approvisionnement
 Délai d ’obtention
 Délai de livraison
 Besoin net Jalonné
 Besoin à lancer
 lancement en production
 lancement au transport
Calcul de charge DRP :
charge de réception de livraison
charge de préparation de commandes
charge de transport
Suivi
commandes
L’approche APS : objectifs
6
Comparaison MRP/DRP
7
Résulte de la volonté de proposer des outils
d’optimisation pour l’aide à la décision
Aide à la décision : fournir des procédures évoluées pour aider à
décider
Aider au suivi : réalisation ≠ prévu ou planifié
Supports
Procédures d’optimisation ou heuristiques évoluées
Récupère les données dans les bases de données de l’entreprise
Récupère les informations de suivi d’exécution
Acheter
ans
Fabriquer
Stocker
Livrer
trimestres
mois
Vendre
Dimensionnement du réseau logistique
Planification stratégique du réseau logistique
FOURNISSEURS
Objectifs :
8
L’approche APS : démarche
Planification tactique du réseau logistique
Planif
production + appros
Prévision
de marché
et de vente
Planif distribution
jour
Ordo
production
Gestion
transports
Temps
réel
Suivi
Suivi
production entrepôts
suivi
transports
Promesse
de vente
CLIENTS
L’approche ERP classique : démarche
suivi
commandes
2
01/10/2013
9
10
Module de prévision
 Rôle : consolider des prévisions de nature différente :
 Prévisions moyen ou long terme de marchés, de
comportement, de réseaux, de familles de produits, de
filiales.
 ≠ prévisions court terme de commandes de clients
Le supply chain Management
 Analyse statistique pour repérer :
 tendance d’évolution de la demande
 effets saisonniers
 impact de promotions
Les APS : …. des modules
valeurs
+
analyse de
corrélations
Modèles de comportement (marchés, réseaux, produits, pays,
familles produits)
Demand Planning
11
Sur quel niveau réaliser les prévisions
12
méthodes de prévisions statistiques
incorporation de facteurs externes
support à la collaboration sur les prévisions
Simulation : études What If
Calcul de stocks de sécurité
Dimension temps
Prévision de vente
Dimension géographique
Définition de hiérarchie sur les 3 niveaux
et aide à la consolidation
3
01/10/2013
Demand Planning
13
Les méthodes statistiques
demande sporadique
ventes perdues ou ventes retardées
Système de sélection des paramètres des modèles
prévision de produits à faible durée de vie
Analyse de l ’erreur de prévision
facteurs externes : intégration de l ’expert
 revised judgment : prevision d ’expert puis statistique puis expert met à
jour sa prévision
 combined forecast : poids fixe à expert et statistique
 revised extrapolation forecast : événements pré-définis
 rule based forecast : rêgle expertes de combinaison de prévision
statistiques
 econometric forecast : expert choisi et valide les séries explicatives
 Objectif : aider à dimensionner une chaîne logistique
 Faut-il un nouveau site de production : où ?
 Faut-il un nouvel entrepôt régional : où ? quels produits ?
 Quelle usine fournit quelle région ?
 Récupération de modèles de marché du module de prévision
+ simulation de planifications stratégiques APS
Evaluation des coûts d’une chaîne logistique donnée
 C’est un module de simulation de chaînes logistiques.
14
Problématiques particulières
Moyenne mobile
lissage exponentiel triple : Holt Winters
ARIMA / Box Jenkins
regression linéaire de séries expicatives
Module de dimensionnement de chaîne
logistique
Demand Planning
Dimensionnement de stocks de sécurité
• 1 ou plusieurs niveaux
15
Module de planification multi-site
16
 Objectif : planification stratégique en 1 passe
 Résolution du modèle précédent
• Horizon : 1 ou 2 ans
• Période : 1 mois ou trimestre
 Permet d’obtenir un PIC validé à capacité finie
• Taille de période grande => peu de problème de
taille de lot
 Optimisation des coûts sur toute la chaîne
4
01/10/2013
Module de planification locale
17
 Objectif : planification tactique sur des sites
 Souvent : DRP pour faire remonter les besoins puis
planification locale multi-site de la production.
