LECTURE 26
LSM733-PRODUCTION
OPERATIONS MANAGEMENT
By: OSMAN BIN SAIF
1
Summary of last Session
 JIT Schedulling
 Kanban
 Toyota Production System
 Lean Operations
2
Summary of last Session(Contd.)
CHAPTER : Maintenance and Reliability
Operations
 Global Company Profile: Orlando Utilities
Commission
 The Strategic Importance of
Maintenance and Reliability
 Reliability
 Improving Individual Components
 Providing Redundancy
3
Summary of last Session(Contd.)
 Maintenance
 Implementing Preventive Maintenance
 Increasing Repair Capabilities
4
Agenda for this Session
 Total Productive Maintenance
 Techniques for Enhancing
Maintenance
5
Agenda for this Session (Contd.)
CHAPTER : Decision Modeling
 Decision Making & Models.
 Decision Tables.
– Decision making under uncertainty.
– Decision making under risk.
– Expected value of perfect information (EVPI).
6
Maintenance Costs
 The traditional view attempted to balance
preventive and breakdown maintenance
costs
 Typically this approach failed to consider
the true total cost of breakdowns



Inventory
Employee morale
Schedule unreliability
7
Maintenance Costs
Total
costs
Costs
Preventive
maintenance
costs
Breakdown
maintenance
costs
Maintenance commitment
Optimal point (lowest
cost maintenance policy)
Traditional View
Figure 17.4 (a)
8
Maintenance Costs
Total
costs
Costs
Full cost of
breakdowns
Preventive
maintenance
costs
Maintenance commitment
Optimal point (lowest
cost maintenance policy)
Full Cost View
Figure 17.4 (b)
9
Maintenance Cost Example
Should the firm contract for maintenance on their printers?
Number of
Breakdowns
Number of Months That
Breakdowns Occurred
0
2
1
8
2
6
3
4
Total: 20
Average cost of breakdown = $300
10
Maintenance Cost Example
1.
Compute the expected number of breakdowns
Number of
Breakdowns
Frequency
Number of
Breakdowns
Frequency
0
2/20 = .1
2
6/20 = .3
1
8/20 = .4
3
4/20 = .2
Expected number of
breakdowns
=
∑
Number of
breakdowns
x
Corresponding
frequency
= (0)(.1) + (1)(.4) + (2)(.3) + (3)(.2)
= 1.6 breakdowns per month
11
Maintenance Cost Example
2.
Compute the expected breakdown cost per month with no preventive
maintenance
Expected
breakdown cost
=
Expected number of
breakdowns
x
Cost per
breakdown
= (1.6)($300)
= $480 per month
12
Maintenance Cost Example
Compute the cost of preventive maintenance
Preventive
maintenance cost
=
3.
Cost of expected
breakdowns if service
contract signed
+
Cost of
service contract
= (1 breakdown/month)($300) + $150/month
= $450 per month
Hire the service firm; it is less expensive
13
Increasing Repair Capabilities
1.
2.
3.
4.
5.
6.
Well-trained personnel
Adequate resources
Ability to establish repair plan and priorities
Ability and authority to do material planning
Ability to identify the cause of breakdowns
Ability to design ways to extend MTBF
14
How Maintenance is Performed
Operator
Maintenance
department
Manufacturer’s field
service
Depot service
(return equipment)
Competence is higher as we
move to the right
Preventive
maintenance costs less and
is faster the more we move to the left
Figure 17.5
15
Total Productive Maintenance (TPM)
 Designing machines that are reliable, easy to operate,
and easy to maintain
 Emphasizing total cost of ownership when purchasing
machines, so that service and maintenance are
included in the cost
 Developing preventive maintenance plans that utilize
the best practices of operators, maintenance
departments, and depot service
 Training workers to operate and maintain their own
machines
16
Establishing Maintenance Policies
 Simulation
 Computer analysis of complex situations
 Model maintenance programs before they are implemented
 Physical models can also be used
 Expert systems
 Computers help users identify problems and select course of action
17
CHAPTER : Decision Modeling
18
The Decision-Making Process
Quantitative Analysis
Problem
Logic
Historical Data
Marketing Research
Scientific Analysis
Modeling
Decision
Qualitative Analysis
Emotions
Intuition
Personal Experience
and Motivation
Rumors
19
Models and Scientific Management
• Can Help Managers to:
– Gain deeper insights into the business.
– Make better decisions!
• Better assess alternative plans and actions.
– Quantify, reduce and understand the
uncertainty surrounding business plans
and actions.
20
Steps to Good Decisions
•
•
•
•
Define problem and influencing factors.
Establish decision criteria.
Select decision-making tool (model).
Identify and evaluate alternatives using
decision-making tool (model).
• Select best alternative.
• Implement decision.
• Evaluate the outcome.
21
Benefits of Models
• Allow better and faster decisions.
• Less expensive and disruptive than
experimenting with the real world system.
• Allow managers to ask “What if…?” questions.
• Force a consistent and systematic approach to
the analysis of problems.
– Require managers to be specific about constraints and
goals.
22
Limitations of Models
• May be expensive and time-consuming to
develop and test.
• May be unused, misused or misunderstood (and
feared!).
– Due to mathematical and logical complexity.
• May downplay the value of qualitative
information.
• May use assumptions that oversimplify the real
world.
23
Decision Theory
Terms:
Alternative: Course of action or choice.
