Discrete Event Simulation Analysis of a Gearbox Manufacturing Plant Using the Software ‘Witness’

ENGINEERING SIMULATION COURSEWORK 2; PDE 4905 May 1, 2015

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Middlesex University, London
School of Science and Technology
PDE 4905

Engineering Simulation

Coursework

For the Academic Year 2014-2015

Rifat Abdul Rahiman
M00514416


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Content

Abstract……………………………………………………………………………………………….…..03
Introduction……………………………………………………………………………………,……….03
Methods…………………………………………………………………………………………….…….04
Results………………………………………………………………………………………………….….07
Discussion…………………………………………………………………………………………….….14
Conclusion……………………………………………………………………………………………….14
Bibliography…………………………………………………………………………………………….15
Appendix…………………………………………………………………………………………………16


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A DISCRETE EVENT SIMULATION ANALYSIS OF A GEARBOX
MANUFACTURING PLANT USING THE SOFTWARE ‘WITNESS’

Abstract
Simulation technology and optimization has been combined in recent years which are used rapidly by various
industries. One such combination is the software Witness. Using the software Witness, a model for the
gearbox manufacturing plant was created and then simulated by conducting it to perform various tasks. Using
the Witness, a stochastic discrete event several companies have improved their functionality. From the given
model, when it was simulated the effect it has on the breakdown of its machines – CNC and conveyor belt, in
the plant and its performance financially was calculated. Here the breakdown done for both the machines is
done for 90 minutes. Results obtained from a series of tests done on the system and more details about the
breakdown are explained extensively in the report.

Introduction
Simulation is a cost effective methodology which is widely used in management science and operations
research. The implementation of given manufacturing process in to simulation model is a typical technique
used to analyse the process and its characteristics.
Simulation is a tool used typically to analyse and understand the characteristics of any set of industrial or
manufacturing processes in which it’s stochastic and dynamic systems can be tested upon. Furthermore, other
parameters like cost and lead time can be accurately understood with rapid configuration adjustments can be
made (Lanner, 2008). On the other hand simulation models are a function that assesses the value of any set of
specifications. They are optimized to observe and convert the input parameters into its potential output
results. Optimization can also be used to control the number of operators and machines in addition to the
management and maintenance of costs and resources respectively. Also cycle times can be minimized based
on the formulation of constraints (April, Glover, Kelly and Laguna, 2003).
When considering discrete problems, the methodology of stochastic approximation is usually applied because
of its relationship with gradient search. It is different from the continuous event simulation such that
instantaneous changes can take place in the system during discrete event simulation, while it is impossible to
change any parameters during continuous event simulation. Moreover this approximation mentioned focuses
mainly on continuous variable problems (April, Glover, Kelly and Laguna, 2003).
One of the simulation software used to process a discrete event simulation is WITNESS, developed by the
Lanner Group Inc. which is mainly used to simulate a production or manufacturing process to analyse and get
proper information on it. A gearbox manufacturing plant has been modelled using the WITNESS software
according to the given conditions and analysed to calculate the probable revenue obtained as per the budget
given. Parts like gears arrive from external suppliers after which it passes through the conveyor belt after
which it is assembled and then inspected followed by the shipment of final products. CNC machines are also
used in the production while defect products are led to the disassembling area.


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The complete model is designed and analysed to find the desired results which are described in the report
along with other breakdown characteristics of the system. The model designed consists of a plant where the
gears supplied arrives external source and fed into a conveyor belt of size 20m in length, whose maximum
capacity is always 180 parts with a maximum speed of 5m/min. Gears purchased are of size 80mm x 80mm x
100 mm in dimension. Similar to gears, bearings and castings are also procured which are fed into the
assembly and CNC units respectively. Likewise the conveyor belt also provides the assembly unit with gears
from which gearbox is manufactured.
Loading and unloading of parts to the machines are done continuously by an operator for which there is a
uniformly distributed cycle time. It is 0.5 – 1.0 minutes and 0.5 – 0.9 minutes respectively. The maximum
capacity of a bin is 50 while the CNC machining’s cycle time is 2.3 minutes. Inspection is done for every 20
th

gearbox passed and its cycle time is triangularly distributed with a minimum, maximum and a mode of 0.8, 2.2
and 1.1 minutes respectively. Assembling is done with two gears and two bearings by an operator which takes
a mean of 3 minutes with a standard deviation of 0.7 minutes, but no less than 2.2 minutes and no greater
than 5. This is followed by the assembly to be put at testing through buffer and then the test bench. It
processes for 10 minutes. An operator who does the loading takes from 0.2 to 0.4 minutes for loading and
unloading for each gearbox. It is also to be noted that as per the given description disassembly takes a mean of
2.3 minutes with standard deviation 0.4, and between 1.7 and 3.5 minutes.