• Problématique de taille de lot
+ problématique de lissage de charge
Résolution du modèle APS par des
heuristiques
Master planning detecte l ’incapacité à faire la
demande
allocation de volume selon la hiérarchie géographique de la
demande
• Par rang de priorité à certaines régions
• en proportion de la prévision faite
• coefficient fixe
18
Objectif :
jugés critiques
ATP et pénurie de produits
Demand Fulfilment et
Available to promise(ATP)
 répartir les pénuries
 donner une date de disponibilité à une commande client
Principe :
Master planning donne un profil de stock dans le temps
ATP réserve ces stocks pour des demandes
Production sur stock => stock de produit fini
Production à la commande => composants ou MP
Configuration à la commande => Capable TP
réservation de capacité d ’assemblage
réservation de composants
19
ATP et promesse de date
20
Actions possible si commande > prévision
réserver sur une autre zone géographique
réserver plus tôt ou plus tard
réserver un produit de rechange
Modules ATP (I2 technologies)
paramétrer les règles pour sélectionner la dimension sur
laquelle choisir.
Les forces de vente ont besoin de ce retour
d ’information pour planifier leurs actions
5
01/10/2013
21
Autres modules
 Ordonnancement : aide interactive à la mise au point
d’un diagramme de Gantt des ordres de fabrication
 Transport : aide à la définition de tournées en fonction
d’un parc de camions
 Peu standardisés = dépendent des éditeurs
Problèmes posés par le modèle APS
22
 1) Taille des problèmes
 Pour une rapidité de résolution, limiter la planification
globale
 2) Taille des lots
 les quantités produits sont multiples de ces tailles de lots
 Introduction de variables entières (nbre de lots)
 3) Compromis charge/capacité difficile à ramener à un coût
 Pour la planification tactique : intérêt à définir des stratégies
heuristiques DRP + MRP est une stratégie.
 Identifier les éléments critiques
 Planification tactique APS sur ces éléments
 Planification classique DRP/MRP sur les autres
23
Comparaison modèle de planification / MRP + DRP
24
Démarche MRP + DRP :
Calcul
charge
Calcul
besoins
Production
PDP
Calcul
charge
Calcul
besoins
livraison
Prévision
vente
Cas APS :
Consolidation des prévisions de vente des magasins
Planification à capacité FINIE en 1 passe pour tous les sites
de production et de distribution
 moins de « bidouilles » sur taille de lot, délai de production
ou d’approvisionnement
Moins de stocks + flux plus tendu
Le supply chain management
Le marché des logiciels
6
01/10/2013
Les éditeurs ERP : 20 % de croissance en 1998
25
Pourcentage de part de marché des éditeurs
ERP en France en 1998
Les éditeurs APS : 45 % de croissance en 1998
2,4%
8% 5%
14,1%
2,9%
12%
5,1%
7,7%
SAP
Oracle
Baan
7,8%
7%
38%
12%
45,5%
3,3%
Parts du marché mondial
1998 en %
Parts du marché français
1998 en %
2,7%
18%
3,6% 3,1% 3,0%
2,5%
4,5%
6,8%
44,8%
7,8%
23,9%
8,4%
Intentia
People Soft
SSA
JD Edwards
Arès
Autres
QAD
Source IDC France 1999
Les tendances du marché logiciel
 1) Les ERP intègrent les modules APS
 SAP, BAAN, Oracle => leur propre APS
 JDE => rachat de Numetrix
 2) Les APS cherchent à optimiser un maximum de fonctions
=>descente dans le court terme
 3) le mouvement vers Internet
 2.1) Planification partagée dans l’entreprise et avec clients et
fournisseurs :e-chain, Collaborative Planning Forecosting and
Replenishment
 2.2) Définition de sites www portail entre fabricants et
distributeurs : Marchés d’échange de produits => cf. Trade
Matrix.com
27
Manugistics
I2 Technologies
Synquest
26
Dynasys
Logility
BAAN
Numetrix
AspenTech
Ilog
SAP
Autres
(source IDC France 1999)
(source Benchmarking Partners)
Tendances du marché méthodologie
28
Le Customer Relation Management
Différentes methodes pour répondre à un besoin client
<=> Où se situe le stock de sécurité, Quel délai promettre
• Pick to Order : stock = produit fini : Réservation de stock et livraison
sur commande
• Assemble to Order : Stock = produit intermédiaire : Configuration
produit + Assemblage + livraison sur commande
• Make to Order : Stock = matière première
Configuration produit + charge + Assemblage + livraison sur
commande
• Engineer to Order : Stock = matière première
Configuration produit + gamme + charge + assemblage + livraison
sur commande.
