Predicting Packaging Sizes Using Machine Learning

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Standard

Predicting Packaging Sizes Using Machine Learning. / Heininger, Michael; Ortner, Ronald.
in: Operations research forum, Jahrgang 43.2022, Nr. 3, 43, 22.08.2022.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

Vancouver

Heininger M, Ortner R. Predicting Packaging Sizes Using Machine Learning. Operations research forum. 2022 Aug 22;43.2022(3):43. doi: 10.1007/s43069-022-00157-5

Author

Bibtex - Download

@article{c523e5586d164f88bb28de32e6c52b28,
title = "Predicting Packaging Sizes Using Machine Learning",
abstract = "The increasing rate of e-commerce orders necessitates a faster packaging process, challenging warehouse employees to correctly choose the size of the package needed to pack each order. To speed up the packing process in the Austrian e-commerce company niceshops GmbH, we propose a machine learning approach that uses historical data from past deliveries to predict suitable package sizes for new orders. Although for most products no information regarding the volume is available, using an approximate volume computed from the chosen packages of previous orders can be shown to significantly increase the performance of a random forest algorithm. The respective learned model has been implemented into the e-commerce company{\textquoteright}s software to make it easier for human employees to choose the correct packaging size, making it quicker and easier to fulfill orders.",
author = "Michael Heininger and Ronald Ortner",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = aug,
day = "22",
doi = "10.1007/s43069-022-00157-5",
language = "English",
volume = "43.2022",
journal = " Operations research forum",
issn = "2662-2556",
publisher = "Springer International Publishing",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Predicting Packaging Sizes Using Machine Learning

AU - Heininger, Michael

AU - Ortner, Ronald

N1 - Publisher Copyright: © 2022, The Author(s).

PY - 2022/8/22

Y1 - 2022/8/22

N2 - The increasing rate of e-commerce orders necessitates a faster packaging process, challenging warehouse employees to correctly choose the size of the package needed to pack each order. To speed up the packing process in the Austrian e-commerce company niceshops GmbH, we propose a machine learning approach that uses historical data from past deliveries to predict suitable package sizes for new orders. Although for most products no information regarding the volume is available, using an approximate volume computed from the chosen packages of previous orders can be shown to significantly increase the performance of a random forest algorithm. The respective learned model has been implemented into the e-commerce company’s software to make it easier for human employees to choose the correct packaging size, making it quicker and easier to fulfill orders.

AB - The increasing rate of e-commerce orders necessitates a faster packaging process, challenging warehouse employees to correctly choose the size of the package needed to pack each order. To speed up the packing process in the Austrian e-commerce company niceshops GmbH, we propose a machine learning approach that uses historical data from past deliveries to predict suitable package sizes for new orders. Although for most products no information regarding the volume is available, using an approximate volume computed from the chosen packages of previous orders can be shown to significantly increase the performance of a random forest algorithm. The respective learned model has been implemented into the e-commerce company’s software to make it easier for human employees to choose the correct packaging size, making it quicker and easier to fulfill orders.

UR - http://www.scopus.com/inward/record.url?scp=85137040865&partnerID=8YFLogxK

U2 - 10.1007/s43069-022-00157-5

DO - 10.1007/s43069-022-00157-5

M3 - Article

VL - 43.2022

JO - Operations research forum

JF - Operations research forum

SN - 2662-2556

IS - 3

M1 - 43

ER -