Chapter 4
Capacity
Strategy
McGraw-Hill/Irwin
Operations Strategy
Copyright © 2008 The McGraw-Hill Companies, Inc. All rights reserved.
4-1
Some issues from Biotech industry
 Strategic impact of long lead times and
(often) high financial impact
 Models for timing and size
 Effects of variability
 Competitive factors
4-2
The Capacity Strategy Decision
 How much capacity should the company have
to cover expected demand in the short,
intermediate and long term?
 In what increments and when, or at what
intervals, should the company add capacity?
 What type of capacity should the company
add?
 Where in the value chain, internal or external
to the company, should capacity be added?
4-3
Some Capacity Definitions
 Capacity: volume of output per period of time
 Maximum or design capacity: the highest rate
of output that a process or activity can
theoretically achieve
 Effective or planned capacity: the output rate
expected for a given activity or process
 Demonstrated capacity: the actual level of
output for a process or activity over time
 Capacity utilization: the percentage of a
facility’s maximum or effective capacity used
by actual production
4-4
Means of Adding Capacity






Human resources
Process technology
Information technology
Facilities
Suppliers or subcontractors
Extracting additional output from existing
resources:
 Quality improvement
 Process optimization
4-5
Capacity Management Time
Horizons
4-6
Capacity Expansion Alternatives Using
Different Time Increments
4-7
Capacity models: The Lead, Lag,
Stay-Even Model
Lead Policy
Volume
Predicted demand
Lag Policy
Stay Even Policy
Time
4-8
The Lead, Lag, Stay-Even Model:
Choosing Among Them
4-9
Economies and Diseconomies of Scale
in Facilities
4-10
Sizing Capacity Increments when Demand is
Known or Certain: Optimum Expansion Intervals
4-11
Sizing Capacity Increments when Demand is
Known or Certain: Guidelines
 As discount rates rise, add capacity in smaller
increments
 Future expenditures on capacity are relatively less
expensive
 Thus, delaying expenditures is more economical
 As scale factors rise, add capacity in smaller
increments
 Cost per increment of capacity goes down
 Making larger investments in capacity up front
isn’t worth it
4-12
Choosing Between Capacity Expansion
Internally and at a Contractor
4-13
Lead time impact
4-14
Hedging for uncertainty – For
specific time in future
 Inventory problem of
 Capacity during a time period (e.g.. kg. per year)
or, equivalently
 Physical size of equipment (liters)
 Set service level or use costs of not meeting
capacity or falling short
 Can use the newsvendor approach
4-15
Capacity expansion in Biotech
 Costs of not meeting demand are extremely large!
 Costs of extra capacity are large, but two orders of
magnitude lower
 Use newsvendor approach of costs of underage and overage
 Co = Cost of overage, or cost of having one too many units of
capacity
 Cu = Cost of underage, or cost of having one too few units of
capacity
 Find z such that P(d<z) = Cu/(Co+Cu)
 For Genentech, this is 99.05%
4-16
Concept: Find percentile corresponding to
cost balance point (critical fractile or
percentile)
Area equal to
percentile
Demand corresponding
To critical percentile
4-17
Concept: Find percentile corresponding to cost
balance point (critical fractile or percentile)
Area equal to percentile
Demand corresponding
To critical percentile
4-18
Normal distribution is an easy way to determine
the appropriate demand levels
84% of area
Under curve
(Z=1)
CSL
84%
90%
1.64
2.33
Z
1
1.28
95%
99%.
Calculate required capacity as:
Average demand + z * standard
deviation of demand
4-19
Note that high services greatly increase
capacity!
Exhibit 4-24: Capacity Required as Service Level Increases
5000
Capacity (Units)
4500
4000
3500
3000
2500
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
Service Level
4-20
How do we generalize for any lead time and
also when the time is uncertain?
 Required capacity increase = Expected demand growth over the lead
time + z * σgL
 σgL2 = E(L) * σg2 + (E(g)) 2 * σL2
 σgL = SQRT (E(L) * σg2 + (E(g)) 2 * σL2)
 Where




E(L) = expected lead time
σL = standard deviation of lead time
E(g) = expected growth
σg = standard deviation of growth
 Example: 2% growth, 5 year lead, 5% uncertainty in five years, 98%
service
 Increase = 5x2% + 1.96x5% = 19.8% increase
4-21
Capacity Expansion under Uncertainty:
Decision Analysis Models
4-22
Capacity Expansion under Uncertainty:
Multistage Decision Analysis Models
4-23
Example for Genentech
Lung,
Breast
approved
Lung
approved
Build
CCP3
Both
approved
Large
expansion
Neither
approved
Lung,
Breast
approved
Option?
.
.
.
.
.
4-24
Capacity Management and
Flexibility
 Capacity that can be used for multiple uses is more efficient
in terms of hedging (safety capacity)
 This can be in terms of flexible capacity or through
postponement (stock components not finished goods)
 Example: Suit one: sigma = 680
Suit two: sigma = 646
2.33 x sum = 3085 (99% service)
Sigma for total demand is 938 (law of
large numbers
Pooled capacity yields 2.33x938 = 2186
4-25
Competition and Gaming with
Capacity
4-26
Developing a Capacity Strategy
 Understand the business strategy and
competitive environment
 Develop a demand forecast
 Identify capacity expansion (or contraction)
alternatives
 Apply relevant models to develop capacity
strategy
 Assess implications for flexibility and balance
 Develop an implementation plan
 Implement, assess and measure results
4-27