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

Lean Six Sigma (LSS) is a step-by-step approach to process improvement. This approach usually follows five steps. (Define, Measure, Analyse, Improve and Control) for improving existing process problems with unknown causes.

Youtube Video

Use the link below to find a short animated video that explains the concept behind this solution.

Article

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.

Scenario

You are the Regional Director of a logistics company (3PL), and you have 22 warehouses in your scope.

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. P.S: Picking Productivity is defined by the number of cartons picked per hour paid.

Objective: find the right incentive policy

Currently, productive operators (those who meet 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 is needed to reach 75% of the picking productivity target?

Experiment

Randomly select operators in your 22 warehouses

Implement a daily incentive amount varying between 1 and 20 euros

Check if the operators reached their target

Code

In this repository, you will find all the code used to explain the concepts presented in the article.

Files

• Logistic Regression.ipynb - Jupyter notebook with step-by-step analysis
• logistic_regression.py - Standalone Python script
• data/ - Folder containing input data (df_incentive.xlsx)

Getting Started

pip install -r requirements.txt
python logistic_regression.py

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scipy
• scikit-learn
• openpyxl

About me 🤓

Senior Supply Chain and Data Science consultant with international experience working on Logistics and Transportation operations.
For consulting or advising on analytics and sustainable supply chain transformation, feel free to contact me via Logigreen Consulting.
Please have a look at my personal blog: Personal Website