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. (Road server) Handouts for most classes are available on the ROAD server. The handouts can be accessed at: _ HYPERLINK http://road.uww.edu __http://road.uww.edu_
