Planning Demand and Supply
in a Supply Chain
Forecasting and Aggregate Planning
Chapters 8 and 9
1
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Learning Objectives
 Overview
of forecasting
 Forecast errors
 Aggregate
planning in the supply chain
 Managing demand
 Managing capacity
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Phases of Supply Chain Decisions
 Strategy
or design:
 Planning:
 Operation/Execution

Forecast
Forecast
Actual demand
Since actual demands differ from the forecasts, …
so does the execution from the plans.
– E.g. Supply Chain degree plans for 40 students per year
whereas the actual is ??
3
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Characteristics of forecasts


Forecasts are always wrong. Include expected value and measure of error.
Long-term forecasts are less accurate than short-term forecasts.
Too long term forecasts are useless: Forecast horizon
– Forecasting to determine
» Raw material purchases for the next week; Ericsson
» Annual electricity generation capacity in TX for the next 30 years; Texas Utilities
» Boat traffic intensity in the upper Mississippi until year 2100; Army Corps of Engineers

Aggregate forecasts are more accurate than disaggregate forecasts
– Variance of aggregate is smaller because extremes cancel out
» Two samples: {3,5} and {2,6}.
Averages: 4 and 4.
Totals : 8 and 8.
» Variance of sample averages/totals=0
» Variance of {3,5,2,6}=5/2
– Several ways to aggregate
» Products into product groups; Telecom switch boxes
» Demand by location; Texas region
» Demand by time; April demand
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4
Forecasting Methods

Qualitative
– Expert opinion
» E.g. Why do you listen to Wall Street stock analysts?
– What if we all listen to the same analyst? S/He becomes right!

Time Series
– Static
– Adaptive


Causal: Linear regression
Forecast Simulation for planning purposes
5
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Master Production Schedule (MPS)
Volume
Firm Orders
Frozen Zone

Forecasts
Flexible Zone
Time
MPS is a schedule of future deliveries. A combination
of forecasts and firm orders.
9
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Aggregate Planning
Chapter 8
10
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Aggregate Planning (Ag-gregate: Past part. of Ad-gregare: Totaled)


If the actual is different than the plan, why bother sweating over
detailed plans
Aggregate planning: General plan for our frequency decomposition
– Combined products = aggregate product
» Short and long sleeve shirts = shirt

Single product
» AC and Heating unit pipes = pipes at Lennox Iowa plant
– Pooled capacities = aggregated capacity
» Dedicated machine and general machine = machine

Single capacity
– E.g. SOM has 100 instructors
– Time periods = time buckets
» Consider all the demand and production of a given month together

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
When does the demand or production take place in a time bucket?
Increase the number of time buckets; decrease the bucket length.
11
Fundamental tradeoffs in Aggregate Planning
Capacity: Regular time, Over time, Subcontract?
Inventory: Backlog / lost sales, combination: Customer patience?
Basic Strategies

Chase (the demand) strategy; produce at the instantaneous demand rate
– fast food restaurants

Level strategy; produce at the rate of long run average demand
– swim wear

Time flexibility; high levels of workforce or capacity
– machining shops, army

Deliver late strategy
– spare parts for your Jaguar
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12
- Which is which?
Level
Deliver late
Chase
Time flexibility
Matching the Demand
Adjust the capacity
to match the demand
Demand
Use inventory
Demand
Demand
Use delivery time
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Demand
13
Capacity Demand Matching
Inventory/Capacity tradeoff
 Level
strategy: Leveling capacity forces
inventory to build up in anticipation of
seasonal variation in demand

