Capacity Planning
Dr. Mohammad Abdul Mukhyi, SE., MM
Capacity
Capacity: Definition
Capacity Types
Capacity of the Supply Chain
Calculating Capacity
Introduction to Facility
Planning
HOW MUCH long range capacity will be
needed?
WHEN will the additional capacity be
required?
WHERE should the facility be located?
WHAT should the layout and
characteristics of the facility be?
Strategic Capacity Planning
Capacity
The amount of resource inputs available
relative to output requirements at a
particular time
How does productivity relate to capacity?
Defining Capacity
the rate of output from an OM system per unit of time
the rate at which the firm withdraws work from the
system
Jumlah masukan sumberdaya-sumberdaya yang
tersedia relatif untuk kebutuhan keluaran pada waktu
tertentu.
unit keluaran
waktu
Defining Capacity
In general, production capacity is the
maximum production rate of an organization
(or maximum conversion rate of a production
system) in any given period.
Sustainable practical capacity is the greatest
level of output that a plant can maintain:
within the framework of a realistic work schedule
taking account of normal downtime
assuming sufficient availability of inputs to
operate the machinery and equipment in place
Berbagai Definisi:
1. Design Capacity : tingkat keluaran per satuan untuk mana pabrik
dirancang.
2. Rated Capacity ; tingkat keluaran per satuan waktu yang
menunjukkan bahwa fasilitas secara teoritik mempunyai kemampuan
memproduksi.
3. Standard capacity : tingkat keluaran per satuan waktu yang ditetapkan
sebagai sasaran pengoperasian bagi manajemen, supervisi dan para
operator mesin, dapat digunakan sebagai dasar bagi penyusunan
anggaran.
4. Actual dan atau operating capacity : tingkat keluaran rata-rata per
satuan waktu selama periode waktu yang telah lewat = kapasitas
standar ± cadangan-cadangan, penundaan, tingkat sisa nyata.
5. Peak capacity : jumlah keluaran per satuan waktu yang dapat dicapai
melalui maksimisasi keluaran dengan kerja lembur, menambah tenaga
kerja, mengurangi jam istirahat dan sebagainya
Steps in the Capacity Planning
Process
Estimate the capacity of the present
facilities.
Forecast the long-range future capacity
needs.
Identify and analyze sources of capacity
to meet these needs.
Select from among the alternative sources
of capacity.
Measures of Capacity
Output rate capacity – Suitable for a single
product or a few homogeneous products
Design capacity - The maximum capacity that
can be achieved under ideal conditions
Effective capacity utilization - The percent of
design capacity actually achieved
Aggregate capacity – Suitable when a
common unit of output is used
. . . more
Measures of Capacity
Rated capacity – Maximum usable
capacity of a particular facility
Input rate capacity – Suitable for service
operations
Percentage utilization of capacity Relates output measures to inputs
available
Capacity Utilization
Capacity used
Utilizatio n =
Best operating level
Capacity used
Rate of output actually achieved
Best operating level
Capacity for which the process was designed
Capacity Utilization--Example
Best operating level = 120 units/week
Actual output = 83 units/week
Utilization = ?
Capacity used
83 units/wk
Utilization =
=
= .692
Best operating level 120 units/wk
Capacity Cushion
A capacity cushion is an additional amount of
capacity added onto the expected demand to
allow for:
greater than expected demand
demand during peak demand seasons
lower production costs
product and volume flexibility
improved quality of products and services
Supply Chain Capacity
Tactical perspective
output driven
units of output, hours worked
strategic perspective
capability
what you can and cannot do
match capabilities with marketing needs
Rated
Jumah
Jam Kerja
Persentase
Efisiensi
=
X
X
X
capacity
Mesin
Mesin
penggunaan
Sistem
Contoh:
Suatu pusat kerja beroperasi 6 hari per minggu
dengan basis dua sift (8 jam per sift), ada empat
mesin dengan kemampuan sama. Bila mesin
digunakan 75% dari waktu pada tingkat efisiensi
sistem sebesar 90%
Jawab :
Rated Capacity = (4) (8x6x2) (0,75) (0,90)
= 259 jam kerja standar/minggu
Types of Capacity
Maximum
Effective
Demonstrated
Planning Effects of Capacity Types
Types of Capacity:
Maximum Capacity (aka Design)
Defined: The highest rate of output that a process
can achieve
Calculation involves the following assumptions:
equally skilled workers
no time loss due to changeovers or product differences
no loss of capacity due to PM or planned downtime
no OT work or heroic employee efforts
Are these assumptions realistic?
