Comparative analysis of limited stock distribution principles in logistics network
MAGAZINE №6 (95) December 2019
AUTHORS ERMOLINA M.V., ZAKHODYAKIN G.V.
CATEGORY Information technologies in logistics and SCM Inventory management Simulation modelling
ABSTRACT
The article considers the situation when a company needs to distribute limited amount of stock to the regional warehouses in its own two-echelon distribution network. The network consists of a single distribution center and several regional facilities which are serving the company's customers. It is supposed that every warehouse calculates its requirements for the replenishment daily basing on the on-hand inventory, demand forecast, safety stocks and lead-times from the central warehouse. Thus, company's managers are aware of the consumption rate and inventory level at each regional facility. Demand forecasting and final replenishment planning decisions are centralized.
Notion of the "limited stock" refers to such inventory quantity at the central warehouse that is insufficient to satisfy the total volume of all regional warehouses' requirements for the product. Limited stock situation may have varying length in time.
A system of rationing rules or principles should be applied to make a distribution decision in such a situation. These set of rules identify the volume and sequence of the shipments from the central to regional warehouses.
So, in this article authors aim to solve the following problems:
- to identify factors that affect the choice of a certain set of rationing rules for the limited stock;
- to attempt to classify existing rationing principles;
- to identify how the business goals affect the choice of the preferred rationing principle;
- to create an imitation model and check experimentally which rationing principles are the best for each of the business goal
The outcomes gained might be used as a base for the choice of the limited stock rationing principles in companies with own distribution network, and for better tuning of the distribution algorithms in DRP systems or modules.
Keywords: inventory management imitation modeling simulation modeling simulation model inventory planning optimization stock rationing stock DRP
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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