Chapter 14
Sales and Operations Planning
Operations Management - 6th Edition
Roberta Russell & Bernard W. Taylor, III
Copyright 2009 John Wiley & Sons, Inc.
Beni Asllani
University of Tennessee at Chattanooga
Lecture Outline
 The Sales and Operations Planning
Process
 Strategies for Adjusting Capacity
 Strategies for Managing Demand
 Quantitative Techniques for Aggregate
Planning
 Hierarchical Nature of Planning
 Aggregate Planning for Services
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Sales and Operations Planning
 Determines the resource capacity needed to
meet demand over an intermediate time
horizon


Aggregate refers to sales and operations planning
for product lines or families
Sales and Operations planning (S&OP) matches
supply and demand
 Objectives


Establish a company wide game plan for allocating
resources
Develop an economic strategy for meeting
demand
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Sales and Operations Planning
Process
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Meeting Demand Strategies
 Adjusting capacity


Resources necessary to meet demand
are acquired and maintained over the
time horizon of the plan
Minor variations in demand are handled
with overtime or under-time
 Managing demand

Proactive demand management
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Strategies for Adjusting Capacity
 Level production
 Overtime and under-time
Producing at a constant rate
 Increasing or decreasing
and using inventory to
working hours
absorb fluctuations in
 Subcontracting
demand
 Let outside companies
 Chase demand
complete the work
 Hiring and firing workers to
 Part-time workers
match demand
 Hiring part time workers to
 Peak demand
complete the work
 Maintaining resources for
 Backordering
high-demand levels
 Providing the service or
product at a later time period

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Level Production
Demand
Units
Production
Time
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Chase Demand
Demand
Units
Production
Time
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Strategies for Managing Demand
 Shifting demand into
other time periods



Incentives
Sales promotions
Advertising campaigns
 Offering products or
services with countercyclical demand patterns
 Partnering with suppliers
to reduce information
distortion along the
supply chain
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Quantitative Techniques For AP





Pure Strategies
Mixed Strategies
Linear Programming
Transportation Method
Other Quantitative
Techniques
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14-10
Pure Strategies
Example:
QUARTER
Spring
Summer
Fall
Winter
Hiring cost
Firing cost
Inventory carrying cost
Regular production cost per pound
Production per employee
Beginning work force
Copyright 2009 John Wiley & Sons, Inc.
SALES FORECAST (LB)
80,000
50,000
120,000
150,000
= $100 per worker
= $500 per worker
= $0.50 pound per quarter
= $2.00
= 1,000 pounds per quarter
= 100 workers
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Level Production Strategy
Level production
(50,000 + 120,000 + 150,000 + 80,000)
= 100,000 pounds
4
QUARTER
Spring
Summer
Fall
Winter
SALES
FORECAST
80,000
50,000
120,000
150,000
PRODUCTION
PLAN
INVENTORY
100,000
100,000
100,000
100,000
400,000
Cost of Level Production Strategy
(400,000 X $2.00) + (140,00 X $.50) = $870,000
Copyright 2009 John Wiley & Sons, Inc.
20,000
70,000
50,000
0
140,000
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Chase Demand Strategy
QUARTER
SALES PRODUCTION
FORECAST
PLAN
Spring
Summer
Fall
Winter
80,000
50,000
120,000
150,000
80,000
50,000
120,000
150,000
WORKERS
NEEDED
80
50
120
150
WORKERS WORKERS
HIRED
FIRED
0
0
70
30
20
30
0
0
100
50
Cost of Chase Demand Strategy
(400,000 X $2.00) + (100 x $100) + (50 x $500) = $835,000
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14-13
Level Production with Excel
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Chase Demand with Excel
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Mixed Strategy
 Combination of Level Production and
Chase Demand strategies
 Examples of management policies


no more than x% of the workforce can be
laid off in one quarter
inventory levels cannot exceed x dollars
 Many industries may simply shut down
manufacturing during the low demand
season and schedule employee
vacations during that time
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Mixed Strategies with Excel
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Mixed Strategies with Excel
(cont.)
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General Linear Programming (LP)
Model
 LP gives an optimal solution, but demand
and costs must be linear
 Let





Wt = workforce size for period t
Pt =units produced in period t
It =units in inventory at the end of period t
Ft =number of workers fired for period t
Ht = number of workers hired for period t
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LP MODEL
Minimize Z = $100 (H1 + H2 + H3 + H4)
+ $500 (F1 + F2 + F3 + F4)
+ $0.50 (I1 + I2 + I3 + I4)
+ $2 (P1 + P2 + P3 + P4)
Subject to
Demand
constraints
Production
constraints
Work force
constraints
P1 - I1
I1 + P2 - I2
I2 + P3 - I3
I3 + P4 - I4
1000 W1
1000 W2
1000 W3
1000 W4
100 + H1 - F1
W1 + H2 - F2
W2 + H3 - F3
W3 + H4 - F4
Copyright 2009 John Wiley & Sons, Inc.
= 80,000
= 50,000
= 120,000
= 150,000
= P1
= P2
= P3
= P4
= W1
= W2
= W3
= W4
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
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Setting up the Spreadsheet
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The LP Solution
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Hierarchical Nature of Planning
Production
Planning
Capacity
Planning
Resource
Level
Product lines
or families
Sales and
Operations
Plan
Resource
requirements
plan
Plants
Individual
products
Master
production
schedule
Rough-cut
capacity
plan
Critical
work
centers
Components
Material
requirements
plan
Capacity
requirements
plan
All
work
centers
Manufacturing
operations
Shop
floor
schedule
Input/
output
control
Individual
machines
Items
 Disaggregation: process of breaking an aggregate plan into more detailed plans
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Collaborative Planning
 Sharing information and synchronizing
production across supply chain
 Part of CPFR (collaborative planning,
forecasting, and replenishment)

involves selecting products to be jointly
managed, creating a single forecast of
customer demand, and synchronizing
production across supply chain
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Rule Based ATP
Product
Request
Yes
Is the product
available at
this location?
No
Availableto-promise
Yes
Is an alternative
product available
at this location?
No
Allocate
inventory
Yes
Is this product
available at a
different
location?
No
Copyright 2009 John Wiley & Sons, Inc.
Is an alternative
product available
at an alternate
location?
Yes
No
Allocate
inventory
Capable-topromise date
Is the customer
willing to wait for
the product?
No
Availableto-promise
Yes
Revise master
schedule
Trigger production
Lose sale
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Aggregate Planning for Services
1.
2.
3.
4.
Most services cannot be inventoried
Demand for services is difficult to predict
Capacity is also difficult to predict
Service capacity must be provided at the
appropriate place and time
5. Labor is usually the most constraining
resource for services
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Yield Management
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Yield Management (cont.)
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Yield Management: Example
NO-SHOWS
PROBABILITY
P(N < X)
0
1
2
3
.15
.25
.30
.30
.00
.15
.40
.70
.517
Optimal probability of no-shows
Cu
75
P(n < x) 
=
= .517
Cu + Co
75 + 70
Hotel should be overbooked by two rooms
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Example: Perishable Inventory
 Selling Price: $12.95/unit
Cu=
 Cost: $5/unit
Co=
 Salvage Value: $0.50/unit
Demand
Probability
75,000
0.15
80,000
0.25
85,000
0.30
90,000
0.20
95,000
0.10
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P(n<X)
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