Types of Capacity:
Effective Capacity
Defined: the output rate that managers
expect for a given process
Why would you operate below maximum?
Types of Capacity:
Demonstrated Capacity
Defined: the actual level of output for a
process over a period of time, i.e., the
average of output over time
Why might this number be different than
maximum or effective capacity?
Types of Capacity:
Demonstrated Capacity
Demonstrated capacity
what we actually observe
can be affected by numerous factors
problems with input
problems internally
nature of the product
new vs standard
Capacity within the Supply
Chain
Must deal with the issue of bottlenecks and
system constraints.
Capacity defined by:
information systems
infrastructure
physical capacity
logistics capacity
supplier capacity
relationship management
Bottlenecks
Must look for bottleneck
constraining resource
how identified
too much or too little inventory
overtime
why important
limits output
determines lead time
determines ability of system to make money
Bottlenecks - Con’t
Types of bottlenecks
output based
time-based
These bottlenecks may the same or they may be
different
Keys to success
keep the bottlenecks busy
inventories/signals
invest in bottlenecks
Capacity - calculating
Level of output of a plant or system is
dependent on how it is organized
capacity in sequence
linear operations
capacity in parallel
multiple alternative operations
any machine can be used
Capacity - Sequential
Capacity of a system or process is based
on the operation with the lowest amount
of capacity
Keys
convert into the same units of measurement
ensure that we are talking about the same
dimensions
effective vs design vs demonstrated
capacity taken over the same time
Capacity - Sequential
We have a process that makes cans
Operation 1 - punches out tops and bottoms
2 lids for every can
produces 250 lids per minute
Operation 2 - body
1 body for every can
produces 175 bodies per minute
Operation 3 - mating
makes the can
produces 7500 cans per hour
Capacity - Parallel
Capacity of the system or operation is based on
the sum of the capacities of the various
machines that make up the operation.
Operation 3 has 4 machines
machine 1 - 90 pieces per minute
machine 2 - 110 pieces per minute
machine 3 -120 pieces per minute
machine 4 - 80 pieces per minute
Total capacity for operation 3 = 400 pieces/min
Capacity Management Tools:
Calculating Capacity
1. Describe the general flow of activities within the process
2. Establish the time period
3. Establish a common unit
4. Identify the Maximum capacity for the overall process
5. Identify the Effective capacity for the overall process
6. Determine the Demonstrated capacity
7. Compare the Demonstrated, Effective and Maximum
Capacities and take appropriate actions
Capacity - Example
Mondavi Plant
Draw a diagram
What is its capacity
Why is it hard to calculate
Other Factors to Consider
Setups
reduce the capacity of the facility
capacity is finite wrt time
Relevant vs irrelevant
relevant also depends on the options
considered
also depends on the level of capacity
utilization
Relevant Analysis
Consider the following
for a machine, what is the impact of one
additional hour of setup if the machine is
50% utilized?
What is the impact of one additional hour
of setup if the machine is 85% utilized?
Importance of Setup Reduction
Setup reduction (SMED) can reduce setup times
by 30 to 50% without significant investments.
When teams work to reduce process variation or
to improve safety, handling, preventive
maintenance or housekeeping, they also tend to
reduce setup times.
When setup times decrease, setup labor and
startup problems decrease.
Setup Reductions
When setup times decrease, it is economical to
run smaller lots, which tend to reduce
inventories, scrap, and rework.
The habit of continuous improvement must not
be discouraged (even if your equipment is not
the bottleneck).
As conditions change, a non-bottleneck can
easily become a bottleneck.
Capacity Management Tools:
Input/Output Control
A tool that manages work flows to
match the demonstrated capacity of
process
Prevent problems by using “plan your
work, work your plan” rule
Economies of Scale
Best operating level - least average unit cost
Economies of scale - average cost per unit
decreases as the volume increases
Diseconomies of scale - average cost per unit
increases as the volume increases
Other considerations
Subcontractor and supplier networks
Focused production
Economies of scope
Economies and Diseconomies
of Scale
Average Unit
Cost of Output ($)
Economies
of Scale
Diseconomies
of Scale
Best Operating Level
Annual Volume (units)
Economies and Diseconomies
of Scale
Average Unit
Cost of Output ($)
Optimum Plant Size
100100-unit
plant
400400-unit
plant
200200-unit
plant
300300-unit
plant
Annual Volume (units)
The Learning Curve Effect
Cost/Time per repetition
100
90
Cost
80
70
60
50
40
30
20
10
0
20
40
60
Number of repetitions (Volume)
80
100
The Learning Curve Effect
Observe, that the per unit cost (or price) of
the product (or service) declines
exponentially as the number of
repetitions increases
Capacity Focus
Should manufacturers attempt to excel
on all production objectives?
