udit-thakkar / logistic-regression-lean-six-sigma

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 method based on a stepwise approach to process improvements.This approach usually follows 5 steps (Define, Measure, Analyze, Improve and Control) for improving existing process problems with unknown causes.

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 Reginal Director of a Logistic 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 (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 not that effective; only 20% of the operators are reaching this target.

Question

What should be the minimum daily bonus 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 to 20 Euros

Check if the operators reached their target

Code

This repository code you will find all the code used to explain the concepts presented in the article.