6 CONTROL DE VERTIMIENTOS A LA RED DE ALCANTARILLADO 6.1 Ámbito de aplicación

Virtual company A manufactures spark plugs. The product is made of a centre electrode, metal casing or shell and side electrode (ground electrode). In other to manufacture

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copper and nickel spark plug, the production process goes through five stages as listed below

• Extruding is made on a metal bar to make a blank.

• For further shaping on the blank to its required dimension as well as to achieve the hard shape the lathing machine is used.

• The casting banding attachment of the side electrode

• Ceramic insulation

• Inserting of terminal stud and welding of the centre electrode

Fig 10. 1 Manufacturing processes 1-3

Fig 10. 2 Manufacturing processes 4 and 5

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10.2. 1 Data collection

Hypothetical data of the manufacturing firms was used in the simulation study.

10.2. 2 Building the model

Using witness, a gearbox manufacturing plan was created for given conditions of the product to achieve a low-cost manufacturing approach and meeting the delivery time. Parts like ceramic and insulator come from external suppliers, which is then passed through the conveyer belt, then assembled and finally inspected before shipment. CNC machines are used in the production.

Witness simulation is an effective tool used in operations research and management science. The application of the particular process is used to simulate a model of manufacturing process, which is then analysed and then put into action. Different types of factors can be calculated using this system. Production factors such as costs and lead-time can be comprehended with the ability to adjust and arrange things into different positions according to what is required.

Simulation models also have the function to assess the value of any set of specifications. They are optimised to convert and observe the input parameters into possible output results.

Optimization can be used to help control different number of operators and machines including the ability to maintain costs and use resources responsibly. The cycle time of the process can also be adjusted depending on the formulation of the constraints.

When manufacturing problems are being considered, the methodology of stochastic approximation is used due to its relationship with gradient search. However, if it’s different from simulation, changes can occur and take place in the system during cautious event simulations, while it is nearly impossible to make any changes to the parameter during continuous event

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simulation. In addition, this approximation mentioned mainly focuses on a wide range of variable problems which can occur during the production runs.

Simulation experimentation

Fig 10.3 demonstrates the complete virtual assembly of copper/nickel flash fittings of spark plugs. Simulation was run for 60 mins and 500 mins and 40 hours respectively and machine statistics were observed.

Fig 10. 3 Virtual assemble of spark plug

The following figures highlights the production runs at various time frames. Only few of the machine statistics have been presented here in this chapter and left the others in the Appendix A, B and C respectively.

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10.2. 3 Machine statistics for 60 min production run:

Fig 10. 4 Machine statistics for Insertion_Assembly (60 mins)

Fig 10. 5 Machine statistics for Stud_Electrode_Welding (60 mins)

Fig 10. 6 Machine statistics for Alloy Steel Stud_Lathe (60 mins)

Fig 10. 7 Machine statistics for Cu_Electrode_Lathe (60 mins)

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10.2. 4 Machine statistics for 500 min production run

Fig 10. 8

Fig 10. 9 Machine statistics for Stud_Electrode_Welding (500 min)

Fig 10. 10 Machine statistics for AlloySteelStud_Lathe (500 min)

Fig 10. 11 Machine statistics for Cu_Electrode_Lathe (500 min)

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10.2. 5 Discussion

This simulation study has replicated a real-time manufacturing environment, where the production is run at different sets of time. These production runs have helped to analyse the issues that would arise in a production unit and identify the possible causes for the delay in delivery of the product, hence affecting the overall manufacturing performance.

This study has helped to identify the capability of a bottle-neck machine to delay the production. In the above production runs, such bottle neck machines have increased idle time of the preceding machines waiting for them to finish the task, thus making the production inefficient. The machine statistics in this simulation shows that there are machines which are in-capable to handle the task on-time and become a bottle-neck.

Bottle neck machines have the limited capacity to do the allocated task. So, the firm has to find an alternative solution.

The solution may be to replace the machine, which could cost more. This suggests that scaling-out may be a better solution for such scenario, if it is comparatively cost effective and deliver the product on-time. To identify if it is cost-effective, the user can utilize the elastic assessment tool to raise concern about the cost of the manufacturing service to the cloud service provider and assess if moving to cloud service would be a better option to improve its manufacturing performance. In the simulation study there were various instances where the production has not been efficient. Analysing the parameters that would affect the manufacturing performance, a set of ten parameters were jotted down.

These parameters are found to affect the manufacturing performance. The statistical analysis in the next section includes these parameters to find out the best combination which has high significance of correlation to achieve on-time delivery, hence improving the manufacturing performance.

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