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 productivity target

Minimum Bonus Problem- — (Image by Author)

When it comes to improving warehouse productivity, there’s no one-size-fits-all solution.

But with Lean Six Sigma methodology and the power of Logistic Regression in Python, you can create a custom incentive policy that works for your business.

In this article, we’ll walk you through estimating the minimum bonus needed to reach 75% of your productivity target so you can achieve your goals and drive success.

Introduction

Lean Six Sigma is a method 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 VAS productivity. (Link)

In this article, we will implement Logistic Regression with Python to estimate the impact of a daily productivity bonus on your warehouse operators' picking productivity.

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SUMMARY
I. Problem Statement
What should be the minimum amount of daily incentive to get 75% of workers that reach their productivity target?
II. Data Analysis
1. Exploratory Data Analysis
Analysis with Python sample data from experiment 
2. Fitted Line Plot of your Logistic Regression
What is the probability of reaching the target for each value of daily incentive?
3. Validation with the p-value
Validate that your results are significant and not due to random fluctuation
III. Conclusion
1. Generative AI: Lean Six Sigma x GPT
2. Next Steps

If you prefer to watch, have a look at the video version of this article

I. Improve Warehouse Productivity with Lean Six Sigma and Logistic Regression

1. Scenario

You are the Regional Director of a Logistic Company (3PL) and have 22 warehouses in your scope.

Fulfilment Centers with Picking, VAS and Packing — (CAD Model by Author)

In each warehouse, the site manager has fixed a picking productivity target for the operators; your objective is to find the right incentive policy to reach 75% of this target.

2. Find the right incentive policy

Currently, productive operators (operators that reach their daily productivity target) receive 5 euros per day in addition to their daily salary of 64 euros (after-tax).

However, this incentive policy applied in 2 warehouses is ineffective; only 20% of the operators are reaching this target.

Question
What minimum daily bonus should be needed to reach 75% of the picking productivity target?

Experiment

1. Randomly select operators in your 22 warehouses
2. Implement a daily incentive amount varying between 1 to 20 euros
3. Check if the operators reached their target

II. Analyzing the Impact of Daily Incentives on Warehouse Operators’ Productivity

1. Exploratory Data Analysis

You can find the full code in this Github (Follow Me :D) repository: Link.
My portfolio with other projects: Samir Saci

Box plot of the sample distribution

Box plot of Incentive distribution by target — (Image by Author)

The median value of incentive for reached target’s day is more than 2 times higher than the one for the days below this target.

2. Fitted Line Plot of your Logistic Regression

Logistic Regression will provide us with a probability plot. We can estimate the probability of reaching the target for each value of the daily incentive.

Fitted line plot of your sample data — (Image by Author)

Confirmation of the current trend: 5 euros -> 20% of the productivity target reached

We need at leat 15 euros incentive per day to ensure 75% of probability to reach the target

Code

Minitab
Menu Stat > Binary Fitted Line Plot

3. Validation with the p-value

In order to check that these results, based on sample data, are significant we need to compute the p-value.

p-value: 2.1327739857133364e-141
p-value < 5%

The p-value is below 5% so we can conclude that the difference of means is statically significant.

Conclusion
If you fix a value of 15 euros per day of incentives, you will reach 75% of your target.

Code

Minitab
Menu Stat > Binary Fitted Line Plot

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 — Kruskal Wallis Test

Lean Six Sigma with Python — Chi-Squared Test

💡 Follow me on Medium for more articles related to 🏭 Supply Chain Analytics, 🌳 Sustainability and 🕜 Productivity.

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.

My first experiment, which I shared in this article, was the design of a LangChain Agent connected to the database to play the role of Supply Chain Control Tower.

Supply Chain Control Tower Agent with LangChain SQL Agent [Article Link] — (Image by Author)

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?

Lean Six Sigma Super Agent — (Image by Author)

The idea 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,

Create GPTs to Automate Supply Chain Analytics

Leveraging LLMs with LangChain for Supply Chain Analytics — A Control Tower Powered by GPT

Conclusion

Based on this experiment, we have fixed a minimum of 15 euros/day for the bonus incentives to reach 75% of your productivity target.

What is the Return of Investments?

Before implementing this new incentive policy, you need to check that you have a positive return on investment:

• What is the total cost to the company (CTC)
  (Basic Salary + Social Contributions) per hour paid for picking operators? (Euros/Hour)
• What is the total amount of hours earned after the productivity increase? (Hours)
• What would be the CTC's reason for hiring temporary workers for this number of hours? (Euros)
• What is the total CTC of incentives?

After answering these questions, you can estimate the return on investment of this new incentive policy. Depending on the hourly cost of the operators, you may lose or save money.

What are the next steps?

However, the operator's productivity may not be only driven by their motivation but can also be impacted by the warehouse layout, the picking process or the order profile.

Therefore, this analysis should be completed with a process optimization study to ensure that operators can exploit their full potential motivated by the right amount of incentives.

For more information, you can check my previous series about warehouse-picking productivity

Improve Warehouse Productivity using Order Batching with Python

Improve Warehouse Productivity using Spatial Clustering with Python

Improve Warehouse Productivity using Pathfinding Algorithm with Python

About Me

Let’s connect on Linkedin and Twitter, I am a Supply Chain Engineer using data analytics to improve logistics operations and reduce costs.

If you are interested in Data Analytics and Supply Chain, have a look at my website

Samir Saci | Data Science & Productivity

References

[1] P values for sklearn logistic regression, Rob Speare

[2] Improve Warehouse Productivity using Order Batching with Python, Samir Saci, Link

[3] Lean Six Sigma with Python — Kruskal Wallis Test, Samir Saci, Link