The Application Effect Measurements And Analysis Of Bill Of Lading

In the sea trade,the traditional bill of lading has played an important role across the globe for centuries,but with the advent of advanced commercial modes of transportation and communication,the central position of this document is under threat.The importance of the bill of lading still prevails as does the need of the functions that this document served in the past,although in a changed format.The effect measurements of bill of lading are different for different commercial circumstances.The commercial circumstances used in this research are focused on both of containerized shipment data and liquid bulk cargo based on Bill of lading data.In this research,the measurements of the application of bill of lading is about the contain information in bill of lading measures.Firstly,this study is focused on the missing data in some observations,and bill of lading from Panjiva is no exception.There are two primary sources of missing data in Panjiva: fields for which a firm request that the U.S.customs and Border Protection(CBP)redact their identity in the shipper or consignee field(among the applications scheme of BL)and fields like TEU,HS code,and value that Panjiva imputes from other information that is not always available.In this section,this paper uses S&P Panjiva as a source of Bo L data,as they provide both the raw data and also a number of useful derivative variables.Secondly,this paper examines the trends cargo damage,trends in cargo shortage and causes of discrepancy in measured weights of loaded and discharged cargoes.For bulk carriers and tankers,as well as for general cargo and container vessels,a shortage claim arises where there is a discrepancy between the quantity of cargo noted on the B/L(Bill of Lading)and the quantity discharged by using Chinese Law for bulk carriers and Pakistan Law for tankers.The draft survey measurement methods were conducted to determine both of the quantities(or weights)loaded and quantities discharged.We use also Chinese method measurement(the CIQ surveys)when there is the large number cargo shortage in China.Finally,this research analyzes the liquid bulk cargo volume and this is the most important part of this paper.Liquid bulk cargo(LBC)volume analysis has received considerably great attention recently since LBC is a valuable and high-demand cargo.Thus,it is important to establish an analysis system for LBC volume,as it can help inform strategies for port planning and management.Nevertheless,LBC volume analysis is a challenging task for researchers because trends in LBC volume are highly volatile and non-stationary.In this paper,a new framework for enabling informative LBC volume analysis based on bill of lading(BL)data is proposed,which consists of three parts: item segmentation,exploratory volume analysis,and volume prediction.Firstly,an innovative item segmentation system using item texts of BL data was developed,which can generate subcategory as well as category information of LBC items that existing system cannot provide.Next,exploratory volume analysis was performed to understand the volume characteristics of each categorized and subcategorized item in terms of geography and timeline.Lastly,manifold learning-and deep learning-based time series techniques were proposed to increase LBC volume prediction accuracy compared with existing statistical models.The experimental results for volume prediction show the accuracy increased by 34% and 18% in average at category and subcategory levels over baseline models.