Operations Management
Lesson 4
Capacity Planning and
Forecasting
1
What you will learn in this unit:
 Capacity Planning
 Making Capacity Planning Decisions
 Forecasting Process
 Types of Forecasting Methods
 Qualitative Methods
 Quantitative Methods
2
Capacity planning


Capacity is the maximum output rate of a
production or service facility
Capacity planning is the process of
establishing the output rate that may be
needed at a facility. Setting the effective
capacity of the operation to meet the
required demands
3
Measuring Capacity Examples



There is no one best way to measure capacity
Output measures like kegs per day are easier to understand
With multiple products, inputs measures work better
Type of Business
Input Measures of
Capacity
Output Measures
of Capacity
Car manufacturer
Labor hours
Cars per shift
Hospital
Available beds
Patients per month
Pizza parlor
Labor hours
Pizzas per day
Retail store
Floor space in
square feet
Revenue per foot
4
Capacity Information Needed

Design capacity:



Maximum output rate under ideal conditions
A bakery can make 30 custom cakes per day
when pushed at holiday time
Effective capacity:


Maximum output rate under normal (realistic)
conditions
On the average this bakery can make 20
custom cakes per day
5
Calculating Capacity Utilization

Measures how much of the available
capacity is actually being used:
actual output rate
100%
Utiliz atio
n
capacity


Measures effectiveness
Use either effective or design capacity in
denominator
6
Example of Computing Capacity Utilization: In the bakery
example the design capacity is 30 custom cakes per day. Currently
the bakery is producing 28 cakes per day. What is the bakery’s
capacity utilization relative to both design and effective capacity?
Utiliz atio
n effective 
Utiliz atio
n design


actual output
28
(100%)  (100%)  140%
e ffe ctivecapacity
20
actual output
28

(100%)  (100%)  93%
de sign capacity
30
The current utilization is only slightly below its design
capacity and considerably above its effective capacity
The bakery can only operate at this level for a short period
of time
7
How Much Capacity Is Best?


The Best Operating Level is the output that results in
the lowest average unit cost
Economies of Scale:



Where the cost per unit of output drops as volume of output
increases
Spread the fixed costs of buildings & equipment over multiple
units, allow bulk purchasing & handling of material
Diseconomies of Scale:


Where the cost per unit rises as volume increases
Often caused by congestion (overwhelming the process with too
much work-in-process) and scheduling complexity
8
Best Operating Level and Size


Alternative 1: Purchase one large facility, requiring one large
initial investment
Alternative 2: Add capacity incrementally in smaller chunks as
needed
9
Other Capacity Considerations

Focused factories:


Plant within a plant (PWP):


Segmenting larger operations into smaller
operating units with focused objectives
Subcontractor networks:


Small, specialized facilities with limited
objectives
Outsource non-core items to free up
capacity for what you do well
Capacity cushions:

Plan to underutilize capacity to provide
flexibility
10
Making Capacity Planning Decisions

The three-step procedure for making
capacity planning decisions is as
follows:

Step 1: Identify Capacity Requirements

Step 2: Develop Capacity Alternatives

Step 3: Evaluate Capacity Alternatives
11
Good forecasts are essential for effective
capacity planning.
But so is an understanding of demand
uncertainty because it allows you to judge the
risks to service level.
Only 5% chance of demand
being higher than this
DEMAND
DEMAND
Distribution of demand
Only 5% chance of demand
being lower than this
TIME
TIME
When demand uncertainty is high the risks to service
level of under provision of capacity are high.
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Forecasting Steps

What needs to be forecast?


What data is available to evaluate?



