The producer focuses on producing parts that match the customer's requirements during manufacturing the automotive engine parts. One of the essential automotive engine parts is a crankshaft used to translate movement from the pistons to the car axle. The crankshaft is a complex shape and difficult to produce accurate dimensions during the machining processes. Many machines are used to create the crankshaft. Therefore, many defects happen during the machining process, reducing reliability and increasing the manufacturing process's production cost. This paper focuses on analyzing failure data and reliability of the crankshaft production line that occurs during the manufacturing process of one year. The common failure associated with the manufacturing process were ring screw, unbalanced crankshaft, broken drill screw, hub machining error, mains part machining error, and setup error. The paper aimed to determine and analyze the best failure fit between the distribution methods, such as Weibull, normal, lognormal, and exponential. Also, the reliability, hazard rate, surviving quantity, and failure density were calculated to evaluate the current situation and predict the reliability of the production line. Results proved the skewness of the data was positive equal to 3.33; the last months had the highest production failure rate, which is 53.8%, the normal method had a proper distribution of data depending on the Anderson-Darling (adj) values which is 1.367 when it compared with other methods, the normal method had the best fitting result depended on failure percentage, from 1% to 95% of the crankshafts production are expected to fail between 47.2676 and 1149.85 months respectively. The reliability of the production line decreased with manufacturing time increased. To reduce the failure and increase reliability, the maintenance system must be supported, analyze the sources that cause failure and downtime of the production line, continue the employee training system on an ongoing basis, and support the production line with modern technology. All analytical results and suggestions could be valuable to the production line to improve reliability and reduce the manufacturing process's failure.