Functional and technological remodelling in optimizing manufacturing networks architecture.

Anghel, Florina ; Popa, Cicerone Laurentiu ; Aurite, Traian 等

1. INTRODUCTION AND DEFINITIONS

Prior to any manufacturing process a simulation for the existing material flow is needed in order to identify the bottlenecks where the material flow is slowed down or even blocked. There are a number of simulation programs which can be used in this matter. For our study case we choose Witness simulation program. Our algorithm is based on the description of the modelling process in Witness, of the machines used and of the manufacturing parametrical values (Ilar et al., 2008).

In this way one can obtain valuable information regarding productivity, the necessary number of machines, the personal involved in the production cycle and both the manufacturer and the beneficiary will know an approximate production cost (Anghel et al., 2008).

We consider a material flow and process synchronous simulation the simulation of a model where at the level at the work point the process simulation is concomitant with the material flow simulation.

A multipolar synchronous simulation can be defined as an integrated system for monitoring more than two manufacturing systems material flow simulations that are interconnected in virtual enterprise architectures (Popa & Cotet, 2008). We agree that the virtual enterprise represents a temporary alliance of enterprises who wish to share resources and aptitudes for the purpose of making a product in the shortest time possible, at the smallest price possible, and with the maximum satisfaction of the client (Camarinha-Matos et al., 1997).

We define diffused manufacturing systems as architectures with more than two working points connected by transport and transfer systems and using buffers. When taking in consideration concentrated systems we can define them as architectures based on a single work point surrounded & assisted by transport, transfer & deposit facilities.

We agree here with the thesis that within the class of stochastic simulation models, one further distinction is necessary: simulations can be either terminating (sometimes called finite) or non-terminating in nature, with specific algorithms for each category (Sanchez, 2001).

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2. MANUFACTURING NETWORKS SIMULATION

The first manufacturing system consists in: debtor machine (D1), lathe machine (S1), milling machine (F1), boring and grinding machine ([MAR.sub.1]), each of them having an operator supervising the production cycle. After studying the activity reports we found a bottleneck at S1 machine caused by the big working times the blank has to spend on this machine. Because of this flow concentrator both D1 machine and C1 conveyor are blocked most of the time (for example D1 is blocked 55% of the time) (fig. 1). For optimising the first manufacturing system we can use functional remodelling; it consists in changing some of the machines placement, the order of some operations, the speed of some conveyors or manufacturing times or technological remodelling; it consists in reconsidering all the system data: the type of the machines, tools, materials used etc. The new system is remodelled and a material flow simulation for the system must be done.

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In the same way we build a model for the second manufacturing system (fig. 2). Taken in consideration the operations required for manufacturing the pulley wheel we could establish the machine-tools used. These are, as symbolised in figure 2: a lathe machine (S2), milling machine (F2), boring and grinding machine (MAR2). For each machine there is an operator present with the role of supervising the operation. All parts are collected in a buffer. For transporting blanks among working points we used conveyor belts. Due to significant concentrators identified on S2, C1 and C2 we obtain low productivity for this system. The third manufacturing system will be used for the product assembly. The entrance for this system will be the outputs from the first and the second system. It contains the following elements: three buffers (B1, B2 and B3), three conveyor belts and a single work point. [P.sub.1] and [P.sub.2] represent the parts produced by systems 1 and 2.

Because of the flow concentrators identified in systems 1 & 2 we obtained a low productivity in the assembly system, thus creating the necessity of system remodeling (fig. 3). Also, after studying the activity report for M1 machine it resulted that this machine is blocked almost half the working time.

3. MANUFACTURING NETWORKS REMODELLING

In order to optimise the first system we used functional remodelling, but the results were not very satisfying and bottlenecks were still present. We had to do a technological remodelling by adding another lathe machine and conveyor. An NPV analysis was performed confirming that the increasing productivity justifies the investment done. In figure 4 we presented the optimised model for the first system. After running the simulation we could notice an important improvement concerning the material flow (fig. 4). The assembly point is now working 97% of the time. For the second system the functional remodelling worked. We eliminated the flow concentrators and, thus we optimised the system by simply changing the order of some operations and the conveyor speed.

[FIGURE 4 OMITTED]

The bottlenecks determined were on F2 and F22 machines (60% of the time the system is blocked; 35% the system is working properly; 4% is waiting for parts and 1% for human activities). After remodelling the results are: 15%--system blocked; 81%--system working; 3% is waiting for parts and 1% for human activities. On the third system one can notice a general improvement due to the fact that the inputs are coming more rapidly and because of the functional remodelling applied on this system. The machine blockage was reduced from 49% to 8%.

4. CONCLUSION

In order to optimize manufacturing network architectures we need to create first isolated models to estimate the productivity level for each node of the network. Using discrete material flow simulation for those models we can identify the bottlenecks generating productivity loss. But for the entire manufacturing network we need a multipolar synchronous simulation model providing more accurate results regarding the network efficiency. The flow concentrators identified on each system have an impact over the entire architecture due to the fact that the three models from our study case function as enterprise nodes. Thus a flow concentrator found on one of the supply systems can reduce productivity for the assembly system. Using multipolar simulation the bottlenecks identified on material flow simulation can be the same with the ones found on isolated simulations, or different ones can appear.

Any manufacturing network architecture can be optimized in two ways using functional or technological remodeling (an economic analysis must confirm the necessary investment). The manufacturing architecture optimisation was done as follows: the first system was technologically remodelled and for systems 2 & 3 we used functional remodelling. In our future research we intend to study the effects of synchronizing process and material flow simulation in optimizing manufacturing networks architecture based on functional and technological remodelling.

5. REFERENCES

Anghel, F.; Cazacu, D. A.; & Dobrescu, T. (2008). Virtual Machine Tools Models in Assembling Systems Material Flow Management (2008). 0025-0026, Annals of DAAAM for 2008 & Proceedings of the 19th International DAAAM Symposium, ISBN 978-3-901509-68-1, ISSN 1726-9679, pp 013, Editor B. Katalinic, Published by DAAAM International, Vienna, Austria 2008, 22-25 Octombrie, Trnava, Slovacia

Camarinha-Matos, L.M.; Carelli, R.; Pellicer, J. & Martin, M. (1997). Towards the virtual enterprise in food industry, Proceedings of the ISIP'97 OE/IFIP/IEEE International. Conference on Integrated and Sustainable Industrial Production, ISBN 0-412-79950-2, Portugal, May 1997, Chapman & Hall, Lisboa

Ilar, T., Powell, J. & Kaplan, A. (2008). Simulation of production lines-The importance of breakdown statistics and the effect of machine position, In: International Journal of Simulation Modelling LJSIAIM, Volume 7, no. 4, December 2008, pp.176-185, ISSN 1726-4529, Vienna.

Popa, C.L. & Cotet C.E. (2008). Multipolar distributed material flow simulation of a virtual enterprise, Proceedings of The 19th International DAAAM SYMPOSIUM, ^Intelligent Manufacturing & Automation:Focus on Next Generation of Intelligent Systems and Solutions, Katalinic, B. (Ed.), pp. 1103-1104, ISBN 978-3-901509-68-1, ISSN 1726-9679, Published by DAAAM International, Vienna

Sanchez, S. M. (2001). ABC's of output analysis, Proceedings of The 2001 Winter Simulation 2001, Peters, B.A., Smith, J.S., Medeiros D.J., CD-ROM, Presses Association for Computing Machinery (ACM), New York.

ANGHEL, F[lorina]; POPA, C[icerone] L[aurentiu] & AURITE, T[raian] *

* Supervisor, Mentor