Operations Management I
Departamento de Organización de
Empresas y Marketing
Área de Organización de Empresas
Dirección de Operaciones I- English teaching
SECTION 2
STRATEGIC DESIGN
3º GADI- 5º DG-ADI-DER
2013-2014
Slide presentation Chapter 6
CHAPTER 6
CAPACITY PLANNING
6.1.
6.2.
6.3.
6.4.
Capacity Measurement
Long-Term Capacity Strategies
Short-Term Capacity Strategies
Break-Even Analysis: Comparison of Alternatives
2
6.1 Capacity
 The throughput, or the number of
units a facility can hold, receive,
store, or produce in a period of time
 Determines a large proportion of
fixed costs
 Determines if demand will be
satisfied or facilities idle
 Three time horizons
3
Planning Over a Time Horizon
Long-range
planning
Add facilities
Add long lead time equipment
Intermediaterange
planning
Subcontract
Add equipment
Add shifts
Short-range
planning
Add personnel
Build or use inventory
*
Modify capacity
*
Schedule jobs
Schedule personnel
Allocate machinery
Use capacity
* Limited options exist
4
6.1 Capacity
 Capacity decisions are often influenced
by economies and diseconomies of
scale:
 Economies of scale are achieved when the
average unit cost of a good or service
decreases as the capacity and/or volume of
throughput increases
 Diseconomies of scale occur when the
average unit cost of the good or service
begins to increase as the capacity and/or
volume of throughput increases.
5
Average unit cost
(dollars per room per night)
Economies and Diseconomies
of Scale
25 - Room
Roadside Motel
50 - Room
Roadside Motel
Economies
of scale
25
75 - Room
Roadside Motel
Diseconomies
of scale
50
Number of Rooms
75
6
Design and Effective Capacity
 Design capacity is the maximum theoretical
output of a system in a given period under ideal
conditions
 Normally expressed as a rate
 Effective capacity is the capacity a firm expects to
achieve given current operating constraints
 Often lower than design capacity
 Safety capacity (or capacity cushion) is defined as
an amount of capacity reserved for unanticipated
events such as demand surges, materials
shortages, and equipment breakdowns
7
Utilization and Efficiency
Utilization is the percent of design capacity
achieved
Utilization = Actual Output/Design Capacity
Efficiency is the percent of effective capacity
achieved
Efficiency = Actual Output/Effective Capacity
8
Bakery Example
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
9
Capacity Considerations
 Forecast demand accurately
 Understanding the technology
and capacity increments
 Find the optimal operating level
(volume)
 Build for change
10
Managing Demand
 Demand exceeds capacity
 Curtail demand by raising prices,
scheduling longer lead time
 Long term solution is to increase capacity
 Capacity exceeds demand
 Stimulate market
 Product changes
 Adjusting to seasonal demands
 Produce products with complimentary
demand patterns
11
Complementary Demand
Patterns
Sales in units
4,000 –
By combining
both, the
variation is
reduced
3,000 –
Snowmobile
sales
2,000 –
1,000 –
Jet ski
sales
JFMAMJJASONDJFMAMJJASONDJ
Time (months)
12
6.2 Long-Term Capacity Strategies
 Firms must anticipate growth or decline
in demand and plan capital investments
 Complementary goods and services:
goods and services that can be
produced or delivered using the same
resources available to the firm, but
whose seasonal demand patterns are
out of phase with each other
 Design higher levels of self-service
(customer labor) into operations
13
Approaches to Capacity
Expansion
Expected
demand
Demand
(c) Capacity lags demand with
incremental expansion
New
capacity
Expected
demand
Demand
New
capacity
(b) Leading demand with
one-step expansion
New
capacity
Expected
demand
(d) Attempts to have an average
capacity with incremental
expansion
Demand
Demand
(a) Leading demand with
incremental expansion
New
capacity
Expected
demand
14
6.3 Short-Term Capacity Strategies
 Managing Capacity by Adjusting ShortTerm Capacity Levels
 Add or share equipment
 Sell unused capacity
 Change labor capacity and schedules
 Change labor skill mix
 Shift work to slack periods
15
6.3 Short-Term Capacity Strategies
 Managing Capacity by Shifting and
Stimulating Demand
 Vary the price of goods or services
 Provide customers information
 Advertising and promotion
 Add peripheral goods and/or services
 Provide reservations (a promise to
provide a good or service at some future
time and place)
16
6.4. Break-Even Analysis: Comparison of Alternatives
 A means of finding the point at which costs
equal revenues
 Assumptions:
 Only one product
 Everything produced is sold
 Cost and revenue are linear functions
 Constant price sale and variable cost. Clear
differentiation between fixed and variable costs
Fixed costs: costs that continue even if no units are
produced
Variable costs: costs that vary with the volume of
units produced
Contribution: difference between selling price and
variable costs
17
6.4 Break-Even Analysis: Comparison of Alternatives
Total revenue: TR = P x
P = price per unit
Total cost: TC = V x + F
x = number of units produced (or demand)
Benefits: B = P x - (V x + F)
V= variable cost per unit
BEP = F / (p - V)
F= fixed costs
Graphic approach
B (u.m)
Alt. B
Alt. A
D < BEP_A
Do nothing
BEP_A  D < X1
BEP_A
X1
FA
BEP_B
FB
X (units)
Alt. A
D = X1
Indifferent
D > X1
Alt. B
18
6.4 Break-Even Analysis: Comparison of Alternatives
RANDOM DEMAND:
• Discrete probability
Demand
Expected
levels
Probability
Benefit
Xo
Po
Bo
X1
P1
B1
....
