การลดเวลานำในการปรับสภาพรถโดยสารประจำทางโดยใช้แนวคิดลีน

• Main Author: รัชนีวรรณ สันลาด
• Other Authors: ผศ.ดร.รุ่งฉัตร ชมพูอินไหว
• Format: Independent Study
• Language: Thai
• Published: เชียงใหม่ : บัณฑิตวิทยาลัย มหาวิทยาลัยเชียงใหม่ 2017
• Subjects: การผลิตแบบลีน
• Online Access: http://cmuir.cmu.ac.th/jspui/handle/6653943832/40011
• Institution: Chiang Mai University

This research focuses on reducing bus overhaul times at a bus company which provides public bus transportation services and studied the overhaul of five “BENZ” buses. Time-variance values for the bus overhaul process were assessed, with the process being divided into two categories: (i) overhauling the buses using the original method (three buses in total) and (ii) overhauling the buses using the lean concept (as suggested by the researcher; two buses in total). The study focused on the lead times for the overhaul process across four divisions, the body, coloring, air compressor and electrical - electronics division. The research began with a current state value stream mapping process, then the value of the sub overhauling process was analyzed, in order to separate out the value-added processes from the non-value adding processes. After that the causes of time and factor analysis of the lead times and any variances found at the bus maintenance station were carried out using a Fish bone diagram and Future State Value Stream Mapping, to assess how the overhaul process might be improved to achieve a smoother flow. To develop the modified process, the researcher used lean tools and techniques such as ECRS, Convert Internal to External Setup, the Two-Bin system and Visual Control. The research results show that the modified process was able to reduce the overhaul lead time from 497.62 hours to 336 hours (a reduction of 161.62 working hours) and also reduce the overhaul time range from 332 hours to 64 hours (faster by 268 working hours). This reduction in time-variance will allow the automotive division to plan a continuous and smoother service, as well as be able to better predict overhaul schedules.