Smart Factory Insights: Digital Transcendence—Fear of The Unknown

The first three industrial revolutions have brought us automation of physical tasks through adoption of mechanical and electrical machines, the benefit of which has been quite easy to appreciate. Industry 4.0 automation, however, is driven almost exclusively from the digital realm, representing a whole new world of intangibility. With manufacturing being rather averse to unplanned change or risk, unless there are very compelling reasons, how do we get to fully trust digital technology needed for our businesses today, taking us toward manufacturing digital transcendence?

Wouldn’t it be amazing to have god-like powers, to be able to look down across the completely automated factory that simply, efficiently, and perfectly, making whatever products are required, whenever needed, and without human dependencies? All actions and activities would be automated in the mix of mechanical and software automation, lights out. This is the extreme and potentially obtainable goal, but with quite a few challenging steps to be taken in getting us there.

Several steps have already been taken. Data captured from machines and operations can be more instantly transmitted and contextualized for various uses today than ever before. We can remotely monitor machines and operations from the other side of the planet, if we so wish. Software has been evolving, not only to bring us, as humans, holistic factory information, with which we make operational decisions in a faster, smarter, and more reliable way, but has already started to make those decisions for us. We accept results, for example, from automated inspection and test machines that alert us to potential quality issues, instruction from Lean supply-chain tools that orchestrate material logistics based on production progress and planned future activities, and actionable information from dashboards that alerts us to trends.

Hang on a minute. Do we trust all these decisions? Smart software technology is still evolving, with many challenges yet to fully overcome. It starts with the problem of data collection. Other than IPC-CFX, for which not every machine vendor has yet received IPC qualification, other data exchange technologies do not guarantee the integrity, content, or meaning of information, leaving gaps in the data that often go unobserved. Decisions are being made by software automation that are based on access to only a certain part of the true holistic picture. Proof of this is apparent as we see examples that include:

  • Instances of work-orders being created and scheduled by ERP without the actual physical materials being available
  • The failure of an in-circuit test operation resulting in a “no fault found”
  • A dashboard indicating a ridiculous number for OEE at the start of a production run

These are all examples of where data and software are incomplete, that contextualization is being assumed or even guessed. How many times do we turn off and on again our computers, phones, tablets, printers, televisions, etc.? Something went wrong, something quite unknown to us, but we know enough about what to do to fix it. This does not inspire confidence.

Today’s reality is that we will not transcend into the full digital world of Industry 4.0 until these kinds of problems have been solved and trust created. This is not an unexpected issue per se. We continue to use human operators for assembly tasks that automation, even after many years of evolution, has not yet found a way to competently and cost effectively replace. The same is, of course, happening in software, with “AI” algorithms evolving over time, with improved visibility and therefore decision-making, though we are much closer to the start of the journey with software than we are with hardware automation, a whole +1.0. We find ourselves therefore on a progressive journey.

The trust element is vital for ongoing adoption. Almost all of us are still driving our cars manually, even though more automated features are being added, such as LIDAR-driven emergency braking and automated steering following the lines on the road, are successfully being introduced. However, we don’t yet totally trust that the software can provide full autonomous driving in all conditions. Is it the software itself that we don’t trust or something else? Hardware failures are also possible, which are addressed, we expect, by a degree of redundancy and fail-safe design. Far more likely would be the problems with the vast variety of road conditions due to unexpected or unrecognized fixed and mobile hazards, weather events, and human nature. Some very clever people work on the development of automotive software, yet the most important people in the mix are those who implement and experience it, then provide feedback as it works in the real world.

The same is true in the evolution of artificial intelligence (AI) manufacturing software. To whatever extent software is “smart” in today’s world, it takes an inspired and informed user to get the most benefit from the software’s functionality, and to understand the real opportunities and limitations. To be afraid of the software, to install it, and then, from a distance, expect to have it work without ongoing interaction and support is counterproductive. With mechanical automation, we have accepted the necessity to keep an eye on the performance and efficiency, as maintenance and repair will ultimately be needed. Software does not wear out, and there is no need for users to get into the bits and bytes levels of detail, but it is essential to understand how the software becomes progressively integrated as a member of the overall manufacturing resources team. As manufacturing practices develop and evolve toward digitalization, more software functions will be utilized, driving further benefits and new functional development, extending the reach of the software “AI,” creating further business opportunities.

For this whole scenario to be an acceptable part of manufacturing, there needs to be visibility, trust, and flexibility between customers and software vendors that reduces the risk of any adverse issue. What is most effective is selecting the right tools that enable low-risk, trustworthy functionality, and interoperability, and that progress at a rate at which manufacturing practices evolve. Bespoke software and middleware components should be avoided, as by their static nature, and bring limitations and barriers which eventually trigger step-changes in solution choices. Products from companies offering holistic, yet fully interoperable solutions, based around industry standards and thought leadership, represent a great start.

