Prediction of ocean import shipment lead time using machine learning methods
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Prediction of ocean import shipment lead time using machine learning methods Saraswathi Hathikal1 · Sung Hoon Chung2 · Martin Karczewski1 Received: 13 February 2020 / Accepted: 22 May 2020 / Published online: 23 June 2020 © Springer Nature Switzerland AG 2020
Abstract This paper focuses on developing a predictive model for estimating the shipment lead time using machine learning methods for ocean import freight considering interests of different stakeholders such as shipper, carrier, freight forwarder, and consignee. Two different terminal criteria for calculating shipment lead time are defined considering different milestones: one with empty container return and the other with delivery confirmation at the destination. Real data obtained from an industry partner are used for implementation, and multinomial logistic regression is identified as the best classifier with the highest accuracy in each binning method, which is followed by a decision tree method. Additionally, commonly used classifiers such as multinomial logistic regression, decision tree, K-nearest neighbors, and support vector machine perform better than Naïve Bayes when the categorical variables are binarized, and vice versa when the categorical variables are converted into ordinal values. The proposed model has the significant potential to benefit different parties in the supply chain by providing improved visibility and predictability for shipment lead times. Keywords Freight forwarder · Ocean freight · Shipment lead time · Machine learning
1 Introduction Globalization is extending local supply chain across boundaries catering to the customers demands throughout the world and is changing the landscape of trade and commerce [22]. With increasing globalization, many of the products and operations are outsourced and are moved across countries. Impact of transportation on the supply chain has always been crucial and is ever expanding. In the logistics and supply chain of import and export operations, intermediate transportation entities such as freight forwarders, also known as non-vessel operating common carriers (NVOCC), play a significant role in the movement of cargo from origin to destination [1, 8, 17, 22, 25]. Freight forwarders are service providers that manage shipment logistics for individuals and/or companies to move cargo from a shipper (manufacturer or producer) to
a consignee (customer) via a carrier (company providing a transportation mode). They are logistics experts and act as a nexus between shipper, carrier, and consignee. Freight forwarders use multiple modes of transportation for the movement of freight, and for global logistics and international shipment, there are two main categories, ocean freight and air freight, based on the mode of transportation. For the US domestic shipment, the primary modes of transportation are airplanes, railroads, and trucks. From an operational point of view, the primary difference between domestic and international shipment is the additional documentation and other activities associated with inte
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