Chase strategy: Carrying low levels of
inventory requires capacity to vary with
seasonal variation in demand or enough
capacity to cover peak demand during season
14
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Case Study:
Aggregate planning at Red Tomato
 Farm
tools:
 Shovels
 Spades
Same characteristics?
Generic tool, call it Shovel
 Forks
Aggregate by similar characteristics
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Aggregate Planning at Red Tomato Tools
Month
January
February
March
April
May
June
Total
Demand
Forecast
1,600
3,000
3,200
3,800
2,200
2,200
16,000
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Aggregate Planning
Item
Materials
Inventory holding cost
Marginal cost of a backorder
Hiring and training costs
Layoff cost
Labor hours required
Regular time cost
Over time cost
Max overtime hrs per employee per month
Cost of subcontracting
Revenue
Cost
$10/unit
$2/unit/month
$5/unit/month
$300/worker
$500/worker
4hours/unit
$4/hour
$6/hour
10hours
$30/unit
$40/unit
What is the cost of production per tool? That is materials plus labor.
Overtime production is more expensive than subcontracting.
17
What is the saving achieved by producing a tool in house rather than subcontracting?
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1. Aggregate Planning (Decision Variables)
Wt = Number of employees in month t, t = 1, ..., 6
Ht = Number of employees hired at the beginning of month t, t = 1, ..., 6
Lt = Number of employees laid off at the beginning of month t, t = 1, ..., 6
Pt = Production in units of shovels in month t, t = 1, ..., 6
It = Inventory at the end of month t, t = 1, ..., 6
St = Number of units backordered at the end of month t, t = 1, ..., 6
Ct = Number of units subcontracted for month t, t = 1, ..., 6
Ot = Number of overtime hours worked in month t, t = 1, ..., 6
Did we aggregate production capacity?
18
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2. Objective Function:
6
6
6
6
6
6
6
6
Min  4  8  20  W t   300 H t   500 L t   6 O t   2 I t   5 S t  10 P t   30 C t
t 1
t 1
t 1
t 1
t 1
t 1
t 1
t 1
3. Constraints
Workforce
size for each month is based on hiring and layoffs
W t  W t  1  H t  Lt, or
W t  W t  1  H t  Lt  0 for t  1,...,6, where W 0  80.
Production (in hours)
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for each month cannot exceed capacity (in hours)
4 Pt  8  20W t  Ot or
40W t  Ot 4  Pt  0, for t  1,...,6.
19
3. Constraints

Inventory balance for each month
I
I
t 1
 Pt  Ct  St  Dt  St 1  It ,
t 1
 Pt  Ct  Dt  St 1  It  St  0,
for t  1,...,6, where
I
0
Pt
 1,000,
0
 0 and
I
6
 500.
Ct
I t 1
It
Period
t-1
Period
t+1
Period
t
S t 1
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S
Dt
St
20
3. Constraints

Overtime for each month
O  10 W or
10 W  O  0 for
t
t
t
t
t  1,...,6.
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Execution

Solve the formulation, see Table 8.3
– Total cost=$422.275K, total revenue=$640K

Apply the first month of the plan
Delay applying the remaining part of the plan until the next
month
Rerun the model with new data next month

This is called rolling horizon execution


22
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Aggregate Planning at Red Tomato Tools
This solution was for the following demand numbers:
Month
January
February
March
April
May
June
Total
Demand Forecast
1,600
3,000
3,200
3,800
2,200
2,200
16,000
What if demand fluctuates more?
23
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Increased Demand Fluctuation
Month
January
February
March
April
May
June
Total
Demand Forecast
1,000
3,000
3,800
4,800
2,000
1,400
16,000
Total costs=$432.858K.
16000 units of total production as before why extra cost?
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With respect to $422.275K of before.
24
Manipulating the Demand
Chapter 9
25
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Matching Demand and Supply
 Supply
= Demand
 Supply < Demand => Lost revenue opportunity
 Supply > Demand => Inventory
 Manage Supply – Productions Management
 Manage Demand – Marketing
26
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Managing Predictable Variability with Supply
Manage capacity
»
»
»
»
Time flexibility from workforce (OT and otherwise)
Seasonal workforce, agriculture workers
Subcontracting
Counter cyclical products: complementary products

Similar products with negatively correlated demands
– Snow blowers and Lawn Mowers
– AC pumps and Heater pumps
» Flexible capacities/processes: Dedicated vs. flexible
a
d
a
b
c
Similar capabilities
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d
a,b,
c,d
c
b
One super facility
27
Managing Predictable Variability with Inventory

Component commonality
– Remember fast food restaurant menus
– Component commonality increase the benefit of postponement.
» More on this later

Build seasonal inventory of predictable products in preseason
– Nothing can be learnt by procrastinating

Keep inventory of predictable products in the downstream supply chain
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Managing Predictable Variability with Pricing
Revisit Red Tomato Tools

Manage demand with pricing
– Original pricing:
» Cost = $422,275, Revenue = $640,000, Profit=$217,725

