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5s training - Statistical View and Tutorials
Rating: 4.3 out of 5(27 ratings)
7,312 students
Created byJarryd Giose
Last updated 6/2021
English

What you'll learn

  • 5s Statistical analysis
  • How to develop Graphs and templates in Excel
  • Explanation of what each S means numerically
  • How to Quantify 5s

Course content

7 sections6 lectures53m total length
  • Introduction10:25

    This is an introduction to the principles of statistically analyzing the 5s cycle

Requirements

  • Quality engineers, manufacturing engineers, process engineers engineer in general.
  • Good head for numbers.
  • Basic Excel Understanding
  • Statistical Background

Description

We can expect to learn the various statistical methods of analyzing 5s.

In this course we will go through a tutorials for each S of the cycle and explain in detail the affects it has on(Sort,Set,Shine,Standardize and Sustain).

This will be analysed in order to make better continuous improvement contributions.


In this course i will be addressing:

- Each step from a blank excel sheet to the fully populated and calculated data will be shown

- Each step describe what is happening as we analyse along

- Fully aware of how to quantify 5s

- Basic lean manufacturing and quality techniques shown and discussed.

- Basic excel technique shown

- Understanding of setting up your own quality control systems

- Foundational mathematics technique and graphs described


Hope you learn a lot more as I describe various scenarios and the analyses of each S at its independent states.

I also describe the profits and loses in real terms and explain how to avoid them or at least be aware of pitfalls.

Its not a strategy, its an analysis and a way of quantify a culture.

I Greatly Appreciate your participation in these lecture and hope you gain a lot of knowledge from them


Remember , Enjoy


Always feel free to ask questions


Who this course is for:

  • 5s
  • Quality
  • Manufacturing
  • Data
  • Process Engineering
  • Excel
  • Adminstration
  • Microsoft Office
  • Data Science