#TITLE_ALTERNATIVE#

• Main Author: (NIM: 13413044), GRACE
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/22301
• Institution: Institut Teknologi Bandung

PT X engages in the furniture industry. One of its products is multi chair. Multi chair is the largest in number of type and has intermediate demand with the type of job shop production. <br /> <br /> <br /> Based on production data on February and March 2017, bending construction of PT X was unable to perform multi chair production process in accordance with the plan so that daily production target was not achieved. Percentage of frequencies not reaching daily production target on February was 74% and on March was 56% with deficiencies 17331 and 13393 units respectively. The inability of bending construction to implement the production plan is influenced by the duration of frame component work. Component work consists of setup and operation time. The comparison of setup and operation time is 22:78. So, both components of this time should be considered. <br /> <br /> <br /> The cause of the high incompleteness of daily production targets is the absence of scheduling models. Currently, the production process is done only on the basis of the machines and materials availability sent from the warehouse. Therefore, this study aims to design the production scheduling model by considering the setup time that can minimize the multi chair makespan in bending construction. The proposed model is a lot-detached setup. This model is a technique of splitting a lot (job) into smaller lot (sublots) so that sublots work on different operations can be done simultaneously. This model also makes it possible to set up certain operations before the sublots of the previous operations are done. Consequently when the sublots of the previous operation have been completed, the transfer and process on the next machine can be done immediately. <br /> <br /> <br /> The proposed scheduling model is tested by using two sets of historical data, which are data on February 2, 2017 and March 24, 2017. This results in the reduction of makespan by 19.62% and 20.71%, respectively. The resulting makespan is 23832 seconds for the set of historical data 1 and 23698 seconds for the set of historical data 2. This indicates that the scheduling with the proposed model is better than the historical model. <br />