Improving Production in Automobile Manufacturing System Using Measurement and Prediction of Man-Machine Performance: A Case Study
This study aims at the use of man and machine input to improve production in an automobile manufacturing system. The goal of this study is carried out using python and MySQL programs to analyze data extracted for parts used to manufacture two car models; Toyota Avalon and Toyota Yaris. Parts where modeled and stored in a Structure Query Language using Python to write an app to help track demand of parts. The current process had a total cycle and rate of 66 weeks and 12hrs 30 minutes to set up a car model in the assembly line. After design of the Value stream map (material and information flow chart) diagram using MSVISIO 2016 for the current process, a key performance factor; reject ratio (rate of parts going out of stock) was identified that affects the lead-time which is caused by equipment failure. During order analysis simulation using excel, parts bearing part numbers 200542,201501,100330 and 200546 fell short within 0, -24, -22, -17 and -16 in the six-sigma chart respectively. A model was built to curb this factor using Material requirement planning (MRP). MRP is used to plan for parts with low inventory in the assembly line. In conclusion it shows that to make one assembly part 200401; four of part 200402 and eleven of part 200403 will be needed having eliminated poor reject ratio. This aids in dropping the initial lead time to 2 hours and 30 minutes to manufacture a car model and decreasing the task time.