Identify needed data & whether it’s available
Select and test the forecasting model


Level of detail, units of analysis & time horizon
required
Cost, ease of use & accuracy
Generate the forecast
Monitor forecast accuracy over time
13
Types of Forecasting Models

Qualitative methods:


Forecasts generated subjectively by the
forecaster
Quantitative methods:

Forecasts generated through mathematical
modeling
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Quantitative Methods

Time Series Models:


Assumes the future will follow same patterns as
the past
Causal Models:



Explores cause-and-effect relationships
Uses leading indicators to predict the future
E.g. housing starts and appliance sales
15
Time Series Data Composition

Data = historic pattern + random variation

Historic pattern to be forecasted:


Level (long-term average)

Trend

Seasonality

Cycle
Random Variation cannot be predicted
16
Time Series Patterns
17
Causal Models




Often, leading indicators can help to predict
changes in future demand e.g. housing starts
Causal models establish a cause-and-effect
relationship between independent and dependent
variables
A common tool of causal modeling is linear
regression:
Y  a  bx
Additional related variables may require multiple
regression modeling
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Linear Regression

b
 XY  X  Y 
 X 2  X  X 

Identify dependent (y) and
independent (x) variables
Solve for the slope of the
line
XY  n XY

b
 X  nX
2

2
Solve for the y intercept
a  Y  bX

Develop your equation for
the trend line
Y=a + bX
19
Linear Regression Problem: A maker of golf shirts has been
tracking the relationship between sales and advertising dollars. Use
linear regression to find out what sales might be if the company
invested $53,000 in advertising next year.
1
Sales $
(Y)
Adv.$
(X)
XY
130
32
4160
X^2
Y^2
XY  n XY

b
 X  nX
2
2304 16,900
2
28202 447.25147.25
2
151
52
7852
2704 22,801
b
3
150
50
7500
2500 22,500
4
158
55
8690
3025 24964
5
153.85
53
a  Y  b X  147.25 1.1547.25
a  92.9
Y  a  bX  92.9 1.15X
Y  92.9 1.1553  153.85
Tot
589
189
9253 447.25
2
 1.15
28202 9253 87165
Avg 147.25 47.25
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How Good is the Fit?

Correlation coefficient (r) measures the direction and strength of the linear
relationship between two variables. The closer the r value is to 1.0 the better
the regression line fits the data points.
r
r
n XY    X  Y 

n X
2
   X
2

* n Y
2
  Y 
2
428,202  189589
4(9253)- (189) * 487,165  589
2
2
 .982
r 2  .982  .964
2

2
Coefficient of determination ( r ) measures the amount of variation in the
dependent variable about its mean that is explained by the regression line.
2
Values of ( r ) close to 1.0 are desirable.
21
How do you cope with
fluctuations in demand?
Absorb
Demand
Adjust
output to
match
demand
Change
demand
22
Absorb
demand
Have
excess
capacity
Keep output
level
Make
to
stock
Part finished,
Finished Goods, or
Customer Inventory
Make
customer
wait
Queues
Backlogs
23
Types of Aggregate Plans

Level Aggregate Plans





Maintains a constant workforce
Sets capacity to accommodate average demand
Often used for make-to-stock products like appliances
Disadvantage- builds inventory and/or uses back orders
Chase Aggregate Plans



Produces exactly what is needed each period
Sets labor/equipment capacity to satisfy period demands
Disadvantage- constantly changing short term capacity
24
Absorb Demand

Level capacity plan
›
Anticipation inventory
25
Principles of the Chase Method

The chase method helps firms match
production and demand by hiring and
firing workers as necessary to control
output
26
Adjust output to match
demand

›
Chase capacity
plan
Adjustment methods




Overtime & idle time
Workforce size
Part-time staff
Sub-contracting
27
The tasks of capacity planning
Some key questions
Forecast Demand or
Revenue Potential
Can you predict the most likely demand at
any point in time?
Can you predict the uncertainty in demand
at any point in time?
Calculate Capability of
Operations Resources
Do you have realistic work standards??
Do you understand the capacity
constraints of all the necessary
resources?
Allocate Resources
Over Time
What are the options for capacity
allocation?
What are their cost, revenue, work capital
and service level implications?
What are their flexibility implications?
Design “Capacity
Control” Mechanisms
Do you monitor actual demand against
forecast?
Do you adapt forecasts accordingly?
Do you replan capacity accordingly?
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