Customers of manufacturing operations today are expecting to see a plan. To not have a plan toward digitalization is increasingly being seen as a wasted opportunity, a risk that competitiveness, and hence customer value, is not being taken seriously. As with climate change, no one expects to create the fully autonomous digital factory overnight, but manufacturers should be putting into place the education, skills and strategy needed that take smart manufacturing toward its end goal. Each company will adopt different paths, as priorities and software selections differ. Having people in the organization with the right skills and expectation is essential. As holistic “AI” utilization within manufacturing is probably not on offer from most universities currently, human resource managers need to search for those candidates who are able to grasp the vision and methodology for manufacturing digitalization, to grow their experience and allow them to gain the trust within the organization.

To achieve manufacturing digital transcendence, we need to know what we are transcending into, and trust those who are enabling that journey for us, in this case, driven by incremental creation of benefits, all of which align to medium- and long-term business goals.

This column originally appeared in the December 2021 issue of SMT007 Magazine.

 

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2021

Smart Factory Insights: Digital Transcendence—Fear of The Unknown

12-22-2021

The first three industrial revolutions have brought us automation of physical tasks through adoption of mechanical and electrical machines, the benefit of which has been quite easy to appreciate. Industry 4.0 automation, however, is driven almost exclusively from the digital realm, representing a whole new world of intangibility. With manufacturing being rather averse to unplanned change or risk, unless there are very compelling reasons, how do we get to fully trust digital technology needed for our businesses today, taking us toward manufacturing digital transcendence?

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Smart Factory Insights: The Costs of Legacy Thinking

12-01-2021

As humans, we learn facts, gain impressions, create solutions, put practices into place, and move onto our next challenge. Over time, our intent is to create a legacy of value, but in many cases, we are creating legacies in a different sense. Our knowledge, experience, and creations age or become superseded, but there is resistance to replace or update. An increasing gap develops between perception and reality. Younger, more agile peers take advantage, get ahead, and we look away, thinking that they don’t know what they are doing. Though a natural human phenomenon, decision-makers in manufacturing today need to bear this mind more than ever.

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Smart Factory Insights: Hands-off Manufacturing

07-12-2021

The use of automation has not eliminated causes of unreliability, nor defects, which ironically continues to drive the need for humans to be hands-on, even as part of SMT operations. There is clearly something missing, so cue our digital twin.

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Smart Factory Insights: Me and My Digital Twin

04-12-2021

A fully functional digital twin involves more than it may initially seem. At first we tend to think about access to information. There is a great deal of trust to be taken into account when creating a digital twin, as there is scope for its use both for good and evil.

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2020

Smart Factory Insights: Changing Roles in the Digital Factory

12-01-2020

Experts once required to have a knowledge of specialized materials and processes are giving way to those experienced in the application of automated and computerized solutions. Michael Ford describes how it is time to reinvent the expectations and qualifications that we seek in managers, engineers, and production operators to attract and support a different kind of manufacturing innovation.

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Smart Factory Insights: Smart Factories—Indirectly the Death of Test and Inspection

11-04-2020

In the smart factory, test and inspection are reinvented, contributing direct added value, playing a new and critically important role where defects are avoided through the use of data, and creating a completely different value proposition. Michael Ford explains how the digitalized Deming Theory can be explained to those managing budgets and investments to ensure that we move our operations forward digitally in the best way possible.

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Smart Factory Insights: Trust in Time

08-05-2020

We’ve all heard of “just in time” as applied to the supply chain, but with ongoing disruption due to COVID-19, increasing risk motivates us to return to the bad habit of hoarding excess inventory. Michael Ford introduces the concept of "trust in time"—a concept that any operation, regardless of size or location, can utilize today.

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Smart Factory Insights: It’s Not What You Have—It’s How You Use It

06-03-2020

According to the reports, all the machines in the factory are performing well, but the factory itself appears to be in a coma, unable to fulfill critical delivery requirements. Is this a nightmare scenario, or is it happening every day? Trying to help, some managers are requesting further investment in automation, while others are demanding better machine data that explains where it all went wrong. Digital technology to the rescue, or is it making the problem worse?

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Smart Factory Insights: Seeing Around Corners

04-20-2020

Each of us has limitations, strengths, and weaknesses. Our associations with social groups—including our friends, family, teams, schools, companies, towns, counties, countries, etc.—enable us to combine our strengths into a collective, such that we all contribute to an overall measure of excellence. There is strength in numbers. Michael Ford explains how this most human of principles needs to apply to IIoT, smart manufacturing, and AI if we are to reach the next step of smart manufacturing achievement.

