AI Enabled Service Market Logistics
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2020
Författare
Ramne, Johan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Uncertainty 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.
Beskrivning
Ämne/nyckelord
delivery performance, RNN, probabilistic forecasting, supply chain management, service market logistics, quantile recurrent neural network.