Discrete Event simulation

Transcription

1 Discrete Event simulation David James Raistrick Shrink Wrap Conveyor Line Submitted in partial fulfilment of the requirements of Leeds Metropolitan University for the Degree of Advanced Engineering Management Faculty of Art s, Environment and Technology December

2 Authorship Declaration I, David James Raistrick confirm that this dissertation/assignment and the work presented in it are my own achievement. Where I have consulted the published work of others this is always clearly attributed; Where I have quoted from the work of others the source is always given. With the exception of such quotations this dissertation is entirely my own work; I have acknowledged all main sources of help; If my research follows on from previous work or is part of a larger collaborative research project I have made clear exactly what was done by others and what I have contributed myself; I have read and understand the penalties associated with Academic Misconduct. I also confirm that I have obtained informed consent from all people I have involved in the work in this dissertation following the School's ethical guidelines Signed: Date: 02/01/2012 Student ID No: C

3 Abstract This report has been published on the results which investigate various scenarios for how the Shrink Wrap Conveyor line at a large glass bottle manufacturing plant can transport the pallets out of the building to the dispatch area where they will be loaded onto the wagons for shipping. This investigation will be carried out using Discrete Simulation modelling software to reconstruct real time outputs. 3

4 Contents 1.0 INTRODUCTION DISCRETE EVENT SIMULATION Basic Concept Benefits of Discrete Event Simulation Deterministic and Stochastic Distribution PROJECT Packing Procedure Production Area Current data Breakdown Evaluation Breakdown Evaluation USING BACK UP SHRINK WRAP MACHINE Backup Shrink Wrap Breakdown Evaluation USING BACKUP SHRINK WRAP MACHINE METHOD Using Backup shrink wrap machine method 2, Evaluation CONCLUSION

5 List of Figures Fig1 Diagram 4 steps of Discrete Event Simulation 10 Fig2 layout of conveyors and shrink wrap machines 15 Fig3 Break down Evaluation 1 - Line after 24 hours 19 Fig4 Break down Evaluation1 - Queue data after 24 hours 20 Fig5 Break down Evaluation 2 - Line after 24 hours 21 Fig6 Break down evaluation2 - Queue data after 24 hours 22 Fig7 Layout of Back up shrink wrap machine 23 Fig8 Back up machine, Break down Evaluation - Line after 24 hours 25 Fig9 Backup machine, Break down Evaluation - Queue data at 24 hours 26 Fig10 Layout of Back up shrink wrap machine method 2 27 Fig11Back up machine method 2, Break down Evaluation 28 Fig12 Backup machine, Break down evaluation - Queue data 29 Fig13 Model results reducing breakdown to 1 every 5 hours 30 Fig14 Queue data results reducing breakdown to 1 every 5 hours 31 5

6 Acknowledgements Tony Pawinski Senior Engineering Manager at AGC Providing technical information on conveyor and process speeds, and proposed solutions to the problems. Ian Pickersgill Cold End Production Manager at AGC Providing the technical information on the current production times for pallets produced, timing for fork truck travel and costing for down time. 6

7 1.0 Introduction This report shows how software can be used to model a process or factory without the need of physically building or utilising hardware or production time. The particular software been used is called Flexsim which is a USA based company who state on their website Accessed 01/11/2011 Flexsim is the most powerful tool for modelling, analyzing, visualizing, and optimizing any imaginable process - from manufacturing to supply chains, abstract examples to real world systems, and anything in between. Learning outcomes within this module are Identify where and how simulation can benefit an organisation and its role in design, planning and control of production systems. Critically evaluate the statistical data a discreet event simulation package can produce via different data points including throughput, content, machine state and utilization, Interpret the financial analysis data generated by a discreet event simulation package and defend a strategy of improvement. Model discrete event data and processes using techniques at the forefront of current best practice to recommend optimized scenarios for a given situation. Within this report a large bottle manufacturing plant based in Leeds will have a section of its production facility modelled and performance analysed using various scenarios. The aim of this research and modelling is to determine which would be the most efficient way to use the backup shrink wrap machine, potentially saving money and down time. 7

8 2.0 Discrete Event Simulation Discrete event simulation is a method used widely today to analyse and investigate processes without actually producing a physical model or factory. Software is used to simulate all factors of a desired project and statically predict outcomes and effect within the processes. A proposed project can be completely modelled with all the known and predicted parameters to evaluate the outcome and the streamline the process to prove or disprove whether the proposed project could actually work in the real world. Document read 12/12/2011 Jerry Banks Marietta, Georgia 30067, Initially published in the Proceedings of the 1999 Winter Simulation Conference Stated A discrete-event simulation model is defined as one in which the state variables change only at those discrete points in time at which events occur. 8

