Chapter 6: Supply Chain Management (SCM) IE 3265 – POM R. R. Lindeke

Chapter 6: Supply Chain
Management (SCM)
IE 3265 – POM
R. R. Lindeke
UMD-MIE
Topics For Discussion:


Defining the issues of SCM
Major Players




How we can work in this new model
Vender relationships
Data Management
Major Issues:



Bullwhip effect
Transportation problems
Location issues
What is SCM?


“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)
“Call it distribution or logistics or supply chain
management... In industry after industry . . . executives
have plucked this once dismal discipline off the loading
dock and placed it near the top of the corporate agenda.
Hard-pressed to knock out competitors on quality or
price, companies are trying to gain an edge through their
ability to deliver the right stuff in the right amount of
time” (Fortune Magazine, 1994)
Growing Interest in SCM – Why?


As manufacturing becomes more efficient (or
is outsourced), companies look for ways to
reduce costs
Several significant success stories:



Efficient SCM at Walmart, HP, Dell Computer
SCM considers the broad, integrated, view of
materials management from purchasing
through distribution
The huge growth of interest in the web has
spawned web-based models for supply
chains: from “dot com” retailers to B-2-B
business models
Mass Customization:
Designing Final Choices into 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:



Hewlett Packard printer configuration
Postponement of final programming of
semiconductor devices – all routines loaded, only
certain ones activated
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
One example is IKEA whose furniture comes
in simple to assemble kits that allows them to
store the furniture in the same warehouselike locations where they are displayed and
sold
Shipping container designs for FedEx and UPS
– airfreight
Dunnage control in Big Auto
Efficient Design of the Supplier Base



Part of streamlining the supply chain is reducing
the number and variety of suppliers
The Japanese have been very successful in this
arena (they’re an Island – so getting materials
there has always been a problem)
In the mid 1980’s Xerox trimmed its number of
suppliers from 5,000 to 400.



Overseas suppliers were chosen based on cost
Local suppliers were chosen based on delivery speed
In 1996, Ford Motor reduced their supplier count
by more than 60%
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 they did not
garner web-based sales
Data Exchange – A Critical Idea



EDI: Electronic Data Interchange
Involves the 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.
Data and Products – E-Tailing

E-tailing: Direct to customer sales on the web
– the so-called Click & Mortor retail model




Perhaps best known e-tailer is Amazon.com,
originally a web-based discount book seller
Today, Amazon.com sells a wide range of products
(we can think of many, many similar
organizations)
Amazon and others spawned so called “dot
com” stock explosion in the NASDAQ (1997 to
April, 2000)
Today, many traditional “bricks and mortar”
retailers also offer sales over the web, often
at lower prices
Dealing with Data – the modern way

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!
Web searches yield 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
Data Transfer in Supply Chains: Vendor
Managed Inventory (the real solution?)





Walmart and P & G
Target and Pepsi/Coke
But … Barilla SpA. An 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
This is in opposition to allowing distributors (or even
retailers) to independently decide on order sizes!
Order Growth – The Bullwhip Effect –
An Important issue
Information Transfer in Supply
Chains: cause of ‘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.
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
There has been a Revitalization in the
Analytical Tools needed to Support SCM



Inventory management and demand
forecasting models such as those discussed in
this course
The transportation problem and more general
network formulations for describing flow of
goods in a complex system
Analytical methods for determining delivery
routes for product distribution – optimal
location of new resources
Focusing on the Distribution Problem:



The Goal is to reduce total
transportation costs throughout the
supply chain
Usually solved with some approach to
the “Transportation Problem”
Our approach will be the Balanced
Matrix model
Lets do one, by example:
\To
From
Des Moines
Evansville
Ft.
Lauderdale
Demand
Cost of moving a
unit of product
from Row to
column location
Albuquerque
Boston
Cleveland
$5
$4
$3
$8
$4
$3
$9
$7
$5
300
200
Capacity
100
300
300
200
/700
700/
In the Transportation Problem:

We must have a Supply/Demand
balance to solve


In this problem that requirement is met
If it is not met, we must create
“Dummy” sources (at $0 move costs) or
Dummy Sinks (also at $0 move costs)
to achieve the require S/D balance
The Transportation problem:



