Smart Factory Insights: Me and My Digital Twin

It would be wonderful to have a digital twin of myself, designed to take on all of the boring aspects of life, leaving me free to focus on what I enjoy doing. How much work would be needed to do this, given that the digital me needs to be developed, trained and configured to make decisions, and do things in the way that I would want? How can we ensure that the benefits from our digital twins outweigh the costs?

A fully functional digital twin involves more than it may initially seem. At first, we tend to think about access to information. To prepare my digital twin, I will need to prepare information about myself, details of where I live, the utilities, where I bank, the cars I drive, contact lists of family, friends and colleagues, security information, as well as my likes and dislikes, access to social media accounts, etc. How about security? There is a great deal of trust to be considered when creating a digital twin, as there is scope for its use both for good and evil. Unlike my physical self, my digital twin can be copied and cloned an unlimited number of times, then used by anyone for anything. Having said that, most of the information is “out there” already. It is really surprising how much personal information is willingly or unwittingly shared through internet-based services, especially social media.

Digital twin lesson number one: Create a secure environment for my digital self. As we baby-proof our homes for newborn humans, we need to baby-proof our digital environment as well.

Likewise, manufacturing digital twins must be protected with cybersecurity measures, as information includes details of production lines, machines, configurations, flows, capabilities, manpower, and other resources. This is intellectual property with commercial value.  

More Than a Database
So far, my personal digital twin is just a database; I need more. As a person, I am connected to the world. Events happen, and as with most humans, I develop opinions which are linked to memories, food for thought, which evolves as I address and solve problems, and interact with other people. The manufacturing digital twin also needs to be connected. Things that we do and say are very tightly linked with events that we experience. Without this link, my digital twin would be limited to endlessly repeating old facts from the original database, not considering any changes to the environment, and unaware of specific needs and constraints. In other words, irrelevant.

The IPC Connected Factory Exchange is not going to help my personal digital twin, but does fulfill requirements of a manufacturing digital twin, providing real-time information that brings live visibility of every event and situation across the whole shop floor through a single interface and language. Wouldn’t communication be great if every human on the planet had at least one common language in which we could all communicate?

With connections made, now we get to the tricky part. I would like my digital twin to not just exist, but to actually do stuff, specifically things I don’t want to have to do myself. Digital assistants can do this today but without any real intelligence beyond what they see about each of us in their database, albeit more than we are likely aware of.

For example, when I want to buy something, it is very satisfying to be able to choose the best item from all the choices offered. There are many products of the type that I need, all of which have different looks and specifications, and of course, prices. I need the best value item that gives me what I need in terms of performance, capability, and expected life; perhaps, I want a little more on top of that to show off a little (we are only human after all). There are likely to be several suppliers offering the same or very similar products that meet the criteria. As well as price comparison, I should also consider the delivery cost, time and reliability, supplier rating, after sales service record, etc.

There is only so much attention span that my human brain can muster, especially when key information seems deliberately hidden. We cannot get into every detail of differentiation; I certainly have better things to do. My digital twin, however, could do it all, and find the perfect solution. Millions of data exchanges across the internet cost virtually nothing. I can be very happy then that the choice “I” end up making is the very best one. For the manufacturing world, the new IPC 2551 Digital Twin provides the definition and structure as to how all of this can be done, linking information and interoperability between digital twin solutions, bridging the once separated worlds of product, manufacturing, and lifecycle digital twin elements.

Two Types of Algorithms
In addition to all the data, however, my thought process to do this needs to be coded into the digital twin, as there will be many conflicts, trade-offs, and compromises to consider during the decision-making process. Lower cost is good, but at the expense of quality? There is an algorithm therefore to be applied to the digital twin data. As a software developer of more than 30 years, I find there to be two choices of types of algorithms.

My favorites are the heuristic-based algorithms, which model the thought processes of humans. Rules, often complex, are followed that determine calculations that lead to a specific answer. The difference between the software and my limited biological approach is that the computer will follow all possible tracks, rather than being limited to those within my own attention span. The danger of this algorithm, however, is that unless written in a very clever way, it is harder to change the thought process based on new ideas or concepts. The benefit, however, is that the results appear very quickly and are effective.

The second type of algorithm is a random mathematical model originally termed “genetic algorithms.” The connection of facts, such as the order in which a process could be done, is laid out at random, the effectiveness measured, then the order changed, and effectiveness re-measured. How the changes are made vary, the original genetics-based idea being to divide them in the same way as genes are shared from parents to a child—slice and dice, then try again, potentially billions of times. No matter how sophisticated genetic algorithms and the like become, the result will take time, geometrically increasing in proportion to the number of variables. The benefit is that unlike the heuristic model, there are no assumptions; a solution that no one may ever have considered could be found to be the best. The downside is that it takes time—a long time—to come up with the best solution. One of my own heuristic machine program optimization algorithms was once beaten by a genetic algorithm, shaving off a second or two of the machine’s run time. I did like to point out that the heuristic algorithm had taken five minutes whereas the genetic algorithm was still going after five days, four days after the production was supposed to have started. An “I’m bored” button then appeared to stop the genetic algorithm and take whatever has been the best result thus far.

The interesting thing, however, about the genetic algorithm is that the “health” of each potential solution discovered is qualified by a function that measures the value of the solution. The need to have a defined method provokes similar limitations as seen with the heuristic method; to truly find new and original solutions is questionable. It is easier to change the ruleset of the genetic algorithm as compared to the logic of a heuristic model. If a solution were able to automatically change the ruleset, based on feedback of the real effectiveness of solutions over time, this would lead to the potential of actual “AI.” The human ability to change the constraints associated with a problem can be termed either intelligence or recklessness, depending on the nature of influencing factors. As a digital twin can be used for good or evil, will we trust the AI to modify itself in a way that we would assume to be in our interest?

We see the trend of increasing amounts of data, more decisions to be made, more complexity, and more security, so the next stage of intelligence may be a hybrid of the two algorithmic types, whereby the simple cause and effect decisions are implemented as heuristic elements of an advanced genetic algorithm type of approach. This can include the building in of “laws” to protect human interest.

Saturation point reached; I don’t want to have to understand all of this “nerd talk” as being simply the user of my digital twin. I simply want it to do the work for me, and just give me answers. Engineers and managers on the manufacturing shop floor will find their decisions augmented using the manufacturing digital twin, providing relief from having to do all the data gathering, formatting, and mind-numbing analysis manually, resulting instead in a clear vision of what will happen, should nothing be done, or perhaps if one of two things were to change—such as the introduction of a new product, material availability issues, or customer demand rates fluctuate.

Put away the abacus, notebook, calculator, Excel spreadsheet, or whatever tools that you have historically chosen to number-crunch and turn to the excellent digital twin solutions that exist within the modern IIoT-based MES solution. As software developers, we do the work, coding in the rules and methods, defining best practices, creating the ontology that turns data into actionable value. To create my personal digital twin would require far more work than I am prepared to put in, but when it comes to manufacturing software, there is an army of developers that have created and continue to evolve a singular solution applicable to all manufacturing environments, bringing true smart Industry 4.0 manufacturing for everyone.

If you are interested in exploring how a digital twin can benefit your factory, you can learn more here.

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

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2021

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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