9 2.1 Basic Concept Discrete Event Simulation works on simple concepts of how long it takes for things to happen, and what implications arise after a period of time. To break this down it can be said that there are 4 simple steps. As shown in Fig1 Website accessed 09/12/

10 Fig1 Diagram 4 steps of Discrete Event Simulation 1. Source This is the beginning of any process were an action or object will arrive and be distributed into the sequence of event it must go through before it can be dispatched at the end. 2. Queue This is the holding area were the object will wait until the next action is ready to accept it 3. Delay This is the action which the object must go through 4. Sink This is the exit or dispatch when the process is complete For each of these 4 steps parameters can be entered in the model such as processing/action times, queue limits distribution event and various other parameters to try and make the model cover all instances within the proposed system that is been modelled. 10

11 2.2 Benefits of Discrete Event Simulation Advantages of using Discrete Event Simulation are 1. Ability to model complex systems 2. Allows a model to be designed and evaluated without laying a brick in terms of actually building a process 3. Simulations save time and money. 4. Within the simulation parameters, layouts and equipment can be altered without changing the real time working system 5. Identify bottle neck areas within the process and experiment how to reduce them Visited 09/12/ Visited 09/12/

12 The research and evaluation carried in this report would not be possible to carry out on the production line, as major re-programming work and electrical work would need to be carried out just to try the various methods. Activities like this would firstly take time to do and test and secondly would need reversing when the test and scenarios have been completed. This would cause major disruption to the factory and production line. Costing allot of money, lost production and missed delivery times. However, using Discrete Event Simulation software it is possible to be able to do an exact model of the machines under investigation, programming actual real to life speeds, timings and Stochastic Distributions. Then the model can be altered physically as well as the working dynamics altered without actually touching the equipment in the factory, therefore not costing any money, down time or production loss. 12

13 2.3 Deterministic and Stochastic Distribution Distributions are how regular or irregular an event could happen, or in the case of this project how frequent the pallets could arrive. In this project we are producing pallets at an average rate of 40/hour, so in the ideal world a pallet would arrive exactly every 90 seconds, the pallets would be processed every 65 seconds and sent down the conveyor line for dispatch leaving exactly 25 seconds of idle time between pallets. If everything did happen this way then it would be referenced as Deterministic Distribution Visited 17/11/2011 The following was quoted on the website A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables. Therefore, deterministic models perform the same way for a given set of initial conditions. Deterministic Distribution is when there is absolutely no randomness to the distribution, no breakdown scenarios and everything happens in the same way and time, every time. It would be very easy to create a model and have it perform in this way but this would not reflect the real world scenarios of what could actually happen, and without applying these real life scenarios the model would not generate true outcomes/results. Creating the model with high variability, and random arrivals, breakdowns and other scenarios which would cause the model to behave completely differently but realistically would be known as Stochastic Distribution 13

14 Visited 17/11/2011 The following was quoted on the website In a stochastic model, randomness is present, and variable states are not described by unique values, but rather by probability distributions. 14

15 3.0 Project This project is based on a production line at a major glass bottle manufacturer in Leeds. There are 8 bottle production lines producing bottles and at the end of each line the bottles are palletised then sent to the shrink wrap machine before been transferred on the conveyors out to the dispatch area where they are loaded onto wagons. Fig2 layout of conveyors and shrink wrap machines This conveyor line and layout is shaped exactly how it is shown in this model due to the positioning of the existing production lines and offices either side of the conveyor line. There is a small roadway that runs the length of the conveyor line which is an access route for the fork lift trucks, but this is only wide enough for 1 truck to pass at any one time. Due to this factory been located very close to Leeds city centre, expanding the factory is not possible as it is already at its limit for the size of the site, and changing the layout of the factory would cause excessive down time and the loss of revenue in loss production would make it un-justifiable. 15

16 3.1 Packing Procedure Each pallet is a standard size and the only variation is the height of each pallet, which is dependent upon the type of container been produced. A shrink wrap machine is designed to measure the height of each pallet and cut a plastic packing bag to the correct length, then fit the bag over the pallet and using heat shrink wrap the bag around the pallet of containers. A fork lift truck is used to transport the pallets from each of the production lines and loads them onto the first loading conveyor. This pallet will then proceed through the shrink wrap machine then down the conveyor line to the dispatch area where they are transferred using a second forklift truck from the conveyor and onto the wagons. 16

