The document discusses a plastic manufacturing facility that produces items like toilet components. It outlines work to optimize the facility layout and logistics. Response surface methodology was used to determine the optimal number of forklifts and transpallet vehicles needed. Simulation results found that 7 transpallet vehicles and 2 forklifts provided the highest production output. Alternative systems were also analyzed.

1. MERT YİĞİT
EDA ACAR MERVE KILIÇ

2.  Founded in 1986 and located in Torbalı İzmir
 Produces plastic materials ; flush tanks, internal
mechanisms, toilet seats etc…
 Eighteen injection machines
 Five assembly lines

4. Work steps
 Modelling current system
 Facility layout optimization and design
 Response surface methodology

5. System is simulated over two product as realistic as
possible by using Arena software
Feasible layout design is obtained by using Flap
Optimum number of forklift and transpalet are
determined by using RSM

6. Before modelling
 Work flow
 Bill of material
 Operation trees
 Time studies
 Input analysis
 Cycle times
 Break downs
 Defect rates
 Size of the boxes and cages

7. Defect rates calculations;

8. Defect Rates of Machines

9. Breakdowns of machine 8

10. Machine breakdowns

11. Product tree

12. Time study of Hero Gömme Rezervuar

13. Input analysis

14. Components are carried ;
 boxes or cages
228 cages and
1000 blue boxes
Capacity of this boxes and cages for each component is
identified with a study in stocking area by portable
terminals.

15. Cage capacity

16. Blue box capacity

17. Contd’
 Distances between each stations both current and
layout optimized system
 Velocity, loading and unloading times of forklift and
transpallet
 Relations between each units

18. Distance calculation
An example;

19. Simulation model composed of;
 Lüx Asma Rezervuar

20.  Hero Gömme Rezervuar

21. SIMULATION MODEL

22. Facility Layout Optimization

25. Relationship Ranks

26. Activity Relationship Chart

27. •Recycling Station and Raw Material
are spread along 14 units
•Semi Product Warehouse I
s spread along 40 units
•Finished Good Warehouse is
spread along 25 units
•Offices are spread along 40units
•Assembly Line 1 is spread along 10 units
•Assembly Line 2 is spread along 15 units
•18 Injection Machines are
spread along 72 units
•1 machine is spread along 4 units

28. FLAP

29. FLAP

34.  Response surface methodology (RSM) is a
collection of mathematical and statistical
techniques that are useful for the modelling and
analyzing problems in which a response of interest
is influenced by several variables and the aim is to
optimize this response.

35.  In our case the response is production amount
which affected by number of transpallet and forklift.
 The production amount is response variable of y and
independent variables are number of transpallet
and forklift
 The finished goods which quits the injection
machines are taken into account when they arrive
the warehouse.
 Hence in simulation model the importance of
transportation units is incontrovertible.

36. In order to develop first order metamodel…
Transpallet Forklift
 Number of transpallet is  Number of forklift is defined
defined as 7 as center. as 2 as center.
 High level of number of  High level of number of
transpallet is defined as 10. forklift is defined as 3.
 Low level of number of  Low level of number of
transpallet is defined as 3. forklift is defined as 1.

37. Design Matrix with
Structure of Design MatrixCorresponding Replication
Results
Transpalet(X1) Forklift(X2) R1 R2 R3 R4 R5
10 3 60500 54300 59900 57500 57500
10 1 58400 58700 59800 58400 59100
3 3 60900 59700 60000 57500 61400
3 1 58100 57500 59700 56400 60700
10 2 57500 61100 57500 60300 57500
3 2 60900 56400 57500 50900 50900
7 3 59200 61100 56400 61300 58200
7 1 61900 59500 61000 56000 58300
7 2 60900 59100 60600 59000 61500
Regression metamodel is developed and analyzed by using MINITAB 15.

38. First Order Metamodel
 The regression equation is:
Y = 55564 + 448 X1 + 1195 X2 - 170 X1X2
S = 2467,20 R-Sq = 4,4%
R-Sq(adj) = 0,0%

39.  R-Sq = coeffcient of determination
 R-Sq expresses that the part of the variability of the
dependent variable with the variability of the
independent variable.
 R-Sq = 4,4% 4,4 % of the variability on the
production amount can be explain by the variability of
the number of transportation units.

