Aggregate Planning
„
Determine the resource capacity needed to
meet demand over an intermediate time
horizon
¾ Aggregate
refers to product lines or families
¾ Aggregate planning matches supply and demand
Ir. Haery Sihombing/IP
Sihombing/IP
Pensyarah Fakulti Kejuruteraan Pembuatan
Universiti Teknologi Malaysia Melaka
„
Objectives
¾ Establish
7
AGGREGATE & CAPACITY
PLANNING
a company wide game plan for allocating
resources
¾ Develop an economic strategy for meeting demand
Aggregate Planning Process
Meeting Demand Strategies
„
Adjusting capacity
™ Resources
necessary to meet demand are
acquired and maintained over the time
horizon of the plan
™ Minor variations in demand are handled
with overtime or underunder-time
„
Managing demand
™ Proactive
Strategies for Adjusting Capacity
„
„
„
Level production
„ Producing at a constant rate
and using inventory to
absorb fluctuations in
„
demand
Chase demand
„ Hiring and firing workers to
„
match demand
Peak demand
„ Maintaining resources for
highhigh-demand levels
„
Overtime and underunder-time
„ Increasing or decreasing
working hours
Subcontracting
„ Let outside companies
complete the work
PartPart-time workers
„ Hiring part time workers to
complete the work
Backordering
„ Providing the service or
product at a later time
period
Level Production
Demand
Production
Units
„
demand management
Time
Chase Demand
Strategies for Managing Demand
¾
Shifting demand into other time periods
™ Incentives
™ Sales promotions
™ Advertising campaigns
¾
Offering products or services with
countercounter-cyclical demand patterns
Partnering with suppliers to reduce
information distortion along the supply
chain
Demand
Units
Production
¾
Time
Quantitative Techniques For APP
Pure Strategies
™ Mixed Strategies
™ Linear Programming
™ Transportation Method
™ Other Quantitative
Techniques
Pure Strategies
Example:
™
QUARTER
SALES FORECAST (LB)
Spring
Summer
Fall
Winter
80,000
50,000
120,000
150,000
Hiring cost = $100 per worker
Firing cost = $500 per worker
Regular production cost per pound = $2.00
Inventory carrying cost = $0.50 pound per quarter
Production per employee = 1,000 pounds per quarter
Beginning work force = 100 workers
Level Production Strategy
Chase Demand Strategy
Level production
(50,000 + 120,000 + 150,000 + 80,000)
= 100,000 pounds
4
SALES
FORECAST
80,000
50,000
120,000
150,000
PRODUCTION
QUARTER
PLAN
INVENTORY
Spring
100,000
20,000
Summer
100,000
70,000
Fall
100,000
50,000
Winter
100,000
0
400,000
140,000
Cost of Level Production Strategy
(400,000 X $2.00) + (140,00 X $.50) = $870,000
QUARTER
SALES PRODUCTION
FORECAST
PLAN
Spring
Summer
Fall
Winter
80,000
50,000
120,000
150,000
80,000
50,000
120,000
150,000
WORKERS WORKERS WORKERS
NEEDED
HIRED
FIRED
80
50
120
150
0
0
70
30
20
30
0
0
100
50
Cost of Chase Demand Strategy
(400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000
General Linear Programming
(LP) Model
Mixed Strategy
„
„
Combination of Level Production and Chase
Demand strategies
Examples of management policies
„
„
„
no more than x% of the workforce can be laid off in
one quarter
inventory levels cannot exceed x dollars
Many industries may simply shut down
manufacturing during the low demand season
and schedule employee vacations during that
time
„
LP gives an optimal solution, but
demand and costs must be linear
„
Let
Wt = workforce size for period t
Pt =units produced in period t
„ It =units in inventory at the end of period t
„ Ft =number of workers fired for period t
„ Ht = number of workers hired for period t
„
„
Transportation Method
LP MODEL
Minimize Z = $100 (H1 + H2 + H3 + H4)
+ $500 (F1 + F2 + F3 + F4)
+ $0.50 (I1 + I2 + I3 + I4)
Subject to
P 1 - I1
I1 + P2 - I2
I2 + P3 - I3
I3 + P4 - I4
1000 W1
1000 W2
1000 W3
1000 W4
100 + H1 - F1
W1 + H2 - F2
W2 + H3 - F3
W3 + H4 - F4
Demand
constraints
Production
constraints
Work force
constraints
= 80,000
= 50,000
= 120,000
= 150,000
= P1
= P2
= P3
= P4
= W1
= W2
= W3
= W4
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
QUARTER
EXPECTED
DEMAND
REGULAR
CAPACITY
OVERTIME
CAPACITY
SUBCONTRACT
CAPACITY
1
2
3
4
900
1500
1600
3000
1000
1200
1300
1300
100
150
200
200
500
500
500
500
Regular production cost per unit
Overtime production cost per unit
Subcontracting cost per unit
Inventory holding cost per unit per period