Demand increases from discounting
– Market growth
– Stealing market share from competitors
– Forward buying
» stealing your own market share from the future
Discount of $1 in a period increases that period’s demand by
10% (market and market share growth) and moves 20% of
next two months demand forward
Can you gather this information –price sensitivity of the
demand- easily? Does your company have this information?
29
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Off-Peak (January) Discount from $40 to $39
Month
January
February
March
April
May
June
Demand Forecast
3,000=1600(1.1)+0.2(3000+3200)
2,400=3000(0.8)
2,560=3200(0.8)
3,800
2,200
2,200
Cost = $421,915, Revenue = $643,400, Profit = $221,485
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Peak (April) Discount from $40 to $39
Month
January
February
March
April
May
June
Demand Forecast
1,600
3,000
3,200
5,060=3800(1.1)+0.2(2200+2200)
1,760=2200(0.8)
1,760=2200(0.8)
Cost = $438,857, Revenue = $650,140, Profit = $211,283
Discounting during peak increases the revenue
31
but decreases the profit!
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Demand Management
 Pricing
and Aggregate Planning must be done jointly
 Factors affecting discount timing and their new values
– Consumption: 100% increase in consumption instead of 10%
increase
– Forward buy, still 20% of the next two months
– Product Margin: Impact of higher margin. What if discount
from $31 to $30 instead of from $40 to $39.)
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January Discount: 100% increase in
consumption, sale price = $40 ($39)
Month
January
February
March
April
May
June
Demand Forecast
4,440=1600(2)+0.2(3000+3200)
2,400=0.8(3000)
2,560=0.8(3200)
3,800
2,200
2,200
Off peak discount: Cost = $456,750, Revenue = $699,560
Profit=$242,810
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Peak (April) Discount: 100% increase
in consumption, sale price = $40 ($39)
Month
January
February
March
April
May
June
Demand Forecast
1,600
3,000
3,200
8,480=3800(2)+(0.2)(2200+2200)
1,760=(0.8)2200
1,760=(0.8)2200
Peak discount: Cost = $536,200, Revenue = $783,520
Profit=$247,320
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Performance Under Different Scenarios
Regular
Price
$40
$40
$40
$40
$40
$31
$31
$31
Promotion
Price
$40
$39
$39
$39
$39
$31
$30
$30
Promotion
Period
NA
January
April
January
April
NA
January
April
% increase
in demand
NA
10%
10%
100%
100%
NA
100%
100%
% forward
buy
NA
20%
20%
20%
20%
NA
20%
20%
Profit
$217,725
$221,485
$211,283
$242,810
$247,320
$73,725
$84,410
$69,120
Average
Inventory
895
523
938
208
1,492
895
208
1,492
Use rows in bold to explain Xmas discounts.
The product, with less (forward buying/market growth) ratio, is discounted more.
What gift should you buy on the special days (peak demand) when retailers
supposedly give discounts?
E.g. Think of flowers on valentine’s day. How about diamonds?
For flowers, what is (forward buying/market growth) due to discounting?
How about for diamonds?
Need empirical data. What is available?
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35
Empirical Data:
Who spends / How much on Valentine’s day


The average consumer spends $122.98 on 2008 Valentine’s Day, similar to
$119.67 of 2007. Total US spending on Valentine’s Day is $17.02 B by 18+.
Spending
– by gender
» Men again dishes out the most in 2008, spending an average of $163.37 on gifts and cards,
compared to an average of $84.72 spent by women.
– by age

»
»
»
»
»
Adults: 25-34 spend $160.37.
Young adults: 18-24 spend $145.59.
Upper Middle age: 45-54 spend $117.91.
Lower Middle age: 35-44 spend $116.35.
Elderly: 55-64 spend $110.97.
»
»
»
»
»
»
»
56.8% of all consumers give a greeting card.
48.2% plan a special night out.
48.0% buy candy.
35.9% buy flowers.
12.3% give a gift card.
11.8% buy clothing.
??.?% buy diamonds
Gifts
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–
Source: National Retail Federation www.nrf.com
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Factors Affecting Promotion Timing
Factor
High forward buying
High stealing share
High growth of market
High margin
Low margin
High holding cost
Low capacity volume
flexibility
Favored timing
Low demand period
High demand period
High demand period
High demand period
Low demand period
Low demand period
Low demand period
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Aside: Continuous Compounding