.....
.....
Xm
Pm
Bm
.....
.....
.....
Xn
Pn
Bn
demand and benefit
n
DE = xiP i
i 1
n
n
i 1
i 1
BE = BiP i   [(p  Cv) xi  CF]P i
n
 Pi1
i 1
19
6.4 Break-Even Analysis: Comparison of Alternatives
Áreas bajo la curva normal estándar. Los valores de la tabla que no
se muestran en negrita representan la probabilidad de observar un
valor menor o igual a z. La cifra entera y el primer decimal de z se
buscan en la primera columna, y el segundo decimal en la cabecera
de la tabla.
•Continuous probability:
B = P x - (V x + F)= P x - V x - F = (P - V) x - F
P (B > Bmín) = P[(P-V) x - F > (P-V) xmín - F =
Since P, V and F are constant
= P(x > xmín) = 1 - P(x  xmín)
Standarizing the variable xmín
x ~ N(µ, )
P (B > Bmín) = 1 - P (Z  Zmín= (xmín - µ )/)
z
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9
3.0
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
0
.5000
.5398
.5793
.6179
.6554
.6915
.7257
.7580
.7881
.8159
.8413
.8643
.8849
.9032
.9192
.9332
.9452
.9554
.9641
.9713
.9772
.9821
.9861
.9893
.9918
.9938
.9953
.9965
.9974
.9981
.9987
.9990
.9993
.9995
.9997
.9998
.9998
.9999
.9999
1
Segunda
1
2
.5040 .5080
.5438 .5478
.5832 .5871
.6217 .6255
.6591 .6628
.6950 .6985
.7291 .7324
.7611 .7642
.7910 .7939
.8186 .8212
.8438 .8461
.8665 .8686
.8869 .8888
.9049 .9066
.9207 .9222
.9345 .9357
.9463 .9474
.9564 .9573
.9649 .9656
.9719 .9726
.9778 .9783
.9826 .9830
.9864 .9868
.9896 .9898
.9920 .9922
.9940 .9941
.9955 .9956
.9966 .9967
.9975 .9976
.9982 .9982
.9987 .9987
.9991 .9991
.9993 .9994
.9995 .9995
.9997 .9997
.9998 .9998
.9998 .9999
.9999 .9999
.9999 .9999
1
1
cifra decimal del valor de z
3
4
5
6
7
.5120 .5160 .5199 .5239 .5279
.5517 .5557 .5596 .5636 .5675
.5910 .5948 .5987 .6026 .6064
.6293 .6331 .6368 .6406 .6443
.6664 .6700 .6736 .6772 .6808
.7019 .7054 .7088 .7123 .7157
.7357 .7389 .7422 .7454 .7486
.7673 .7704 .7734 .7764 .7794
.7967 .7995 .8023 .8051 .8078
.8238 .8264 .8289 .8315 .8340
.8485 .8508 .8531 .8554 .8577
.8708 .8729 .8749 .8770 .8790
.8907 .8925 .8944 .8962 .8980
.9082 .9099 .9115 .9131 .9147
.9236 .9251 .9265 .9279 .9292
.9370 .9382 .9394 .9406 .9418
.9484 .9495 .9505 .9515 .9525
.9582 .9591 .9599 .9608 .9616
.9664 .9671 .9678 .9686 .9693
.9732 .9738 .9744 .9750 .9756
.9788 .9793 .9798 .9803 .9808
.9834 .9838 .9842 .9846 .9850
.9871 .9875 .4878 .9881 .9884
.9901 .9904 .9906 .9909 .9911
.9925 .9927 .9929 .9931 .9932
.9943 .9945 .9946 .9948 .9949
.9957 .9959 .9960 .9961 .9962
.9968 .9969 .9970 .9971 .9972
.9977 .9977 .9978 .9979 .9979
.9983 .9984 .9984 .9985 .9985
.9988 .9988 .9989 .9989 .9989
.9991 .9992 .9992 .9992 .9992
.9994 .9994 .9994 .9994 .9995
.9996 .9996 .9996 .9996 .9996
.9997 .9997 .9997 .9997 .9997
.9998 .9998 .9998 .9998 .9998
.9999 .9999 .9999 .9999 .9999
.9999 .9999 .9999 .9999 .9999
.9999 .9999 .9999 .9999 .9999
1
1
1
1
1
8
.5319
.5714
.6103
.6480
.6844
.7190
.7517
.7823
.8106
.8365
.8599
.8810
.8997
.9162
.9306
.9429
.9535
.9625
.9699
.9761
.9812
.9854
.9887
.9913
.9934
.9951
.9963
.9973
.9980
.9986
.9990
.9993
.9995
.9996
.9997
.9998
.9999
.9999
.9999
1
9
.5359
.5753
.6141
.6517
.6879
.7224
.7549
.7852
.8133
.8389
.8621
.8830
.9015
.9177
.9319
.9441
.9545
.9633
.9706
.9767
.9817
.9857
.9890
.9916
.9936
.9952
.9964
.9974
.9981
.9986
.9990
.9993
.9995
.9997
.9998
.9998
.9999
.9999
.9999
1
20
6.4 Break-Even Analysis: Comparison of Alternatives
EXAMPLE:
NORTEL Inc. is opening a new plant in Malaga. They have to decide
regarding the capacity of the new plant between the following
alternatives:
Size
Annual
Capacity
Fixed Costs
Unit
Variable
Costs
Selling
Price
Big
7.000
1.800.000
1.500
2.000
Medium
5.000
1.200.000
1.600
2.000
Small
2.500
740.000
1.700
2.000
With the information provided we want to determine which is the best
option in the following cases:
1º) Using the graphic approach of the break-even analysis.