Beginning inventory
$20
$25
$28
$3
300 units
Burruss’
Burruss’ Production Plan
Transportation Tableau
PERIOD OF USE
PERIOD OF PRODUCTION
1
Beginning
1
2
2
0
Inventory
300
Regular
600
—
—
300
26
—
29
1000
31
100
34
100
Subcontract
28
31
34
37
500
1200
20
100
—
25
28
Subcontract
23
—
26
1200
28
150
31
150
31
Regular
1300
Overtime
200
20
25
28
Subcontract
4
—
28
300
23
Capacity
9
25
Regular
20
Unused
Capacity
4
6
Overtime
Overtime
3
3
3
250
—
—
500
Regular
1300
Overtime
200
Subcontract
Demand
500
900
1500
1600
3000
34
250
23
500
1300
28
200
31
500
20
1300
25
200
28
500
250
REGULAR
SUBENDING
SUBPERIOD DEMAND PRODUCTION OVERTIME CONTRACT INVENTORY
1
2
3
4
Total
900
1500
1600
3000
7000
1000
1200
1300
1300
4800
100
150
200
200
650
0
250
500
500
1250
500
600
1000
0
2100
Other Quantitative Techniques
Linear decision rule (LDR)
„ Search decision rule (SDR)
„ Management coefficients model
„
Available-to-Promise (ATP)
„
Quantity of items that can be promised to the
customer
„
Difference between planned production and
customer orders already received
Hierarchical Nature of Planning
Items
Production
Planning
Capacity
Planning
Resource
Level
Product lines
or families
Aggregate
production
plan
Resource
requirements
plan
Plants
Individual
products
Master
production
schedule
Rough-cut
capacity
plan
Critical
work
centers
Components
Material
requirements
plan
Capacity
requirements
plan
All
work
centers
Manufacturing
operations
Shop
floor
schedule
Input/
output
control
Individual
machines
ATP: Example
AT in period 1 = (On-hand quantity + MPS in period 1) –
- (CO until the next period of planned production)
ATP in period n = (MPS in period n) –
- (CO until the next period of planned production)
ATP: Example (cont.)
ATP: Example (cont.)
Take excess units from April
ATP in April = (10+100) – 70 = 40 = 30
ATP in May = 100 – 110 = -10
=0
ATP in June = 100 – 50 = 50
Rule Based ATP
Aggregate Planning for Services
Product
Request
Yes
Is an alternative
product available
at an alternate
location?
Is the product
available at
this location?
No
Availableto-promise
No
No
Allocate
inventory
Allocate
inventory
Is the customer
willing to wait for
the product?
Is this product
available at a
different
location?
Yes
Availableto-promise
Capable-topromise date
Is an alternative
product available
at this location?
Yes
Yes
Yes
Revise master
schedule
1.
2.
3.
4.
Most services can’
can’t be inventoried
Demand for services is difficult to predict
Capacity is also difficult to predict
Service capacity must be provided at the
appropriate place and time
5. Labor is usually the most constraining
resource for services
Trigger production
No
Lose sale
No
Yield Management
Yield Management (cont.)
Yield Management: Example
TIME - BREAK
NONO-SHOWS
PROBABILITY
P(N < X)
0
1
2
3
.15
.25
.30
.30
.00
.15
.40
.70
Optimal probability of nono-shows
P(n
P(n < x) d
Cu
75
=
= .517
C u + Co
75 + 70
Hotel should be overbooked by two rooms
.517
OBJECTIVES
Strategic Capacity Planning Defined
Capacity Utilization & Best Operating
Level
„ Economies & Diseconomies of Scale
„ The Experience Curve
„ Capacity Focus, Flexibility & Planning
„ Determining Capacity Requirements
„ Decision Trees
„ Capacity Utilization & Service Quality
„
„
Strategic Capacity Planning
Strategic Capacity Planning
Capacity Utilization
Defined
„
Capacity can be defined as the
ability to hold, receive, store, or
accommodate
„
Strategic capacity planning is an
approach for determining the
overall capacity level of capital
intensive resources, including
facilities, equipment, and overall
labor force size
Capacity utilization rate
Where is it used
„ Capacity used
„
„ rate
„
Average
unit cost
of output
Overutilization
Underutilization
Best Operating
Level
Volume
of output actually achieved
Best operating level
„ capacity
for which the process was
designed
Best Operating Level
Example: Engineers design engines and assembly lines to
operate at an ideal or “best operating level” to maximize
output and minimize ware
Capacity used
Best operating level
Example of Capacity Utilization
„
During one week of production, a plant
produced 83 units of a product. Its historic
highest or best utilization recorded was 120
units per week. What is this plant’
plant’s capacity
utilization rate?
x
Answer:
Capacity utilization rate =
Capacity used .
