Predicting Packaging Sizes Using Machine Learning
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In: Operations research forum, Vol. 43.2022, No. 3, 43, 22.08.2022.
Research output: Contribution to journal › Article › Research › peer-review
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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 -
