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Статті в журналах з теми "Lissages exponentiels":
Indjehagopian, Jean-Pierre, and Sandrine Macé. "Mesures d'impact de promotion des ventes : Description et comparaison de trois méthodes." Recherche et Applications en Marketing (French Edition) 9, no. 4 (December 1994): 53–79. http://dx.doi.org/10.1177/076737019400900403.
Leclerc, André, and Mario Fortin. "Économies d’échelle et de gamme dans les coopératives de services financiers : une approche non paramétrique (DEA)." Articles 85, no. 3 (November 10, 2010): 263–82. http://dx.doi.org/10.7202/044877ar.
Дисертації з теми "Lissages exponentiels":
Cifonelli, Antonio. "Probabilistic exponential smoothing for explainable AI in the supply chain domain." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR41.
The key role that AI could play in improving business operations has been known for a long time, but the penetration process of this new technology has encountered certain obstacles within companies, in particular, implementation costs. On average, it takes 2.8 years from supplier selection to full deployment of a new solution. There are three fundamental points to consider when developing a new model. Misalignment of expectations, the need for understanding and explanation, and performance and reliability issues. In the case of models dealing with supply chain data, there are five additionally specific issues: - Managing uncertainty. Precision is not everything. Decision-makers are looking for a way to minimise the risk associated with each decision they have to make in the presence of uncertainty. Obtaining an exact forecast is a advantageous; obtaining a fairly accurate forecast and calculating its limits is realistic and appropriate. - Handling integer and positive data. Most items sold in retail cannot be sold in subunits. This simple aspect of selling, results in a constraint that must be satisfied by the result of any given method or model: the result must be a positive integer. - Observability. Customer demand cannot be measured directly, only sales can be recorded and used as a proxy. - Scarcity and parsimony. Sales are a discontinuous quantity. By recording sales by day, an entire year is condensed into just 365 points. What’s more, a large proportion of them will be zero. - Just-in-time optimisation. Forecasting is a key function, but it is only one element in a chain of processes supporting decision-making. Time is a precious resource that cannot be devoted entirely to a single function. The decision-making process and associated adaptations must therefore be carried out within a limited time frame, and in a sufficiently flexible manner to be able to be interrupted and restarted if necessary in order to incorporate unexpected events or necessary adjustments. This thesis fits into this context and is the result of the work carried out at the heart of Lokad, a Paris-based software company aiming to bridge the gap between technology and the supply chain. The doctoral research was funded by Lokad in collaborationwith the ANRT under a CIFRE contract. The proposed work aims to be a good compromise between new technologies and business expectations, addressing the various aspects presented above. We have started forecasting using the exponential smoothing family which are easy to implement and extremely fast to run. As they are widely used in the industry, they have already won the confidence of users. What’s more, they are easy to understand and explain to an unlettered audience. By exploiting more advanced AI techniques, some of the limitations of the models used can be overcome. Cross-learning proved to be a relevant approach for extrapolating useful information when the number of available data was very limited. Since the common Gaussian assumption is not suitable for discrete sales data, we proposed using a model associatedwith either a Poisson distribution or a Negative Binomial one, which better corresponds to the nature of the phenomena we are seeking to model and predict. We also proposed using Monte Carlo simulations to deal with uncertainty. A number of scenarios are generated, sampled and modelled using a distribution. From this distribution, confidence intervals of different and adapted sizes can be deduced. Using real company data, we compared our approach with state-of-the-art methods such as DeepAR model, DeepSSMs and N-Beats. We deduced a new model based on the Holt-Winter method. These models were implemented in Lokad’s work flow
Rostami, Tabar Bahman. "ARIMA demand forecasting by aggregation." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00980614.
Частини книг з теми "Lissages exponentiels":
Aragon, Yves. "Lissage exponentiel." In Pratique R, 121–32. Paris: Springer Paris, 2011. http://dx.doi.org/10.1007/978-2-8178-0208-4_6.
"Chapitre 6 Lissage exponentiel." In Séries temporelles avec R, 123–34. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2-009.
"Chapitre 6 Lissage exponentiel." In Séries temporelles avec R, 123–34. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-1994-2.c009.