PPS system as a management tool for modern manufacturing plant.

Horvath, Stefan ; Danisova, Nina ; Velisek, Karol 等

1. INTRODUCTION

By help of computing simulation in PPS system (Plant Simulation) is able to design and simulate flexible assembly system that is realized at Institute of production systems and applied mechanics. On the ground of analysis in the simulation process of flexible assembly system is able to practise some variable experiments with changing of parameters in material and informatics flow.

Digital Factory as a simulation tool has large representation in praxis. This method of design and planning of new production factory or system raises real perspective in this problematic area. By help of digital simulation are decreased production costs and also time for designing and planning.

2. MATERIAL FLOW SIMULATION IN A MANUFACTURING

Literature defines simulation as an academic or engineering proceeding which simulates processes or objects. (Bangsow, 2010), (Koether, 2001). Its purpose is to examine dynamic systems in a given model in which further examination is available, with the goal to achieve information and outcome for practical purposes. (Matusova & Hruskova, 2010), (Bangsow, 2008).

Exploitation of a material flow simulation:

1. Designing new manufacturing systems

2. Optimalization of existing manufacturing systems Production Flow Scheme (PFS) is used both for designing new manufacturing systems and for optimalization of existing manufacturing systems. When we use PPS system, this information ought to be processed in advance: (Kuhn, 2006).

* Design time case studies for processes on each production, operating, transport and auxiliary device. These define the output of machines and also make it possible to follow dependency between them. (wait state, setups, work time, failure rate,...) For such analysis it is necessary to define data for shift calendar.

* Define all work zones for each facility--section plan,

* Define possible barriers, following optimalization,

* Analysis of occurred failures,

* Analysis of necessary number of human capital,

* Collecting information about the operating manufacturing process,

* Define possible management strategies,

* Test each available alternative.

When analyzing an existing manufacturing system by PPS, it is possible to optimalize: (Kuhn, 2006).

* Management strategy of the examined system,

* Technological processes,

* Daily output examination.

Expected changes after implementing material flow simulation in production: (Kuhn, 2006).

* Increase of machine utilization,

* Increase of system utilization,

* Productivity and output increase,

* Decrease of incidental time,

* Decrease of human force necessity,

* Decrease of storage,

* Estimation of storage,

* Estimation of AVG cart number and technological palettes,

* Examining proposed alternatives of system management,

* Optimalization of management strategies,

* Reducing possible errors in manufacturing system projecting,

* Protection of invested capital.

The simulation is able to applicable in the four basic steps: (Kuhn, 2006).

1. Modelling of experiment,

2. Simulation experiment,

3. Results and outputs of simulation,

4. Implications for real system, application and adjustment.

3. DESCRIPTION OF THE ASSEMBLY LINE AND CREATE THE MODEL

Described example in the article is aimed at finding a production increase when changing from a two-shift operation to three-shift operation.

The first step is to analyze the current state of the production line and then create the model. The accuracy of data survey by simulating depends on accurate data from FAL and administrative structure of the model. Creation of a model and its details depends on required analysis.

Flexible assembly line (FAL) consists of following sections:

* Assembly in the dust-free environment S01-S03,

* Assembly in the current environment S04-S07,

* Installation and inspection S08-S14,

* Packing and shipping S15.

[FIGURE 1 OMITTED]

Workstations are mounted directly on the conveyor and thus are a part of it. Conveyor has a length of the 38 m and the transportation speed of 0.25 m/s. A defectiveness of the whole assembly line is 0.02%. However the failure occurrence increases with the complicacy of the operation on the individual workstation. Details of individual workstations necessary to complete the model are shown in tab. 1.

The model also provides workers' average speed (1.63 m/s) and efficiency is considered 100%.

The production plan consists of three types of products (A, B and C). Product mix: A (120), B (100), C (120), B (145), A (268), B (230) and then repeated after a period of one year. Every product in the system can be monitored from beginning to the end of its production.