Plants within plants (Skinner)
Extend focus concept to operating level
Capacity Flexibility
Flexible plants
Flexible processes
Flexible workers
Capacity Planning
Stage 1
Units
per
month
6,000
Stage 2
7,000
Stage 3
4,500
What will happen to WIP inventory?
Issue: How to maintain system balance?
Analyzing Capacity-Planning
Decisions
Break-even Analysis
Present-Value Analysis
Decision Tree Analysis
Computer Simulation
Waiting Line Analysis
Linear Programming
Determining Capacity
Requirements
Forecast sales within each individual
product line
Calculate equipment and labor
requirements to meet the forecasts
Project equipment and labor availability
over the planning horizon
Example: Capacity
Requirements
A manufacturer produces two lines of ketchup, FancyFine
and a generic line. Each is sold in small and family-size
plastic bottles.
The following table shows forecast demand for the next
four years.
Y ear:
F a n cyF in e
S m all (0 0 0 s )
F am ily (0 0 0 s )
G en er ic
S m all (0 0 0 s )
F am ily (0 0 0 s )
1
2
3
4
50
35
60
50
80
70
100
90
100
80
110
90
120
100
140
110
Example: Capacity
Requirements
The Product from a Capacity Viewpoint
Are we really producing two different types of
ketchup from the standpoint of capacity
requirements?
Example: Capacity Requirements
Equipment and Labor Requirements
Year:
Small (000s)
Family (000s)
1
150
115
2
170
140
3
200
170
4
240
200
Three 100,000-units-per-year machines are available for smallbottle production. Two operators required per machine.
Two 120,000-units-per-year machines are available for familysized-bottle production. Three operators required per
machine.
Example: Capacity Requirements
Equipment and Labor Requirements
• Total machine capacity available for small-bottle
production: 3*100,000=300,000 units/year
• Total machine capacity available for family-sized-bottle
production: 2*120,000=240,000 units/year
• Total labor capacity required for small-bottle production:
3*2=6 operators
• Total labor capacity required for family-sized-bottle
production: 2*3=6 operators
Example: Capacity
Requirements
Y ear:
S m all (0 0 0 s)
F amily (0 0 0 s)
S m a ll
F a m ily-siz e
S m a ll
P ercen t cap acity u s ed
M ach in e req u iremen t
Lab o r req u iremen t
F a m ily-siz e
P ercen t cap acity u s ed
M ach in e req u iremen t
Lab o r req u iremen t
1
150
115
2
170
140
3
200
170
4
240
200
M ach . C ap .
M ach . C ap .
3 0 0 ,0 0 0
2 4 0 ,0 0 0
Lab o r
Lab o r
6
6
5 0 .0 0 %
1 .5 0
3 .0 0
5 6 .6 7 %
1 .7 0
3 .4 0
6 6 .6 7 %
2 .0 0
4 .0 0
8 0 .0 0 %
2 .4 0
4 .8 0
4 7 .9 2 %
0 .9 6
2 .8 8
5 8 .3 3 %
1 .1 7
3 .5 0
7 0 .8 3 %
1 .4 2
4 .2 5
8 3 .3 3 %
1 .6 7
5 .0 0
The Decision-Making Process
Quantitative Analysis
Problem
?
Logic
Historical Data
Marketing Research
Scientific Analysis
Modeling
Qualitative Analysis
Emotions
Intuition
Personal Experience
& Motivation
Rumors
Decision
!
Six Steps of the Decision
Process
1
2
3
4
5
6
Defining the problem and the factors that influence it
Establishing decision criteria and goals
Formulating a model or relationship between goals and variables
Identifying and evaluating alternatives
Selecting the best alternative
Implementing the decision
Fundamentals of Decision
Theory
The three types of decision models:
1 Decision making under certainty
2 Decision making under risk
3 Decision making under uncertainty
Terms:
Alternative: course of action or choice
State of nature: an occurrence over which the
decision maker has no control
Decision Making Under
Uncertainty
Maximax
find the alternative that maximizes the maximum
outcome for every alternative.
Maximin
find the alternative that maximizes the minimum
outcome for every alternative.
