Statistics Article

Donald J. Wheeler’s picture

By: Donald J. Wheeler

On Sept. 29, 2020, the recorded worldwide death toll from Covid-19 reached 1 million. Six days earlier the United States reached 200,000 Covid-related deaths. So how did the United States with only 4 percent of the world’s population manage to capture 20 percent of the world’s deaths in this pandemic?

The 19 countries listed in figure 1 account for 85 percent of the Covid-related deaths worldwide, as reported by the European CDC. Here we can see how the U.S. death toll exceeds all others.

 
Figure 1: Number of Covid-related deaths reported by 19 countries as of Sept. 26, 2020

The short explanation for this dubious achievement is that between April 1 and the present, the United States had an average of 27 percent of the worldwide total number of confirmed cases of Covid-19. With that kind of market share, the high death toll was sure to follow. But a more detailed answer requires that we look at the number of deaths per capita and the rate at which these death tolls are growing.

Eric Weisbrod’s picture

By: Eric Weisbrod

The idea of digital transformation can be scary. The growth of technology is outpacing a comfortable pace of adoption for many manufacturers. But remaining content with the status quo often means being left behind. Digital transformation has become an imperative to give manufacturing organizations the flexibility and agility required to overcome business disruptions and adapt to rapidly changing and demanding global markets.

Digital transformation of quality management is a process that depends on something you already have: quality data. Your quality management system is key to optimizing all your quality operations, including supplier and materials management, production processes, quality checks, packaging, and shipping.

InfinityQS calls this holistic approach “manufacturing optimization.” It starts with improving the way you use data to answer the strategic, big-picture questions that truly matter to your business.

Limits of the status quo

The barriers to transformation are often a result of operational and resource challenges that typically boil down to one thing: everyone’s plate is already full. Whether managing and maintaining servers and IT projects, or running day-to-day production, no one has the time to take on new transformation projects.

Steve Wise’s picture

By: Steve Wise

The importance of data analysis in manufacturing operations can’t be overstated. Over the years, manufacturers have used statistical process control (SPC) methods and tools to study historical data and reveal differences between comparable items: shifts, products, machines, processes, plants, lot codes, and more.

The foundational benefit of statistical methods is predicting future behavior from historical data. That’s why control charts, box-and-whisker plots, Pareto charts, and the like are so valuable: They indicate that if processes are not changed, then performance (positive or negative) will continue as it is.

Control charts are brilliant tools for assessing performance over time, and their related “control limits” are predictions of normal future behavior. The problem is that many SPC software products struggle to move beyond just data collection to offer truly insightful data analysis.

Multiple Authors
By: Dirk Dusharme @ Quality Digest, Jason Chester

In previous articles of this series, we discussed how to master quality at the tactical and strategic levels. If you are like most readers, you probably nodded your head through article two’s tactical shop-floor view and vigorously shook your head through article three’s strategic view because your organization has the same challenges.

There is understandable hesitation from nearly any organization to make the transition to ultimately master quality at the enterprise level. This hesitation stems from organizations trying to view this transition as an all-or-nothing endeavor. As a result, that gets people seeing roadblocks that don’t necessarily exist.

Let’s take a look at a few.

Solve all the deployment issues in advance

Because previous deployments took a tremendous amount of time, resources, and expense, most organizations want to address every deployment problem before they even begin considering strategy.

That won’t happen. Ever. No matter what system you deploy, you will discover and learn things you could never have anticipated up front.

Jason Chester’s picture

By: Jason Chester

Before we get into a case study about how enterprisewide SPC software would work on both the shop floor and the C-suite, let’s talk about a long-held bias about “blue-collar” workers: That because they’ve traditionally been associated with manual labor, they should use manual tools; “white-collar” front-office workers, on the other hand, need the slick technology tools.

Imagine walking around the offices of a large manufacturing organization and finding salespeople managing customers’ information using a Rolodex. In a planning meeting, the CEO is using acetates on an overhead projector. In the procurement office, staff are issuing purchase orders using a Telex machine.

Now imagine walking the plant floor at that same manufacturer. The production supervisor is writing machine settings for the next shift on a board next to the machine. The quality engineer is writing the results of a critical quality check on a clipboard with a blunt pencil. A bunch of people stand around murmuring, scratching their heads, and wondering why a machine isn’t working properly.

