This repository contains my submissions for the Deloitte STEM Virtual Program. As part of this program, participants assist Daikibo Industrials, a global leader in heavy machinery manufacturing, in tackling various operational challenges.
This repository documents my completion of three courses in the Deloitte Virtual Internship on Forage.
Daikibo is integrating Industrial Internet-of-Things (IIoT) devices to monitor and analyze their manufacturing processes. However, their infrastructure handles telemetry data in two different formats. The challenge is to merge these data files efficiently.
Key Learnings:
- Manipulating JSON files in Python
- Writing and executing unit tests using the
unittestframework
Real-time monitoring and smart alerts enhance operational efficiency. Daikibo's tech team needs a real-time manufacturing status dashboard. The first step in this project involves drafting a development proposal.
Key Learnings:
- Writing clear and persuasive software development proposals
A major news outlet has leaked sensitive information about Daikibo Industrials, revealing a production halt impacting supply chains. The client suspects a security breach in their new status dashboard.
Key Learnings:
- Analyzing web log files to detect anomalies
- Identifying suspicious activities through log analysis
Findings:
The dashboard is hosted on Daikibo’s intranet, with remote access restricted to VPN tunneling. Log analysis revealed that a user with IP 192.168.0.101 initially accessed the dashboard normally but later exhibited automated hourly access to status updates across four factories—indicating potential unauthorized activity.
Daikibo's tech team compiled one month’s telemetry data (May 2021) from four factories:
- Daikibo Factory Meiyo (Tokyo, Japan)
- Daikibo Factory Seiko (Osaka, Japan)
- Daikibo Berlin (Berlin, Germany)
- Daikibo Shenzhen (Shenzhen, China)
Each site operates nine types of machines, reporting status updates every 10 minutes. The goal is to analyze:
- Which location experienced the most machine failures?
- Which machines had the highest failure rate at that location?
Key Learnings:
- Utilizing Tableau to create interactive dashboards
- Extracting insights from large datasets
Due to internal concerns over gender pay inequality, Daikibo wants to quantify the fairness of compensation across various job roles and locations. The Forensic Technology team has developed an algorithm to measure gender pay equality.
Key Learnings:
- Applying Excel functions for data analysis and pattern recognition