7
01/10/2013
Efficient Consumer Response (ECR)
29
 Démarche introduite aux USA en 1992
 Objectif : comment améliorer l ’efficacité de la chaîne logistique
dans le secteur de la grande distribution.
 Liste de bonnes pratiques généralisables à d ’autres secteurs :
Par action sur l ’organisation des flux physiques
Par action sur l ’organisation des flux d ’information
 11 thèmes = 11 types de bonnes pratiques
ECR : bonnes pratiques (2)
31
 4/ Production synchroniséé :
Utiliser l ’information des ventes pour réduire les tailles de
lots et améliorer réactivité
=> baisse des stocks, meilleure tension du flux, réduction des
délais
 5/ Production flexible et fiable
Réduction des stocks exige livraison fréquente et fiable +
production adaptable
=> augmenter la flexibilité des sites de production pour
s ’adapter aux fluctuations
 6/ Intégrer les fournisseurs
Partager les informations avec Ss-traitants et fournisseurs en
conception et production
ECR : bonnes pratiques
30
 1/ optimiser les unités de conditionnement :
 unité de conditionnement fournisseur = unité des magasins de
distribution
=> réduction des places en magasins + réorganisation des unités de
transport
 2/ Cross docking, GPA : limiter au maximum opérations logistiques
Palettes livrées par fabricant limitent les opérations à réaliser chez
distributeur
=> éclatement facile de palettes, rassemblement de commandes,
codes barres, …
 3/Mise à jour continue :
Information Tps réel des ventes en magasins à tous les
fournisseurs, fabricants.
=> fournisseur s ’engage sur un taux de service et Stock sécurité
ECR : bonnes pratiques (3)
32
 7/ Catégories de produits d ’après la vente finale
Établir des familles de produits et objectifs par famille sur
l ’ensemble de la chaîne d ’après les implications pour les
clients finaux.
 8/ Réduire les erreurs lors du lancement d ’un nouveau produit
par une coordination sur l ’ensemble de la chaîne
D ’autant plus important que rythme d ’innovation s ’accélère
 9/ Réapprovisionnement de stock automatique
Utiliser les ventes réelles pour définir des points de
réapprovisionnement automatique => limité délai
administratif de commande + facture par période.
8
01/10/2013
ECR : bonnes pratiques (4)
33
 10/ gestion des coupons automatisée
Informatiser complètement la gestion (émission, traitement,
enregistrement) des bons de réduction
 11/ échanges automatisés dans les contrats
Simplifier la structure des contrats avec fournisseurs.
Demander un engagement sur certains objectifs moyennant
échange d ’information
Les objectifs du CPFR
Les intégrateurs : Collaborative Planning,
Forecasting and Replenishement (CPFR)
34
Le CPFR est un processus global de collaboration
industrie-commerce visant l’alignement de l’offre
et de la demande dans le secteur des produits de
grande consommation.