40. Transpalet(X1) Forklift(X2) R1 R2 R3 R4 R5
10 3 60500 54300 59900 57500 57500
10 1 58400 58700 59800 58400 59100
3 3 60900 59700 60000 57500 61400
3 1 58100 57500 59700 56400 60700
10 2 57500 61100 57500 60300 57500
3 2 60900 56400 57500 50900 50900
7 3 59200 61100 56400 61300 58200
7 1 61900 59500 61000 56000 58300
7 2 60900 59100 60600 59000 61500
• Some changes on the number of transpallet and forklift
seems do not affect the production amounts seriously.
• Besides, sometimes it is observed that more production
amounts in the system in which less transporter units are
used.

41. • If the product is carried with box, the choice of transporter (transpallet or
forklift) is depends on the decision of the operator.
• The criterion of the selection is determined in terms of probability because the
exact criterion of that decision is not certain.
• This decision is based on the assumption that if the product is carried with
box it should be transferred by transpallet with % 50 probability and
transferred by forklift with % 50 probability.
• Since transpallet and forklift have different carrying capacities for
boxes, process times are entered to the system as distribution, not constant.
• Hence, variety in production amount is acceptable. If we look at only amounts
of products which are transported with cages we can see the direct proportion
between number of transporters and production quantity.

43. • Assumptions of Regression
– Normality of the error terms: Error terms must be normally
distributed.
– Constant variance: The variance of distribution of the error
terms must be constant.
– Independency of the error terms: Error terms must be
statistically independent.

44.  Normality of the Error
Terms
 Analysis of Linearity
 Residual Analysis for
Constant Variance
(Homoscedasticity)
 Residual Analysis for
Independency

45.  The regression equation is:
Y = 54749 + 1880 X1 - 2232 X2 - 112 X1^2 + 857 X2^2 -
170 X1X2
S = 2398,98 R-Sq = 14,1%
R-Sq(adj) = 3,0%

46. • Since increase on R-Sq and R-Sq(adj) is observed and p
value is decreased, it is decided to continue the RSM
process from this second order model

47.  Canonical form
 The regression equation is
Y = 57012 + 1540 X1 - 3363 X2 - 112 X1^2 + 857 X2^2
S = 2406,00 R-Sq = 11,3%
R-Sq(adj) = 2,5%

48.  Canonical analysis is implemented considering the
stationary points.

49. • Number of forklift and transpallet should be integer. Hence:

50. • are known as eigenvalues
or characteristic roots of the
matrix B.
• If the all are
negative, is a point of
maximum response,
• If the all are
positive, is a point of
minimum response,
• If the have different
signs, is a saddle
point.

51.  However in our model
is a point of maximum
response.
Transpalet (X1) Forklift (X2) R1 R2 R3 R4 R5 Average
10 3 60500 54300 59900 57500 57500 57940
10 1 58400 58700 59800 58400 59100 58880
3 3 60900 59700 60000 57500 61400 59900
3 1 58100 57500 59700 56400 60700 58480
10 2 57500 61100 57500 60300 57500 58780
3 2 60900 56400 57500 50900 50900 55320
7 3 59200 61100 56400 61300 58200 59240
7 1 61900 59500 61000 56000 58300 59340
7 2 60900 59100 60600 59000 61500 60220
7 transpallets and 2 forklifts provide the highest amounts of production units.

52.  Design table
Transpalet(X1) Forklift(X2) R1 R2 R3 R4 R5
10 3 59600 62400 62700 60400 60200
10 1 58600 59300 61000 62500 60200
3 3 60700 57800 61500 55800 62200
3 1 62900 54600 60800 62200 62200
10 2 60100 61900 62700 62500 60200
3 2 62900 57800 61500 61300 62200
7 3 61300 62900 60200 63000 63000
7 1 59500 62200 60200 62100 63900
7 2 62100 61900 60200 61700 63000
 First order metamodel
Y = 62008 - 157 X1 - 772 X2 + 123 X1X2

