Multi-Vari Studies, How to Quickly Find 85% of the Variation in a Product or Service

The name “Multi-Vari” was given to this form of analysis by L.A. Seder in his classic paper “Diagnosis with Diagrams,” which appeared in Industrial Quality Control in January and March 1950.  The premise is to utilize graphics to understand where the variation in a process exists.  Is it excessive variation within a single piece, excessive variation from piece to piece, or is the variation excessive from time to time.  If we are relating this to service delivery substitute “service delivery to customers”, “service delivery from customer to customer”, and “service delivery from time to time” in the previous sentence.

Multi-Vari Studies are often classified as either a “Nested Design” or a “Crossed Design.”  In the Nested Design the data is collected without making changes to the process to investigate where the variation is coming from.  It could be positional which is within piece variation, it could be cyclical which is consecutive piece-to-piece variation, or it could be temporal which is time-to-time variation such as day-to-day, or week-to-week.  The following graphic is an example where we are trying to find the source of process variation with regard to warp in a glass container.

From this Nested Design Multi-Vari Chart we can clearly see that Machine Section 7 is very different than any other section on the machine.  Section 7 becomes the target for variation reduction.  The question becomes, “Why is it so different than the rest of the machine?”

In the Crossed Design the plan is to test changes to the process in a balanced manner following an on or off strategy.  In the Crossed Design either 2 or 3 potential variation contributor process variables are studied at 2 different settings.  Analysis of Variance is often added as part of the study to provide detailed statistics that support what the graphic analysis portrays.  The ANOVA provides the verdict of “guilty beyond a shadow of a doubt” to support what we see graphically.  The following graphic is an example where we are trying to minimize the time it takes to boil a cup of water in a microwave oven.

From this Crossed Design Multi-Vari Chart it is clear that to minimize the time to boil a cup of water in a microwave oven the container should be rotated, located 4 inches off center, and covered.  To add further proof to this graphic finding an ANOVA (analysis of variance) was conducted with the following results.

The sources of variation are Cover, Rotate, and Location.  Each are significant with p values that are less than .0009 (assume worst case for unknown digit of 9) which equates to a confidence level of at least of 99.91%.

Multi-Vari Studies provide a graphic means to quickly find 85% of the variation in a product or service.  I think you will find this technique to be useful.

Lean Root Cause Analysis

Do you have processes that don’t perform the way you would like them to?

Do you have processing errors and customer complaints?

Does it seem like nothing ever gets out the door on time?

Are you tired of the inefficiencies and waste in your processes?

If you are in this situation then finding the root causes in your processes is the answer. Lean Root Cause Analysis begins with mapping of the current state, which is where you are now. All it takes is a walk through review of your current process, a digital camera to capture actual evidence, some sticky notes, sharpies, and a roll of brown paper.

Depicting the current state of your process on a Process Map, whether it is a Value Stream Map or a Deployment Process Map, gives you a visual representation from the beginning to the end of your process. You will be able to take a step back and review what’s actually going on objectively.

Photographs provide reminders of what is actually taking place. Sometimes the photos show some pretty scary things taking place in the process.

Information that should also be captured are cycle times, processing times, lead times, quantities of good and bad process outputs, travel distances, and inventory. The list is not all encompassing, but a good start.

The next step is to classify and quantify the Value Adding activities and the Non Value Adding Activities. We define Value Adding activities as ones that (1) the customer considers to be important and would be willing to pay for them, (2) the “THING” that travels through the process is physically changed, and (3) the process activity is done correctly the first time through the process. All three requirements must be met or the process activity is considered to be Non Value Adding.

To reduce or eliminate the Non Value Adding activities in the process requires an understanding of the root causes that created the need for them. We use Cause and Effect Diagrams, Failure Mode Effects Analysis, and 5 Why Brainstorming to uncover the root causes.

Devise your corrective actions and implement the improvements. Sounds pretty easy and it can be. Just follow the 5 step DMAIC improvement process. Define your current state, measure what is actually happening, analyze the information to uncover the root causes, develop creative solutions to improve the process, and then implement the solutions and install controls to maintain your gains.

If you need some help getting started then take our course Lean Root Cause Analysis

The Lean Root Cause Analysis course teaches practical application tools for uncovering the root causes in your processes. Lean concepts are demonstrated with a simulation. You will then learn how to define your current state and uncover the root causes that are the impediments to your future state success. Budget friendly at only $69.95

Register for a Course Today at EducateVirtually.com