Best operating level
= 83/120
=0.69 or 69%
The Experience Curve
Economies & Diseconomies of Scale
As plants produce more products, they
gain experience in the best production
methods and reduce their costs per unit
Economies of Scale and the Experience Curve
working
Yesterday
100-unit
plant
Average
unit cost
of output
200-unit
plant
300-unit
plant
400-unit
plant
Cost or
price
per unit
Today
Tomorrow
Diseconomies of Scale start working
Total accumulated production of units
Volume
Capacity Flexibility
Capacity Focus
„
„
The concept of the focused factory
holds that production facilities work
best when they focus on a fairly
limited set of production objectives
Plants Within Plants (PWP) (from
Skinner)
„ Extend
focus concept to operating
„
Flexible plants
„
Flexible processes
„
Flexible workers
level
Capacity Planning: Balance
Units
per
month
Unbalanced stages of production
Stage 1
Stage 2
6,000
7,000
Stage 2
6,000
6,000
Frequency of Capacity Additions
„
External Sources of Capacity
5,000
Balanced stages of production
Stage 1
„
Stage 3
Maintaining System Balance:
Balance: Output of one stage is
the exact input requirements for the next stage
Units
per
month
Capacity Planning
Stage 3
6,000
Example of Capacity Requirements
Determining Capacity Requirements
„
1. Forecast sales within each individual
product line
A manufacturer produces two lines of mustard,
FancyFine and Generic line. Each is sold in small
and family-size plastic bottles.
„
2. Calculate equipment and labor
requirements to meet the forecasts
The following table shows forecast demand for
the next four years.
„
Year:
FancyFine
Small (000s)
Family (000s)
Generic
Small (000s)
Family (000s)
3. Project equipment and labor availability
over the planning horizon
Example of Capacity Requirements
(Cont): Product from a Capacity Viewpoint
„
„
1
2
3
4
50
35
60
50
80
70
100
90
100
80
110
90
120
100
140
110
Example of Capacity Requirements
(Cont) : Equipment and Labor Requirements
Question:
Question: Are we really producing two
different types of mustards from the
standpoint of capacity requirements?
Answer:
Answer: No, it’
it’s the same product just
packaged differently.
Year:
Small (000s)
Family (000s)
1
150
115
2
170
140
3
200
170
4
240
200
•Three 100,000 unitsunits-perper-year machines are available
for smallsmall-bottle production. Two operators required
per machine.
machine.
•Two 120,000 unitsunits-perper-year machines are available
for familyfamily-sizedsized-bottle production. Three operators
required per machine.
47
Year:
Small (000s)
Family (000s)
1
150
115
2
170
140
3
200
170
48
Question: What are the values for columns 2, 3 and 4 in the table below?
Question: What are the Year 1 values for capacity, machine,
and labor?
4
240
200
Small
Mach. Cap.
300,000
Labor
6
Family-size
Mach. Cap.
240,000
Labor
6
150,000/300,000=50%
At 1 machine for 100,000, it
Small
takes 1.5 machines for 150,000
Percent capacity used
50.00%
Machine requirement
1.50
Labor requirement
3.00
At 2 operators for
Family-size
100,000, it takes 3
operators for 150,000
Percent capacity used
47.92%
Machine requirement
0.96
Labor requirement
2.88
©The McGraw-Hill Companies, Inc., 2004
Year:
Small (000s)
Family (000s)
Small
Family-size
Small
Percent capacity used
Machine requirement
Labor requirement
Family-size
Percent capacity used
Machine requirement
Labor requirement
1
150
115
2
170
140
3
200
170
4
240
200
Mach. Cap.
Mach. Cap.