Goal is to minimize the total cost of shipping
We will allocate products to cells – any
allocation means the row resource will ship
product to the column demand
The process is an iterative one that requires a
feasible starting point


Can start by using NW Corner approach
Can start using a more structured VAM (Vogel
Approximation method)
Starting with a VAM Solution
technique:

Determine Row Penalty number (PNi) –the difference
between lowest and 2nd lowest cost in row


Determine Column Penalty Number (PNj) – the
difference between the lowest and 2nd lowest col.
Cost


Here: C1: 3; C2: 0; C3: 0
Choose R or C with greatest penalty cost – here is C1



Here: R1 is 1; R2 is 1: R3 is 2
If there is a tie, break tie by choosing C or R with smallest
costs
Max out the allocation in chosen C or R at lowest cost cell
then x-out the C or R
And so on after allocation (after we recompute PN’s!)
Phase 1 of VAM:
Step
A
B
C
PNr1
PNr2
PNr3
DM
$5
$4
$3
1
--
--
E
$8
$4
$3
1
1
5 x-out
FL
$9
2
2
4
100
200
$7
100
$5
200
100
PNc1 3
0
PNc2 1
3
1
--
0
xout
2
2
X-out
Costing The model:

Current:


Before proceeding, check if the Feasible
solution is (or isn’t) degenerate:



100*5 + 200*9 +200*4 + 100*3 + 100*5 =
$3900
Number of allocation must be at least: m +n - 1 =
3 + 3 - 1 = 5 (we have 5 is the above set so the
solution is not degenerate! See next slide if it was)
Now, we must determine if it’s optimal?
We must continue to a second phase to
determine this!
Dealing with Degeneracy
(when it is
found)


We must allocate a very small amount of material
movement (call it ) to any independent cell
An independent cell is any one where we can not
complete a “stepping motion” of only horizontal and
vertical movements through filled cells to return to the
originating cell




We call this the -path. (this would be done by alternating adding or
subtracting assignments of material to any filled cell we step on)
Note any cell were a path can be build is a dependent cell
We would add sufficient ’s to reach allocated cells count of
m + n – 1 number (make the solution non-degenerate)
Here since R1C1 is independent – check for yourself – we
will fill it with  units – this makes our solution nondegenerate – 5 cells are allocated!
Entering Phase II:
Determining Optimality




We will explore the MODI (modified
distribution) algorithm
After finding a non-degenerate initial solution,
add a row of Kj’s and a column of Ri’s to the
Matrix
To begin, Assign a zero value to any R or K
position
For each allocated cell, the following
expression must be satisfied:

Ri + Kj + ci,j = 0
Starting MODI with a possible
Indicator cost for this
R/K allocation:
cell (= R+K+c)
Kj
Ri
DM
E
FL
Demand:
0
-2
-4
A
B
C
-5
-2
-1
$5
$4
+2 $3
$4
$3
+2
100
100
$8
+1
200
$9
$7
200
300
+1
300
100
$5
300
100
200
200
MODI continued:





Examine all indicator values for empty cells – if all are
non-negative the solution is optimal
If some are negative then develop a -path
beginning at most negative cell (here is R3C3)
Complete the -path by stepping only to filled cells
(and pivoting) while alternatively subtracting then
adding  allocation
After completing the path, determine the “-” cell
with the smallest quantity and choose its value for
“” – substitute it along the whole path
Note here: all indicators are positive thus we have
the optimal solution!
Forming the -Path (starts in R2C3)
– if we had erroneously allocated as seen below and requiring an
 addition
Smallest “-” Allocated amount
Kj
Ri
DM
E
FL
Demand:
0
0
-4
$5
A
B
C
-5
-4
-3
+ $4

$8
Ind: +3
$9
- $3
100
Ind: 0
$4
+ $3
100
- $7
300
Ind: -1
300
200
100
- 300
200
$5
+ 300
Ind: -2
200
After 100 unit re-allocation – now
recompute R’s and K’s & Indicators
Kj
Ri
DM
E
FL
Demand:
-1
-3
-5
A
B
C
-4
-1
0
$5
Cap.
$4
+2 $3
+2 100
$4
$3
300
+100
$8
+1
200
$9
$7
200
300
100
+1 $5
300
100
200
200
Looking at this Matrix

All indicators are now positive – this indicates
an optimal solution!