Equally likely
find the alternative with the highest average
outcome
Decision Tree Analysis
Structures complex, multiphase decisions
Allows objective evaluation of
alternatives
Incorporates uncertainty
Develops expected values
Decision Tree Analysis
Symbols used in decision tree:
A decision node from which one of several
alternatives may be selected.
A state of nature node out of which one
state of nature will occur.
Decision Tree Analysis
Define the problem
Structure or draw the decision tree
Assign probabilities to the states-of-nature
Estimate the payoffs for each possible combination of
alternative and state-of-nature
5 Solve the problem by computing expected monetary
values (EMV) for each state-of-nature node
1
2
3
4
Example 1: Decision Tree
Analysis
Good Eats Café is about to build a new restaurant.
An architect has developed three building designs, each
with a different seating capacity. Good Eats estimates that
the average number of customers per hour will be 80,
100, or 120 with respective probabilities of 0.4, 0.2, and
0.4. The payoff table showing the profits for the three
designs is on the next slide.
Example 1: Decision Tree
Analysis
Payoff Table
Average Number of Customers Per Hour
c1 = 80
c2 = 100 c3 = 120
Design A
Design B
Design C
$10,000
$ 8,000
$ 6,000
$15,000
$18,000
$16,000
$14,000
$12,000
$21,000
Example 1: Decision Tree Analysis
Expected Value Approach
Calculate the expected value for each decision. The
decision tree on the next slide can assist in this calculation.
Here d1, d2, d3 represent the decision alternatives of designs A, B,
C, and c1, c2, c3 represent the different average customer volumes
(80, 100, and 120) that might occur.
Example 1: Decision Tree Analysis
Payoffs
Decision Tree
2
d1
1
c1
.4
c2
c3
.2
.4
10,000
15,000
14,000
d2
3
d3
c1
.4
c2
c3
.2
8,000
18,000
.4
12,000
4
c1
.4
c2
.2
c3
.4
6,000
16,000
21,000
Example 1: Decision Tree Analysis
Expected Value For Each Decision
d
Design A
1
Design B
1
d
2
Design C
2
EV = .4(10,000) + .2(15,000) + .4(14,000)
= $12,600
3 EV = .4(8,000) + .2(18,000) + .4(12,000)
= $11,600
d
3
EV = .4(6,000) + .2(16,000) + .4(21,000)
4
= $14,000
hoose the design with largest EV -- Design C.
A Sequence of Decisions
National Decision
Political, social, economic stability;
Currency exchange rates; . . . . .
Regional Decision
Climate; Customer concentrations;
Degree of unionization; . . . . .
Community Decision
Transportation system availability;
Preference of management; . . . . .
Site size/cost; Environmental impact;
Zoning restrictions; . . . . .
Site Decision
Region Location Decision
Corporate desires
Attractiveness of region (culture, taxes, climate, etc.)
Labor availability, costs, attitudes toward union
Cost and availability of utilities
Environmental regulations of state and town
Government incentives
Proximity to raw materials & customers
Land/construction costs
Site Location Decisions
Site size and cost
Air, rail, highway, waterway systems
Zoning restrictions
Nearness of services/supplies needed
Environmental impact issues
Factors Affecting the
Location Decision
Economic
Site acquisition, preparation and construction
costs
Labor costs, skills and availability
Utilities costs and availability
Transportation costs
Taxes
. . . more
Factors Affecting the
Location Decision
Non-economic
Labor attitudes and traditions
Training and employment services
Community’s attitude
Schools and churches
Recreation and cultural attractions
Amount and type of housing available
Facility Types and Their
Dominant Locational Factors
Mining, Quarrying, and Heavy Manufacturing
Near their raw material sources
Abundant supply of utilities
Land and construction costs are inexpensive
Light Manufacturing
Availability and cost of labor
Warehousing
Proximity to transportation facilities
Incoming and outgoing transportation costs
. . . more
Facility Types and Their
Dominant Locational Factors
R&D and High-Tech Manufacturing
Ability to recruit/retain scientists, engineers, etc.
