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Dirk Dusharme @ Quality Digest’s picture

It’s Time to Start Really Using Our Data
By: Dirk Dusharme @ Quality Digest 06/22/2020

Blame it on Moore’s law. We live in a digital Pangaea, a world of borderless data driven by technology, and the speed and density with which data can be transmitted and handled. It’s a world in which data-driven decisions cause daily fluctuations in markets and supply chains. Data come at us so fast that there is almost no way business leaders can keep abreast of changing supply chains and customer preferences, not to mention react to them.

Operating any kind of manufacturing today requires agility and the means to turn the flood of largely meaningless ones and zeros into something useful. The old ways of treating data as nothing more than digital paper won’t cut it in the “new normal.” We need to reimagine how we view quality.

Ryan E. Day’s picture

The Problem With Unexamined Processes
By: Ryan E. Day 06/22/2020

It’s no secret that manufacturing companies operate in an inherently unstable environment. Every operational weakness poses a risk to efficiency, quality, and ultimately, to profitability. All too often, it takes a crisis—like Covid-19 shutdowns—to reveal operational weaknesses that have been hampering an organization for a long time.

The nature of the problem

It is not just a manufacturing company’s production facility that faces operational challenges, either. The entire organization must address a host of risks and challenges; shifting consumer and market trends necessitate improving agility and responsiveness; dynamic and global competition force innovation not only in product development, but also service and delivery; evolving sales channels, including online outlets, challenge established profit margins. And these challenges are not going away any time soon.

The real problem, however, lies not with the challenges themselves but with a company’s reluctance to see the operational weakness that makes it susceptible to a particular risk in the first place.

Quality Digest 2021 Editorial Calendar

2021 Editorial Calendar
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Landing Pages That Generate Leads
By: Quality Digest 10/21/2019

Let’s get this out of the way right up front: You have the best product in your sector of the industry. You know it, your people know it—heck, even your competitors know it in the bottom of their competitive hearts. But do the thousands upon thousands of potential customers across the world know it? If not, how do you communicate that value to them in a flash?

Welcome to the power of a well-thought-out and elegantly executed media marketing strategy. If there’s one maxim you need to keep in mind constantly, it’s the words we all mutter to ourselves whenever we land somewhere online: “Why do I care about this?” You have fewer than five seconds to show your prospects why they should care. 

Notice we said “show” and not “tell.” Showing them starts with a great landing page, which must be visual, enticing, and most of all, simple. If looks like it’s too much work, too complicated, then prospects will bounce. 

What makes a great landing page? A few simple elements:

Do

Don’t

Be simple

Be complicated

Use few words

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By: Quality Digest 09/09/2019

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Quality Digest   |  Fri, 05/10/2019
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Quality Digest   |  Fri, 05/10/2019

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Ryan E. Day   |  Thu, 05/09/2019 Great stuff here

William A. Levinson’s picture

When and How to Use Zero Acceptance Number Sampling
By: William A. Levinson 04/11/2019

Inspection is a mandatory but nonvalue-adding activity, and our objective is to do as little as possible, provided that we continue to fulfill the customer’s requirements. The zero acceptance number (c = 0) sampling plan requires far less inspection than the corresponding ANSI/ASQ Z1.4 (formerly MIL-STD 105) plan, and becomes viable when the supplier is extremely confident in its level of quality.1

An ANSI/ASQ Z1.4 plan consists of a sample size n, and an acceptance number c. The inspector checks n items, and accepts the lot if c or fewer defects or nonconformances are found. These plans are designed to give (roughly) a 95-percent chance of acceptance at the acceptable quality level (AQL), which is one of the parameters for the plan’s selection.

The c = 0 plan, on the other hand, rejects the lot if any defects or nonconformances are found, but it requires a considerably smaller sample size. The drawback is that the producer’s risk (α) of rejecting a lot at the AQL is usually far greater than the textbook 5 percent, so the c = 0 plan should be used only when quality is much better than the AQL. This reinforces a basic principle of industrial statistics: We can have low risks or small sample sizes, but we can’t have...

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