In this paper, in the process of explosion-proof inspection at offshore plant, a meaningful knowledge was extracted for the accumulated data (event log) from the production and quality management system and this was analyzed a frequency perspective and a duration perspective using a process mining technique. It was found that there had more lead time than 1~2 days predicted for explosion-proof inspection. Three of improved methods were proposed to improve the process of explosion-proof inspection. The first was a combine inspection, which is conducted together with the prerequisite inspection and explosion-proof inspection. There are many duplicate inspection items in the electric and instrument prerequisite inspection items and explosion-proof inspection items. For this reason, it was expected that the total inspection time will be reduced due to the reduced burden on duplicate inspection items and prompt on-site action on the faults found. As a result of the combine inspection for 4 days, the efficiency of the combine inspection was lower than the separate inspection due to the waiting for the explosion-proof inspector during the prerequisite inspection, the waiting for the prerequisite inspector during the explosion-proof inspection, and the waiting for the explosion-proof inspector for equipment that does not require explosion-proof inspection. The second was the cable gland pre-inspection. Cable gland inspection is required for most explosion-proof inspections and can be conducted with or without the completion of prerequisite inspections. As the cable gland inspection is only one check item for explosion-proof inspection, it was not available to proceed with a the inspection as official inspection and was conducted when the explosion-proof inspector had a time to spare. Therefore, no objective figures to confirm the improvement were obtained. However, it was found that the explosion-proof inspection time was reduced in the applied module compared to the non-applied module. Third, an algorithm was developed to check tags that is able to perform the explosion-proof inspection through the system, which had checked by manual method. The algorithm is composed of an input, a process, and an output. The input is equipment tag list, cable tag list, and quality verification document (QVD) extracted from the quality management system that require explosion-proof inspection. The process checks the completion status of all the prerequisite QVDs such as the cable inspection and the prerequisite equipment inspection required for the individual explosion-proof equipment from the extracted QVD status data. The output is that the system screen shows the tag which is able to carry out an explosion-proof inspection if all the prerequisites for explosion-proof inspection are completed. For empirical verification of this algorithm, two modules that did not start the inspection were applied for about 11 months. In the first module, the time from the completion of the prerequisite QVD to the completion of explosion-proof QVD is improved up to 11 days. The second module, maximum 8 days was improved. In particular, it was found that the QVDs with high frequency were improved more. Although some of the prerequisite QVDs had an additional delay of 1 to 4 days and no improvement after application of the algorithm, the frequency was not high and did not significantly affect the process delay. From this paper, a significant delay improvement was achieved through an algorithm for confirming the explosion-proof inspection possibility in the explosion-proof inspection process. In addition, it was found that the cable gland pre-inspection, which could not confirm the improvement result with quantitative values, also contributed to the improvement of the explosion-proof inspection productivity. Through the improvement of the explosion-proof inspection process, it helped the schedule of MC (Mechanical Completion) Walk-down and commissioning, which is a follow-up process, and helped the entire project completion schedule reduction. In addition, improvements in human and material resources that occurred in the previous manual method were also found.