17 3.2 Production Area Each of the 8 production lines produce glass containers 24 hours a day 7 days a week. Due to the nature of the procedure and implications of controlling liquid glass, it is not efficient to stop the moulding procedure at the hot end of the production line. Therefore the glass containers will never stop been produced. There are also other implications which can happen at the cold end of the procedure, and the containers can be held in holding areas, packed by hand or even scrapped and re-cycled. It has been estimated that if any one of the 8 lines is not producing bottles the production lost revenue is approximately 75/minute. A major bottle neck in this whole manufacturing procedure is shrink wrapping the completed pallets and loading them onto the awaiting wagons. On the shop floor there is only a maximum capacity to hold 150 pallets. Once this is reached there is a major problem and palletising on each line has to be stopped and the containers scrapped. As this is the case backup scenarios must be in place to accommodate any breakdowns. Maintenance engineers are on hand full time and this critical part of the production is classed as priority 1, which means that if any problem occurs on this conveyor line which causes at to stop, then all other maintenance jobs/breakdowns will be left until the problem is resolved and the conveyor line is set running again. A second shrink wrap machine is integrated into the conveyor line in case the main shrink wrap machine has a major problem. 17

18 3.3 Current data From the 8 production lines 40 pallets are produced per hour, with an average pallet distribution of 90 seconds with a standard deviation of 15 seconds max. The following parameters have been set to the loading and unloading fork trucks Max Speed = 0.5 Load Time = 5 seconds Unload Time = 5 seconds When the pallet is placed on the loading conveyor, it must go through the shrink wrap procedure Squaring Bagging and shrinking = 20 seconds = 45 seconds Giving an overall process time of 65 seconds Then the pallets set off down the conveyor line and outside to dispatch Each conveyor is 3m long and travels the distance in 5 seconds When the simulation is run and everything is working as it should be then the whole process runs faultlessly and well within it capacity, producing no back log of pallets on the shop floor. To show the impact on the bag log of pallets on the shop floor various breakdown scenarios have been introduced and monitored over a 24 hour period. In the event of a breakdown the whole line would stop sending the pallets outside for dispatch regardless of where it stops working. 18

19 In this case the simulated breakdown is on the squarer at the beginning of the line. The main repercussion of the line breaking down is that it backs the pallets up on the shop floor (in this case queue 2). 3.4 Breakdown Evaluation 1 For this break down evaluation, an statistical distribution has been applied which will execute exponentially over 3600 seconds (1 hour) and the break down will have a down time of 600 seconds (10 minutes). This simulation was run for seconds (24 hours) Fig3 Break down Evaluation 1 - Line after 24 hours 19

20 Fig4 Break down evaluation1 - Queue data after 24 hours From this data the throughput of pallets at 99.39% and neither of the forklift trucks are used excessively with idle times of 26.4% for loading fork truck and 28.4% for the unloading fork truck. The backlog of pallets reached a maximum of 22 during the down time periods but this was soon cleared when the line was running at full speed. 20

21 3.5 Breakdown Evaluation 2 For this break down evaluation, an statistical distribution has been applied which will execute exponentially over 3600 seconds (1 hour) and the break down will have a down time of 1800 seconds (30 minutes). This simulation was run for seconds (24 hours) Fig5 Break down Evaluation 2 - Line after 24 hours 21

22 Fig6 Break down evaluation2 - Queue data after 24 hours These breakdown times were pushing the system to the limit and were entered as an exercise to determine what would happen under these circumstances. A shown from the data, the backlog reaches the maximum capacity at the 24 hour mark, which is built up due to not been able to fully clear the backlog between down times. The throughput of pallets was down to 69% which would also start having an impact on the Logistic of the delivery wagons. This extreme scenario would not be allowed to happen without switching over to the backup shrink wrap machine and repairing the primary machine correctly. 22

23 4.0 Using Back up shrink wrap machine Situated part way down the conveyor line is the backup shrink wrap machine which is available to be used if the primary shrink wrap machine is going to be down for any considerable amount of time. When the backup machine is in use then the conveyor line is disabled through to conveyor 4 and the pallets are loaded direct onto the bagging process on conveyor 5. There are 2 major disadvantages when using this backup machine, 1. The process time is slightly longer and impacts on the recovery of time after a breakdown or stoppage 2. The line has to be manually supervised at all times whilst running due to its vulnerability of stoppages. This cost the company an estimated 45/hour. Fig7 Layout of Back up shrink wrap machine 23

