Retail Giant Sales Forecasting using Machine Learning
Ashwini Rekha. Banjanagari1, Vijay Kumar. B2
1Ashwini Rekha. Banjanagari, Department of Information Technology, G. Narayanamma Institute of Technology and Science, Hyderabad (Telangana), India.
2Vijay Kumar. B, Department of Information Technology, G. Narayanamma Institute of Technology and Science, Hyderabad (Telangana), India.
Manuscript received on 15 October 2019 | Revised Manuscript received on 24 October 2019 | Manuscript Published on 02 November 2019 | PP: 2408-2411 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B12770982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1277.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.
Keywords: Machine Learning, ARIMA, Sale-Forecasting, Smoothing, COV And Classical Decomposition.
Scope of the Article: Machine Learning