300,000
240,000
Labor
Labor
6
6
50.00% 56.67%
1.50 1.70
3.00 3.40
66.67%
2.00
4.00
80.00%
2.40
4.80
47.92% 58.33%
0.96 1.17
2.88 3.50
70.83%
1.42
4.25
83.33%
1.67
5.00
©The McGraw-Hill Companies, Inc., 2004
Example of a Decision Tree Problem
A glass factory specializing in crystal is experiencing a
substantial backlog, and the firm's management is
considering three courses of action:
A) Arrange for subcontracting
B) Construct new facilities
C) Do nothing (no change)
Example of a Decision Tree Problem
(Cont): The Payoff Table
The management also estimates the profits when
choosing from the three alternatives (A, B, and C) under
the differing probable levels of demand. These profits,
in thousands of dollars are presented in the table
below:
The correct choice depends largely upon demand, which
may be low, medium, or high. By consensus, management
estimates the respective demand probabilities as 0.1, 0.5,
and 0.4.
Example of a Decision Tree Problem (Cont):
Step 1. We start by drawing the three decisions
A
B
C
0.1
Low
10
-120
20
0.5
Medium
50
25
40
0.4
High
90
200
60
Example of Decision Tree Problem (Cont):
Step 2. Add our possible states of nature, probabilities & payoffs
$90k
$50k
$10k
High demand (0.4)
Medium demand (0.5)
Low demand (0.1)
A
A
B
High demand (0.4)
Medium demand (0.5)
B
C
Low demand (0.1)
$200k
$25k
-$120k
C
$60k
$40k
$20k
High demand (0.4)
Medium demand (0.5)
Low demand (0.1)
Example of Decision Tree Problem (Cont):
Step 3.
3. Determine the expected value of each decision
Example of Decision Tree Problem (Cont):
Step 4. Make decision
High demand (0.4)
High demand (0.4)
Medium demand (0.5)
$62k
Low demand (0.1)
$90k
$50k
$10k
Medium demand (0.5)
$62k
A
B
A
EVA=0.4(90)+0.5(50)+0.1(10)=$62k
$80.5k
Low demand (0.1)
High demand (0.4)
Medium demand (0.5)
Low demand (0.1)
$90k
$50k
$10k
$200k
$25k
-$120k
C
High demand (0.4)
$46k
Medium demand (0.5)
Low demand (0.1)
$60k
$40k
$20k
Alternative B generates the greatest expected profit, so our
choice is B or to construct a new facility
Capacity Utilization &
Service Quality
Planning Service Capacity
vs. Manufacturing Capacity
„
Time:
Time: Goods can not be stored for later
use and capacity must be available to
provide a service when it is needed
„
Location:
Location: Service goods must be at the
customer demand point and capacity
must be located near the customer
„
Volatility of Demand:
Demand: Much greater than
in manufacturing
„
Best operating point is near 70% of
capacity
„
From 70% to 100% of service capacity,
what do you think happens to service
quality?
Question Bowl
The objective of Strategic Capacity Planning
is to provide an approach for determining
the overall capacity level of which of the
following?
a.
b.
c.
d.
e.
Facilities
Equipment
Labor force size
All of the above.
above.
None of the above
Question Bowl
To improve the Capacity Utilization Rate
we can do which of the following?
a.
b.
c.
d.
e.
Reduce “capacity used”
used”
Increase “capacity used”
used”.
Increase “best operating level”
level”
All of the above
None of the above
(This increases the numerator in the Capacity Utilization Rate
ratio, which is desirable.)
Question Bowl
Question Bowl
When adding capacity to existing
operations which of the following are
considerations that should be included in
the planning effort?
When we talk about Capacity Flexibility
which of the following types of flexibility are
included?
a.
b.
c.
d.
e.
Plants
Processes
Workers
All of the above .
None of the above
a.
b.
c.
d.
e.
Maintaining system balance
Frequency of additions
External sources
All of the above.
above.
None of the above
Question Bowl
Which of the following is a term used to
describe the difference between projected
capacity requirements and the actual
capacity requirements?
a.
b.
c.
d.
e.
Capacity cushion.
cushion.
Capacity utilization
Capacity utilization rate
All of the above
None of the above
Question Bowl
In determining capacity requirements we
must do which of the following?
a.
b.
c.
d.
e.
Address the demands for individual product lines
Address the demands for individual plants
Allocate production throughout the plant
network
All of the above.
above.
None of the above
Question Bowl
In a Decision Tree problem used to evaluate
capacity alternatives we need which of the
following as prerequisite information?
a.
b.
c.
d.
e.
Expect values of payoffs
Payoff values.
values.
A tree
All of the above
None of the above
(Expected values are what is computed, not prerequisite to the
analysis.)
THE END