35
Processus du CPFR
36
 Aligner les objectifs commerciaux du client et du fournisseur
pour une (des) catégorie(s) de produits donnée(s)
 Intégrer les plans commerciaux dans les plannings opérationnels
 Fiabiliser les prévisions
 Augmenter l’efficacité des promotions
 Réduire les stocks dans la chaîne d’approvisionnement
 Dynamiser les ventes
9
01/10/2013
GPA / CPFR, deux processus complémentaires
37
 En tant que système de calcul de commande efficace, la GPA
peut se substituer en l’état aux étapes 6, 7, 8 et 9 du CPFR
 Maintenir les 2 systèmes à part :
les prévisions concertées sont faites au plan national entre les
fonctions commerciales, marketing, merchandising et
opérationnelles
la GPA fonctionne à l’échelle locale pour le calcul de
commandes optimisées
 Donner de la visibilité à la GPA concernant les promotions
concertées à 3 mois
Les résultats sont substantiels à la fois pour les
industriels et les distributeurs :
Bénéfices :
Amélioration des prévisions de vente 10 – 40%
Réduction des stocks 10 – 15%
Amélioration du taux de service 0.5 – 4.0%
Augmentation des ventes 2 – 25%
Source: Derived from VICS published CPFR pilots and Transora participant
company results
38
 Johnson & Johnson UK
 Collaboration aval avec Superdrug
 Par article / semaine / entrepôt distributeur
 Extension à d’autres produits et distributeurs
 Marks & Spencer
 Collaboration quotidienne avec les fournisseurs de sandwiches
 Fenêtre de visibilité : 14 jours + 10 semaines
 Extension à l’ensemble des produits et fournisseurs de la catégorie
 Kimberly Clark France
 Collaboration sur les promotions avec un distributeur (75 produits ; 3 entrepôts)
 Extension progressive du périmètre
 Procter & Gamble
 CPFR et VMI (Vendor-Managed Inventory) avec différents distributeurs et
fournisseurs de matières premières en Europe
 Pfizer and Unichem
 Collaboration sur les prévisions de vente et de livraison (50 produits, 11 entrepôts)
 Donner de la réactivité aux prévisions grâce à l’analyse des
premiers jours de vente afin de corriger le tir, si besoin est.
Exemples de résultats obtenus avec
une démarche CPFR
Quelques projets CPFR en Europe
 Par article / semaine / entrepôt distributeur
39
Synthèse sur le CPFR
40
 Le CPFR n’est pas un mythe, mais une réalité
 On peut l’assimiler à un puissant « système de contrôle » de la bonne marche
des affaires internes et externes d’une entreprise
 Par essence, il stimule la croissance et le reengineering orienté marché
 Il n ’y a pas un mais plusieurs CPFR selon les scénarios
 Il s’applique particulièrement aux produits dont la demande est irrégulière
(nouveaux produits, promotions, produits saisonniers...)
 Ses performances sont telles que les entreprises qui réussissent le mieux sont
celles qui en parlent le moins !
 Mais sa mise en œuvre requiert une forte motivation de la direction générale
et la dynamique d’une équipe de projet pluridisciplinaire
10
Strategic fit
1. How would you characterize the competitive strategy of a high-end department store chain such
as Nordstrom ? What are the key customer needs that Nordstrom aims to fill?
2. What level you place the demand faced by Nordstrom on the implied demand uncertainty
spectrum? Why?
3. What level of responsiveness would be most appropriate for Nordstrom’s supply chain ? What
should the supply chain be able to do particularly well?
4. How Nordstrom expand the scope of strategic fit across its supply chain?
5. Reconsider the previous four questions for other companies such as Amazon, a supermarket
chain, an auto manufacturer, and a discount retailer such as Wal-Mart.
2. Consider the purchase of a can of soda at a convenience store. Describe the various stages in the
supply chain and the different flows involved.
3. Why should a firm like Dell take into account total supply chain profitability when making
decisions?
4. What are some strategic, planning, and operational decisions that must be made by an apparel
retailer like The Gap?
5. Consider the supply chain involved when a customer purchases a book at a bookstore. Identify
the cycles in this supply chain and the location of the push-pull boundary.
6. Consider the supply chain involved when a customer orders a book from Amazon. Identify the
push/pull boundary and two processes each in the push and pull phases.
7. In what way do supply chain flows affect the success or failure of a firm like Amazon? List two
supply chain decisions that have a significant impact on the supply chain profitability.
See www.emse.fr/~xie/MasterGI/Ch1_supplement.pdf for questions below.
A. Quelles sont les principales caractéristiques de Supply Chain Management ?
B. Décrire les principaux flux d’une supply chain et leurs caractéristiques.
C. Décrire le rôle de la frontière Push/Pull dans une chaîne logistique. Expliquer son importance
dans une supply chain.
D. Quels sont les leviers de performances des supply chains.
E. Quels sont les principaux processus identifiés par le modèle SCOR ? Qu’apporte une analyse de
son supply chain par la méthode SCOR ?
F. Quels sont les avantages et les désavantages d'un réseau de distribution centralisée par rapport à
un réseau de distribution décentralisé.
G. Pour faire face à la diversité des produits en automobile, les constructeurs développent un
nombre limité des modules génériques pour couvrir la plupart des demandes spécifiques. Quels
sont les avantages et les inconvénients de cette méthode?
