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

The indirect role played by test and inspection has no place in the smart factory if it is there simply to detect defects, providing a filter, at best, and acting for the improvement of product reliability in the market—a response to mistakes and weaknesses within manufacturing, effectively, or a tax on inadequacy. 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 create a completely different value proposition. How can the digitalized Deming Theory be explained to those managing budgets and investments to ensure that we move our operations forward digitally in the best way possible?

Going back to basics, the concept of “direct” vs. “indirect” value to manufacturing is a fundamental measurement of the core business performance of any manufacturing operation. The value of the metric is the measurement of efficiency of any expense or cost related to manufacturing support, including non-added-value processes, in comparison to resources that are performing added-value manufacturing. Most other metrics focus on breaking down production activities, allowing inefficiencies in support, and enabling activities to go unchallenged. These include the operational costs of test and inspection, as well as physical activities orchestrated, managed, and monitored by MES, such as supply-chain logistics and quality management.

The origin of this metric predates automation and is associated with the work of W. Edwards Deming, though his dream had to be fulfilled with armies of staff, paperwork, and processes. Now, we have digitized it. As production operators represent the major operational fixed cost, those who perform assembly are seen as direct added-value, whereas other activities—including repair and re-work—are seen as being indirect.

Applying the same principle to test and inspection, imagine a set of production processes where there is one machine, such as a pick-and-place, and a reflow oven both involved in the making of the product, and then an SPI, AOI, and ICT machine. Applying the direct/indirect rule, the direct ratio of this line is just 40%. In a perfect world, we would only need the two direct machines making the product. It means that considerable investment has been made, together with a high proportion of the operational cost, which is—in a sense—waste.

However, few would be bold enough to do without the test and inspection machines, as quality performance would no doubt suffer, bringing potentially more substantial costs associated with poor quality later. The reluctance to invest in equipment and software solutions that are regarded as being indirect is understandable. But as this and other similar business-level metrics are adversely affected, this leads to concerns with business sponsors.

How poor is the actual team at the site that they need so much more indirect support than others? Why should we invest in their inadequacy? These are old-school questions that the smart factory still has to answer if investment in smart technology associated with test and inspection, as well as software solutions, are to be readily accepted, as opposed to being a continuous battle for funding.

The fact that the smart factory is run on the contextualization of data provided by the IIoT-driven MES solution’s built-in defined ontology is very fortunate for vendors of test and inspection equipment. These processes actually provide a large part of the required data for smart decision-making. Can we finally put aside the stigma that these machines and solutions are indirect costs, and position them as adding direct value to the production operation? We need to bear in mind that the key people we need to convince are not technically minded. The engineering-orientated explanation of how the role of these machines has changed is not going to be easy.

Let’s first take a look at where we are in terms of maturity with the use of test and inspection data, as the technology is not yet at the point where it can be regarded as mature. Many years ago, the application of Six Sigma showed that defect occurrence is not required and is avoided by understanding the variations in processes and how to control them. Analysis of the data statistically as machine learning, closed-loop solutions, or as MES-level quality analysis exposes trends that risk defects, communicating the issue—and even executing the appropriate corrective action—before any defect occurs.

Whilst this is simple in theory, the practice itself is multi-dimensional, as there are so many complex and interacting factors. Machine-learning and closed-loop algorithms are yet to be perfected to the point where the root cause of variation can be correctly identified and appropriate action taken. A simple example of this is the analysis of the deviation of placement coordinates, comparing positions as specified in the machine program against those inspected after placement.

Simply put, too much deviation raises the alarm before the risk of making a poor joint or an unwanted short. However, how much is too much depends on many factors, such as the size, shape, and orientation of the pads on the PCB. Consider the size, shape, pitch, and contact profile of component leads, as well as the location, size, shape, and thickness of the solder paste.

Then, there is the need to understand the nature of the patterns of deviation, especially with respect to adjacent parts. Patterns in the data may suggest a PCB condition—for example, stretch or twist, which is an issue around a specific nozzle that has been used—or be related to the specific part itself. Each of these factors comes with its own set of rules as to what can or cannot be tolerated, as well as what potential adjustments in any preceding processes may be applied.

In reality, all of these effects happen concurrently with different levels of contribution. With all the analysis done, that is not the end of it, as certain aspects of trend calculations need to be reset when, for example, PCB packs change, nozzles are cleaned or replaced, materials replenished, machine parameters adjusted, etc. A truly challenging AI application running within a truly big data environment, with data from design, the supply chain, machine programs, MES, and other sources all needed to provide a holistic machine-learning or closed-loop solution. And this was a “simple” example. It is not quite a done deal as to whether we can say that “defects are history” already, though a lot of people are working hard on these algorithms from many directions, so we get closer and closer.

Business-wise, to get the buy-in to invest in smart factory inspection and test machines—as well as IIoT-based MES solutions—we need to create a convincing argument that shows that they are an essential and intrinsic part of the value-added process. Direct machines today already include operations that are not strictly of direct added value. Reading fiducial locations on the PCB increases the accuracy of placement, as does the taking of the picked-up component to a camera for recognition and alignment before placement. A significant amount of machine run-time can be dedicated to these tasks, which are in addition to the basic pickup and placement.

As a whole, the industry sees these functions as being essential to the SMT placement operation, and that it is only through the use of these technologies that the machines have become capable of placing the newer, smaller, or higher pitch components successfully. The addition of post-placement inspection and test could be positioned as being an evolution of this. The only difference is that it takes a separate machine to perform the test and inspection, as well as external software for the machine-learning, line-learning closed-loop, or factory-learning at the MES level. However, they are all essentially the same in that they facilitate increased direct performance of the line itself.

As we have seen with the state of the technology as it evolves, the transition of test and inspection to become a zero-defect driver as opposed to being a filter of defects may not yet be black and white. For some time, there will be an element of both happening. As line and factory layers evolve, the expectation is that defects will be reduced and eliminated over time, as the data captured from test and inspection is improved, linked with increasing sources of data—such as that from MES—and algorithms at the machine.

The process has to start somewhere, though. Smart factory management must invest in initiatives that strive to make the transition from defect-based quality management to zero-defect quality management through the use of test and inspection data. I have seen yield losses reduce by an order of magnitude already through the use of software that utilizes test and inspection data to improve placement accuracies—a benefit that simply cannot be ignored.

Test, inspection, and IIoT-based MES software that support machine, line, and factory-learning belong in the must column of any smart factory shopping budget, justified as being of direct contribution to manufacturing, with an expectation that actual defect rates become very close to zero over a reasonable period.

If we are to truly embrace the potential of smart factories, we need to be able to utilize the latest revolutionary steps forward in technology that the industry has seen with standards-driven IIoT data exchange in the form of CFX, for instance, which virtually eliminate the costs of data acquisition. The use of IIoT-based machine-learning, closed-loop line learning—as well as contextualization of big data at the factory layer through the use of IIoT-driven MES solutions based on built-in ontology—are all things that are available now.

Significant benefits are already achievable, with the risk of non-added value investment eliminated—for example, in middleware and locked proprietary solutions. It is altogether great support for a manufacturing business strategy for success as we come to terms with increased global challenges, promoting flexible, sustainable, environmentally responsible, and profitable local manufacturing.

Michael Ford is the senior director of emerging industry strategy for Aegis Software.

This column originally appeared in the November 2020 issue of SMT007 Magazine.

 

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2020

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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