Automated lead time estimation for manufacturing networks with dynamic demand

Askin, Ronald G.; Hanumantha, Girish Jampani

doi:10.1109/coase.2017.8256232

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…This is part of using the correct inventory control policy. The prediction of lead time was necessary for planned order releases and shop scheduling in a study by Askin and Hanumantha [23]. Therefore, they investigated the prediction of lead times under dynamic conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

2021

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This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.

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Section: Literature Reviewmentioning

confidence: 99%