A Machine Learning Approach to Shipment Consolidation

Bas van Andel


This research analyzes the current approach used by a client of DHL LLP for the transportation of shipments from suppliers to production sites. As a result of this analysis, several improvements are proposed that can be used to reduce the costs of transportation in the logistics network. Focus is put on shipment consolidation, rather than on the rerouting of shipments. In this paper, two consolidation methods are introduced. Cluster analysis groups together suppliers that are geographically close and generally ship to the same production sites, and a time-based policy that introduces a maximum waiting time for shipments before they are released. While only marginal improvements are obtained when applying these techniques indepen- dently, a combination of the two provides a powerful synergy. A trade-off between the savings and proportion of on-time shipments arises, when a maximum waiting time is introduced. The potential savings, therefore, depends on the company’s tol- erance for late shipments. While the proposed techniques work in theory, practical and organizational challenges emerge when applying them in the real world. 


Machine Learning; Shipment Consolidation; DHL; cluster analysis, poisson process

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DOI: http://dx.doi.org/10.26481/marble.2018.v2.616


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