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Sep 17, 2022

5 stories

Lean Six Sigma

In

TDS Archive

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Samir Saci

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Aug 9, 2021

Lean Six Sigma with Python — Kruskal Wallis Test

Use the Kruskal Wallis Test in Lean Six Sigma with Python to evaluate the impact of training on warehouse productivity.

A diagram showing the process to evaluate the impact of warehouse operator training on productivity using Kruskal Wallis Test with Python. Two groups of operators are randomly selected: one group consists of trained operators, and the other group consists of untrained operators. The productivity of both groups is measured using stopwatches, aiming to determine whether training has a significant impact on operator productivity.

In

TDS Archive

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Samir Saci

·

Aug 24, 2021

Central Limit Theorem for Process Improvement with Python

Estimate the workload for returns management assuming a normal distribution of the number of items per carton received from your stores.

An infographic explaining the application of the Central Limit Theorem for process improvement in returns management. The image has three sections: (1) Assumption of a normal distribution (N(σ, μ)) for the number of items per return carton, (2) Calculation of the probability of receiving between n1 and n2 items per carton, and (3) Estimation of the workforce needed for returns management. The graphic includes icons for data distribution, a work desk for processing returned items, and workers for

In

TDS Archive

by

Samir Saci

·

Aug 31, 2021

Lean Six Sigma with Python — Logistic Regression

Perform a Logistic Regression to estimate the minimum bonus needed to reach 75% of a productivity target.

A diagram illustrating the “Minimum Bonus Problem.” The title asks, “What is the minimum bonus level needed to reach 75% of your productivity target?” Three rows show warehouse operators being randomly selected, each row with a worker icon holding a money bag. Arrows point from these workers toward stopwatch icons labeled “Productivity Target?”. Use of Kruskal Wallis Test with Python to evaluate how different bonus levels impact operator productivity, specifically aiming to achieve the target.

In

TDS Archive

by

Samir Saci

·

Sep 28, 2021

Statistical Sampling for Process Improvement using Python

Use sample data to estimate the average lead time for processing customer orders in a customer service department.

A visual representation of order processing lead time estimation using statistical sampling with Python. The left shows the population of customer service representatives, represented by icons, with a distribution (σ, μ). The right displays a sample of the population used to estimate the average lead time, with a 90% confidence interval (x̄ ∈ [μ-b, μ+b]). The image explains how sample data is used to infer the overall lead time for processing customer orders in a customer service department.

Samir Saci

·

Oct 30, 2021

Lean Six Sigma with Python — Chi-Squared Test

Perform a Chi-Squared Test to explain a shortage of drivers impacting your transportation network

Diagram illustrating the driver allocation problem in a transportation network. A factory sends goods to two warehouses — North and South — via an ERP system. Both warehouses are served by a transport company, which allocates drivers (D1, D2, D3). Black arrows represent balanced driver allocations to the North Warehouse, while red arrows indicate an unbalanced allocation to the South Warehouse, causing delays. This introduce Lean Six Sigma with Python using Chi-Squared Test.