Open AccessImplementation of Demand Forecasting – A Comparative Approach
Author(s) -
Punit Gupta,
Harshit Ladia,
Kabir Kakkar,
Kriti Rai,
Y. C. Agrawal,
Rishika Mamgain,
Navaditya Gaur
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1714/1/012003
Subject(s) - demand forecasting , backup , computer science , supply and demand , predictive analytics , profit (economics) , profit margin , field (mathematics) , operations research , margin (machine learning) , economics , engineering , machine learning , microeconomics , finance , pure mathematics , mathematics , database
Forecasting is a crucial factor in development of store head businesses either commercial or economical that require frontend sales to outgrow. Demand forecasting hence deals with providing our stores a significant amount of backup supply prediction which hence can deal with the supply to meet the ups and downs of our demand. Hence, a predictive model has been developed to maintain the supply of goods and featuring the predictions of upcoming demand for the next few selected time period. The model involves being highly trained in the field of Machine Learning using various predefined models such as Linear Regression, GBT Model and even using the time series analysis for the defined period and using the outcome from the factor of inputs provided by the user to finally predict the upcoming demand and provide (increase or decrease) the given supply in the specific field, thereby saving the businesses from any uncertain downhill and hence helping analytics to provide more reasonable theories for increasing the profit margin in the upcoming foreseeable future and even get a track record and deep insights of the previous demand using a business intelligence software, like PowerBI.