Open in app

Sign in

Medium Logo
Write

Sign in

Samir Saci
Samir Saci

Sep 17, 2022

·

5 stories

Lean Six Sigma

TDS Archive

In

TDS Archive

by

Samir Saci

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.

Aug 9, 2021
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.
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.
Aug 9, 2021
TDS Archive

In

TDS Archive

by

Samir Saci

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.

Aug 24, 2021
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
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
Aug 24, 2021
TDS Archive

In

TDS Archive

by

Samir Saci

Lean Six Sigma with Python — Logistic Regression

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

Aug 31, 2021
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.
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.
Aug 31, 2021
TDS Archive

In

TDS Archive

by

Samir Saci

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.

Sep 28, 2021
2
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.
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.
Sep 28, 2021
2
Samir Saci

Samir Saci

Lean Six Sigma with Python — Chi-Squared Test

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

Oct 30, 2021
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.
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.
Oct 30, 2021
Samir Saci

Samir Saci

4K followers

Top Supply Chain Analytics Writer — Case studies using Data Science for Supply Chain Sustainability 🌳 and Productivity: https://bit.ly/supply-chain-cheat

Following
  • Data Science Collective

    Data Science Collective

  • BoredGeekSociety

    BoredGeekSociety

  • Austin Starks

    Austin Starks

  • Ben Huberman

    Ben Huberman

  • Nadezda Yusyuz

    Nadezda Yusyuz

See all (31)

Help

Status

About

Careers

Press

Blog

Privacy

Rules

Terms

Text to speech