Download Supply Chain Management

Supply Chain Management
Part I
Supply chain management is the combination
of art and science that goes into improving
the way a company finds the raw components
it needs to make a product or service,
manufactures that product or service and
delivers it to customers.
Supply Chain
• The entire network related to the activities of a
firm that links suppliers, factories, warehouses,
stores, and customers.
• It requires management of goods, money, and
information among all relevant players.
• Traditionally, particularly in the military, referred
to as logistics except there is more of a system
and integrated view of the process
Traditional Military Logistics Functions
• Procurement – obtaining goods and services
from suppliers and contractors
• Supply – storing and managing the flow of
inventory among base supply points and depots
• Maintenance – base and depot level restoration
of repairable components
• Transportation – the movement of inventory
among suppliers, depots, and bases
What is Supply Chain
Management?
Supply Chain management deals with the
control of materials, information, and
financial flows in a network consisting of
suppliers, manufacturers, distributors, and
customers (Stanford Supply Chain Forum
Website).
I look at supply chain management as a continuously evolving
management philosophy that seeks to unify the collective
productive competencies and resources of business functions
found both within the enterprise and outside the firm's allied
business partners located along intersecting supply channels
into a highly competitive, customer-enriching supply system
focused on developing innovative solutions and synchronizing the
flow of marketplace products, services, and information to
create unique, individualized sources of customer value!
… And it has certainly improved my business.
A Postulate
• Manufacturing has become relatively
efficient
– Therefore fewer opportunities to trim costs
– However significant opportunities still exist for
cutting costs in the supply chain.
A Supply Chain Visualization
information flow
customer
raw material
manufacturing
retail store
Financial flow
suppliers
warehousing
The Ripple Effect
• Actions by one member of the chain can
influence the profitability of other members
• Compete against other supply chains
rather than other firms
The Variability Curse
• Constant demands will still generate high
variability for manufacturer
– trade promotions, volume discounts, long lead times,
sales incentives, economic order quantities (batch
ordering), over reaction to shortages, etc.
– variability increases from consumer  retail store 
distribution center  warehouse  factory 
supplier.
• Cost of variability can be high
– inefficient use of production and warehouse
resources
– high transportation costs
– high inventory costs
Five Basic Components to Manage
1.
Planning - a strategy for managing all the resources that go toward
meeting customer demand for a product or service.
–
2.
includes metrics to monitor the supply chain so that it is efficient, costs
less and delivers high quality and value to customers.
Sourcing - select suppliers that will deliver the goods and services
–
–
3.
Develop a set of pricing, delivery and payment processes with suppliers
and create metrics for monitoring and improving vendor relationships.
Manage goods & services received from suppliers: receiving shipments,
inspection, transferring to production facilities & authorizing supplier
payments.
Making -the manufacturing step.
–
–
4.
Schedule the activities necessary for production, testing, packaging and
preparation for delivery.
The most metric-intensive portion of the supply chain measuring quality
levels, production output, and worker productivity.
Delivering - the "logistics."
–
5.
Coordinate the receipt of customer orders, develop a network of
warehouses, pick carriers to get products to customers, and set up an
invoicing system.
Returning - the problem part of the supply chain.
–
Create a network for receiving defective and excess products back from
customers & supporting customers having product problems.
Supply Chain Efficiency
Collaboration
Product Postponement
Design for Logistics
Supply Chain Collaboration
• P&G hooked up to Wal-Mart's distribution centers.
• When P&G's products run low at the distribution
centers, the system sends an automatic alert to
P&G to ship more products.
– In some cases, the system connects to the individual
Wal-Mart store.
– It lets P&G monitor the shelves through real-time
satellite link-ups that send messages to the factory
whenever a P&G item swoops past a scanner at the
register.
More supply chain collaboration
• With this kind of minute-to-minute information,
P&G knows when to make, ship and display more
products at the Wal-Mart stores.
• No need to keep products piled up in warehouses
awaiting Wal-Mart's call.
• Invoicing and payments happen automatically
too.
