Open AccessUsage of time series forecasting model in Supply chain sales prediction
Author(s) -
Ashwin G. Raiyani,
Amit Lathigara,
H. D. Mehta
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1042/1/012022
Subject(s) - autoregressive integrated moving average , computer science , supply chain , time series , sales forecasting , supply chain management , model selection , contest , transformation (genetics) , series (stratigraphy) , operations research , data mining , econometrics , artificial intelligence , machine learning , engineering , economics , marketing , paleontology , biochemistry , chemistry , biology , political science , law , business , gene
This paper representing a study of supply chain operation data that was used on 100 different store items from 10 stores using 5 years history of sales through open sources contest to compare the performance of time-series forecasting model mainly, decomposition, Auto-Regressive Integrated Moving Average(ARIMA), Prophet, Box-Cox transformation. Here data is collected from 2013 to 2018 were used in real-time transaction at different store, initially model was applied on 2013 to 2017 data and based on the that predicted for 2018 then again cross checked with actual 2018 with proceed predicted data of 2018. To improve the performance and evaluation of the supply chain management system, scrutiny 3 metrices that will help to make decision on the model selection. The accuracy of the Machine learning model in forecasting future sales of supply chain store. Although the result on comparison indicates that there is no single method gives better and superior result. But present study indicates that prophet and ARIMA hybrid model gives better result compare to individual model.