Project Description
The transportation and logistics industry forms a critical part of the supply chain, ensuring products are delivered on time and efficiently. In this project, we will evaluate the performance of shipments, the efficiency of warehouses, the quality of carriers and customer satisfaction by analyzing transportation and logistics data. Our aim is to optimize logistics processes, improve delivery times and reduce customer complaints. This analysis will help logistics managers and operations teams improve processes and make strategic decisions.
Project Usage Areas
This project has several uses for logistics managers, supply chain specialists and operations teams:
Analysis of Shipment Performance: Improving logistics processes by analyzing on-time delivery rates and costs of shipments.
Evaluation of Warehouse Efficiency: Optimizing storage processes by analyzing the capacity utilization and efficiency of warehouses.
Carrier Quality Review: Identifying the best carriers by analyzing carriers' performance and customer reviews.
Increasing Customer Satisfaction: Identifying ways to increase customer satisfaction by analyzing customer complaints.
Logistics Strategies: Developing effective logistics strategies and improving supply chain processes using data analysis.
Dataset Description
The data set to be used in this project includes transportation and logistics data. The dataset consists of four main files in total:
Shipments
ShipmentID: Shipment ID
WarehouseID: Warehouse ID
CarrierID: Carrier ID
ShipmentDate: Shipment date
DeliveryDate: Delivery date
Weight: Weight
Cost: Cost
Status: Status (Delivered, In Transit, Delayed, Cancelled)
Warehouses (warehouses)
WarehouseID: Warehouse ID
WarehouseName: Warehouse name
Location: Location (City, Suburb, Rural)
Capacity: Capacity
Manager: Administrator
Carriers
CarrierID: Carrier ID
CarrierName: Carrier name
FleetSize: Fleet size
Rating: Evaluation (between 1-5)
Customer Complaints (customer_complaints)
ComplaintID: Complaint ID
ShipmentID: Shipment ID
CustomerID: Customer ID
ComplaintDate: Complaint date
Issue: Problem (Late Delivery, Damaged Goods, Lost Package, Other)
Resolution: Solution (Resolved, Pending, Unresolved)
There are various dirty data problems in this dataset, such as missing data, outlier data, and wrong data type. This is an ideal data set to experience data cleaning and processing processes commonly encountered in real life.
Student Benefits
This project provides many benefits for students:
Data Manipulation: Students develop skills in examining, cleaning, and analyzing data sets.
Using Pandas: They learn to use the data processing and analysis methods of the Pandas library effectively.
Data Cleaning: They gain skills in cleaning missing data, outliers and incorrect data types.
Business Intelligence: By analyzing data sets, they improve their ability to evaluate the efficiency of logistics processes and make strategic decisions.
Reporting: Provides skills to effectively report and present analysis results.
Real Life Applications: Provides practical information about data problems and analysis processes encountered in real life.
If you want to lay the foundations of Python and gain competence in data analysis and science, you can immediately register for a 1-month intensive Python camp. Take a look now at the interactive and practice-oriented training program developed in Helsinki, inspired by Finnish education models, consisting of ~40 hours of live lessons, ~50 comprehensive projects, ~15 quizzes and countless coding exercises!
Comments