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Get a Running Start at Six Sigma

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Get a Running Start at Six Sigma

Wouldn’t It Be Great To Start At Six Sigma?

Wouldn’t it be great to hit the ground running with a Six Sigma capable (3.4 defects per million) process for delivering your product or service? Of course it would, but most 3-Sigma companies don’t have the stomach for the kind of rigorous thinking it takes to design and launch a new product or service at these levels. Unless, of course, you understand the horrendous costs associated with a typical “seat of the pants” implementation. Design for Six Sigma requires the rigorous application of three key tools: QFD, FMEA, and DOE. Let’s look at each of these.

Quality Function Deployment (QFD)QFD is a rigorous method for translating customer needs, wants, and wishes into step-by-step procedures for delivering the product or service. While delivering better designs tailored to customer needs, QFD also cuts the normal development cycle by 50%, making you faster to market. QFD uses the “QFD House of Quality” (a template in the QI Macros) to help structure your thinking, making sure nothing is left out.

There are four key steps to QFD thinking:

1. Product Planning – Translating what the customer wants (in their language, e.g., portable, convenient phone service) into a list of prioritized product/service design requirements (in your language, e.g., cell phones) that describes how the product works. It also compares your performance with your competition, and sets targets for improvement to differentiate your product/service from your competitors.

2. Part Planning – Translating product specifications (design criteria from step 1) into part characteristics (e.g., lightweight, belt-clip, battery-driven, not-hardwired but radio-frequency based).

3. Process Planning – Translating part characteristics (from step 2) into optimal process characteristics that maximize your ability to deliver Six Sigma quality (e.g., ability to “hand off” a call from one antenna to another without interruption).

4. Production Planning – Translating process characteristics (from step 3) into manufacturing or service delivery methods that will optimize your ability to deliver Six Sigma quality in the most efficient manner (e.g., antennas installed with overlapping coverage to eliminate dropped calls).

Even in my small business, I use a template to evaluate and design a new product or service. It helps me think through every aspect of what my customers want and how to deliver it. It saves me a lot of “clean up” on the backend. It doesn’t always mean that I get everything right, but I get more of it right, which translates into greater sales and higher profitability with less rework on my part. That’s the power of QFD.

Preventing Disaster with Failure Modes and Effects Analysis (FMEA)

When you’ve got a product or a process that can affect human life (e.g., radiology or amputation in a hospital), what do you do to prevent a disaster? You want to anticipate all of the ways that the product or process could go wrong and affect your customer. The automobile and airline industries do this routinely to mitigate and prevent potential problems. Let’s face it, if it wasn’t pretty safe, none of us would drive or fly. The tool you use to analyze and prevent disasters is the FMEA template. You can use it to analyze a product (car, x-ray, MRI), part (car door), or process (part stamping process or elective surgery). to analyzing a process you would use a PFMEA.

Here’s how you do it:

1. Identify each Part or Process Step (e.g., preparation for an MRI-magnetic resonance imaging-in a hospital)

2. Identify Potential Failure Modes – All of the manners in which the part or process could fail: cracked, loosened, deformed, leaking, oxidized, overlooked, etc. (e.g., MRIs produce intense magnetic fields. One patient was killed by a flying fire extinguisher pulled off the wall by the MRI.)

3. Identify any Potential Effect(s) of Failure – Consequences on other systems, parts, or people: noise, unstable, inoperative, impaired, injury, death, etc.

4. Rank Severity of the Effect (1-10)- from none to “hazardous without warning” (e.g., tire blow out or fire extinguisher to the skull).

5. Evaluate Potential Cause(s) / Mechanism(s) of Failure – List every potential cause and/or failure mechanism: incorrect material, improper maintenance, fatigue, wear, etc.

6. Rank the Possibility of Occurrence (1-10) – remote (6-sigma) to high (3-sigma).

7. List Your Current Design Controls – List prevention and detection activities to assure design adequacy and prevent or reduce occurrence.

