School of Six Sigma

In this sec­tion we’re intro­duced to both lean and six sig­ma while also learn­ing how they can be used togeth­er in harmony.

Now that we’ve cov­ered some impor­tant foun­da­tion­al work it’s time to learn how to iden­ti­fy a good project. This step is extreme­ly impor­tant since a poor­ly scoped project has lit­tle hope of success.

The abil­i­ty to under­stand how mate­r­i­al and process­es flow, while also iden­ti­fy­ing the ​“inputs” or ​“Xs” to a process, is at the heart of the DMA­IC method­ol­o­gy. So, in this sec­tion we’ll learn about dif­fer­ent types of process maps while also learn­ing how to take the infor­ma­tion learned from process map­ping into addi­tion­al tools like the C&E Matrix and FMEA in order to nar­row the lists of inputs down to the ​“crit­i­cal few.” To be sure, many prob­lems can be solved dur­ing this phase of a project, so it’s real­ly time to dig in!

Sta­tis­tics is the sci­ence of col­lect­ing, ana­lyz­ing, pre­sent­ing, and inter­pret­ing data. Sta­tis­tics pro­vides meth­ods for design­ing exper­i­ments, tech­niques for ana­lyz­ing data, and mak­ing informed deci­sions. Learn the dif­fer­ences between descrip­tive and infer­en­tial sta­tis­tics, and how to use each. This dis­ci­pline is essen­tial for enabling pro­fes­sion­als and researchers to eval­u­ate trends, test the­o­ries, and dri­ve improve­ment and inno­va­tion in any industry.

Accu­rate­ly inter­pret­ing data col­lect­ed dur­ing test­ing phas­es is cru­cial for enhanc­ing process­es and work­flows. Descrip­tive sta­tis­tics are sum­ma­ry mea­sures that describe, show, or sum­ma­rize the char­ac­ter­is­tics of data in a mean­ing­ful way. Ensur­ing that all team mem­bers are pro­fi­cient in descrip­tive sta­tis­tics and their prop­er appli­ca­tion, we can make informed deci­sions that dri­ve per­for­mance improvements. 

The abil­i­ty to graph data is supreme­ly impor­tant. But with so many graphs to choose from how do we choose the cor­rect one? This series of videos answers that ques­tion for you!

Hav­ing a sol­id under­stand­ing of the sci­en­tif­ic method and how to study a small sam­ple of data in order to make deci­sions about an entire pop­u­la­tion is what usu­al­ly sep­a­rates excel­lent Con­tin­u­ous Improve­ment Prac­ti­tion­ers from aver­age ones. In the next series of videos we real­ly dig into top­ics such as hypoth­e­sis test­ing and nor­mal­i­ty. Final­ly, you will nev­er need to use ALL of these tools in a sin­gle project… but it’s still impor­tant you under­stand the tool allow­ing you to use it when the time is right.

Sta­tis­ti­cal Process Con­trol, or SPC, is a method to mon­i­tor, con­trol, and enhance process­es. SPC enables us to build con­sis­tent, pre­dictable process­es by study­ing data over time and min­i­miz­ing vari­a­tion. Learn the five steps of Sta­tis­ti­cal Process Con­trol, how to cre­ate a sam­pling plan, set sub­group sizes and sam­ple sizes, the dif­fer­ence between attribute and vari­able data, how to select and cre­ate the appro­pri­ate type of Con­trol Chart for each sit­u­a­tion, and more.

Have you ever won­dered what ​“Six Sig­ma” real­ly means from a sta­tis­ti­cal per­spec­tive? If so, you’re about to learn. Addi­tion­al­ly, you’re also going to learn the key rela­tion­ship between the Voice of the Cus­tomer and the Voice of Process as well as how to mea­sure this rela­tion­ship with Process Capa­bil­i­ty Analysis.

ANO­VA, which stands for Analy­sis of Vari­ance, is a fun­da­men­tal sta­tis­ti­cal con­cept that is used in things such as Mea­sure­ment Sys­tems Analy­sis, Regres­sion, and DOE. In this sec­tion, we’ll learn what ANO­VA is as well as how we can lever­age it in order to run experiments.

When applic­a­ble, being able to deter­mine how ​“cor­re­lat­ed” vari­ables are can be extreme­ly enlight­en­ing as it can allow us to build pre­dic­tive mod­els. But, as you will learn, there is a key dif­fer­ence between cor­re­la­tion and cau­sa­tion so set­tle in and pre­pare to have some fun with regression!

Can you trust the data that you’re ana­lyz­ing? If you’re not sure… you def­i­nite­ly need to per­form a mea­sure­ment sys­tem analy­sis. Now, to be sure, we’ll typ­i­cal­ly per­form an MSA ear­ly in our project but since there are so many advanced sta­tis­ti­cal con­cepts, like ANO­VA, used in an MSA we had to make you wait a bit before shar­ing it with you. The good news is the wait is over!

Design of Exper­i­ments, or DOE, is one of the most pow­er­ful tools avail­able to Lean & Six Sig­ma prac­ti­tion­ers. Sad­ly, many peo­ple sim­ply don’t under­stand what an authen­tic DOE is or, in some cas­es, some prac­ti­tion­ers mis­tak­en­ly believe their one fac­tor at a time exper­i­ment is in fact a DOE when, real­ly, it isn’t.

Once process­es are improved it’s crit­i­cal we keep them from back­slid­ing. As such, the abil­i­ty to lever­age tools like Con­trol Plans, Poka Yoke, and SPC are must use tools.

Fail­ure Modes and Effects Analy­sis, abbre­vi­at­ed FMEA, is a struc­tured approach for iden­ti­fy­ing the ways that a prod­uct or process can fail, what caus­es the fail­ure, and how this affects the cus­tomer. Learn how to apply FMEA to improv­ing the safe­ty, reli­a­bil­i­ty, and per­for­mance of process­es, prod­ucts, and services.

DMA­IC is a five-phase approach for design­ing, deliv­er­ing, and sus­tain­ing improve­ment projects. Learn the objec­tives of each phase, the struc­tured activ­i­ties of each phase, com­mon sta­tis­ti­cal tools and analy­sis tech­niques used dur­ing each phase, how to con­duct toll­gate reviews between each phase, and how project lead­ers must han­dle project closure.

The 8D prob­lem-solv­ing process was estab­lished in the ear­ly 1980s by the Ford Motor Com­pa­ny with the goal of stan­dard­iz­ing and improv­ing prob­lem-solv­ing capa­bil­i­ties in their sup­ply chain. Launched under the name of ​“Team Ori­ent­ed Prob­lem-Solv­ing Using the Eight Dis­ci­plines,” it’s com­mon­ly prac­ticed as 8D. Learn how to form teams, con­tain prob­lems, scope projects, iden­ti­fy root caus­es, imple­ment cor­rec­tive actions and pre­ven­tive mea­sures, and rec­og­nize the team’s success.

In the Six Sig­ma DMA­IC approach we iden­ti­fy, eval­u­ate, and opti­mize the crit­i­cal inputs, or Xs, that sig­nif­i­cant­ly influ­ence our pri­ma­ry met­ric and over­all process per­for­mance. Learn how to use tools like process map­ping, the Cause & Effect Matrix, and FMEA to iden­ti­fy process inputs and nar­row them down to the crit­i­cal Xs.