Note this agrees with optimal solution found
earlier!!!
Relax  value to zero – makes cell 1,1
allocation 100 units
Optimal transportation cost is:

5*100 + 4*200 + 3*100 + 9*200 + 5*100 =
3900
Lets try one:
/To
D
Fr/
A
B
C
Demand
E
F
G
8
6
4
2
10
6
6
2
4
2
3
8
3
3
3
Cap.
4
3
6
4
/13
13/
But the Transportation
Problem can be solved by LP!


Define an
Objective
Function:
Subject to:
  c
m
n
i 1
j 1
ij
 X ij 
where :
cij is cell cost and X ij is amount moved


n
j 1
m
i 1
X ij  ai for 1  i  m
X ij  b j for 1  j  n
where:
a i are all shipment from a source (capacity)
b j are all shipments into a "sink" (Demand)
Our Example (by LP Solver)
Variables
XDM-A
XDM-B
XDM-C
Values:
0
0
0
0
0
0
0
0
0
V*C
0
0
0
0
0
0
0
0
0
Costs:
5
4
3
8
4
3
9
7
5
CapC1
1
1
1
CapC2
XE-A
XE-B
1
XE-C
1
XFL-A
DemC2
DemC3
1
1
1
1
1
1
1
1
1
XFL-C
1
CapC3
DemC1
XFL-B
1
1
1
OBJ.Fn.
0
0
100
0
300
0
300
0
300
0
200
0
200
Applying Solver:
Examining Results:




Optimal Value = $3900 (as we found
‘by hand’!)
Ship: 100 DM-A; 200 FL-A; 200 E-B;
100 E-C; 100 FL-C
All as we found using the VAM Heuristic
Much Faster and easier using LP!
Expansion to Transshipment Problem


When a system is allowed to use intermediate
‘warehousing’ sites – they are Source sites or
even Sink sites in regular transportation
problem – for reducing the total cost of
transportation we call the problem the
transshipment network problem
We require more costs to be obtained but
typically, in most complex S. Chains,
companies find savings of from 7 to 15% (or
more) in implementations that allow
transshipment
Expansion to Transshipment Problem
In the general Transshipment problem the transport network
is expanded to allow movement between sources and
between sinks (and even back to other sources)
Expansion to Transshipment Problem
Extracted from: J.
P. Ignizio, Linear
Programming in
Single- & MultipleObjective Systems,
Prentice Hall 1982
The original
transportation
problem
Another Level of Transport – the
delivery route problem


This problem is usually one of very large scale
(classically called the Traveling Salesman Problem)
Because of this, we typically can not find an
absolutely optimal solution but rather only near
optimal solution – as seen in our textbook



Here, the knowns are the costs of travel from point to point
throughout the network and we try to save costs by
“ganging up” trips
Solution typically follows along a line of attack based
on the “Assignment Problem”
See Handout, focus on the Shortest Route Problem
Delivery Optimization:



Realistically, a delivery vehicle can
only carry so much – so this may
reduce effectiveness of solutions
Delivery’s take Real Time – again
this must be considered during
scheduling and routing
Loading of vehicles is very critical to
control step 2 time – load in reverse
delivery order!
Looking at the Locating of New
Facilities:

Considerations:


Labor Climate
Transportation issues:




Proximity to markets
Proximity to suppliers & resources
Proximity to parent company (sharing
expertise, purchasing, drop routing) – could be
plus or minus!
Quality of transportation system
Looking at the Locating of New
Facilities:

Consideration, cont.


