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Abstract
The effect of automatic identification and data capture (AIDC) on developing LEAN manufacturing is studied via:
1. Reducing the idle time of the machines to minimize the make span of the manufacturing time by applying the proposed rule named ITPF. This rule depends on the network-based schedule of jobs with precedence constraints, which improves the machine utilization.
2. Development of the tabular method to be a modified tabular method (MTM) via revisement of the starting point, which is called the value joint factor (VJF). Using the value joint factor reduces the number of iterations to formulate cells. The (MTM) reduces the exceptional parts and voids to increase the utilization of the machines and throughput. The Cellular manufacturing design is a LEAN prerequisite.
3. Development of a mathematical model to rearrange the machines layout (a LEAN prerequisite) to reduce the traveling distance and costs. When applying the proposed layout algorithm the setup times are reduced, also the intra-cell movement is reduced.
4. On applying the automatic identification and data capture on the shop floor and studying its effect on the manufacturing time, the response time is reduced and any errors occurring during the manufacturing could be repaired. The recording data is enhanced by scanning every step executed in the shop floor.
5. Applying the LEAN principals and logistics using the PROMODEL.
6. A case study is performed using PROMODEL. The case study was an automotive spare parts workshop. The data were gathered, the workshop is divided into cells, and then the layout of the machines is found. The new layout is simulated using the PROMODEL. The results of the simulation proved the effectiveness of the proposed models.