As a logistics professional, it’s essential to constantly improve your operations.
That’s where Lean Six Sigma with Python comes in. In this article, we will use the Chi-Squared Test to analyze transportation delays caused by a bad allocation of drivers.
Follow our step-by-step guide to optimize your supply chain management.
Lean Six Sigma is a method that can be 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.
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
If 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 busy 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 Selected Hub to 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.
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.
Are there drivers avoiding as much as possible to be allocated to the north route?
We have analyzed the shipments of the last 18 months to build a sample of 269 records.
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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.
82.65 % of shipments handled by Driver 1 are from SOUTH HUB
38.89 % of shipments from SOUTH HUB are handled by Driver 1
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.
We’ll calculate the significance factor to determine whether the relation between the variables is of considerable significance using the Chi-Squared Test.
p-value is 0.410
Because the p-value >0.05, there is no significant proof that the driver’s allocation is linked to the Hub.
If you are interested in other applications of Lean Six Sigma Methodology using Python you can have a look at the articles below:
Lean Six Sigma with Python — Logistic Regression
Replace Minitab with Python to perform a Logistic Regression to estimate the minimum bonus needed to reach 75% of a…
By applying the Chi-Squared Test in Python, we were able to 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 lead to significant improvements in productivity and efficiency.
Stay tuned for more data-driven solutions to optimize your operations and reduce costs.
If you are interested in Data Analytics and Supply Chain, have a look at my website
- 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