[FIGURE 2 OMITTED]

The number of accepted and rejected products is listed in the next table (Tab. 3). The numbers are subdivided according to their type per two-shift operation.

Three-shift operations model simulation shows production increase in 34.46%. It is not necessary to shown more efficiency diagrams for individual workstation as well as corresponding tables of their negligible differences. Negligible variations caused by different lengths of breaks.

4. CONCLUSION

This article depicts the necessity of using modern IT technologies in the areas of designing, planning and optimalization of material flow by computer simulations. The results are intended to assess management issues in the transition from two-shift operation to tree-shift operation. The next step will be options to reduce the percentage of blocking workstations.

5. ACKNOWLEDGEMENTS

This article was created thanks to the national grant VEGA 1/0206/09--Intelligent assembly cell at the Institute of Production Systems and Applied Mechanics, Faculty of Material Science and Technology--STU

6. REFERENCES

Bangsow, S. (2010). Manufacturing Simulation with Plant Simulation and Simtalk: Usage and Progrmming with Examples and Soliutions, Springer, ISBN 978-3- 642-050732, Berlin

Bangsow, S. (2008). Fertigungssimulation mit Plant Simulation und SimTalk: Anwendung und Programmierung mit Beispielen und Losungen, Hanser Fachbuchverlag, ISBN 978-3-446-41490-7, Fachbuchverlag

Koether, R. (2001). Technische Logistik, FH Munchen, ISBN 3-446-21759-2, Munchen

Kuhn, W. (2006). Digitale Fabrik: Fabriksimulation fur Produktionsplaner, Hanser Fachbuchverlag, ISBN 344-640619-0

Matusova, M.; Hruskova, E. (2010). Projektovanie vyrobnych systemov: Ndvody na cvicenia, AlumniPress, ISBN 978-808096-116-9, Trnava

Tab. 1 Production parameters of individual workstations

WS S01 S02 S03 S04 S05

PT 51.3 51.3 51.3 51.6 52.3
ST 15 15 15 30 30
Fr 0,01 0,01 0,01 0,01 0,01
W 2 1 1 1 1

WS S06 S07 S08 S09 S10

PT 52 52.3 32.7 32.8 32.6
ST 30 30 300 300 300
Fr 0,01 0,01 0,02 0,02 0,02
W 1 1 -- -- 1

WS S11 S12 S13 S14 S15

PT 32.1 32.6 41.8 32.8 54.5
ST 300 300 300 300 30
Fr 0,02 0,02 0,02 0,02 0
W 1 -- 1 -- 3

WS--Work Station, PT--Process Time [s],
ST--Setup Time [s], Fr--Failure rate [%], W--Worker.

Tab. 2 Efficiently of individual workstation a period of one year

WS S01 S02 S03 S04 S05

Wo 65.81 65.81 65.81 66.20 67.09
Su 0.12 0.12 0.12 0.24 0.24
Wa 0 5.13 5.13 5.14 5.14
Bl 21.56 16.43 16.42 15.92 15.02
Fr 0.01 0.01 0.01 0.01 0.01
P 12.5 12.5 12.5 12.5 12.5

WS S06 S07 S08 S09 S10

Wo 66.71 67.09 41.95 42.08 41.82
Su 0.24 0.24 2.35 2.35 2.35
Wa 5.14 5.14 5.17 6.9 8.66
Bl 15.41 15.03 38.02 36.16 34.66
Fr 0.01 0.01 0.01 0.02 0.02
P 12.5 12.5 12.5 12.5 12.5

WS S11 S12 S13 S14 S15

Wo 41.18 41.42 53.62 42.07 69.82
Su 2.35 2.35 2.35 2.35 0.24
Wa 10.4 12.14 13.88 15.7 17.43
Bl 33.56 31.17 17.62 27.36 0
Fr 0.01 0.02 0.03 0.02 0.01
P 12.5 12.5 12.5 12.5 12.5

Tab. 3 Executed products for two-shift operation per year

Products A B C along

accepted 67938 83030 20974 171942
rejected 82 99 26 207
along 68020 83129 21000 172149