If my $1investment earns an interest of r per year, what is my
interest+investment at the end of the year?
Answer: (1+r)
If I earn an interest of r/2 per six months, what is my interest+ investment at the
end of the year?
Answer: (1+r/2)2
If I earn an interest of (r/m) per (12/m) months, what is my interest+investment?
Answer: (1+r/m)m
Think of continuous compounding as the special case of discrete-time
compounding when m approaches infinity.
What if I earn an interest of (r/infinity) per (12/infinity) months?
m
r

Answer : lim 1    e r where
m 
 m

1 1 1 1 1 1
1
e     

1 1 2 6 24 120
n  0 n!
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See the appendix of scaggregate.pdf for more on continuous compounding.
38
Deterministic Capacity Expansion Issues

Single vs. Multiple Facilities
– Dallas and Atlanta plants of Lockheed Martin

Single vs. Multiple Resources
– Machines and workforce; or aggregated capacity

Single vs. Multiple Product Demands
– Have you aggregated your demand when studying the capacity?

Expansion only or with Contraction
– Is there a second-hand machine market?

Discrete vs. Continuous Expansion Times
– Can you expand SOM building capacity during the spring term?

Discrete vs. Continuous Capacity Increments
– Can you buy capacity in units of 2.313832?


Resource costs, economies of scale
Penalty for demand-capacity mismatch
– Recallable capacity: Electricity block outs vs Electricity buy outs
» Happens in Wisconsin Electricity market
» What if American Airlines recalls my ticket

Single vs. Multiple decision makers
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39
A Simple Model
Units
Capacity
x
Demand
D(t)=  t
x
x/
Time (t)
No stock outs. x is the size of the capacity increments.
δ is the increase rate of the demand.
40
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Infinite Horizon Total Discounted Cost
 f(x)
is expansion cost of capacity increment of size x
f ( x)  x ; r  5%;   1.
0.5
C(x) is the long run (infinite horizon) total discounted
expansion cost



x 
f ( x)

C ( x)   exp   r (k )  f ( x)  f ( x) (exp( rx /  )) k 
 
1  exp( rx /  )

k 0
k 0
41
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Solution of the Simple Model
Discounted Expansion Cost
25
20
15
10
5
0
1
10
20
30
40
50
60
70
80
90
100
Expansion Size
Solution can be: Each time expand capacity by an amount
that is equal to 30-week demand.
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42
Shortages, Inventory Holding, Subcontracting
Units
Capacity
Subcontracting
Demand
Surplus
capacity
Inventory
build up
Inventory
depletion
Time

Use of Inventory and subcontracting to delay capacity expansions
43
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Stochastic Capacity Planning:
The case of flexible capacity
1
A
y1A=1, y2A=1, y3A=0
B
y1B=0, y2B=0, y3B=1
2
3
Plants



Products
Plant 1 and 2 are tooled to produce product A
Plant 3 is tooled to produce product B
A and B are substitute products
– with random demands DA + DB = Constant
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44
Capacity allocation
 Say
capacities are r1=r2= r3=100
 Suppose that DA + DB = 300 and DA >100 and DB >100
With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=0, y3B=1.
Scenario
DA
DB
X1A
X2A X3A X1B X2B
X3B
1
200
100
100
100
100
0
2
150
150
100
50
100
50 B
3
100
200
100
0
100
100 B
If the scenarios are equally likely, expected shortage is 50.
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Shortage
45
Capacity allocation with more flexibility
 Say
capacities are r1=r2= r3=100
 Suppose that DA + DB = 300 and DA >100 and DB >100
With plant flexibility y1A=1, y2A=1, y3A=0, y1B=0, y2B=1, y3B=1.
Scenario
DA
DB
X1A
X2A X3A X1B X2B
X3B
Shortage
1
200
100
100
100
0
100
0
2
150
150
100
50
50
100
0
3
100
200
100
0
100
100
0
Flexibility can decrease shortages. In this case, from 50 to 0.
46
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A Formulation with Known Demands: Dj=dj








i denotes plants
j denotes products, not necessarily substitutes
cij tooling cost to configure plant i to Max -  c ij yij   m j x ij
i, j
i, j
produce j
Subject to
mj contribution to margin of
producing/selling a unit of j
j x ij  ri for each plant i
ri capacity at plant i
x ij  ri yij for each plant i and product j
Dj=dj product j demand
yij=1 if plant i can produce
product j, 0 o.w.
xij=units of j produced at plant i
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x
ij
 d j for each product j
i
x ij  0 , y ij  {0,1}
Solutions depend on scenarios:
- If DA=200 and DB=100, then y1A=y2A=y3B=1.
- If DA=100 and DB=200, then y1A=y2B=y3B=1.
47
Unknown Demands: Dj=djk with probability pk

