How smart manufacturers bring Industry 4.0 principles to quality

Quality matters. Many manufacturers will tell you that poor quality affects both the top and bottom line. They will tell you that the consequences of poor quality are on the rise and that social media can make quality issues devastating to an OEM’s brand. They will also tell you that they are struggling to respond. The problem? While the consequences of poor quality are rising, so too are margin pressures. Margin pressures means that budgets are tight and few can respond in traditional ways. Smart manufacturers need to think differently.

Cost of quality is on the rise

Beyond brand impact, poor quality also has a real impact to the bottom line. We must scrap or rework defective products which can eat into overall equipment effectiveness (OEE) measures and lead to plant inefficiency. Challenges only increase once a defective product leaves the factory. Growing supply chain complexity means that products are increasingly costly to find and recall.

Flex – one of the world’s largest electronics makers – estimates that for every $ 1 spent in product creation, they spend $ 100 creating resolutions to quality problems.

Flex on quality for smart manufacturers

 

That is incredible. Solutions may include tracing root cause analysis to identify problems upstream with raw material inputs or downstream with the manufacturing process. It may also mean going back to product development to understand how product design contributes to quality.

Different industries; different impacts on quality

The implication of poor quality changes depending on the industry. For example, volume matters in the electronics industry. OEMs produce dozens of products per minute which means that a defect in a cell phone casing or circuit board assembly can be replicated hundreds of times before they identify and resolve the issue. That is a lot of potential scrap or rework. Smart electronics OEMs look for solutions that impact inspection speed. They need to quickly identify a quality issue, understand the root cause, and implement a resolution before hundreds of defective units are created.

Automotive companies have a different challenge. Here, OEMs are focused on manufacturing precision. Millimeters matter and a high degree of automation means that poorly calibrated equipment can cause small but meaningful variances. Sometimes these variances can be small – so small that only highly trained human inspectors with sophisticated testing tools can spot the difference. Smart automotive OEMs look for solutions that can help human inspectors identify these very small deviations with more accuracy.

Traditional inspection methods can be costly, slow, ineffective, and sometimes dangerous

Traditional manual inspections can be problematic. Quality inspection is a high-pressure job and the sole reliance on humans without new tools or methods can be slow and ineffective. Humans make mistakes. We get tired and have bad days. We require extensive training to spot defects and retraining to keep pace with new models. All this can hinder agility – a problem which intensified as our labor force ages and retires.

Some OEMs are getting smarter

Increasingly smart OEMs across a range of industries are approaching IBM about ways to get smarter with their quality inspection process. These firms are looking for help bringing technology – specifically machine learning and AI to bear on the problem. Fortunately, IBM has a number of solutions that can help.

Some firms are seeking to better understand the factors that contribute to quality. Have we exceeded quality thresholds? Does temperature or humidity play a role? What about equipment age and maintenance cycles? IBM has a statistical-based solution – called Prescriptive Quality – that dynamically weighs variables that might contribute to issues. This is a great solution when inspectors cannot identify quality based on an image or sound.

One of the hottest areas of interest from OEMs is how AI technology can identify visual or acoustic patterns related to quality defects. Can an image be used to identify a scratch on a cell phone casing or car paint job? Can acoustic sensors “hear” a poorly functioning dishwasher before the product is released from testing? The answers are yes and yes. IBM has two solutions – Visual Insights and Acoustic Insights – that use sophisticated AI to spot defects. What is even more impressive? These solutions can start with a small number of defective images or sounds and can learn over time to get smarter.

Does this mean we don’t need quality inspectors?

It is easy to position many of these AI-based solutions as replacing the jobs of quality inspectors. Yet this is rarely the case. Smart companies see these solutions as tools that help quality inspectors improve throughput and effectiveness. Put simply, technology like Visual Insights or Acoustic Insights help inspectors inspect products more quickly, with fewer misses, and fewer false positives. Rather than replacing inspectors, these technologies become important aids that help OEM respond better to the rising cost of quality without sacrificing margins.

Take the next step

Want to learn more about how IBM views quality and how we can help OEMs address quality challenges?

Learn more about quality management in the era of AI here.

Get more detail on specific quality solutions for your business:

 

 

 

 

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Internet of Things blog

Five Principles for Organizing Collective Intelligence

Collective intelligence is nothing new. Back around 400 BC, the Greek historian Thucydides described how a “great many” soldiers counted the bricks in the wall of a besieged town and their individual totals were compared to determine the correct height for the assault ladders needed to capture it.

Geoff Mulgan, CEO of Nesta, the U.K.’s National Endowment for Science, Technology and the Arts, and a senior visiting scholar at Harvard University’s Ash Center, relates the story in his insightful new book, Big Mind. The book is about how the “collective” in collective intelligence works. That is, of course, a very timely topic now that technology has not only enabled us to muster larger collectives of intelligence than ever, but also has expanded them to include smart machines. Witness the entire open-source movement.

Big Mind is notable for a number of reasons. One of them is that we don’t have a lot of guides for managing and optimizing collective intelligence, in contrast to the shelves and shelves of books describing how to optimize the output of individual brains. Another reason is the five fundamental principles that Mulgan offers, in the excerpt below, in a nuanced answer to the question: “What is it, at the micro and macro levels, that allows collective intelligence to flower?


MIT Sloan Management Review

Ignore The IoT Hype And Stay Focused On Key Principles

If you can ignore the PR white noise plaguing the Internet of Things (IoT) phenomenon and instead focus in on what the technology is and how to use it, you’ll have an easier time appreciating the fundamental principles that underpin it. Once you understand these first principles, determining exactly how IoT can help your business is a much more straightforward process. So, what are the first principles?

  1. When planning your IoT solution, begin with the end in mind. Do not build off older systems, but consider it a greenfield opportunity. Be creative and construct an innovative IoT vision.
  1. IoT is in part about having a more granular view of a situation, an operation, a process, or a piece of equipment, once it has been instrumented and interconnected and had intelligence applied to it.
  1. Standards are still evolving, so protocols for IoT remain in flux. It is important to consider the protocols you adopt.
  1. IoT is natively an IPv6 technology, so if you have not started using IPv6, now is the time to begin.
  1. Open architecture and standards-based networks should be part of your approach.
  1. Networks should be vendor-agnostic.
  1. All IoT wireless technologies, regardless of vendor, are classified as constrained networks, meaning they cannot handle much in the way of data rates. Ten to 30 kbps should be considered average.
  1. Intelligence needs to be pushed outward towards the edge of the network. Not all data needs to go to the center or to the cloud. A good percentage of data will live only on the network fabric.
  1. Security and federated protection points – firewalls, intrusion detection and protection systems, AAA systems – all need to use a hybrid blend of centralized and distributed architectures operating in concert.
  1. Privacy is required by law, so it must be respected.

As IoT evolves, more principles will join this list. The scenario is highly dynamic and demands an agile approach. The ability to see into your systems at a highly granular level will be extremely beneficial for lowering costs, improving quality, and speeding up processes. The key adage to consider pertaining to IoT is “situational awareness drives better and faster decision-making.” It’s this ultra-granularity that produces the greatest value for your enterprise.

Join me on December 6 for an interactive webcast, in which I’ll explain each one of these foundational first principles with industry examples from healthcare, manufacturing, smart cities, and more.


Internet of Things – Digitalist Magazine