Analytics for cargo industry (Record no. 6068)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03105nam a22001937a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220107122839.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 180525b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIITMK |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Maryl John (92216015) |
| 9 (RLIN) | 14084 |
| 245 ## - TITLE STATEMENT | |
| Title | Analytics for cargo industry |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | MSC DA 2016-2018 |
| 500 ## - GENERAL NOTE | |
| General note | <br/>In the increasing business or aviation market, air cargo is the major contributor. The commercial<br/>flight has now become secondary. This is because air is the fastest mean of transportation. The<br/>market requires means for transportationof all kinds of goods, air cargo carried in aircrafts. Air<br/>cargo goods such as these have three categories: air freighter, air express and air mail.<br/><br/>IATA (International Air Transport Association) is one of the trade association for all the world’s<br/>airlines. They help in driving a safe secure profitable and suitable air cargo supply chain<br/>throughout the airlines industry.<br/><br/>The major or the most important focus is to meet the challenging needs of the customers. As<br/>cargo is interconnected it is challenging to manage it on a global level. The increase in fuel rates<br/>affects the opening price. Inventory needs to be managed to ensure we have enough resources to<br/>handle peak demand. There are many delays, that need to be predetermined in order to devise<br/>appropriate steps to handle them.<br/><br/>Analytics has brought us different solutions for these problems using data description on the<br/>improvements of the supply chain. Identifying a solution model’s category helps us find the right<br/>approach that works best for a problem. Here we can also have a situation where the data is<br/>unavailable as labels to perform supervised learning, we probably can use other methods in those<br/>cases.<br/><br/>Also, before we analyze the data we also need to process the data with all the missing values,<br/>lack of data, replacing the data with meaningful placeholders. Some kinds of prediction models<br/>can be used to recommend force actions. There are also different methods used to predict the<br/>future requirements of the cargo. <br/><br/>Cargo delays can be modelled to predict and handle delays. This also helps in decision-making<br/>based on historical data. Different models can also be compared to obtain consistent accuracy.<br/>This prediction is critical as the charges for delay and cancellation are very high which a problem<br/>that needs immediate solutions is. <br/><br/>The visualization of the cargo can also be performed using before and after Modelling. This<br/>helps in finding the critical areas. Data Visualization is a very important factor to analyze for <br/>extracting meaningful information. Data Science for freight industry is becoming efficient due to <br/>long run improvement. The global economy has also benefited from this. This will bring<br/>exceptional output and help us to find the unpredictable outcomes in near future. Applied<br/>technology will have bright future for Cargo Industry analysis. |
| 502 ## - DISSERTATION NOTE | |
| Degree type | MSC DA |
| Name of granting institution | 2016-2018 |
| Year degree granted | INT |
| -- | Dr. Manoj Kumar T K |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | AIR CARGO |
| 9 (RLIN) | 14085 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | DATA VISUALIZATION |
| 9 (RLIN) | 14086 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | DATA SCIENCE |
| 9 (RLIN) | 14087 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Non Fiction | IIITM-K | Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre | 25/05/2018 | R-1362 | 25/05/2018 | 25/05/2018 | Project Reports |