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 |