Costs to operate (utilities, taxes, real estate
costs, construction)
Expansion considerations




Room available for growth?
Construction to modify structure?
Any local incentives to re-locate?
Quality of Life (schools, recreational
possibilities, health care – cost, availability)
Looking at the Locating of New
Facilities:


Most organization compare several alternatives
They identify weighting factors for the characteristics
then narrow choices





1st consider regions
2nd narrow search to communities
3rd consider specific sites
Done by collecting data addressing the various factors under
study
After data is collected and weighted, make selection
(typically by starting with quantitative decision follow
with qualitative analysis)
Location Decisions in SCM:

In the final analysis the decision
typically comes down to a “Center of
Gravity Solution” that minimizes the
total travel distance between the Facility
and all possible Contact facilities

Contact facilities may be sources of Raw
Materials or other Suppliers or they may be
destinations for Product stored in or made
at the new facility under design
Lets Consider an Example:
Store C
Store E
Store B
DC 1
Store G
(where should we put our
new Distribution Center?)
Store A
Store D
Store F
DC 2
Given this information:
Store
A
B
Ca
D
E
Fb
G
a
Location (X,Y)
(2.5, 2.5)
(2.5, 4.5)
(5.5, 4.5)
(5, 2)
(8, 5)
(7, 2)
(9, 3.5)
Site of Possible DC 1;
b
C. Sales
5
2
10
7
10
20
14
Site of Possible DC 2
Solution is a Type of Transportation
Minimization:
Using Either Euclidian or Recta-linear offsets

Euclidean Distance:
deuclid 
 X  X   Y  Y 
2
i
DC j
i
2
DC j
here : X's or Y's are map coordinates
of stores or Distribution Center

Recta-Linear (RL) Distance:
d RL  X i  X DC j  Yi  YDC j
Now What?

Best Location is the one that minimizes the
sum of the total needs of all Demands times
the travel distances involved:
D d
i
all i
euclid j
 for all Sinks
and each possible new Source
Leading to this analysis:
DC 1
5.5
4.5
DC 2
7
2
STORE
X Location
Y Location
D Euclid 1
D Euclid 2
D RL 1
D RL 2
Demand
D*DE 1
D*DE 2
D*Drl 1
D*Drl 2
A
2.5
2.5
3.605551
4.527693
5
5
5
18.02776
22.63846
25
25
B
2.5
4.5
3
5.147815
3
7
2
6
10.29563
6
14
C
5.5
4.5
0
2.915476
0
4
10
0
29.15476
0
40
D
5
2
2.54951
2
3
2
7
17.84657
14
21
14
E
8
5
2.54951
3.162278
3
4
10
25.4951
31.62278
30
40
F
7
2
2.915476
0
4
0
20
58.30952
0
80
0
G
9
3.5
3.640055
2.5
4.5
3.5
14
50.96077
35
63
49
176.639
142.711
225
182
From the Analysis: DC 2 minimizes costs
But is this the Optimal location?




Perhaps we could place the ‘Center’ at the
Median of all the current demand locations?
In an ‘RL’ sense, form the Cumulative
Weighting (Cum. Demand)
1st: order each target location in increasing
level of X and then Y
Determine the average of this CumWt for X &
Y and place location the is the same as the
site that first exceeds this value
C. Wt. Average is 34
Store Dem. X
coor
C.
Wt.
A
5
2.5
B
2
D
Sel.
Store Dem. Y
coor
C.
Wt.
5
D
7
2
7
2.5
7
F
20
2
27
7
5
14
A
5
2.5
32
*
C
10
5.5
24
*
G
14
3.5
46
**
F
20
7
44
**
B
2
4.5
48
E
10
8
54
C
10
4.5
58
G
14
9
68
E
10
5
68
Locate at: 7 (in X) and 3.5 (in Y) as a rule*
Sel
Optimization using Euclidian Distances:

1st Compute the Center of Gravity:
X

D  x 


D
D  y 


D
i
i
i
Y

i
i
i
for each location i
Optimization:


Start with X*, Y* determined above as Xcur, Ycur
Compute:
Di
g i  x, y  
2
2
x

x

y

y
 cur i   cur i 
then :
xnew
x  g  x, y  



 g  x, y 
y  g  x, y  



 g  x, y 
i
i
i
ynew
i
i
i

Stop iteration when X and Y stop changing
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
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