Managing Bottlenecks

Read this article. It details a bottleneck analysis and proposes a bottleneck management process. From the reading, can you describe why bottlenecks occur in a production process?

Simulation Modelling

5.1 Development of simulation model

In discrete-event simulation (DES), the operation of a system is represented as a chronological sequence of events. Each event occurs at an instant in time and marks a change of state in the system. Input data to the simulation model was extracted directly from the data files of the process control department. The assembly plant facility layout was used as the basis for the layout of the simulation model to enable it to be accurate and realistic. Discrete-event simulation is an experimental approach that is often used; it allows a high level of detail to be modelled since assumptions about buffer space, processing time distributions, or priority dispatching can be modelled. It was assumed that all stations would be available for assembly operations and that the new spray booth had negligible downtime. It is also critical to note that the final vehicle inspection station would not constrain the main assembly line, since the finished vehicle could be moved away from the line and not block station 15. Figure 1 is a schematic diagram of the facility layout of the assembly line that was used for the Showflow model.


The model was coded using trigger language interpreter (TLI). For example, a table for processing times is first created, and a job parameter such as the processing time for the first vehicle model on the first workstation will be coded as elem[product[E,1],1]. TLI was also used to control entry of vehicles into the first workstation - i.e.

if elqueue[E]>1 then elaccept[E]:=false.


5.2 Verification and validation of the simulation model

After building the model, verification and validation were carried out to check the accuracy of the model. The productions' historical data was compared with the simulation data, and showed acceptable accuracy - less than five per cent deviation from real values. The objective of model verification was to ensure that the conceptual model was reflected accurately. Table 4 shows simulation results for a 30-day period for vehicles entering the assembly line hourly, in the sequence VW-HB-TGM-TGS-CLA.

Table 4: Simulation results for validation

Station number Produced Average queue Utilisation
1 236 0.64 54.3
2 235 0.55 45.74
3 234 0.6 56.23
4 233 0.58 41.49
5 233 0.63 56.11
6 233 0.56 35.27
7 233 0.64 62.79
8 232 0.54 46.84
9 231 0.5 50.16
10 231 0.24 19.25
11 230 0.62 39.07
12 229 0.69 49.6
13 228 0.77 64.31
14 228 0.74 73.94
15 228 0.45 38.13
16
227
0.79
78.85

We used the time representation 60 units = 1 minute, 60 minutes = 1 hour, 8 hours = 1 day. The simulation results for vehicles produced are in line with the average monthly sales shown in Table 1 - an indication that the lumped model input/output relations accurately map those of the real system.