Risk Management Article

William A. Levinson’s picture

By: William A. Levinson

The U.S. House of Representatives has passed the HEROES Act (Health and Economic Recovery Omnibus Emergency Solutions Act)1 which will, if approved by the Senate and president, require OSHA to develop a standard for workplace protection against Covid-19.

Under section 120302 the legislation says specifically (emphasis is mine):

“(a) EMERGENCY TEMPORARY STANDARD

(1) In general—in consideration of the grave danger presented by COVID-19 and the need to strengthen protections for employees, notwithstanding the provisions of law and the Executive orders listed in paragraph (7), not later than 7 days after the date of enactment of this Act, the Secretary of Labor shall promulgate an emergency temporary standard to protect from occupational exposure to SARS-CoV-2

(A) employees of health care sector employers;
(B) employees of employers in the paramedic and emergency medical services, including such services provided by firefighters and other emergency responders; and
(C) other employees at occupational risk of such exposure. ...

Denrie Caila Perez’s picture

By: Denrie Caila Perez

A new report from Trend Micro Research illustrates how advanced hackers are using unconventional attack vectors against smart manufacturing environments. Smart manufacturing technology generally operates through proprietary systems, which use their own proprietary language. However, these systems also still run on the computing power of traditional IT systems. While typically designed to function independently from other systems, it’s this particular loophole that leaves these systems vulnerable to IT threats.

“Past manufacturing cyberattacks have used traditional malware that can be stopped by regular network and endpoint protection,” says Bill Malik of Trend Micro. “However, advanced attackers are likely to develop operational technology-specific attacks designed to fly under the radar. As our research shows, there are multiple vectors now exposed to such threats, which could result in major financial and reputational damage for Industry 4.0 businesses. The answer is IIoT-specific security designed to root out sophisticated, targeted threats.”

Leigh Turner’s picture

By: Leigh Turner

Given the death, suffering, social disruption and economic devastation caused by Covid-19, there is an urgent need to quickly develop therapies to treat this disease and prevent the spread of the virus.

But the U.S. Food and Drug Administration (FDA), charged with the task of evaluating and deciding whether to approve new drugs and other products, has a problem. The FDA’s standards appear to be dropping at a time when rigorous regulatory review and robust oversight are crucial.

Tom Taormina’s picture

By: Tom Taormina

Each article in this series presents new tools for increasing return on investment (ROI), enhancing customer satisfaction, creating process excellence, and driving risk from an ISO 9001:2015-based quality management system (QMS). They will help implementers evolve quality management to overall business management. In this article we look at the clauses and subclauses of section 8 of the standard.

Clause 8: Operation

Clause 8 contains the requirements for planning, designing, and bringing to fruition your products or services. The processes within this clause must be robustly implemented to achieve business excellence. They must also be continually scrutinized for foreseeable risk.

8.1 Operational planning and control

8.1 and excellence
The “plan” is a series of interrelated process, each with acceptance criteria, and each with metrics that tie to the organization’s key objectives and key process indicators. Or, at least that has been my interpretation while leading scores of implementations.

Vanessa Bates Ramirez’s picture

By: Vanessa Bates Ramirez

Long before coronavirus appeared and shattered our preexisting “normal,” the future of work was a widely discussed and debated topic. We’ve watched automation slowly but surely expand its capabilities and take over more jobs, and we’ve wondered what artificial intelligence will eventually be capable of.

The pandemic swiftly turned the working world on its head, putting millions of people out of a job and forcing millions more to work remotely. But essential questions remain largely unchanged: We still want to make sure we’re not replaced, we want to add value, and we want an equitable society where different types of work are valued fairly.

To address these issues—as well as how the pandemic has impacted them—this week Singularity University held a digital summit on the future of work. Forty-three speakers from multiple backgrounds, countries, and sectors of the economy shared their expertise on everything from work in developing markets to why we shouldn’t want to go back to the old normal.

Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

What is quality intelligence, exactly? It’s more than marketing spin. More, even, than the sum of its many control charts. It’s not collecting data simply to further go/no-go actions. And it doesn’t mean turning the cognitive wheel entirely over to artificial intelligence, either—far from it.

We might think of quality intelligence as a natural progression of quality control. It’s both granular, in that core quality tools underpin it, and forward-looking because quality data are used to improve not only products and processes but also operational performance. It’s very deliberate in that its goal is to wring the maximum value possible from reliable data.

To do this, quality intelligence employs four key tools: ensuring compliance, grading collected data, exploiting software, and implementing data strategically.

Ensuring compliance

People often assume that compliance applies solely to government or industry standards, but the term surfaces in many shop-floor conversations and processes. For instance, there is compliance to limits: Are data in specification? Are the appropriate statistical rules being met? There’s also compliance to procedures: Are people collecting data in the right way, and on time?

Ryan E. Day’s picture

By: Ryan E. Day

An organization can achieve great results when everyone is working together, looking at the same information generated from the same data, and using the same rules. Changes can be made that affect a company’s bottom line through operational improvements, product quality, and process optimization. There are quality intelligence (QI) solutions that can help reveal hidden opportunities.

Companies can save money and improve operational efficiency by effectively focusing resources on the problems that matter most from both a strategic and tactical perspective. A proper QI system makes this practical in several ways.

The QI advantage

With a QI system, data are captured and analyzed consistently in a central repository across the organization. This means there aren’t different interpretations of the truth, and there is alignment among those on the shop floor, site management, and corporate quality.

Alignment is possible because of a positive cascade of events:
• Notifications are sent to the appropriate people, and workflows trigger the required actions. This means people are appropriately accountable for addressing issues. Those issues can then be analyzed to understand recurring problems and how to avoid them.

Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

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

By: Ryan E. Day

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.

Mary Rowzee’s picture

By: Mary Rowzee

During the first six months after the publication of its first edition in June 2019, the AIAG & VDA FMEA Handbook gained popularity in the global automotive industry. Both U.S. and European OEMs have started to require the AIAG VDA approach to failure mode and effects analysis (FMEA) in their programs. Like the AIAG Guidebook, fourth edition, the handbook provides guidance, instruction, and illustrative examples of the requisite analytical techniques. The activities and analyses historically involved in FMEA have been formalized as discrete steps in the handbook.

 The seven-step approach described in the handbook and outlined here guides the development of design, process, and supplemental monitoring and system response of FMEA through the sequencing (and the iteration) of described activities.

[Read More]

Syndicate content