Retail demand forecasting using light gradient boosting machine framework
dc.contributor.author | Hewage, C | |
dc.contributor.author | Perera, N | |
dc.date.accessioned | 2022-10-12T08:05:18Z | |
dc.date.available | 2022-10-12T08:05:18Z | |
dc.date.issued | 2022-09 | |
dc.description.abstract | Preparing product-level demand forecasts is crucial to the retail industry. Importantly, reliable in-ventory and replenishment decisions for retail products depend on accurate demand forecasts. This allows retailers to enable better pricing and timely promotion plans while leading to huge cost reductions [1]. Often, retail promotions create demand irregularities for products. Customers may change their buying behavior by purchasing more products for future consumption (stockpiling), thereby increasing sales in the promotional period. Then, for a brief time, sales may fall below normal levels before gradually returning to normal levels. The period with a dip in demand is known as the post-promotional period [2]. Thus, a retail promotion has three distinct periods: (1) normal, (2) promotional, and (3) post-promotional, each with its own set of demand fluctuations | en_US |
dc.identifier.doi | https://doi.org/10.31705/BPRM.v2(1).2022.8 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.journal | Bolgoda Plains Research Magazine | en_US |
dc.identifier.pgnos | pp 28-31 | en_US |
dc.identifier.uri | http://dl.lib.uom.lk/handle/123/19095 | |
dc.identifier.volume | 2 | en_US |
dc.identifier.year | 2022 | en_US |
dc.language.iso | en | en_US |
dc.subject | Retail demand forecasting | en_US |
dc.subject | Gradient boosting machine | en_US |
dc.title | Retail demand forecasting using light gradient boosting machine framework | en_US |
dc.type | Article-Full-text | en_US |