In this episode we look at a history of quality, how you serve your customer in the housing industry, and what makes a good review.

“Young couples ‘trapped in car dependency’”

Building entry-level housing along highways may give couples the chance to buy a home, but at what cost to them and the environment?

“The Quality Profession: Where It's Been, Where It's Going,” an interview with Barbara Cleary of PQ Systems

We talk to a 35-year veteran of the quality industry about the changes she has seen in the industry.

“Perfect Information: Customer Reviews That Influence Purchasing Decisions”

Want to leave a customer review that is meaningful and gets readers attention? Here’s how.

Metrics are an important part of an effective quality management system (QMS). They are necessary to understand, validate, and course-correct the QMS. They should be used to verify that it is achieving the goals and objectives defined by management. In an ISO 9001 system, metrics must be available to assess risk, and validate changes made to the QMS and individual processes. Metrics are also used to validate improvement and verification of corrective action implementation during the management review.

I have seen and used many metrics in the past, and in my experience not all metrics are equally good; in fact, many are totally inappropriate for the purpose for which they are being used. This article, the first in a three-part series, will help readers distinguish good metrics from bad—or as the title suggests, the downright ugly. Once the characteristics of a good metric are known, a bad metric can be converted into a good metric. This series is divided into three parts: Part one explains what a good metric is, part two identifies bad metrics and explains how to convert them, and part three looks at ugly metrics and explains why they have no hope for conversion.

In this episode we look at data, data, more data, and then... engineering the perfect human?

“Your Data Are Your Most Valuable Assets”

Just what the heck is Quality 4.0? Remember this acronym: CIA. No, not that CIA. Nicole Radziwill explains.

“Applying Smart Manufacturing Technology to Conduct Smart Inspections”

There is an easier way to do inspections on the shop floor than using a clipboard and pencil (remember those skinny yellowey-orange things?). It's called your mobile device.

In my first article, the merits and cautions of AS9138 c=0 sampling plans were discussed and a simple formula was provided to determine the required sample size to detect nonconforming units. In the second article, the process control properties of MIL-STD-105 c>0 sampling plans were demonstrated, and the connectivity to other process control techniques was discussed. Here, a third alternative will be explored that applies the procedures of MIL-STD-105 to “imaginary limits” which are set proportionally inside the real “engineering specification.” This imaginary limit procedure thereby does not allow nonconforming units in the sample and has superior detection capabilities.

In my previous article, I discussed the merits and cautions of the “acceptance number” equal zero (c=0) sampling plans contained within AS9138. A simple formula was provided to determine appropriate sample size, and it was illustrated that twice the inspection does not provide twice the consumer protection. Although there is an undeniable emotional appeal to implement sampling procedures that have an acceptance number of zero, readers must not jump to the conclusion that c=0 sampling procedures provide better consumer protection at the designed lot tolerance percent defective (LTPD) point.

In this article the merits and limitations of MIL-STD-105 will be illustrated, and its link to process control will be demonstrated. Before discussing the technical merits of MIL-STD-105, its impressive evolution deserves some recognition.

Aerospace standard AS9138—“Quality management systems statistical product acceptance requirements” was issued this year (2018), a few years after its accompanying guidance materials in section 3.7 of the International Aerospace Quality Group’s (IAQG) Supply Chain Management Handbook. The new aerospace standard supersedes the aerospace recommended practice of ARP9013 and, related to MIL-STD-105 (ANSI/ASQ Z1.4), claims to shift focus from the producer’s risk to the consumer’s risk with sampling plans having an acceptance number of zero (c=0).

Somewhere along this evolutionary path, the sampling procedures of MIL-STD-105 have fallen out of favor, even though the consumer risks of MIL-STD-105 at their designed lot tolerance percent defective (LTPD) point are superior to most plans found within AS9138.