24 This shrink wrap process works slightly differently as the bagging has to be done separately which in this case is shown by using an additional process. Also the heating process is gas which takes longer than the electrical heating process due to having to run through purging sequences before firing off the burners. The processing parameters for the backup shrink wrap machine are Bagging Bagging Squaring Shrinking = 20 seconds = 10 seconds = 50 seconds Giving an overall process time of 80 seconds. Which is 15 seconds longer than using the primary shrink wrap machine and only gives a 10 second window between each pallet, thus increasing the recovery time between down time. Again when the simulation is run and everything is working as it should be then the whole process runs faultlessly and well within it capacity, producing no back log of pallets on the shop floor. 24

25 4.1 Backup Shrink Wrap Breakdown Evaluation 1 As with the primary shrink wrap simulation, this break down evaluation, an statistical distribution has been applied which will execute exponentially over 3600 seconds (1 hour) and the break down will have a down time of 600 seconds (10 minutes). Fig8 Back up Shrink Wrap machine, Break down Evaluation - Line after 24 hours 25

26 Fig9 Backup machine, Break down evaluation - Queue data after 24 hours From observing the data with the breakdown events used, the throughput of pallets is down to 93% and the shop floor would backup to 76 pallets within the 24 hour period, this gives a maximum running time for using the backup system of 48 hours 2 days, before it would start causing serious problems with the overall production facility. 26

27 5.0 Using Backup shrink wrap machine method 2 Fig10 Layout of Back up shrink wrap machine method 2 One of the main reasons for stoppages on the conveyor line and the backup shrink wrap machine is that the control system for the whole system is tied together. So if any of the conveyors trip off out side or the safety ropes get knocked then the whole procedure will stop and it takes allot of effort to reset the gas system on the backup shrink wrap machine. Because most of the vulnerability is on the sections of conveyor which are outside the building n investigation has been simulated just using a shorter section of the conveyor line. For this method the pallets will be loaded onto Conveyor 5 and removed from conveyor 10, allowing the remaining conveyors to be isolated and thus potentially reducing the down time frequency. This time the unloading fork truck has a narrow path to follow which has been simulated by using network nodes. 27

28 5.1 Using Backup shrink wrap machine method 2, Evaluation Fig11 Back up machine method 2, Break down Evaluation - Line after 24 hours 28

29 Fig12 Backup machine, Break down evaluation - Queue data after 24 hours Running this scenario with break down evaluation, an statistical distribution has been applied which will execute exponentially over 3600 seconds (1 hour) and the break down will have a down time of 600 seconds (10 minutes). Produces better results and does have the ability to be able to clear the back log with a throughput of 95.8%. However, using the shortened conveyor method would reduce the stoppage time to a worst case scenario of once every 5 hours. Feeding the new breakdown parameters into the model produces the following results. 29

30 Fig13 Model results reducing breakdown to 1 every 5 hours 30

31 Fig14 Queue data results reducing breakdown to 1 every 5 hours Running this new criteria through the model shows a significant improvement and the backlog never gets chance to backup on to the shop floor. The throughput is up to 99% and the maximum backlog reaches 12 which is soon cleared when the line is running correctly again. Only the unloading fork truck driver is effected by their idle time been reduced to 8.5%, but the drivers have job switch time tables so they would be swapped around every 2 hours with a 3 rd driver covering breaks every 4 hours. 31

32 6.0 Conclusion Using the Primary shrink wrap machine is the most efficient way to run the conveyor line, and can sustain large amounts of down time and still recover quickly. In the event of a major breakdown of this part of the line and the backup conveyor is required, the chances of line stoppages are highly increased and the duration and risk of the floor shop filling up with pallets and stopping the bottle production lines is also increased. This ultimately costs the company money, as the line has to be manually supervised at a cost of 45/hour and a further 75/minute would be lost if any of the lines are forced to start scrapping bottles due to the shop floor reaching capacity. By modifying the backup shrink wrap machine procedure and only using a shortened conveyor line, reduces the risk of line stoppages and there for the risk of pallets backing up shop floor. Also by removing this venerability means that the line will not need to be supervised by a dedicated full time person and the fork lift truck drivers can handle this roll, as they would when running with the primary shrink wrap machine. 32

33 References Internet Website Accessed 01/11/2011 Page 7 Pdf Document Document read 12/12/2011 Page 8 Internet Website Accessed 09/12/2011 Page 9 Internet Website Visited 09/12/2011 Page 11 Internet Website Visited 09/12/2011 Page 11 33

34 Internet website Visited 17/11/2011 Page 13 Internet website Visited 17/11/2011 Page 14 34