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dc.contributor.author Saradha, RMS
dc.contributor.author Samadhi, MA
dc.contributor.author Manawadu, I
dc.contributor.author Ganegoda, GU
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Mahadewa, KT
dc.date.accessioned 2022-11-10T03:13:02Z
dc.date.available 2022-11-10T03:13:02Z
dc.date.issued 2021-12
dc.identifier.citation R. M. S. Saradha, M. A. Samadhi, I. Manawadu and G. U. Ganegoda, "A Framework to Detect Sale Forecasting with Optimum Batch Size," 2021 6th International Conference on Information Technology Research (ICITR), 2021, pp. 1-6, doi: 10.1109/ICITR54349.2021.9657288. en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19456
dc.description.abstract Today, sales forecasting plays a key role for each business. To maintain the sales process successfully, every manufacture focus on retaining optimum production batch size. Therefore, this study aims to develop a framework to detect sale forecasting with optimum batch size. This work focuses on predict future sales and optimum production batch size by using different machine learning techniques and trying to determine the best algorithm suited to the problem. Here, Auto-Regressive Integrated Moving Average (ARIMA) model is used to predict future sales and Artificial Neural Network (ANN) model is developed to determine the optimum level of production as a function of product unit, setup cost, and holding cost in our approach and have found these models have better result than other machine learning models. en_US
dc.language.iso en en_US
dc.publisher Faculty of Information Technology, University of Moratuwa. en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9657288/ en_US
dc.subject sales forecasting en_US
dc.subject Auto-regressive integrated moving average en_US
dc.subject Optimum batch size en_US
dc.subject Artificial neutral network en_US
dc.title A framework to detect sale forecasting with optimum batch size en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2021 en_US
dc.identifier.conference 6th International Conference in Information Technology Research 2021 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.proceeding Proceedings of the 6th International Conference in Information Technology Research 2021 en_US
dc.identifier.doi doi: 10.1109/ICITR54349.2021.9657288 en_US


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  • ICITR - 2021 [39]
    International Conference on Information Technology Research (ICITR)

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