Hiryuu Project Overview
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| Background | Data | Methodology |
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Project Background
With the increasing regional presence of logistics providers, the logistics data used by them have been increasingly complex for the different contexts involved. Unlike a logistics provider providing services for just one country, regional logistics providers will face the complexities involved in having geospatial address data in different formats, and data records of different languages corresponding to the local cultures of the different locales. The lack of a universal uniform format in writing affects the data recorded in the system, which becomes a complication faced when running analysis on such data. In this paper, we will mainly discuss on the complexities in geocoding, for the different address formats in the different countries, and the differences in geospatial data accuracy, as a result of the different countries using different methods in anchoring address fields to physical geographical locations. Data records of different languages, and different vendors employed to fulfill the logistics services also add on to the complexity of the data, for the lack of uniformity necessitates the need to prepare the data in a uniform format to be able to better draw on the insights in the data for analysis. The paper seeks to highlight the complexities involved in regional logistics data and discuss the methods we have applied to overcome them.
Business Problem
Despite the amount of data handled by the team, the lack of a dashboard to show summary tables regarding the status of the shipments has made it difficult for the program managers and the sales team to monitor and follow up with requests from the client regarding specific shipments. The data used by them comes from a number of different sources, which needs to be mapped to their own templates to be used effectively by them.
Furthermore, the shift of the company’s approach to be proactive instead of reactive has prompted the development of systems to aid in monitoring and also provide analysis for users to not just follow up on cases, but to also seek avenues for further improvements.
With this opportunity, we have begun working with the company to take the first step into developing a tool that would provide them with better insights into their data.
Motivation
As final year students graduating soon from university, we sought to find a project that would suit our future directions in career path. All of us in the team are keen to take on a career related to either e-Commerce or logistics. Hence, we believe that the project we have chosen would familiarise us with the workings of logistics and distribution and aid us greatly in managing e-Commerce solutions and systems that we will have the opportunity to work on in the future.
For our choice of project, we engaged a logistics company in the market that has partnered with various firms handling the logistics solutions and e-Commerce platforms for them. From this project, we will have the opportunity to learn from the hands on experience with the data provided to us by the sponsor. This project involves various countries and has data originating from each of the countries, hence the language used for certain data columns such as address and instructions are in the country’s native language (E.g. Japanese, Chinese, Thai). The challenge to handle such non-standardized data fields and to gain insights from it gives us the opportunity to learn beyond the classroom.
From this project, we will gain many opportunities like these to familiarize ourselves with handling real world data unlike what we have used thus far in our classes. Having to handle the unexpected and learning how projects are managed in the real world through hands-on are unlike what we have experienced thus far in our classes. We believe that this project will provide us with invaluable experiences that allow us to put what we learnt into practice, while receiving further guidance on how to better improve our work before we step out into the real world to handle such things ourselves.
