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forecasting tools for optimizing the supply chain

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dc.contributor.author CHAANBI, BOUTHAINA
dc.date.accessioned 2020-11-30T14:33:15Z
dc.date.available 2020-11-30T14:33:15Z
dc.date.issued 2020
dc.identifier.uri http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/21510
dc.description.abstract Supply chain planning is a critical activity in the supply chain management strategy. Having smart work tools to develop concrete plans is a necessity in today's business world Demand forecasting plays an important role in many areas, including supply chain management and inventory management. In fact, inventory control and efficient inventory management are often based on forecasts that make it possible to estimate the demand, for weeks or even months in advance, and thus order the necessary amount. We present our project with a growing online retail platform, Stock&Buy, servicing hundreds of customers all over the world, as an example of how retailers can use the wealth of data to optimize supply chain and inventory decisions through demand forecasting. One of the main challenges of Stock&Buy is estimating demand for thousands of products exhibiting different underlying demand patterns (seasonality, intermittency, etc.) In practice, many quantitative and qualitative methods are used to forecast demand and minimize the forecasting error. Within this context, and in order to tackle Stock&Buy challenges, we provide a review of the theoretical framework and the state of the art related to demand forecasting, essentially time-series forecasting. We also propose demand-forecasting tool to estimate demand for different product variants, which are, for the most part, associated with intermittent and lumpy demand patterns, whereby only a handful of distinct product units are sold every day. en_US
dc.language.iso en en_US
dc.subject Supply Chain, Demand Forecasting, Time Series, Machine Learning, Statistical Methods Resumé en_US
dc.title forecasting tools for optimizing the supply chain en_US
dc.type Thesis en_US

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