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Smart Factory Insights: Size Matters—The Digital Twin

02-01-2020

In the electronics manufacturing space, at least, less is more. Michael Ford considers what the true digital twin is really all about—including the components, uses, and benefits—and emphasizes that it is not just an excuse to show some cool 3D graphics.

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Smart Factory Insights: What You No Longer Need to Learn

01-14-2020

Naturally evolving layers of technological applications allow us to build and make progress, layer by layer, rather than staying relatively stagnant with only incremental improvement. To gain ground in manufacturing, Michael Ford explains how we need to embrace next-layer hardware and software technologies now so that we can focus on applying these solutions as part of a digital factory.

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2019

Smart Factory Insights: Dromology—Time-space Compression in Manufacturing

11-25-2019

Dromology is a new word for many, including Microsoft Word. Dromology resonates as an interesting way to describe changes in the manufacturing process due to technical and business innovation over the last few years, leading us towards Industry 4.0. Michael Ford explores dromology in the assembly factory today.

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Smart Factory Insights: Trends and Opportunities at SMTAI 2019

10-14-2019

SMTAI is more than just a simple trade show. For me, it is an opportunity to meet face to face with colleagues and friends in the industry to talk about and discuss exciting new industry trends, needs, technologies, and ideas.

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Smart Factory Insights: Recognizing the Need for Change

09-24-2019

We are reminded many times in manufacturing, that "you cannot fix what you cannot see" and "you cannot improve what you cannot measure." These annoying aphorisms are all very well as a motivational quip for gaining better visibility of the operation. However, the reality is that there is a lot going on that no-one is seeing.

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Accelerating Tech: Standards-driven, Digital Design Flow for Industry 4.0

04-24-2019

The term “fragmented manufacturing” is a good way to describe current assembly manufacturing challenges in an Industry 4.0 environment. Even in Germany, productivity reportedly continues to decline. To reach the upside of Industry 4.0, data flows relating to design play a major role—one that brings significant opportunity to the overall assembly business.

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The Truth Behind AI

02-28-2019

The term "artificial intelligence" or "AI" has become a source of confusion for many—heralded as part of Industry 4.0, yet associated with the threat of automation replacing human workers. AI is software rather than hardware, and it's time to put these elements of AI into context, enabling us as an industry to embrace the opportunities that so-called AI represents without being drawn in, or pushed away, by the hype.

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2018

Resolving the Productivity Paradox

12-22-2018

The productivity paradox continues to thrive. To a growing number of people and companies, this does not come as a surprise because investment in automation alone is still just an extension of Industry 3.0. There has been a failure to understand and execute what Industry 4.0 really is, which represents fundamental changes to factory operation before any of the clever automation and AI tools can begin to work effectively.

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The Truth About CFX

10-23-2018

A great milestone in digital assembly manufacturing has been reached by having the IPC Connected Factory Exchange (CFX) industrial internet of things (IIoT) standard in place with an established, compelling commitment of adoption. What's the next step?

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Advanced Digitalization Makes Best Practice, Part 2: Adaptive Planning

08-27-2018

For Industry 4.0 operations, Adaptive Planning has the capability of replacing both legacy APS tools, simulations, and even Excel solutions. As time goes on, with increases in the scope, quality and reliability of live data coming from the shop-floor, using for example the CFX, it is expected that Adaptive Planning solutions will become progressively smarter, offering greater guidance while managing constraints as well as optimization.

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Advanced Digitalization Makes Best Practice Part 1: Digital Remastering

07-02-2018

As digitalization and the use of IoT in the manufacturing environment continues to pick up speed, critical changes are enabled, which are needed to achieve the levels of performance and flexibility expected with Industry 4.0. This first part of a series on new digital best practices looks at examples of the traditional barriers to flexibility and value creation, and suggests new digital best practices to see how these barriers can be avoided, or even eliminated.

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Configure to Order: Different by Design

01-15-2018

Perhaps in the future, sentient robots looking back at humans today will consider that we were a somewhat random bunch of people as no two of us are the same. Human actions and choices cannot be predicted reliably, worse even than the weather. As with any team however, our ability to rationalize in many different ways in parallel is, in fact, our strength, creating a kind of biological “fuzzy logic.”

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2017

Counterfeit: A Quality Conundrum

10-01-2017

There is an imminent, critical challenge facing every manufacturer in the industry. The rise in the ingress of counterfeit materials into the supply chain has made them prolific, though yet, the extent is understated. What needs to be faced now is the need for incoming inspection, but at what cost to industry, and does anyone remember how to do it?

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