• The system saves P&G so much in time, reduced
inventory and lower order-processing costs that it
can afford to give Wal-Mart "low, everyday prices"
without putting itself out of business.
Postponement in Supply Chains
• Several companies have been able to cut costs
and improve service by postponing the final
configuration of the product until the latest
possible point in the supply chain. Examples:
– Bennetton producing “grey stock” wool clothing
– Hewlett Packard printer configuration
– Postponement of final programming of semiconductor
devices
– Assemble to order rather than assemble to stock (Dell
Computer)
Design for Logistics
• Many firms now consider SCM issues in
the design phase of product development.
(This has been referred to DFL or Design
for Logistics).
• One example is IKEA whose furniture
comes in simple to assemble kits that
allows them to store the furniture in the
same warehouse-like locations where they
are displayed and sold.
Efficient Design of the Supplier Base
• Part of streamlining the supply chain is
reducing the number and variety of
suppliers.
• Another example: In the mid 1980’s Xerox
trimmed its number of suppliers from
5,000 to 400. Overseas suppliers were
chosen based on cost, and local suppliers
were chosen based on delivery speed.
Dell Designs the Ultimate Supply Chain
Dell Computer has been one of the most successful
PC retailers. Why? To solve the problem of
inventory becoming obsolete, Dell’s solution:
Don’t keep any inventory! - All PC’s are made to order
and parts shipped directly from manufacturers when
possible. Compare to the experience of Compaq
Corporation. (initial success selling through low cost
retail warehouses, but did not garner web-based
sales).
Information Transfer
Electronic Data Interchange
Vendor Management Inventory
The Bullwhip Effect
Business to Business
Electronic Data Interchange (EDI)
• Transmission of documents electronically in a
predetermined format from company to
company. (Not web based.)
• The formats are complex and expensive. It
appears to be on the decline as web-based
systems grow.
– offer discounts to those that transact business over
internet
• Requires cooperation and coordination among
all the players
Vendor Managed Inventory
• Barilla SpA. Italian pasta producer.
Pioneered the use of VMI (Vendor
Managed Inventory). They obtained sales
data directly from distributors and decide
on delivery sizes based on that information
(as opposed to allowing distributors to
independently decide on order sizes).
The Bullwhip Effect
• First noticed by P&G executives examining the order patterns for
Pampers disposable diapers. They noticed that order variation
increased dramatically as one moved from retailers to distributors to
the factory. (See next slide.) The causes are not completely
understood but have to do with batching of orders and building in
safety stock at each level. Problem: increases the difficulty of
planning at the factory level.
• Causes
–
–
–
–
demand forecasting updates
batch orders
price fluctuations
shortage gaming
• Cures
–
–
–
–
information sharing through EDI
channel alignment through multi-echelon inventory models
price stabilization using value rather than promotional pricing
discourage shortage gaming by allocating on sales not orders
Example of the Bullwhip Effect in Supply Chains
The Explosive Growth of E-tailing
• E-tailing: Direct to customer sales on the
web. Perhaps best known e-tailer is
Amazon.com, originally a web-based
discount book seller. Today, sells wide
range of products. The so called “dot com”
stocks fueled large gains in the NASDAQ
in 1999 to be followed by a major decline
since April, 2000. Today, many traditional
“bricks and mortar” retailers also offer
sales over the web, often at lower prices.
B2B Supply Chain Management
• B2B (business to business) supply chain
management:. While not as visible and “sexy” as
E-tailing, it appears that B2B supply chain
management is the true growth industry. A
search on Yahoo yielded over 80 matches for
supply chain software providers. Some of the
major players in this market segment include:
• Agile Software based in Silicon Valley.
• i2 Technologies based in Dallas.
• Ariba based in Silicon Valley
Global Concerns in SCM
• Moving manufacturing offshore to save
direct costs complicates and adds
expense to supply chain operations, due
to:
– increased inventory in the pipeline
– Infrastructure problems
– Political problems
– Dealing with fluctuating exchange rates
– Obtaining skilled labor
Trends in Supply Chain Management
• Outsourcing of the logistics function
– example: Saturn outsourced their logistics to Ryder
Trucks.