Methods
The basis upon which the plant was modelled is by using Petri Net with a stochastic set up. Petri Net founded
by Carl Adam Petri in 1962 is tool used to create synchronized and concurrent model in distributed systems.
They are comprised of 3 types of components which include places, transitions and arcs. These are indicated as
circles, rectangles and arrows which represents state of the system, actions that result in change of state and
the connection between place and transition or vice versa respectively. It is to be noted that every place and
arc has a capacity and a weight respectively. This allows separate tokens to stay in a place to model more
complex systems (Ling, 2001).
The required model was created using the WITNESS simulation software. It consists of various components like
machines, buffers and parts. Among these buffers used in the system represent bins and test actions while
variables are also used to specify their actions. Initially the gears arrived externally is supplied to the assembly
through the conveyor set a fixed speed. Simultaneously along with the gears, bearings and casting are supplied
to the assembly and CNC respectively.


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A Typical example of a Petri Net Model Courtesy: Techfak

The assembly has a sequential input rule as casting, gear and bearing are pulled in as per the given sequence.
The function used to define the structure is

SEQUENCE /Wait Casting out of Bin#(1),
Gear out of Conveyor_Belt at Front#(2),
Bearings out of WORLD#(2)

Furthermore the final output from the assembly is to be defined as Gearbox where the function is
CHANGE ALL to Gearbox

Moreover once the product has reached the inspection station, as per the description 78% of the parts will be
in the upper tolerance limit. This function for the system has been structured as given below.


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IF Binomial (0.78,20) <= N_passed
Clean_CNC = 1
N_passed = 0
ELSE
N_passed = N_passed + 1
ENDIF
Castings which are bored down are then let to the inspection station from which it proceed to the assembly via
bin if qualified; else it is scrapped. The final product namely Gearbox is then led to the test bench where it is
tested. If there’s any error, it is then disassembled from the disassembling unit from which gears proceed to
the conveyor belt to repeat the cycle while the rest is scrapped. The operator has been allocated shifts as
required to work for 8 hours per week.

The Model created using Witness simulation software as per the description
Revenue and Budget
The revenue is determined using Witness as well whereas the cost is defined in the system. Meanwhile the
machine rate is calculated by obtaining the time needed to run the machine to get the investment on machine
back. The analysis is done on the basis of direct and indirect costs. On the other hand, the total budget
required has been mentioned as per the description of the plant. The CNC workstation was purchased for a
cost £ 72000 which is needed to be repaid in three years. Nevertheless a budget of £ 15000 was allocated to
set up the remainder of the plant where the costs for assembly stations, bins and test benches (per gearbox) is
£1800, £200 and £2200 respectively. In addition the cost for hiring an operator is $ 1500 where the hourly rate
is £15.60 which includes on-costs. This expenditure is to be repaid within one year. The expected income
from the sale of each completed gearbox is £80. According to Yang, the revenue for his kind of system is
obtained by using the equation

Return = NShip (Gearbox) * 80 – [(NQty (Assembly) * 0.9+NQty (CNC) * 24 + NQty (Test_Buffer) * 0.1 +NQty
(Test_Bench) * 1.1+ NQty (Operator) * 16.35)*80]


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Breakdown
One of the common occurrences in the manufacturing sector is the breakdown of equipments and units.
Multiple breakdowns of units can be set in Witness which has to be done through proper distribution.
According to the description a 90 minutes breakdown has to be considered in the system, which indicates
the time required to repair the specified unit. A Breakdown counter has been used and defined to indicate
the number of breakdowns that occur. The time taken between each breakdown for the conveyor belt and
the CNC machine is 8000 and 4000 respectively. Its function is defined as
Breakdown_counter = Breakdown_counter+1

Results
The required model was created and simulation was done to an optimum level manually to observe and
analyse the performance. The Revenue for the plant was deduced by manually setting up the plant at different
conditions within the given budget o £ 15000. This was done by using several variables, functions and variables
along with the manufacturing unit like the inspection and assembly base. Operators were assigned uniform
shifts with a maximum of 8 hours per day. The system was run for 14400 minutes to obtain the results, and out
of 6 different calculations as given below, the combination that gave the maximum revenue for two weeks was
£43964 for a budget of £13700. One of the tasks required was to identify the characteristics of the system
after and before each of the machines or unit has broken down.

Number of
Shipments
Operator
Shift
Quantity
Assembly
Quantity
Bin
Quantity
Conveyor
Belt
Quantity
Test
Bench
Quantity
Test
Buffer
Quantity
CNC
Quantity
Budget Revenue
585 1 2 1 1 2 1 2 9700 41324
620 1 3 1 1 2 1 3 11500 42132
585 2 2 1 1 2 1 3 11200 38096
620 1 3 1 1 3 1 2 13700 43964
605 1 3 1 1 3 1 3 13700 40844
555 1 1 1 1 1 1 1 5700 41004

Table1. Revenue and Budget Statistics

One of the tasks required was to identify the characteristics of the system after and before each of the
machines or unit has broken down. Understanding the breakdown characteristics and its aftereffects will assist
in managing the system and reduce the risk associated with it. This is highly helpful in maintaining the income
as well. It is to be noted that when any one part of the system breaks down, it affects the whole setup
completely. The conditions for the breakdown as mentioned earlier are set in system and the performance for
each unit was investigated before and after the breakdown. The data and statistics for each of the units are
given below.