H. Dans d'une chaîne logistique composée d'un producteur et des distributeurs appartenant à des
entreprises différentes, traditionnellement, chaque distributeur détermine ses
approvisionnements en fonction des demandes des clients finaux, le producteur considère les
distributeurs comme des clients finaux et établit son plan de production pour répondre aux
ordres d'approvisionnement de ces derniers. Quels sont les problèmes posés par cette pratique?
Que peut apporter le partage des informations sur la demande client? Quel est l'avantage de
laisser producteur gérer les stocks de distributeur? Ces stratégies sont-elles réalisables?
1. (maîtrise de concepts, soyez bref et clair)
Chapitre 1. Introduction
Exercices
Usine
4 wk
4 wk
DC2
DC1
Marché 2
Marché 1
Usine
4 wk
DC
Marché 2
Marché 1
On vous demande de :
1) Calculer la quantité économique de chaque DC
2) Déterminer le stock de sécurité de chaque DC
3) Déterminer le niveau de stock moyen ainsi que le coût de stockage annuel de chaque DC
(suggestion : dessinez la courbe de l’évolution du niveau de stock)
4) Déterminer le niveau de stock maximal, la capacité de stockage de chaque DC. On suppose
que chaque produit a un volume de 0,1 m3 et la capacité d’entreposage est de 3 fois le
volume des produits (pourquoi ?).
5) Déterminer le coût total de stockage et de construction des DC sur un horizon de 5 ans. On
suppose que le coût de construction est de 1000€ par m3 pour un DC de moins de 2000 m3 et
de 900 € par m3 pour un DC de plus de 2000 m3.
6) Quelles conclusions en tirez vous ?
Supply chain design
Une entreprise souhaite savoir si il est possible d’optimiser son réseau de distribution.
Actuellement, elle distribue ses produits via deux centres de distribution (DC1 et DC2). Elle
cherche à savoir le gain en regroupant DC1 et DC2 en un centre de distribution central (DC). La
demande hebdomadaire du marché 1 est de moyenne (1000) et d’écart-type (500). La hebdomadaire
demande du marché 2 est de moyenne (1600) et d’écart-type (700). Les deux demandes sont
indépendantes. Le délai d’approvisionnement est de 4 semaines dans tous les cas. Chaque produit a
une valeur de 1000€, le coût de stockage annuel est de 13% de la valeur du produit, le coût de
commande est de 10000 € par commande. Les centres de distribution sont gérés pour niveau de
service de 95%.
SC drivers and metrics
1. How could a grocery retailer use inventory to increase the responsiveness of the company’s
supply chain ?
2. How could an auto manufacturer use transportation to increase the efficiency of its supply chain?
3. How could a bicycle manufacturer increase responsiveness through its facilities?
4. How could an industrial supplies distributor use information to increase its responsiveness?
5. Motorola has gone from manufacturing all its cell phone in-house to almost completely
outsourcing the manufacturing. What are the pros and cons of the two approaches?
6. How can a home-delivery company like Peapod use pricing of its delivery services to improve its
profitability?
7. How has globalization made strategic fit even more important to a company’s success?
8. What are some industries in which products have proliferated and life cycles have shortened?
How have the supply chains in these industries adapted?
9. How can the full set of logistical and cross-functional drivers be used to create strategic fit for a
PC manufacturer targeting both time-sensitive and price-conscious customers ?
6. Give arguments to support the statement that Wal-Mart has achieved very good strategic fit
between its competitive and supply chain strategies.
a) Considérons le réseau actuel. Déterminer le stock de sécurité à constituer dans chaque magasin.
Déterminer le coût de stock de sécurité de l'ensemble des magasins.
b) Déterminer le niveau de stock de sécurité de chaque magasin dans le nouveau réseau.
c) Déterminer la variance de demande du DC du nouveau réseau et son stock de sécurité.
Dans le deux cas, le niveau de service exigé est de 95%.
Afin d'améliorer la chaîne logistique, l'entreprise procède à une refonte de son réseau de
distribution. La nouvelle structure comporte un Centre de Distribution DC permettant d'alimenter
les magasins en 1 semaine. Le DC s'approvisionne seul auprès du fournisseur avec un délai
d'approvisionnement de 8 semaines (voir la figure de la page suivante).
