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[Information] Let's start analyzing logistics data!

[Information] Let's start analyzing logistics data!

For those who want to start data analysis! Introducing tips for data analysis.

This document provides a detailed explanation of how to handle and analyze data effectively. It includes insights on data analysis, representative data and analysis methods, and data analysis in transportation and delivery performance (TMS). Additionally, it offers clear explanations of data analysis in warehouse operation performance (WMS) and data analysis regarding loading and unloading work time (WS method). 【Contents】 ■ Principles for data analysis ■ Representative data and analysis methods ■ Data analysis in transportation and delivery performance (TMS) ■ Data analysis in warehouse operation performance (WMS) ■ Data analysis regarding loading and unloading work time (WS method) *For more details, please download the PDF or feel free to contact us.

  • Process Control System

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Basic Edition: Analyzing Logistics Data with Python and Statistics

Basic Edition: Analyzing Logistics Data with Python and Statistics

A detailed introduction in blog format about multiple regression analysis and the comparison of observed values and predicted values!

In this blog, I would like to incorporate a more practical approach (such as correlation and multiple regression analysis) to advance the learning of Python and statistics. I will output the correlation between variables from the dataset read using pandas' read.csv() as a heatmap. The value of the correlation coefficient can range from -1 to 1, where a value close to 1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, and a value close to 0 indicates a weak correlation. *For more details about the blog, you can view the related links. Please feel free to contact us for more information.*

  • Logistics and warehouse management systems

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Introduction: Analyzing Logistics Data with Python and Statistics

Introduction: Analyzing Logistics Data with Python and Statistics

Explaining how data analysis can be useful for decision-making and improvement in the logistics industry!

The data analysis approach in logistics is required in various aspects such as inventory management, route optimization, and demand forecasting. However, insights gained from data are essential for its realization. This article focuses on the statistical analysis of logistics data using the programming language Python, examining how data analysis can aid decision-making and improvements in the logistics industry. Specifically, it will explain data processing and statistical analysis examples using Python libraries such as Pandas and NumPy, through case studies using real data. *For more details about the blog, you can view it through the related links. Please feel free to contact us for more information.*

  • Logistics and warehouse management systems

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