Near companies with similar technology interests
Retailing and For-Profit Services
Near concentrations of target customers
Government and Health/Emergency Services
Near concentrations of constituents
Some Reasons the
Facility Location Decision
Arises
Changes in the market
Expansion
Contraction
Geographic shift
Changes in inputs
Labor skills and/or costs
Materials costs and/or availability
Utility costs
. . . more
Some Reasons the
Facility Location Decision
Arises
Changes in the environment
Regulations and laws
Attitude of the community
Changes in technology
Analyzing Location Decisions
Quantitative Approaches
Qualitative Approaches
Integrating Qualitative and Quantitative
Data
Analyzing Location Decisions
Quantitative Approaches
Decision trees
Center of Gravity Method
Finds best distribution center location
Location Breakeven Methods
Special case of breakeven analysis
Transportation Method
Special case of LP method
NPV analysis
Computer Simulation
Analyzing Location Decisions
Mixed Approaches
Weighted Methods which:
Assigns weights and points to various factors
Determines tangible costs
Investigates intangible costs
Examples:
Rating scale approach
Relative-aggregate-scores approach
Example 1: Locational Breakeven
Analysis
250
Akron
Bowling Green
Chicago
200
150
100
50
Akron
Lowest cost
0
0
200
400
600
Bowling Green
Lowest cost
Chicago
Lowest cost
800 1000 1200 1400 1600 1800 2000 2200
Penentuan kebutuhan
kapasitas
x
Hstd = ∑ [Oi ( Ti + Si ) + BiNi ]
i =1
Hstd
Oi
Ti
Si
Bi
Ni
X
= jumlah total jam sumber daya yang dibutuhkan untuk
memenuhi permintaan
= jumlah unit keluaran X yang diperlukan
= waktu pengoperasian standar per unit X
= waktu persiapan standar peru unit keluaran X
= waktu standar untuk mempersiapkan sekumpulan X
= jumlah kumpulan X yang diperlukan
= jumlah jenis produk
Hact
Hact
Hstd
Eo
Pw
Em
Hstd
=
Eo Pw Em
= jumlah sumberdaya nyata yang dibutuhkan
= jumlah total jam sumber daya yang dibutuhkan untuk
memenuhi permintaan
= efisiensi organisasional
= produktivitas operator
= efisiensi mesin, faktor pemeliharaan, faktor mesin rusak
Hact
Nr =
Havl
Nr
Havl
= jumlah unit sumberdaya yang dibutuhkan
= jumlah jam yang tersedia per unit sumberdaya selama
periode waktu tertentu
Permintaan produk sebesar 200 unit, ada 22 hari kerja per bulan.
Waktu pengoperasian standar per unit sebesar 8 jam, waktu
persiapan setengah jam setiap unit. 200 unit akan diproses dalam 10
kumpulan, pada setiap akhir kumpulan, mesin harus diuji dan
disesuaikan kembali sebelum kumpulan berikutnya diproses, waktu
penyiapan memerlukan 4 jam. Efisiensi organisasional diperkirakan
95% dan mesin beroperasi dengan efisiensi 90%. Selama mesin
dioperasikan dengan kecepatan wajar diperlukan waktu penundaan
untuk pemeliharaan selama 48 menit per hari, mesin-mesin
dijalankan 8 jam per hari dan para operator mesin bekerja sesuai
dengan standar (1,00).
Berapa jumlah mesin yang dibutuhkan untuk memenuhi permintaan
bulanan?
H std = 200(8 + 0,5) + 4(10) = 1.740 jam standar
H act =
Nr =
1 . 740
= 2.035,1 jam nyata
0,95 (1,0 )( 0,90
2 . 035 ,1
= 11,56 mesin
22 ( 8 )
Learning Curve dan Kapasitas
Y = C XS
Log Y = S log X + Log C
X = jumlah unit produk yang dibuat
C = jam kerja langsung yang diperlukan oleh produksi
pertama
Y = jumlah jam kerja rata-rata per unit produk
S = slope = (log % -2)/log 2
Untuk kurva 80%S = log 80-2/log 2
= 1,90309 – 2 / 0,30103 = -0,322
Perusahaan menerima kontrak pembuatan produk sejumlah 50 unit. Produk
pertama memerlukan 2.000 jam tenaga kerja langsung, dengan learning curve
yang berlaku sebesar 80% waktu yang diperlukan per unit produk :
Log Y = -0,322. log 50 + log 2.000
= -0,322(1,69897) + 3,30103
= 2,75396
Y = 567,491 jam kerja langsung
literatur
T. Hani Handoko, Dasar-dasar manajemen produksi dan
operasi, bPFE, UGM, Yogyakarta.
Sheri Nemeth and Mick Peters, Production and Operating
Management.
N. Gaither and Frazier, Production and Operations
Management, 8th Edition, Duxbury Press, NY, NY, 1999.
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