Dj=djk product j demand
under scenario k
 xijk= units of j produced
at plant i if scenario k
happens
Max -  c ij yij   p k  m j x ijk
i, j
k
i, j
Subject to
k
x
 ij  ri
for each plant i
j

yij=1 if plant i can produce
product j, 0 o.w.
 Does yij differ under
different scenarios?
Should my variable depend on
scenarios? (Yes / No)
Anticipatory variable and
Nonanticapatory variable
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and scenario k
x ijk  ri yij for each plant i, product j
and scenario k
k
k
x

d
 ij j
for each product i
i
and scenario k
x ijk  0 , y ij  {0,1}
48
Reality Check:
How do car manufacturers assign products to plants?




With the last formulation, we treated the problem of assigning products to
plants.
This type of assignment called for tooling/preparation of each plant
appropriately so that it can produce the car type it is assigned to.
These tooling (nonanticipatory) decisions are made at most once a year and
manufacturers work with the current assignments to meet the demand.
When market conditions change, the product-to-plant assignment is revisited.
– Almost all car manufacturers in North America are retooling their previously truck
manufacturing plants to manufacture compact cars as consumer demand basically
disappeared for trucks with high gas prices.
– Also note that the profit margin made from a truck sale is 2-5 times more than the
margin made from a car sale. No wonder why manufacturers prefer to sell trucks!

