Improving Efficiency of Mineral Terminal Performance using Lean Railroading

Abstract

The purpose of this study is to investigate and identify the waste from the unloading station of mineral cars using the lean thinking approach. In this paper, one of the largest steel producers is investigated as a case study. The time information was collected from the database system of the factory and railway company from April to July 2017. The wastes were identified by using the value stream map, and to reduce the waste, ten solutions have been proposed based on the changes in: (a) operation management; (b) purchase of new equipment; and (c) infrastructure development. Implementation of solutions using the Planimate simulator software shows that the best strategy consists of a combination of three operational management solutions without having to purchase new equipment and without any infrastructure development, which can improve station performance by up to 52% with regards to the total time for the unloading process. In addition, the valuable time of the whole process has been increased from 9 to 19%. This improvement will result in an increase of 21% in the annual turnover of cars to the station destination. As a result, each car annually carries on average 450 tons more freight, in which case the railway’s revenues from carrying iron ore would be increased by 24% for this destination.

Improving operational efficiency of iron ore logistics by rail using simulation

“…In sum, contrary to the study of Fakoor et al 14 in which the operations management had the greatest improvement in system performance, it can be found, according to Figure 6, that the fifth scenario reached the best overall performance in improving the average total stop time of a car in the system by 20%. Based on this scenario, using a mechanized loading system will increase the speed and efficiency of the loading system at station O.…”

“…This scenario is defined as a superior scenario in improving the unloading system performance. This is consistent with the findings of Fakoor et al 14 Figure 5 presents the impact of the scenarios on the average improvement of the total stop time of a car at station O. As can be seen, the fifth scenario causes the highest improvement in the performance of loading terminal, by 44%.…”

“…According to the findings, using the optimal policy, it is possible to reduce the total stopping time of the train at the unloading terminal up to 50% at best. 14 This study was similar to the research done by Straka et al; 15 however, it only addressed the impact of the policies on the performance of the unloading terminal and did not consider the impact on the performance of predecessor stations, especially the loading terminals, that is, considering the origin-destination network. This gap can also be found in the research focusing on other logistics facilities such as container terminals.…”

“…In an attempt to reduce the delays, Boisen et al 13 used dynamic programming for scheduling the train loading and unloading process. Finally, Fakoor et al 14 investigated the impact of implementing different policies on terminal performance by examining a mineral unloading station. According to the findings, using the optimal policy, it is possible to reduce the total stopping time of the train at the unloading terminal up to 50% at best.…”