جستجوی مقالات مرتبط با کلیدواژه "statistical quality control" در نشریات گروه "مدیریت"

The objective of this research is to predict and monitor the ton-kilometers and waybill on the Iran's roads to find abnormal Behavior. In this study, data was collected from monthly observations over a period of 6 years (1395 to 1400) by the Iran Road Maintenance and Transportation Organization, categorized by province. Different machine learning techniques, deep learning, and time series methods were employed to predict ton-kilometers, and the results were monitored for abnormal behavior following an increase in interest rates and taxes. For model implementation, a dataset of 72 records of ton-kilometers and 72 records of issued waybill, collected from 32 provinces over six years of road transportation, was utilized. Initially, four different prediction methods, including random forest, LSTM neural network, ARIMA, and ETS, were extensively examined. The empirical results indicate that the random forest outperforms the other models. this study employs the statistical quality control tool, the z-score, to detect outliers and abnormal behavior in the data. The empirical findings reveal that out of the 32 provinces, three provinces exhibit abnormal behavior, and one of them is attributed to factors other than an increase in interest rates and transportation taxes.

Earned Duration Management (EDM) method has been developed to enhance Earned Value Management and to solve some of its problems, such as separation of measuring time and cost dimensions. By providing performance indicators, this method makes it possible for managers to accurately measure the progress of a project and to calculate the time and cost required to complete it. Nevertheless, the accepted rate of progress indexes of a project is not specified in this method, which brings about ambiguity. Intending to improve earned duration management performance, statistical quality control charts were used, which had previously been applied in Earned Value Management to determine the acceptable cut-off points of indexes of project management. Using Monte Carlo simulation, these charts were calculated at the time of project planning, but not during project implementation. Such calculations give project managers prospective view; in addition, they provide a basis for evaluating and comparing the performance of projects during their progress.