Simulasi sistem
persediaan
Outline
• What is Simulation?
• Advantages and Disadvantages of
Simulation
• Monte Carlo Simulation
• Simulation and Inventory Analysis
• The Role of Computers in Simulation
Learning Objectives
When you complete this chapter, you should be able
to Identify or Define:
– Monte Carlo simulation
– Random numbers
– Random number interval
– Simulation software
Explain or be able to use:
– The advantages and disadvantages of modeling
with simulation
– The use of Excel spreadsheets in simulation
Simulation
• Numerical technique of experimentation
• Attempts to duplicate a system
– Features
– Behavior
• Requires description of system
• Many application areas
– Operations management
– Finance & economics
Some Applications of Simulation
Ambulance location and dispatching
Bus scheduling
Assembly-line balancing
Design of library operations
Parking lot and harbor design
Taxi, truck, and railroad
dispatching
Distribution system design
Production facility scheduling
Scheduling aircraft
Plant layout
Labor-hiring decisions
Capital investments
Personnel scheduling
Production scheduling
Traffic-light timing
Sales forecasting
Voting pattern prediction
Inventory planning and control
Simulation
The idea behind simulation is to:
• Imitate a real-world situation
mathematically
• Study its properties and operating
characteristics
• Draw conclusions and make action
recommendations based on the results
of the simulation
The Process of Simulation
Define the
Problem
Introduce
important
variables
Construct
simulation
model
Specify values of
variables to be
tested
Conduct the
simulation
Examine the
results
Select best
course of
action
Advantages of Simulation
– flexible, straightforward
– can analyze large, complex real-world
problems for which no closed-form
analytical solutions exists
– can include real-world complications
which most other techniques cannot
– enables “time compression”
– allows “what if” type questions
– does not interfere with the real-world
system
– allows study of relationships
Disadvantages of Simulation
Simulation:
• Can be expensive
and time consuming
• Does not yield optimal
solution
• Requires good
managerial input
• Results not
generalizable to other
situations
© 1984-1994 T/Maker Co.
The Monte Carlo
Simulation Technique
• Setup probability distribution for important
variables
• Build cumulative distribution for each variable
• Establish interval of random numbers for
each variable
• Generate random numbers
• Simulate a series of trials
Partial Table of Random
Numbers
(upper left corner)
52
37
06
63
50
28
88
02
53
74
30
35
10
24
47
03
99
29
37
60
66
74
91
85
35
90
82
69
98
96
33
50
88
90
50
27
45
57
02
94
52
69
33
32
30
48
88
14
68
36
90
62
27
50
18
36
61
21
46
28
49
36
87
21
95
50
24
18
62
32
05
71
06
49
11
13
62
60
85
69
13
94
99
78
56
60
44
57
82
23
64
49
03
32
23
49
95
34
34
51
08
48
66
11
10
67
23
89
62
56
74
54
31
62
27
75
89
78
68
63
62
30
17
12
74
79
21
85
71
48
39
31
35
12
73
41
90
95
29
72
17
55
15
36
80
02
86
87
90
21
90
89
29
40
85
69
68
98
92
94
25
57
34
30
90
01
24
00
92
Real World Variables Which Are
Probabilistic in Nature
•
•
•
•
•
•
•
Inventory demand
Lead time for orders to arrive
Time between machine breakdowns
Times between arrivals at a service facility
Service times
Times to complete project activities
Number of employees absent from work each
day
Simulation and Inventory
Analysis - the Basic Model
demand >
begin inv?
Begin
# of
lost sales
end inv =
begin-demand
Order
arrived?
End inv = 0
End inv < reorder
point?
Increase
current inv
by qty order
random #
for today's
demand
Order placed
& not arrived?
Enough Days
in simulation?
Compute
averages
Place
order
Generate Random
lead time