8. Rank Your Ability to Detect a Failure Using these Controls (1-10) from almost certain to absolute uncertainty.

9. Calculate the risk-priority number (RPN) for each part or step RPN = severity * occurrence * detection.

10. Design Recommended Improvement Action(s) – Design additional actions to reduce severity, occurrence and detection ratings. Severity of 9 or 10 requires special attention.

11. Assign Responsibility & Target Completion Date for implementing designed improvements.

12. Monitor Actions Taken and effects on RPN.

Use the FMEA or PFMEA to analyze and mitigate the effects of potential failures before they happen. Can you afford an injury or a death? Probably not. Can you afford the time and money involved in litigation? Probably not. Is it cheaper to use the FMEA and prevent potential problems? No doubt, especially when the consequences are extreme. Are you going to catch everything? No, because some things are outside of our ability to imagine them. Save yourself time and money by analyzing the potential causes of failure using the FMEA.

Fine Tuning Your Production Process using Design of Experiments (DOE)Many manufacturing processes and some service processes can benefit from using DOE-Design of Experiments to optimize their results. Without DOE, you’re stuck with the world’s slowest method for success-trial and error. With DOE, you just have to test at the high (+) and low (-) values for any particular “design factor” (e.g., pressure, temperature, time) from your QFD House of Quality, not every increment in between. And you can test more than one factor at a time. You can make DOE wildly complex or straightforward and simple.

In my first DOE class we spent an inordinate amount of time understanding “orthogonal arrays” and all of the other “behind the scenes” mathematics, but you don’t need to know all of that to conduct a DOE study.

Manufacturing Example

For simplicity, let’s assume you are writing a cookbook and want to find the best directions for baking a cake (which is similar to baking paint on a car finish). To do this, you will want to establish the high-low settings for each “factor” in your study. Let’s suppose you have four factors (a four-factor experiment):

1. Pan shape: Round (low) vs square (high) pan

2. Ingredients: 2 vs 3 cups of flour

3. Oven temperature: 325 vs 375 degrees

4. Cooking Time: 30 vs 45 minutes

Let’s say that you’ll rank each resulting cake on a 1-10 scale for overall quality. You then use the +/- values in the orthogonal array to guide your test of every combination (16 total):

• High: all high values (+ + + + = square pan, 3 cups, 375 degrees, 45 minutes),
• Low: all low values (- – – – = round pan, 2 cups, 325 degrees, 30 minutes),
•I n Between: every other combination (“+ + + -“, and so on).

To optimize your results, you might want to run more than one test of each combination. Then you just plug your data into the 16-factor DOE template (Taguchi or Plackett-Burman format) in the QI Macros and observe the interactions.

In DOE, they talk about “confounding” which simply means that one factor affects another. You’d expect a higher temperature to result in a shorter cooking time, and vice versa, but does a square pan take longer than a round one?

Using the results, a DOE program will draw the interactions between each of the factors as a line graph. If the two lines are parallel, there’s no interaction. Is one end higher than the other? If so, you can immediately tell which value (high/low) gives you the best result. If the two lines cross, there is an interaction (confounding). And, by looking at where the two lines intersect on the graph, you can determine the optimum settings (e.g., time and temperature) to get the best cake. To do this using trial-and-error would take hundreds, maybe even thousands of trials, not just 16.

Service Example

People who send direct mail rigorously tally their results from each mailing. They will test one headline against another headline, one sales proposition against another, or one list of prospects against another list, but they usually only do one test at a time. What if you can’t wait? Using DOE, you could test all of these factors simultaneously. Design your experiment as follows:

• Headline: Headline #1 (high), Headline #2 (low)

• Sales proposition: Benefit #1 (high), Benefit #2 (low)

• List: List #1 (high), List #2 (low)

• Guarantee: Unconditional (high), 90 days (low)

This way you might find that headline #1 works best for list #2 and vice versa. You might find that one headline works best with one benefit.

DOE can help you shorten the time and effort required to discover the optimal conditions to produce Six Sigma quality in your delivered product or service. Don’t let the +/- arrays baffle you. Just pick 2, 3, or 4 factors, pick sensible high/low values, and design a set of experiments to determine which factors and settings give the best results. Start with a 2-factor and work your way up. Have fun! It’s just not that hard.

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