53.  S = 1946,67 R-Sq = 4,2%
R-Sq(adj) = 0,0%

54.  Facility layout optimization and design implemented
model has the same situation of the model which has
no implementation of the facility layout.
 Because of the system is already optimized the R-Sq
and R-Sq(adj) are very low.
 However we continue with the Second order
metamodel:
 The regression equation is
Y = 57019 + 1005 X1 + 1642 X2 - 90,5 X1^2 - 603
X2^2 + 123 X1X2

55. • S = 1894,81 R-Sq = 13,7%
R-Sq(adj) = 2,6%

56.  Canonical form
 The regression equation is
Y = 55376 + 1251 X1 + 2463 X2 - 90,5 X1^2 - 603 X2^2
 S = 1895,84 R-Sq = 11,4%
R-Sq(adj) = 2,5%

57. • Canonical analysis is implemented considering the
stationary points.

58. • Number of forklift and transpallet should be integer. Hence

59. • are known as eigenvalues
or characteristic roots of the
matrix B.
• If the all are
negative, is a point
of maximum response,
• If the all are
positive, is a point of
minimum response,
• If the have different
signs, is a saddle point.

60. • It is proven that the 7 transpallets
and 3 forklifts are efficient to
increase production amount by
running the simulation model with
upgrading number of forklift and
transpallet.
Transpalet(X1) Forklift(X2) R1 R2 R3 R4 R5 Average
10 3 59600 62400 62700 60400 60200 61060
10 1 58600 59300 61000 62500 60200 60320
3 3 60700 57800 61500 55800 62200 59600
3 1 62900 54600 60800 62200 62200 60540
10 2 60100 61900 62700 62500 60200 61480
3 2 62900 57800 61500 61300 62200 61140
7 3 61300 62900 60200 63000 63000 62080
7 1 59500 62200 60200 62100 63900 61580
7 2 62100 61900 60200 61700 63000 61780

62.  Current System • Alternative System 2
LAYOUT NOT OPTIMIZED LAYOUT NOT OPTIMIZED
RESPONSE SURFACE METHODOLOGY NOT APPLIED RESPONSE SURFACE METHODOLOGY APPLIED
NUMBER OF FORKLIFT 1 NUMBER OF FORKLIFT 2
NUMBER OF TRANSPALLET 6 NUMBER OF TRANSPALLET 7
TOTAL: 60000 TOTAL: 61500
Utilization of Forklift 85.412 Utilization of Forklift 45.635
Utilization of Transpallet 59.680 Utilization of Transpallet 59.768
• Alternative System 1 • Alternative System 3
LAYOUT OPTIMIZED LAYOUT OPTIMIZED
RESPONSE SURFACE METHODOLOGY NOT APPLIED RESPONSE SURFACE METHODOLOGY APPLIED
NUMBER OF FORKLIFT 1 NUMBER OF FORKLIFT 3
NUMBER OF TRANSPALLET 6 NUMBER OF TRANSPALLET 7
TOTAL: 62900 TOTAL: 63000
Utilization of Forklift 97.325 Utilization of Forklift 37.325
Utilization of Transpallet 59.505 Utilization of Transpallet 59.505

64.  Firstly works are begining modeling the current
system as realistic as possible with Simulation
software Arena.
 After determining the most produced product of
the factory, All the data which are related to
production such as time studies , bill of materials,
downtimes, defect rates, cycle times are taken.
 The second step of the work is adding second
product which is Hero Gömme Reservoir in
simulation model.

65.  While doing this works in addition to the first step
of the work
 Transportation times and distances informations
are added to the model.
 According to product type cage and box capacities
are determined.
 Relationship chart is constructed and Facility
Layout Optimization and Design is applied by
using Flap.
 Response Surface Methodology are used to
determine the optimum number of transpalet and
forklift in factory.

67.  Work orders can be created according to customer
orders by this way WIP can be decreased and facility
becomes appropriate for Lean philosophy.
 In this system injection machines work independently and
work orders are constant.
 Schedule decision criteria can be added to simulation
model.
 There is no scheduling method for assignment of work orders
to the machines.

68.  There are two more lines in the real system. These can
be added.
 We added two assembly lines
 If simulation model decides which product will be
produced by checking demands and arranges which
product is going to be produced, how much, etc…
Modelcan be replicated monthly so, it can be analyzed
in long-term
 Instead of daily