Decision-maker chooses among alternatives.
State of nature: An occurrence over which the
decision maker has no control.
24
Decision Table
States of Nature
State 1
State 2
Alternative 1
Outcome 1
Outcome 2
Alternative 2
Outcome 3
Outcome 4
A-25
Example - Decision Making Under
Uncertainty
A firm has two options for expanding production of a product:
(1) construct a large plant; or (2) construct a small plant.
Whether or not the firm expands, the future market for the
product will be either favorable or unfavorable.
If a large plant is constructed and the market is favorable, then
the result is a profit of $200,000. If a large plant is constructed
and the market is unfavorable, then the result is a loss of
$180,000.
If a small plant is constructed and the market is favorable, then
the result is a profit of $100,000. If a small plant is constructed
and the market is unfavorable, then the result is a loss of
$20,000. Of course, the firm may also choose to “do nothing”,
which produces no profit or loss.
26
Example - Decision Making Under
Uncertainty
States of Nature
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Market
$200,000
Market
-$180,000
$100,000
-$20,000
$0
$0
27
Decision Making Under Uncertainty Criteria
• Maximax - Choose alternative that maximizes
the maximum outcome for every alternative
(Optimistic criterion).
• Maximin - Choose alternative that maximizes
the minimum outcome for every alternative
(Pessimistic criterion).
• Expected Value - Choose alternative with the
highest expected value.
28
Example - Maximax
States of Nature
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Market
$200,000
Market
-$180,000
$100,000
-$20,000
$0
$0
Maximax decision is to construct large plant.
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Example - Maximin
States of Nature
Market
$200,000
Market
-$180,000
Minimum
in Row
-$180,000
$100,000
-$20,000
-$20,000
$0
$0
$0
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Maximin decision is to do nothing.
(Maximum of minimums for each alternative)
30
Decision Making Under Risk
• Probabilistic decision situation.
• States of nature have probabilities of
occurrence.
• Select alternative with largest expected
value (EV).
– EV = Average return for alternative if decision
were repeated many times.
31
Expected Value Equation
Number of states of nature
N
EV
Value of Payoff
( A i ) =  V i *P (V i )
Probability of payoff
i =1
= V1 *P (V1 ) + V 2 * P (V 2 ) + ... +V N * P (V N )
Alternative i
32
Example - Expected Value
Suppose: Probability of favorable market = 0.5
Probability of unfavorable market = 0.5
States of Nature
Market
$200,000
Market
-$180,000
Expecte
d Value
$10,000
$100,000
-$20,000
$40,000
$0
$0
$0
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Decision is to “Construct small plant”.
33
Example - Expected Value
Suppose: Probability of favorable market = 0.7
Probability of unfavorable market = 0.3
States of Nature
Market
$200,000
Market
-$180,000
Expecte
d Value
$86,000
$100,000
-$20,000
$64,000
$0
$0
$0
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Now, decision is to “Construct large plant”.
34
Example - Expected Value
Over what range of values for probability of favorable market is “Construct
large plant” preferred?
States of Nature
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Expected Value
Market
$200,000
Market
-$180,000
380,000x - 180,000
$100,000
-$20,000
120,000x - 20,000
$0
$0
Solve for x: 380000x-180000 > 120000x-20000
35
Example - Expected Value
Over what range of values for probability of favorable market is “Construct large
plant” preferred?
Solve for x: 380000x - 180000 > 120000x - 20000
x > 0.6154
So, as long as probability of a favorable market exceeds 0.6154, then
“Construct large plant”.
36
Expected Value of Perfect
Information (EVPI)
• EVPI places an upper bound on what one
would pay for additional information.
– EVPI is the maximum you should pay to learn
the future.
• EVPI is the expected value under certainty
(EVUC) minus the maximum EV.
EVPI = EVUC - maximum EV
37
Expected Value Under Certainty
(EVUC)
EVUC
n
=  (Best outcome for the state of nature j) * P(S )
j
j =1
where:
P(Sj ) = The probability of state of nature j.
n = Number of states of nature.
38
Example - EVUC
States of Nature
Alternatives Favorable Unfavorable
Construct
large plant
Construct
small plant
Do nothing
Market
$200,000
Market
-$180,000
$100,000
-$20,000
$0
$0
Best outcome for Favorable Market = $200,000
Best outcome for Unfavorable Market = $0
39
Expected Value of Perfect
Information
Suppose: Probability of favorable market = 0.5
Probability of unfavorable market = 0.5
EVPI = EVUC - max(EV)
= ($200,000*0.50 + 0*0.50) - $40,000
= $60,000
Thus, you should be willing to pay up to $60,000 to
learn whether the market will be favorable or not.
40
Expected Value of Perfect
Information
Now suppose: Probability of favorable market = 0.7
Probability of unfavorable market = 0.3
EVPI = EVUC - max(EV)
= ($200,000*0.70 + 0*0.30) - $86,000
= $54,000
Now, you should be willing to pay up to $54,000 to
learn whether the market will be favorable or not.
41
Summary of the Session
 Total Productive Maintenance
 Techniques for Enhancing
Maintenance
42
Summary of the Session (Contd.)
CHAPTER : Decision Modeling
 Decision Making & Models.
 Decision Tables.
– Decision making under uncertainty.
– Decision making under risk.
– Expected value of perfect information (EVPI).
43
THANK YOU
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