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.
Highlights
In temporal freight consolidation, small orders are aggregated over time in the consolidation center to make bigger shipments to the customer [1]
This paper investigates the forecasting problem for a make-to-order supply chain based on real data, where the purpose is to predict if a certain order will be delivered to the consolidation center in the delivery week or not
The lead time is usually long, and accurate forecasting based on little information is very challenging
Summary
In temporal freight (shipment) consolidation, small orders are aggregated over time in the consolidation center to make bigger shipments to the customer (retailer) [1]. A sustainable supply chain is about the achievement of an organization’s social, environmental, and economic goals. Consolidation reduces the number of vehicles and the size of the workforce needed This is important in the time of the COVID-19 pandemic, where a shortage in the workforce in logistics is a big problem. It will be useful for customers because they will have a lower number of shipments, and lower handling costs. All these three goals of sustainability and the response to COVID-19 are some of the motivations for this study. This study is the first one that considers temporal consolidation as the objective for forecasting
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