In the first example, you might think you’d traveled back in time. The scenes are absurd. But the second example is a common reality.

Multiple Authors
By: Ryan E. Day, Dirk Dusharme @ Quality Digest, Taran March @ Quality Digest

In order to best illustrate how enterprisewide SPC software can help address shop-floor problems and then funnel the captured data to the corporate level where strategic issues can be analyzed, here is a case study of a hypothetical manufacturing facility. In it, the company makes effective use of SPC for data-driven decisions.

A global food products manufacturing company with 11 sites worldwide had chosen to master quality, both tactically and strategically, as its top goal. Each site collected and analysed data in the company’s enterprisewide SPC software, both to monitor and respond to quality issues at the site, and to share those same data with the corporate office.

At the company’s Prague site, the quality manager looked at her shop-floor data for the previous month. As figure 1 indicates, the software reported a total of 737 events, which at first glance seemed like a big deal to the manager. However, on closer inspection, she could see that these weren’t massive quality issues with the product or processes. However, there were 517 missed data checks. Although not a line-stopping issue, missed checks could result in noncompliance to agreements with customers or industry requirements.

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Eric Weisbrod’s picture

By: Eric Weisbrod

In recent months, we’ve learned that manufacturing during a global health crisis puts organizations under immense pressure to maintain operational efficiency while upholding product quality and employee safety.

Initially, organizations focused simply on taking the steps required to survive. However, as organizations around the globe have pivoted to overcome those initial challenges, manufacturers are taking the opportunity to explore how they will not just survive but become more resilient—even thrive—going forward.

Recent operational challenges have shined a light on existing process weaknesses and technology limitations. Manufacturers are taking their cue and proactively identifying opportunities to optimize processes, empower workers, and make operations across the organization more effective.

Enact, InfinityQS’ cloud-native quality intelligence platform, offers plant leadership a variety of ways to make their operations more effective. Here are six Enact benefits that can help your organization make critical shifts that are necessary for the future of manufacturing.

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Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

It’s been 40 years since “If Japan Can, Why Can’t We?”, W. Edwards Deming, and total quality management. More than 33 years have passed since the release of the first iteration of ISO 9001 (remember checklists?). For four decades the importance of building quality into processes rather than trying to “inspect in” quality has been pounded into our little quality management brains. Proactive good, reactive bad. We get it.

Or do we?

Despite “risk management” or “risk-based thinking” becoming part of everyday quality parlance, quality on the ground is still largely reactionary. Tactical rather than strategic. In short, we still seem to be just “doing” quality rather than mastering it, i.e., we are using traditional quality processes and tools at only the shop-floor level rather than using next-generation enterprisewide quality tools throughout our entire organization.

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Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

In the intro to this series we noted that, too often, quality tools and the data we glean from them are used only to solve immediate, mostly shop-floor problems. These gold nuggets of opportunity aren’t used in an equally valuable way to address a company’s strategic goals.

Here we’ll consider how to master quality at the shop-floor, tactical level. More than just byproducts of the production process, quality data are the vital signs that determine if individual processes—and by extension, the entire production system—are healthy. That information can in turn help drive business strategy.

It starts on the shop floor

For most companies, learning to use data to their best advantage entails drifting between visionary potential and problematic reality—the strategic vs. tactical tension. As part of this inevitable tug-of-war, data are rounded up, variability is tamped down, and quality anxiously measured.

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Ryan E. Day’s picture

By: Ryan E. Day

Those of you involved in matters of business strategy know: Strategy matters. Your strategies guide you to reach your objectives. Behind every successful business are purposeful strategies. Then again, as Alvin Toffler said, “The absence of strategy is fine if you don’t care where you’re going.”

We’re talking specifically about data-driven strategies like using “improving operational efficiency” to support a goal of increasing your profit margin. Or “improving product standardization” to increase international market share. The question is, how do you support your data-driven strategies? Where do your data come from?

Many leaders don’t realize they are probably sitting on a gold mine of data just waiting to be transformed into actionable information to support their strategies. I’m talking about the quality control data that are collected every day on the shop floor.

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