Industry 4.0 Series: Data-Driven Stamping, Part 2

Welcome to the final article in this blog series on Industry 4.0! In this series, we address unanswered questions and roadblocks surrounding the term and its implementation in factories or small job shops. We tackle the "what, "why", and "how" of Industry 4.0, and why it should matter to you.

Our previous article discussed why data matters in Industry 4.0 and how to become Industry 4.0-ready. In this article, we'll finish the series by covering what data matters and 3 crucial factors to consider when implementing data-driven stamping.

Full knowledge about every detail involved in a manufacturing process is ideal. But, collecting, storing, and processing a vast amount of data is challenging. Industry 4.0 is the vision where machines provide the data that can then be processed by computers which are capable of storing large amounts of data and performing complex computations in real-time.

The efficacy of the data process algorithm depends on the completeness and accuracy of the data and the intelligence built into it. Industry 4.0 is ideal to be attained over time because it seems to be virtually open-ended. In relation to this, our goal is to provide a good starting point.

Starting Point: 3 Crucial Factors to Consider

1. Gathering, storing, organizing, and prioritizing data                                                            Many do not even know where to begin and suffer from information paralysis. It is impossible to anticipate all the requirements upfront. One must become comfortable to view the transformation as a journey, rather than perfect out of the box. It is okay, or even necessary, to organize and prioritize before gathering and storing information.

   It is paramount to question what information already exists, how it is stored, and how it is used. Why not start with data that is already available? Discoveries of the existence of information that no one is aware of are not uncommon. Remember that with Industry 4.0, computers can relay commands to machines to take corrective actions. Therefore, it is important to filter out data that is not relevant and prioritize the relevant information.

   Ask yourself, “What data or information is already available, easily retrievable, and easily communicated with stakeholders?”

   As far as a starting point for collecting data, some elementary data can be gathered and processed by simple means. The most basic data that should be part of any KPI metric is: Is the equipment in operating condition?

   If the answer is No: Questions that determine why it is not in operating condition, what needs to be done to restore operation, and when the equipment is back in service will be the governing path.

   If the answer is Yes: The next question would be: Is the equipment operating? If No – why not? If yes, is it producing good parts?

   These basic yes or no questions can be answered with simple means. However, the answers to the "why" questions are not so straightforward, and therefore cannot be further processed until a concise, binary reporting system exists.

2. The accessibility and ease of distributing data
   

   If not existing in a raw format, data may be easily retrievable by running a report from an ERP system. The following questions can help uncover if there is an issue with the distribution or accessibility of information:

   • Do the stakeholders know that the information exists?
   • Do the stakeholders know how the information can benefit them?
   • Do the stakeholders know where to find the information?
   • Do the stakeholders work off the same information?
   • Do the stakeholders have to provide information?
   • Is the information accurate, complete, and reliable?
   • Does the information need to be protected, and to what level?
   • Is there accountability for the information provided, or its use?
   
   
3. The use and misuse of information
   

   Information is power; hence, as soon as data is collected, one must consider its use and misuse. The importance of managing the storage, distribution, and access cannot be overstated! While Industry 4.0’s focus is on the use of information, it cannot ignore the threats that come with it. There is an entire security aspect to the data world.

   It may be costly and possibly dangerous to collect and manage information that will not lead to a decision or an action. However, it may be necessary to act pre-emptively by gathering data, even without immediate use, for the future.

   For example, some machine data may contain useful information, but no processing algorithm exists. Should the data drive the development of the algorithm (Bottom Up approach) or should the intelligence demand the necessary data (Top Down approach)? The current day affairs suggest that it is the combination of both approaches.

Who's Best Positioned to Progress in the Digital Transformation?

Companies that effectively aggregate and relay available information have already a culture of data-driven decision-making. These are well-positioned to progress in the digital transformation. Companies that don’t have this culture will fall further and further behind.

Unfortunately, there are still too many companies that operate ad-hoc, or with an “always done it this way” mentality. Leaders of Industry 4.0 will be highly automated industries and factories. Here’s why.

If you compare a highly automated factory containing lots of robots to one with very little or no automation, you may come to the following conclusions about the latter’s operators and production processes:

• Biased, subjective statements about data and production processes
• Limited memory and intelligence due to human limitations
• Need instructions
• Don’t follow instructions
• Cannot be controlled

A huge reason highly automated industries and factories are best positioned to achieve Industry 4.0 is that they don’t have the roadblocks of human bias, human limitation, and process inefficiency. Essentially, highly automated factories with robots can do it better, faster, and more precisely than humans.

Flexibility vs. Efficiency: Where Do You Fall?

Aside from having the right mindset and possessing a culture of continuous improvement, there’s another force at play with data-driven stamping. This second force is more pragmatic: flexibility vs. efficiency. Ideally, you want your production processes to strike the perfect balance between flexibility and efficiency. In some industries and factories, this perfect balance exists, but for many of us, manufacturers lean toward one side of the scale.

Automotive manufacturers are a great example. Their factories are highly automated and focused on high volume, low mix, and mass production consumables. They benefit from efficiency but lack the flexibility to pivot to other product lines.

As Alastair Orchard, VP of SIEMENS puts it, “An automotive factory is incredibly efficient, but not flexible”. Unless they spend a fortune to build a factory that accommodates this flexibility and automation and pays off the investment with continual high product demand, Orchard’s statement still rings true.

Aside from mass production automotive manufacturers, what about the typical stamper? Where do they fall? Many will fall into the third category: low volume/high mix, focused on capability + set-up, and with the advantage of specialized, customized fabrication.

This means that they are more flexible than efficient. This also means that there’s less automation happening in their facilities. Since these stampers focus more on capability than meeting high production demands, they also have less incentive to collect data that will improve production efficiencies.

Final Thoughts

The benefits and importance of reliable and complete information have been previously discussed. Data can be used to observe trends and to compare actual performance with historical performance, or a benchmark.

As previously observed, raw data is honest and true if the measuring system is reliable. This is in itself a huge advantage because humans communicate with biases and emotions. In the automation world, data gives you an edge over existing production systems. Once you understand the role, importance, and impact of data-driven stamping within the digital transformation of Industry 4.0, you can then start to implement it.

We hope this series on Industry 4.0 provided a practical breakdown of how digital transformation applies to stampers. We also hope you gained helpful insight and answers to the question, “What is Industry 4.0 and why should it matter to you?”

Please feel free to leave a comment below or reach out if you have any questions, thoughts, or ideas to add. We're always happy to chat with you!

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