21
6.4 Break-Even Analysis: Comparison of Alternatives
EXAMPLE (cont.):
2º) Knowing different demand levels and their probability:
Demand (Di)
Probability (Pi)
1.000
0,05
2.000
0,15
3.000
0,25
4.000
0,25
5.000
0,15
6.000
0,1
7.000
0,05
3º) Assuming demand is normally distributed with mean 4.000 and
standard deviation 1.000, we will choose the alternative with the
highest probability of achieving, at least, an expected benefit of
100.000 €
22
6.4 Break-Even Analysis: Comparison of Alternatives
PROBLEM 1
MISOL, S.L. (LTD.) produces and installs prefabricated solar systems (domestic
solar equipments), whose objective is to produce warm water for domestic
purposes. Currently, there are several points still to decide in the enterprise,
one of them related to the capacity of a new production plant located in Seville.
Therefore, there are two options:
a) Installing a new production plant in Mairena del Aljarafe, with a capacity of
3,000 upy (units per year).
b) Installing a new production plant in Osuna, with a capacity of 2,300 upy.
The sale price of an equipment is known to be of € 4,000 and the main costs
are as follows:
Production plant
Variable cost
Fixed cost
Mairena del Aljarafe
€ 3,000
€ 2,400,000
Osuna
€ 3,500
€ 1,100,000
23
6.4 Break-Even Analysis: Comparison of Alternatives
PROBLEM 1 (cont.)
We would like to find out:
1. Using the break-even point graphic, which of the plants would be more
appropriate for each demand volume?
2. Assuming that the demand acts like a continuous variable, following a
normal distribution of average 2500 and standard deviation 400, what is
the probability that this enterprise reaches a maximum profit of € 400,000
in the Mairena del Aljarafe production plant?
24
6.4 Break-Even Analysis: Comparison of Alternatives
PROBLEM 2
At A SU GUSTO, S.A., a factory of jet planes for celebrities, their facilities aren’t
enough for their purposes. That’s why a specialised consultant has been asked
to produce a report on the best option for the board:
a) A factory in Seville, smaller but modern, with a maximum production
capacity of 50 units per year, though with relatively small fixed costs: 500
million € per year. Each unit cost would raise to 75 million €.
b) A factory in Huelva, with a production capacity of 100 units per year, but 2
billion (2,000 million) € as yearly fixed costs and 50 million € as variable
costs.
25
6.4 Break-Even Analysis: Comparison of Alternatives
PROBLEM 2 (cont.)
1. Knowing that, in both cases, the sales price for each jet plane is 100 million
€, you have to propose to A SU GUSTO, S.A. the best option according to
the expected demand volume. For this purpose, the technique of the breakeven point graphic is to be used.
2. The marketing department reports to A SU GUSTO, S.A. that the expected
demand of jet planes acts like a continuous variable following a normal
distribution of average 26 and standard deviation 20. What is the
probability of not being able to fulfill all the demands of the clients if they
choose the option of Seville?
26
6.4 Break-Even Analysis: Comparison of Alternatives
PROBLEM 3
Café 1492, Inc. (S.A.) is thinking on increasing its capacity due to the
extraordinary outcomes. Therefore, there are two options:
Option 1: setting up a new café in a different location with a capacity of
13,600 customers per year. Its estimations are € 60,000 per annum for fixed
costs and € 7 as unitary variable cost. This option would lead to the shutdown
of the current café.
Option 2: increasing the current capacity in the same location, enabling a
night shift. This means assisting 13,900 customers per year more than option
1. The night-shift work would increase fixed costs €40,000 more.
In the café business, there are different sorts of products. For the sake of this
case, a representative equivalent value would be € 12 per product.
We would like to find out:
Using the break-even point graphic, which option would be more
appropriate for each demand value?
27