– Outsourcing of manufacturing is a major trend these
days.
• Moving towards more web based transactions
systems
• Improving the information flows along the entire
chain.
Inventory Implications
End Item
Multi-echelon
Lateral re-supply
Indentured parts
Factory
(Dependent demand)
Central
Warehouse
Regional
Warehouses
Regional
Warehouses
Retail
Outlets
Retail
Outlets
Retail
Outlets
customers
Components
Parts
Parts
Components
Parts
Regional
Warehouses
Retail
Outlets
Retail
Outlets
Parts
Analytical Methods
• The transportation problem and more
general network formulations for
describing flow of goods in a complex
system
• Inventory management and demand
forecasting models such as those
discussed in this course
• Analytical methods for determining
delivery routes for product distribution.
A Micro Supply Chain
Vehicle Routing
– determine the optimal delivery sequence of a
single truck to n customers
– same as the classical traveling salesman
problem
– for n customers, there are n! possible routes
• for small n, enumerate and cost all routes
• for large n, use heuristic algorithm (np hard)
– problem increases in difficulty for multiple
trucks
The single truck problem
A truck is located in City A and must
deliver to each of n customers
returning to city A. What route will
minimize the total distance driven?
5
B
C
2
5
11
A
E
3
7
4
D
G
8
6
F
6! = 720
(n-1)! possible routes.
30
A From-to Matrix
From /
To
City A
City A
City B
City C
City D
25
30
12
City B
25
17
23
City C
30
17
City D
12
23
37
37
distances in miles
the formulation
1 if travel from city i to city j
let xij  
0 otherwise
n
n 1
min z   cij xij
j 1 i 1
i j
st
n
x
1
for each j – must enter each city exactly once
 xij  1
for each i – must leave each city exactly once
i 1
i j
n
j 1
j i
ij
32
Oh! The sub-tour problem.
B
eliminate 3-city subtours:
xij + xjk + xki  2
C
A
eliminate 2-city subtours:
xij + xji  1
E
D
G
F
33
Sizing the problem
• For a 10-city tour there are
–
–
–
–
–
–
10 x 10 – 10 = 90 binary variables
20 constraints to enter and leave each city exactly once
C(10,5) = 252 – 5 city tour constraints (5,5)
C(10,4) = 210 – 4 city tour constraints (4,6), (4,4,2), etc.
C(10,3) = 120 – 3 city tour constraints (3,7), (3,3,2,2) etc.
C(10,2) = 45 – 2 city tour constraints (2,8), (2,2,2,2,2)
• Any others required?
– Total constraints = 647
– 290 = 1.23794 x 1027 potential solutions
A Real Example Problem
The Commissioner of baseball must deliver new baseballs
to all the national league ballparks. Delivery will start and
end in Cincinnati where the All American Baseball Plant is
located. What route should the delivery truck follow in
order to minimize total distance?
Distances between parks in miles
ATL
ATL
CHI
CIN
HOU
LA
MON
NYK
PHI
PIT
STL
SD
SF
702
454
842
2396
1196
864
772
714
554
2363
2679
CHI
702
324
1093
2136
764
845
764
459
294
2184
2187
CIN
454
324
1137
2180
798
664
572
284
338
2228
2463
HOU LA
MON
842 2396 1196
1093 2136
764
1137 2180
798
1616 1857
1616
2900
1857 2900
1706 2844
396
1614 2752
424
1421 2464
514
799 1842
1058
1521
95
2948
2021 405
2951
NY
PHI PIT STL SD
SF
864 772 714 554 2363 2679
845 764 459 294 2184 2187
664 572 284 338 2228 2463
1706 1614 1421 799 1521 2021
2844 2752 2464 1842
95 405
396 424 514 1058 2948 2951
92 386 1002 2892 3032
92
305
910 2800 2951
386
305
622 2512 2646
1002 910 622
1890 2125
2892 2800 2512 1890
500
3032 2951 2646 2125 500
Heuristics -algorithm development
Heuristic: A procedure for solving problems by an intuitive
approach in which the structure of the problem can be interpreted
and exploited intelligently to obtain a reasonable solution.
