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

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 p...

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Autores principales:	Mohammed Alnahhal, Diane Ahrens, Bashir Salah
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	freight consolidation lead-time forecasting make-to-order machine learning supply chain Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/57cbc066c5524738a057357fdd837277
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spelling	oai:doaj.org-article:57cbc066c5524738a057357fdd8372772021-11-11T15:10:05ZDynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain10.3390/app1121101052076-3417https://doaj.org/article/57cbc066c5524738a057357fdd8372772021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10105https://doaj.org/toc/2076-3417This 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.Mohammed AlnahhalDiane AhrensBashir SalahMDPI AGarticlefreight consolidationlead-time forecastingmake-to-ordermachine learningsupply chainTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10105, p 10105 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	freight consolidation lead-time forecasting make-to-order machine learning supply chain Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	freight consolidation lead-time forecasting make-to-order machine learning supply chain Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Mohammed Alnahhal Diane Ahrens Bashir Salah Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
description	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.
format	article
author	Mohammed Alnahhal Diane Ahrens Bashir Salah
author_facet	Mohammed Alnahhal Diane Ahrens Bashir Salah
author_sort	Mohammed Alnahhal
title	Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
title_short	Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
title_full	Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
title_fullStr	Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
title_full_unstemmed	Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
title_sort	dynamic lead-time forecasting using machine learning in a make-to-order supply chain
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/57cbc066c5524738a057357fdd837277
work_keys_str_mv	AT mohammedalnahhal dynamicleadtimeforecastingusingmachinelearninginamaketoordersupplychain AT dianeahrens dynamicleadtimeforecastingusingmachinelearninginamaketoordersupplychain AT bashirsalah dynamicleadtimeforecastingusingmachinelearninginamaketoordersupplychain
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Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

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