In the following pages, you will find the product to plant assignment of major
car manufacturers in the North America. These assignments were updated in
the summer of 2008 just about the time when manufacturers started talking
49
about retooling plants to produce compact cars.
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All of Toyota Plants in the North America
Toyota. Cambridge
Corolla, Matrix, Lexus, Rav4
Toyota-Subaru. LaFayette
Camry
Nummi: Toyota-GM. Freemont.
Corolla, Tacoma, Pontiac Vibe
Toyota. Princeton
Tundra, Suquoia, Sienna
Toyota. Georgetown
Avalon, Camry, Solara
Toyota. Long Beach
Hino
Toyota. Blue Springs
Highlander
Toyota. Tijuana, Mexico
Tacoma
Toyota. San Antonio
Tundra
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All of Honda Plants in the North America
Honda. Alliston, Ca.
Civic, Acura, Odyssey,
Pilot, Ridgeline
Honda. Decatur
TBO in 2008
Honda. Marysville
Accord, Acura
Honda. Lincoln
Odyssey, Pilot
Honda. El Salto, Me
Accord
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All of Nissan Plants in the North America
Nissan. Smyrna
Frontier, Xterra, Altima,
Maxima, Pathfinder
Nissan. Canton
Quest, Armada,
Titan, Infiniti, Altima
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All of Hyundai-Kia Plants in the North America
Kia. LaGrange
TBO in 2009
Hyundai. Montgomery
Sonata, Santa Fe
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All of Mercedes and BMW Plants in the North America
BMW. Spartanburg
Z4, X5, X6
M roadster, coupes
Mercedes. Tuscaloosa
M, R classes
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All of Ford Plants in the North America
Ford. Dearborn
F-150, Lincoln Mark LT
Ford. Flat Rock
Mustang, Mazda 6
Ford. Oakville, Ca.
Edge, Lincoln MKX
Ford. Saint Paul
Ranger, Mazda B series
Ford. Saint Thomas, Ca.
Crown Victoria, Grand Marquis
Ford. Wayne
Focus, Expedition, Lincoln Navigator
Ford. Chicago
Taurus, Mercury Sable
Ford. Avon Lake
E Series
Ford. Kansas City
Escape, Escape Hybrid,
Mazda Tribute,
Mercury Mariner, F-150
Ford. Louisville
F-250, F-550, Explorer,
Mercury Mountaineer
Ontario, Michigan, Illinois,
Indiana, Ohio in Focus
Ford. Hermosillo, Mex.
Ford Fusion, Lincoln
MKZ, Mercury Milan
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Ford. Cuatitlan, Mex.
F-150, 250, 350, 450, 550,Ikon
All of Chrysler Plants in the North America
Chrysler. Sterling Heights
Dodge Avenger, Chrysler Sebring
Chrysler. Brampton, Ca
Chrysler 300,
Dodge Challenger,
Dodge Charger
Chrysler. Warren
Dodge Ram, Dakota, Mitsubishi Raider
Chrysler. Detroit-Jefferson North
Jeep Grand Cherokee and Commander
Chrysler. Detroit-Conner Ave.
Dodge Viper, SRT 10 Roadster
Chrysler. Windsor, Ca
Dodge Grand Caravan, Chrysler Town
Chrysler. Belvidere
Dodge Caliber, Jeep
Compass, Jeep Patriot
Chrysler. Toledo
Jeep Liberty, Dodge Nitro
Chrysler. Newark
Dodge Durango, Chrysler Aspen
Will close in 2009
Chrysler. Fenton-North
Dodge Ram
Chrysler. Fenton-South
Grand Voyager, Grand
Caravan, Cargo Van
Ontario, Michigan, Illinois,
Indiana, Ohio in Focus
Chrysler. Saltillo, Mex.
Dodge Ram
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Chrysler. Toluca, Mex.
Chrysler PT Cruise, Dodge Journey
All of GM Plants in the North America
GM. Lansing-Grand River
Cadillac E-SRX
GM. Lansing-Delta Township
Buick Enclave, Saturn
Outlook, GMC Acadia
GM. Orion
Pontiac G6,
Chevrolet Malibu
GM. Oshawa, Ca
Chevy Impala, Buick Allure,
Chevy Silverado, GMC Sierra.
Trucks will stop in 2009.
GM. Flint
GMC Sierra, Chevy Silverado,
Chevy - GMC medium trucks.
GM. Pontiac
Chevy Silverado, GMC Sierra
GM. Detroit
Buick Lucerne,
Cadillac DTS
GM. Janisville
Chevy Tahoe,
Suburban, GMC Yukon
Will stop in 2010
GM. Wilmington
Saturn L series,
Pontiac Solstice
GM. Fort Wayne
Chevy Silverado,
GMC Sierra
GM. Moraine
Chevy Trailblazer, GMC
Envoy, Oldsmobile Bravada,
Isuzu Ascender, Saab 9-7X
Will stop in 2010
GM. Fairfax
Chevy Malibu,
Malibu Maxx,
Saturn Aura
GM. Lordstown
Chevy Cobalt, Pontiac
Pursuit, G4, G5
GM. Wentzville
Chevy Express,
GMC Savana
GM. Bowling Green
Cadillac XLR,
Chevy Corvette
GM. Spring Hill
Saturn Ion and Vue
Currently down
GM. Doraville
Chevy Uplander,
Pontiac Montana
Ontario, Michigan, Illinois,
Indiana, Ohio in Focus
GM. Arlington
Chevy Tahoe,
Suburban,
GMC Yukon,
Cadillac Escalade
GM. Shreveport
Chevy Colorado,
GMC Canyon,
Isuzu brands,
Hummer H3
GM. Ramos Arizpe, Mex.
Pontiac Aztek, Chevy Cavalier, Chevrolet
Checy, Pontiac Sunfire, Buick Rendezvous
GM. Silao, Mex.
Chevrolet Suburban, Chevrolet Avalanche,
GMC Yukon, Cadillac Escalade
utdallas.edu/~metin
GM. Toluca, Mex.
Chevrolet Kodiak Truck
Stopping in 2008
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Summary of Learning Objectives
 Forecasting
Aggregate planning
 Supply and demand management during
aggregate planning with predictable demand
variation

– Supply management levers
– Demand management levers

Capacity Planning
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Material Requirements Planning
 Master
Production Schedule (MPS)
 Bill of Materials (BOM)
 MRP explosion
 Advantages
– Disciplined database
– Component commonality

Shortcomings
– Rigid lead times
– No capacity consideration
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Optimized Production Technology
 Focus
on bottleneck resources to simplify
planning
 Product mix defines the bottleneck(s) ?
 Provide plenty of non-bottleneck resources.
 Shifting bottlenecks
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Just in Time production







Focus on timing
Advocates pull system, use Kanban
Design improvements encouraged
Lower inventories / set up time / cycle time
Quality improvements
Supplier relations, fewer closer suppliers, Toyota city
JIT philosophically different than OPT or MRP, it is not
only a planning tool but a continuous improvement scheme
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