Paper Details
Predictive Modeling for Demand-Driven Distribution Planning Based on Hybrid LSTM-DANN Approach
Authors
Narendra Sharad Fadnavis
Abstract
Recent developments in business and technology have prompted distribution channels in a number of industries to adjust to new performance criteria. Increased service level expectations from retail consumers and the trend of suppliers and manufacturers outsourcing distribution have introduced new difficulties into the field of supply chain management. As a result, most companies will face the challenging challenge of restructuring their distribution networks. The other side is that not many companies look at the customer journey as a complete.Training the model, selecting features, and preprocessing are the main components. As part of the data pre-processing phase, data cleansing is carried out, which involves identifying and fixing dataset anomalies, quantization normalization, and smoothing. Attribute or feature selection refers to the process of choosing a subset of pertinent features to simplify the challenge. In order to accomplish this, we trained our models using the LSTM-DANN framework. To the contrary, it makes LSTM and DANN ineffective. The numbers point to a success percentage of 97.48%.
Keywords
Citation
Predictive Modeling for Demand-Driven Distribution Planning Based on Hybrid LSTM-DANN Approach. Narendra Sharad Fadnavis. 2023. IJIRCT, Volume 9, Issue 1. Pages 1-11. https://www.ijirct.org/viewPaper.php?paperId=2504034