scholarly journals Predicting the Demand for Fmcg using Machine Learning

Author(s):  
Anish Mebal. P ◽  
Hema. S ◽  
Jothika. S.J ◽  
Manochitra M

Now-a-days the more accurate prediction of the demand for fast-moving consumer goods (FMCG) is a competitive factor for both the manufacturers and retailers, especially in the super markets, wholesale manufacturers and fresh food sectors and other consumable industries. This proposed system presents the benefits of Machine Learning in sales forecasting for short shelf-life and highly-perishable products, as it predict the statistical information as a result, improves inventory balancing throughout the chain, improving availability to consumers and increasing profitability. This performance is done with various classification algorithms and comparative study is done with some metrics like accuracy, precision, recall and f-score. So that it helps in finding customer need and to increase the profit of the manufacturers

2019 ◽  
Vol 52 (13) ◽  
pp. 737-742 ◽  
Author(s):  
Elcio Tarallo ◽  
Getúlio K. Akabane ◽  
Camilo I. Shimabukuro ◽  
Jose Mello ◽  
Douglas Amancio

Omega ◽  
2020 ◽  
pp. 102389
Author(s):  
Xavier Andrade ◽  
Luís Guimarães ◽  
Gonçalo Figueira

2013 ◽  
Vol 694-697 ◽  
pp. 3480-3483
Author(s):  
Shou Wen Ji ◽  
Zeng Rong Su ◽  
Zhi Hua Zhang

The paper analyzes the extended spanning trees elements corresponding to fast-moving consumer goods (FMCG) logistics quality. According to extended spanning tree, we establish a logic model of FMCGs logistics quality causal tracing. At last, the paper gives out tracing algorithm and specific tracing process of FMCG logistics quality based on extended spanning tree.


2021 ◽  
Vol 10 (3) ◽  
pp. 179
Author(s):  
Zane Simpson ◽  
Anneke De Bod ◽  
Jan Havenga ◽  
Esbeth Van Dyk ◽  
Isabel Meyer

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