Prospects of Application of Logistics Indicators Monitoring Systems in Manufacturing Enterprises
MAGAZINE №6(89) December 2018
AUTHORS
BARASHKOV A.V. - Postgraduate student, Department of Logistics and Supply Chain Management, National Research University Higher School of Economics (St.-Petersburg, Russia)
CATEGORY Controlling Information technologies in logistics and SCM Industrial companies’ corporate logistics
ABSTRACT
This article discusses perspectives of the use of monitoring systems in the enterprises of the manufacturing sector of logistics. The case for the implementation of such systems on real production facilities was considered. The economic efficiency of using monitoring systems was analyzed. For this, three scenarios were created under which it is possible to make mistakes when performing the business process of determining the need for inventory items. The first of them described a situation in which the time of error detection was 1 day; the second scenario suggested an increase in the time of failure to 2 days; scenario 3 described the use of the real-time monitoring system and described situation in which failure in the supply chain caused by shortage of goods was determined instantly. After that, these scenarios were analyzed on the basis of existing contract for production of certain number of finished products. It is shown that the monitoring system can significantly reduce batch production time due to a significant reduction in the probability of making mistakes. Finally, it was shown how the implementation of monitoring and automation system for a number of tasks affects the efficiency of business processes.
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Comparison of the Artificial Data Generation Methods for Deep Training of Monitoring System
MAGAZINE №3(86) June 2018
AUTHORS
SOBOLEVSKIY V.A. - Graduate student, Russian Academy of Sciences, Laboratory of Information Technologies in System Analysis and Modeling, St. Petersburg Institute of Informatics and Automation (St. Petersburg, Russia)
CATEGORY Analysis in logistics and SCM Simulation modelling
ABSTRACT
This article deals with the problem of input data generated for the creation and training of an artificial neural network which is the basis of the classification module of a dynamic monitoring system of the manufacture performance indexes. The input data that was used to train the neural network was divided into the following categories: real data, generated data for a given distribution, and data obtained using the simulation approach. The simulation model was created using the apparatus of Petri nets. Further, for the data used in the work, classification rules were set, after which the artificial neural network was trained on each data set. At the next stage, real data was submitted to the monitoring system which are previously did not appear in the training and validation of neural networks. The final step of this study was to compare the results of the classification of the described approaches of artificial generation of values of enterprise input parameters with respect to the control data set.
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