In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a “robot-based cargo loading and unloading system”. In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the “robot-based cargo loading system”. The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.

Continuing and Developing the growth, the company should focus on 'time to market of high quality production' and 'cost down'. Due to the complexity of the production, reliability is being one of the important factor. In this study, in particular, focus on parts reliability test improvement about printer product and proceeded. Need test improvement for time to market that is developed, and reduce expense with test period that is required at Reliability Test time of new parts and should high quality level of product. Finally huge Failure Cost (F-COST) occurs because quality level of product drops if parts that accomplish product does not ensure reliability and this is associated by malfunction in marketplace.

As part of a hierarchical production control system, a policy is proposed for the admittance for new parts to a flexible manufacturing system (FMS) together with two different heuristics for dynamic routing of parts. The heuristics that consider the process plan's flexibility with respect to machines and operations are based upon operating time and machine reliability. To quantify flexibility, it is developed a priority type of the measurement considering machine's reliability that incorporates all the job and machine characteristics that contribute to routing flexibility. HUR which considers machine reliability is applied to drive some simulated systems and compared with the HUS using SPT rule. It is found that HUR outperforms HUS using SPT in terms of makespan and machine utilization.