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Table 2a. Conveyor Belt Statistics before Breakdown
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Table 2b. Conveyor Belt Statistics after Breakdown


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Table 3a. CNC Statistics before Breakdown

Table 3b. CNC Statistics after Breakdown


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Table 4a. Inspection Statistics before Breakdown

Table 4b. Inspection Statistics after Breakdown


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Table 5a. Assembly Statistics before Breakdown

Table 5b. Assembly Statistics after Breakdown


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Table 6a. Disassembly Statistics before Breakdown

Table 6b. Disassembly Statistics after Breakdown


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Table 7a. Test Bench Statistics before Breakdown

Table 7b. Test Bench Statistics after Breakdown
Further statistics are encompassed in the appendix as they are insignificant to the simulation analysis.


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Discussion
The simulated results obtained for the system modelled using Witness was analysed which give an
idea about its characteristics. It was found that through manual calculations setting the budget
within the required 15000 pounds limit, a maximum revenue of 43964 pounds was achieved when using
3 assemblies and test benches along with 2 CNC machines. In addition a conveyor belt was used. A budget of
13700 pounds was used in this case. Using Witness and through optimization excellent forecasts can be
achieved for the revenue and budget which gives a good indication on what business model to use and proceed
to improve and maximize the income. Nevertheless it is only a forecast and is liable to change when compared
with the performance in the real world as the values can end up being higher or lower than the initial forecast.
The breakdown statistics was also observed during the analysis to understand its impact on the
system. The plant is affected hugely when any one of the units breakdown especially the conveyor
belt and the CNC machine because they are the units from which the system starts to work. The
plant tends to become idle when they breakdown as shown in the data given earlier. This in addition
has an adverse impact on the revenue of the plant as well. Also the reduced number of shipped
gearboxes can also be seen from the data after the different sections have undergone a breakdown,
when compared with their data prior to breakdown.
Even though the breakdown statistics for each machine is valuable and has to be considered, like the
optimization of the software, it doesn’t incur the real time aspects and tends to be different in the
real world compared to the system.
Conclusion
The model was designed using the simulation software Witness and the maximum revenue obtained
for a period of two weeks was 43964 pounds for a budget of 13700 pounds. This indicates that the
plant can incur a revenue of 402300 pounds per year based on the forecast, which actually is a
respectable amount of income when considering the requirements for the plant. Moreover from the
detailed analysis of breakdown, specifically the breakdown counter it can be seen that there is
always one breakdown in the plant every week. This shows that the average number of breakdown
the plant is expected to suffer in a year is 52.
When the machines or units break down, especially the CNC and conveyor belt, the complete plant
is affected and is subjected to idleness and blockage in different machines. Nonetheless these results
are not precise as they don’t consider the human, external or supply and demand factors which also
play a huge role on the working of the industries and focusing solely on the simulated forecast can
also lead to poor performance.


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Bibliography

Chris Ling, 2001. The Petri Net Method. [Online]. Available on:
www.utdallas.edu/~gupta/courses/semath/petri.ppt. [Accessed on 29/03/2015].
Jay April Fred Glover James P. Kelly Manuel Laguna, 2003. PRACTICAL INTRODUCTION TO SIMULATION
OPTIMIZATION. [online]. Available on: http://www.courses.vcu.edu/MATH-
jrm/OPER641/Papers/AprilPracticalSimulationOptimization.pdf. [Accessed on 29/03/2015].
Lanner, 2008. Simulation and Simulation Software Explained. [Online]. Available on:
http://www.lanner.com/en/simulation-explained.cfm. [Accessed on 29/03/2015].
Ming Chen, n.a. Petri Net. [Online]. Available on: http://www.techfak.uni-
bielefeld.de/~mchen/BioPNML/Intro/pnfaq.html. [Accessed on 29/03/2015].
Yang X.S, 2014. Discrete-Event Simulation: Introduction to Witness. Middlesex University. London


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Appendix

Table 8a. Gear Statistics before Breakdown

Table 8b. Gear Statistics after Breakdown


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Table 9a. Bearing Statistics before Breakdown

Table 9b. Bearing Statistics after Breakdown

Table 10a. Casting Statistics before Breakdown


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Table 10b. Casting Statistics after Breakdown

Table 11a. Bin Statistics before Breakdown


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Table 11b. Bin Statistics after Breakdown

Table 12a. Test Buffer Statistics before Breakdown


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Table 12b. Test Buffer Statistics after Breakdown

Table 13a. Gearbox Statistics before Breakdown

Table 13b. Gearbox Statistics after Breakdown


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Table 14a. Operator Shift Statistics before Breakdown

Table 14b. Operator Shift Statistics after Breakdown

Discrete Event Simulation Analysis of a Gearbox Manufacturing Plant Using the Software ‘Witness’

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