A Framework for Modeling Efficient Demand Forecasting Using Data Mining in Supply Chain of Food Products Export Industry

2010 ◽  
pp. 1387-1397
Author(s):  
Pongsak Holimchayachotikul ◽  
Nuanlaor Phanruangrong
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Food Products ◽  
Export Industry ◽  
Using Data

scholarly journals Garment Categorization Using Data Mining Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 984
Author(s):  
Sheenam Jain ◽  
Vijay Kumar
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Random Forest ◽  
Good Deal ◽  
Classification Model ◽  
Upper Body ◽  
Whole Body ◽  
Product Data ◽  
Soft Classification ◽  
Using Data

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Demand forecasting and Route Optimization in Supply chain industry using Data Analytics

2021 ◽  
Author(s):  
Mohan B A ◽  
Harshavardhan B ◽  
Karan S ◽  
Mohammed Jawaad Shariff ◽  
Pranav M G
Keyword(s):  
Supply Chain ◽  
Data Analytics ◽  
Demand Forecasting ◽  
Route Optimization ◽  
Using Data

Clustering Return Items in a Multi-Layer Multi-Product Reverse Supply Chain Using Data Mining

2013 ◽  
Vol 1 (4) ◽  
pp. 136
Author(s):  
Mohammadi Lida ◽  
Fazlollahtabar Hamed ◽  
Mahdavi Iraj
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Reverse Supply Chain ◽  
Using Data
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