AI Enabled Service Market Logistics

dc.contributor.authorRamne, Johan
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerLang, Annika
dc.contributor.supervisorLang, Annika
dc.date.accessioned2020-06-11T08:48:40Z
dc.date.available2020-06-11T08:48:40Z
dc.date.issued2020sv
dc.date.submitted2019
dc.description.abstractUncertainty about upstream suppliers’ ability to deliver ordered quantities on time is one the reasons that manufacturers and retailers need to keep safety stock in inventory. Through accurate prediction of suppliers’ delivery performance the uncertainty can be quantified and used by material planners in their decision-making process. Representing the deliver performance of an individual supplier as a time series, the uncertainty can be predicted through probabilistic forecasting: estimation of the future probability distribution given past observations. This thesis presents two recurrent neural network models, using encoder-decoder architectures, for multi-step ahead probabilistic forecasting of the delivery performance of suppliers to Volvo Group Trucks Operations Service Market Logistics. The models are evaluated on mean quantile loss for a number of quantiles over a 14 week forecast range. One model, DeepAR, outperformed exponential smoothing models generated by the forecast package in R on four out of five quantiles.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/300824
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectdelivery performance, RNN, probabilistic forecasting, supply chain management, service market logistics, quantile recurrent neural network.sv
dc.titleAI Enabled Service Market Logisticssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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