Back to Search
Start Over
Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
- Source :
- Applied Sciences, Vol 11, Iss 10105, p 10105 (2021), Applied Sciences, Volume 11, Issue 21
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- 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.
- Subjects :
- Technology
Operations research
Computer science
Build to order
QH301-705.5
Supply chain
QC1-999
ComputerApplications_COMPUTERSINOTHERSYSTEMS
freight consolidation
Consolidation (business)
Linear regression
Production (economics)
General Materials Science
Biology (General)
Instrumentation
QD1-999
supply chain
Fluid Flow and Transfer Processes
Process Chemistry and Technology
Physics
General Engineering
lead-time forecasting
Engineering (General). Civil engineering (General)
Computer Science Applications
Chemistry
machine learning
Order (business)
make-to-order
TA1-2040
Lead time
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 11
- Issue :
- 10105
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....511090384b1b77a6c1fc14b71dbde533