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Supply Chain Forecasting Employing Auto-Regressive Integrated Moving Average Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 52 |
| Year of Publication: 2022 |
| Authors: Jagriti Singh, Rahul Maheshwari |
10.5120/ijca2022921937
|
Jagriti Singh, Rahul Maheshwari . Supply Chain Forecasting Employing Auto-Regressive Integrated Moving Average Model. International Journal of Computer Applications. 183, 52 ( Feb 2022), 23-27. DOI=10.5120/ijca2022921937
Abstract
Supply chain Management has remained an active area of research due to its widespread applications in several domains of manufacturing business. Off late, supply chain forecasting has emerged as a very effective tool which is useful in streamlining production, logistics and manpower thereby critically affecting the profit margin. Supply chain forecasting predominantly deals with forecasting of demands of the products and goods based on previously available data. The estimation of demands directly impacts the production, which in turn influences the supply. The current supply has a critical impact on the future demands. Due to the enormity of data to be analyzed, supply chain forecasting is prone to errors in forecasting. Off late, machine learning based algorithms have been in the forefront for supply chain forecasting. This work presents an Auto-Regressive Integrated Moving Average Machine Learning Model for Supply Chain Forecasting. It is shown that the proposed work attains higher accuracy of forecasting compared to existing technique.
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