Nearest Neighbor Heuristic – a
greedy heuristic algorithm
ATL
ATL
CHI
CIN
HOU
LA
MON
NYK
PHI
PIT
STL
SD
SF
702
454
842
2396
1196
864
772
714
554
2363
2679
CHI
702
324
1093
2136
764
845
764
459
294
2184
2187
CIN
454
324
1137
2180
798
664
572
284
338
2228
2463
HOU LA
MON
842 2396 1196
1093 2136
764
1137 2180
798
1616 1857
1616
2900
1857 2900
1706 2844
396
1614 2752
424
1421 2464
514
799 1842
1058
1521
95
2948
2021 405
2951
NY
PHI PIT STL SD
SF
864 772 714 554 2363 2679
845 764 459 294 2184 2187
664 572 284 338 2228 2463
1706 1614 1421 799 1521 2021
2844 2752 2464 1842
95 405
396 424 514 1058 2948 2951
92 386 1002 2892 3032
92
305
910 2800 2951
386
305
622 2512 2646
1002 910 622
1890 2125
2892 2800 2512 1890
500
3032 2951 2646 2125 500
MON
SNF
NYK
CHI
LAX
STL
PIT PHI
CIN
SND
ATL
HOU
DISTANCE
= 8015 MILES
MON
SNF
NYK
X
CHI
LAX
STL
PIT PHI
CIN
SND
X
ATL
DISTANCE
= 8015 MILES
HOU
OPTIMUM
= 7577 MILES
The n-truck problem
• Let c0j = cost of one trip from the depot to customer j
• cij = cost of trip from customer i to customer j (assume cij
= cji)
• If separate vehicle assigned to each customer then cost
is given by:
n
2 coj
j 1
• If go from depot to i to j then back to depot, the saving
would be found from:
sij  c0i  c0 j  cij
The Algorithm
• Compute sij for all possible customer pairs
• Rank the sij in decreasing order
• Consider each of the links in descending
order of savings and include link (i,j) in a
route if it is feasible
– if infeasible, go to the next link
• Once the list is exhausted, eliminate those
on the current route and begin a new route
Example 6.4
• Whole Grains delivers bread to 5
customers each morning
• goal is to meet delivery requirements at
minimum delivery costs
• capacity of 300 loaves on delivery trucks
customer
1
2
3
4
5
requirements (loaves)
85
162
26
140
110
Delivery Costs in Miles
customer
0 (plant)
1
2
3
4
0 (plant) 1
35.5
2
3
4
5
30.4
22.4
7.1
22.4
10.0
11.2
26.9
20.6
11.2
25.0
25.0
15.8
14.1
15.8
The Algorithm in Action
compute savings
s12 = c01 + c02 – c12 = 33.5 + 30.4 – 10 = 53.9
s13 = c01 + c03 – c13 = 33.5 + 220.4 – 11.2 = 44.7
s14 = 13.7 s34 = 13.7
s15 = 35.3 s35 = 30.7
s23 = 41.6 s45 = 13.7
s24 = 12.5
s25 = 27.8
rank: (1,2), (1,3), (2,3), (1,5), (3,5), (2,5), (1,4), (3,4),
(4,5), (2,4)
The Algorithm Continues in Action
• Try assigning customer 1 and 2 to the same
truck
– 85 + 162 = 247 loaves < 300, okay
• Try assigning customer 3 to the same truck
– 247 + 26 = 273 < 300, okay
• Try assigning customer 5 to the same truck
– 273 + 110 > 300, not okay
• Try assigning customer 4 to the same truck
– 273 + 140 > 300, not okay
• Start a new route with customer 4 and 5
– 140 + 110 = 250 < 300, okay
Other Considerations
•
•
•
•
Frequency requirements
Time windows
Time-dependent travel time
Multiple constraints
– weight and volume of trucks
• Varying truck sizes and costs
• Split deliveries
• Uncertainty – random processes
The End of the Supply Chain
Goods and services flowing
through the supply pipeline
The bottom line:
The supply chain locks in money!