Lean Six Sigma with Python — Chi-Squared Test
Perform a Chi-Squared Test to explain a shortage of drivers impacting your transportation network
As a logistics professional, it’s essential to improve your operations constantly.
That’s where Lean Six Sigma with Python comes in. Follow our step-by-step guide to optimize your supply chain management.
Introduction
Lean Six Sigma is defined as a stepwise approach to process improvements.
In a previous article, we used the Kruskal-Wallis Test to verify the hypothesis that specific training positively impacts operators' Inbound Value-Added Services (VAS) productivity.
In this article, we will implement the Chi-Squared Test with Python to understand if transportation delays are due to a bad allocation of drivers.
💌 New articles straight in your inbox for free: Newsletter
📘 Your complete guide for Supply Chain Analytics: Analytics Cheat Sheet
SUMMARY
I. Problem Statement
Transportation delays are due to drivers' allocation issues?
II. Data Analysis
1. Exploratory Data Analysis
Analysis with Python sample data from historical records
2. Perform Cross Tabulation
Summarise the relationship between several categorical variables.
3. Pearson’s Chi-Square Test
Validate that your results are significant and not due to random fluctuation
III. Conclusion
1. Generative AI: Lean Six Sigma x GPT
2. Next StepsIf you prefer to watch, have a look at the video version of this article
I. Problem Statement
Addressing Transportation Delays with Chi-Squared Test
You are the Inbound Transportation Manager of a small factory in the United States.
Your transportation network is simple; you have two routes:
- Route 1: coming from your northern regional hub (with difficult road conditions and heavy traffic)
- Route 2: coming from your southern regional hub (with no traffic and a beautiful modern road)
Transportation is managed by an external service provider with a fleet of three trucks (with three different drivers: D1, D2, D3).
Replenishment Process: Understanding Driver Allocation in Transportation Network
- The Factory sends a replenishment order to your ERP
- The Southern regional hub receives the order first
- If the stock in the southern hub is too low, then the order is transferred to the northern hub
- ERP sends a pick-up request to the transportation service provider (From the Selected Hub to the Factory)
- The first driver accepting the request is delivering the raw materials to the factory
P.S.: As a customer, we do not have any visibility on the process of driver allocation.
Problem
When an order is allocated to the northern regional hub the lead time to get the request accepted is 35% higher than the southern hub.
Question
Are there drivers avoiding as much as possible to be allocated to the north route?
Experiment
We have analyzed the shipments of the last 18 months to build a sample of 269 records.
💡 Follow me on Medium for more articles related to 🏭 Supply Chain Analytics, 🌳 Sustainability and 🕜 Productivity.
II. Data Analysis
Exploratory Data Analysis: Stacked Bar Charts to Visualize Driver Allocation
Cross Tabulation: Analyzing Shipments by Hub and Driver
A cross-tabulation of the data can provide some insights and help us to discover a potential pattern in the repartition of driver’s allocation.
Example
82.65 % of shipments handled by Driver 1 are from SOUTH HUB
Example
38.89 % of shipments from SOUTH HUB are handled by Driver 1
Minitab
Menu Stats> Tables > Cross Tabulation and Chi-Square Pearson’s Chi-Squared Test: Evaluating the Significance of Driver Allocation
The first table is called also called a Contingency table. It is used in statistics to summarise the relationship between several categorical variables.
Using the Chi-Squared Test, we’ll calculate the significance factor to determine whether the relation between the variables is of considerable significance.
p-value is 0.410Conclusion
Because the p-value >0.05, there is no significant proof that the driver’s allocation is linked to the Hub.
Code
If you are interested in other applications of Lean Six Sigma Methodology using Python, you can have a look at the articles below:
III. Conclusion
Generative AI: Lean Six Sigma GPT Agent
After the recent adoption of Large Language Models (LLMs) like GPT, we can enhance the user experience of analytics products with smart agents.
I shared my first experiment in this article, which was the design of a LangChain Agent connected to a TMS.
The outputs are impressive, as we have an agent that can answer operational questions by querying a database autonomously.
What if we create a Lean Six Sigma super agent?
My objective is to equip a GPT agent with
- Python Scripts of Lean Six Sigma Tools
- Context, articles and knowledge about LSS mathematical tools
So we have an agent that can find the right test, perform it on data uploaded by users and provide an answer.
For more information,
2. Next Steps
By applying the Chi-Squared Test in Python, we could determine that driver allocation was not the root cause of the transportation delays.
This data-driven approach helped us identify areas for further investigation to find the actual cause of the problem.
Therefore, leveraging Lean Six Sigma methodologies with Python can significantly improve productivity and efficiency.
Stay tuned for more data-driven solutions to optimize your operations and reduce costs.
References
- Pearson’s Chi-Squared Test, geeks for geeks, link
- “Optimize Luxury Warehouse Value-Added Services Scheduling with Python”, Towards Data Science, Samir Saci
- “Lean Six Sigma with Python — Kruskal Wallis Test”, Towards Data Science, Samir Saci
- “4 Impacting Projects to Start Your Data Science for Supply Chain Journey”, Towards Data Science, Samir Saci