1. (Lead time pooling)
Une entreprise possède 300 magasins en France. Les magasins sont localisés de telle sorte que leur
demande est similaire. La demande hebdomadaire de chaque magasin est une variable aléatoire de
distribution normale N(1000, 5002) de moyenne 1000 et de l'écart-type 500. Le coût de chaque
produit est de 100 euros par unité. Le coût de stockage annuel d'un produit est 13% de son coût.
Chaque magasin s'approvisionne auprès d'un même fournisseur et le délai d'approvisionnement de 8
semaines.
Risk pooling
3. (Aggregation with capacity constraint) WW Grainger sources from hundreds of suppliers and is
considering the aggregation of inbound shipments to lower costs. Truckload shipping costs 500$
per truck along with 100$ per pickup. Average annual demand from each supplier is 10000 units.
Each unit costs 50$ and Grainger has an annual holding cost of 20%. What is the optimal order
frequency and order size if Grainger decides to aggregate 4 suppliers per truck? What is the optimal
order size and order frequency if each truck has a capacity of 2500 units?
2. (aggregating multiple products in a single order) Consider 4 different product modes of Best Buy
store each with data of problem 1 and all four products are sourced from the same source.
a) what is the total cost and order quantity if each product model is managed independently.
b) What if the four product managers coordinate their purchasing to ensure that all four products
arrive on the same truck, i.e. shared the same fixed order cost?
c) What do you think about the fixed cost structure? Is it reasonable?
1. (EOQ) Demand for Despro Computer at Best Buy is 1000 units per month. Best Buy incurs a
fixed order placement, transportation, and receiving cost of 4000$ for each order. Each computer
costs Best Buy 500$ and the retailer has an annual holding cost of 20%.
a) Evaluate the order size, order frequency, cycle inventory, annual ordering cost, annual holding
cost, average flow time.
b) Evaluate the total inventory cost of a lot size of 1100 units. What observation can you make?
(Robustness)
c) Determine the order quantity, cycle inventory, flow time if the demand of Best Buy increases to
4000 computers per month (demand increased by a factor of 4). What observations can you make?
d) For the situation D = 1000, what if the manager would like to reduce to lot size to Q = 200 units?
What if the fixed cost is reduced to 1000$ per order?
e) How much should the fixed order cost be reduced to in order to reduce the optimal order size to
200?
Ch5 : Cycle inventory
store 300
store 1
Supplier
8 wk LT
retail
DC
1 wk LT
store 300
store 1
1
16
40
1
43
67
1
64
99
t
demande 1
demande 2
t
demande 1
demande 2
t
demande 1
demande 2
2
94
72
2
84
8
2
92
59
3
70
20
3
54
44
3
11
18
4
52
67
4
77
30
4
3
12
5
8
42
5
42
55
5
7
14
6
56
68
6
74
6
6
42
5
7
19
62
7
56
26
7
34
44
8
7
41
8
6
66
8
30
22
9
50
93
9
50
88
9
33
37
10
39
17
10
10
93
10
78
73
2. Risk pooling
Pour chacun des trois historiques de demande de deux produits similaires, (i) vérifions pour chacun
si il y a une corrélation entre les deux produits, (ii) déterminer la moyenne et l'écart type de chaque
demande i et de l'ensemble de deux demandes 1+2; (iii) déterminer pour les paramètres des
politiques de gestion de stock (s, S) pour deux scénarios (a) les demandes sont satisfaites à partir de
deux stocks différents et (b) les demandes sont satisfaites à partir d'un seul stock en utilisant les
paramètres suivants: coût de commande K = 100 euros/ordre, coût de stockage h = 1 euro
/produit/période, niveau de service  = 95% et le délai d'approvisionnement L = 2, (iv) quelles
conclusions en tirez vous?
Supplier
8 week LT
Déterminer le coût de stocks de sécurité du nouveau réseau.
Quelle conclusion en tirer?
Que se passe-il si il y ait que 2 magasins?
Que se passe-il avec toujours 300 magasins mais avec 7 semaines de délai pour
l'approvisionnement des magasins auprès du DC dans le nouveau réseau?
h) Que pensez vous des coûts de transport?
d)
e)
f)
g)