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.
Find in the link below a short animated explained video to understand the concept behind this solution
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.
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.
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.
What minimum daily bonus should be needed to reach 75% of the picking productivity target?
Randomly select operators in your 22 warehouses
Check if the operators reached their target
This repository code you will find all the code used to explain the concepts presented in the article.
Senior Supply Chain Engineer with an international experience working on Logistics and Transportation operations.
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