Development of diagnostic analytics to maximize customer profitability in AC Logistics

Date of Publication

12-2-2022

Document Type

Master's Thesis

Degree Name

Master of Information Technology

Subject Categories

Computer Sciences | Data Science

College

College of Computer Studies

Department/Unit

Information Technology

Thesis Advisor

Oliver A. Malabanan

Defense Panel Chair

Lissa Andrea K. Magpantay

Defense Panel Member

Ma. Rowena Caguiat
Marivic S. Tangkeko

Abstract/Summary

AC Logistics Philippines helps customers establish a successful supply chain by tracking and controlling their shipments through its network. The company collaborates with numerous industries to create innovative, value-added solutions. The convergence of technology has transformed logistics competitiveness, requiring flexibility. Thus, the company's tight partnerships with major airlines enable the most reliable and cost-effective air cargo solutions. The purpose of this capstone project is to develop diagnostic analytics to maximize customer profitability for AC Logistics in order to provide solutions to the company's problems, which include understanding what and why trends are occurring, aggregating data managed by different resources, and addressing errors in calculations by having set measures and calculated fields within the tool. The proponent used the CRISP-DM model which is flexible and iterative. In lieu of modelling, it is focused on data exploration and visualization to uncover insights on financial data trends. The proponent started with descriptive analytics to do diagnostics analytics. AC Logistics' data was visualized using visual vocabulary to present the appropriate visualization based on the company's needs and requirements. The results of the diagnostic analytics showed insights regarding which customers are profitable and the shipment commodity that generated the most profit. This has to lead the company to identify which customers to invest in. In addition, it has led to insights to handle operations during peak periods. The capstone project showed that customer profitability data, when properly used with descriptive and diagnostic analytics, provides insights that help the organization succeed financially. To enhance customer profitability, future studies should examine external variables such as flight, pandemic restrictions, seasonality, and inflation.

Key Words: data analytics; air logistics; descriptive; diagnostic; seasonality

Abstract Format

html

Language

English

Format

Electronic

Physical Description

69 leaves

Keywords

Data mining; Logistics

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Embargo Period

4-24-2023

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