International Business Machines Corporation

Ulrich B Schimpel

Pawan Raghunath Chowdhary

Markus Ettl

Zhengliang Xue

Roger Douglas Lederman

Tim Nonner

Hongxia Yang

Richard A Boedi

Abderrahim Labbi

Peter Korevaar

A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

Training a machine to automate spot pricing of logistics services in a large-scale network

The present invention relates to a method, computer system and computer program for stock keeping. An embodiment of the invention determines forecast values of quantities to be consumed of a stock keeping unit for at least two different forecast time spans depending on a historical consumption data. The invention further determines order values of order quantities for each forecast value depending on a respective forecast value, stock keeping costs and ordering costs. The invention further determines an associated order quantity time span for each order value. The invention further determines a respective pair of forecast value and order value with the least deviation between forecast time span and order quantity time span from forecast values, forecast values respective forecast time spans, the order values and the order values respectively associated with order quantity time spans. The invention further processes the determined pair of forecast value and determined order value.

Method for stock keeping, computer system and computer program

The invention is directed to a method for assigning to a set of products a set of corresponding continuous demand densities. In a conversion step for each product its demand time series is converted into a discrete demand density. In a normalization step the discrete demand densities are transformed into normalized discrete demand densities. In a clustering step each of the normalized discrete demand densities is assigned to a cluster and for each cluster a cluster-representative discrete density is determined. In a selection step for each cluster-representative discrete density out of a predetermined set of continuous model densities a cluster-representative continuous density is selected. In a parameter-determination step for each product for its cluster-representative continuous density product-individual density parameters are determined under use of which for each product a continuous density is determined. In an adjustment step for each product the continuous density is adjusted into the continuous demand density.

Safety stock determination

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Neuheim, Switzerland

Ulrich B Schimpel