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

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?

Having machine data—or not having it—is not an indication of good or bad. My evil self could go into any factory shut down due to COVID-19 today and find ways to take out production reports that show that over the recent period, no scrap was made, no parts were lost, there were no line imbalances, the factory achieved zero-defects, there were no missed deliveries, and there was no sign of productivity loss on any machine. No time was lost for manual operators taking restroom breaks, there were no accidents, nobody was late for work, and there was no need for any overtime. In different circumstances, these statistics would be excellent news, and while they may be true, they don’t have any meaning. These statistics would show that you can take a certain view with data and make a case to justify all manner of things. 
 
Though perhaps not as extreme, this practice is happening all the time. Metrics are crafted from simple data sources to promote the positive; after all, we each look forward to a good review at the end of the year, but isolated “facts” can hide an overall negative situation. Money first seeps, then pours through the cracks, covering over fundamental operational issues for which there is little or no visibility other than symptoms that appear as business limitations or even failure.
 
Analysis of data needs to be more intelligent. Machine learning and line-based closed-loop systems are great at using raw machine data to automate process improvement. Beyond these narrow solutions, however, the analysis of machine data in isolation is relatively pointless. We would expect these days that automated machines work well when able to do so, providing that positive spin opportunity. The real challenge is how to perform analyses of what is happening in between the machines, where there is no data being reported. 
 
At the simple level, no machine data means potential loss. Machines are stopped and perhaps blocked, starved, not needed, or broken down; there could be material issues, quality concerns, lack of operators, a scheduled vacation, or even a pandemic. The machines don’t know; they only say that they are stopped. Through the correlation of data from multiple  disciplines—such as material logistics, planning, and quality management—what needs to be discovered are the root causes and net effects of any key exception in the process preventing operational progress.
 
Then, there is a more complex level. We should look at the progress of a product through manufacturing rather than just looking at the performance of the machines themselves. Consider the typical international tourist experience at an airport. How much time is actually needed to check-in, drop a bag, enjoy the security check, walk to the gate, and get on the plane? Probably about 10 minutes door to door, but we are told to arrive at the airport at least two hours before the flight leaves. Therefore, added-value time at the airport is about 8%, and the other 92% is waste, but owners of the shops and cafés may tend to disagree. 

To report about 8% efficiency in manufacturing would probably get you fired, but I could go into most factories working normally today and get reports that show efficiencies measured in such a way as being much worse than 8%. We are fixated by looking at machine data rather than using the data to truly analyze the effectiveness of the factory in doing its job, taking materials, and making end products. The stock of raw materials should be minimized, as should be the holding of sub-assemblies, areas of semi-finished goods, and finished goods in the warehouse. We should not have so many products awaiting repair or retest, being repaired or tested, going through quality inspection, being in quarantine, or being piled up in front of processes that are not yet set up and ready to execute. All of these aspects of manufacturing have a far more significant effect on the business than the simple operation of any particular machine. 
 
Machine data acquisition has been revolutionized of late, with data gathering from machines being easier, more detailed, timely, and accurate without the need for middleware or customized machine interfaces. This is notably true in the case of using the IPC Connected Factory Exchange (CFX) standard. 
 
There are no interfaces for the gaps in between the machines. These are the areas that have a major impact on the operation. Take the example of an individual product simply leaving one process and moving to another. The product gets to the end of the line and stops. It is stored—somewhere, somehow—waiting for the others in the batch, job, or work order to be completed. The next process has to be as efficient as possible, so planning delayed the start time until it was sure that all products had been completed by the prior process, a vacancy had opened up, and it was optimal timing to do so. 

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Minutes, hours, or days could pass. There’s so much increased opportunity for handling issues, further delays from failing equipment, missing materials, effects of engineering revisions, and contamination, resulting potentially in more inspection, cleaning, processing, and more delays and storage. Being able to digitally track the paths of products during assembly creates significantly more opportunities for efficiency improvement and cost savings. To do this, the data from individual machines and processes also needs to be used to create a live virtual “movie” of everything happening in the factory—a live manufacturing “digital twin” that’s the real thing, not a simulation. 
 
Unlike people, software does not need to create fancy 3D animations and images to be able to apply a rules-based engine that takes contextualization of data holistically from machines, materials, quality, and planning, as well as knowledge of working line configurations and products to create an omniperspective-based digital twin model. The manufacturing digital twin “movie” extends back in time in terms of near-term performance history to learn what works well and where everything currently is. It also extends forward in time, as extrapolations based on current trends are analyzed, to detect any issues that could be avoided by implementing changes and decisions now. 
 
In effect, this rules-based manufacturing digital twin is controlling and managing the whole production operation, creating visibility and automation around challenges and addressing the core business needs and improvement opportunities within the factory. This is no ordinary MES solution; instead, it is the redefined, modern IIoT-driven MES solution built specifically around the rules-based digital twin architecture.
 
Therefore, gathering data from around the factory is step one toward making digital solutions work for the betterment of manufacturing. However, you do need to take more than one step to get to the next level. How you use the data is far more significant than just having it, making dashboards from it, and performing machine learning and analytics. The true digital twin for manufacturing within IIoT-based MES execution is here.

This column originally appeared in the June issue of SMT007 Magazine.

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2020

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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