Smart Factory Insights: Dromology—Time-space Compression in Manufacturing

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Dromology is a new word for most of us, I expect; it certainly is for me (and 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. It is easy to lose track of the many changes that are happening, as our experience of the compression of time and space has taken place over many years. Paradigm shifts that new technologies enable today should not be surprises if the trends and direction of business needs are understood. Best practices going forward should be based on achievable goals, edge towards expected future business needs, and be driven by the opportunities that technologies bring. Let’s look at dromology in the assembly factory today.

Digital Manufacturing Engineering

We started with what was once industrial engineering. For many, the earliest applications of computer system technology in manufacturing were related to electronic product design and the entry of data into manufacturing. Previously, data was created through reverse engineering, taking measurements and paper drawings to feed the need for automation. What took weeks, if not months, could now be done in days using computers.

Though the use of engineering systems helped, it was not enough to change the paradigm. Production configurations continued to be defined and decided by engineering and driven through the singular assignment of product to line configuration. Best practices today, enabled by digital manufacturing engineering tools, have broken through to support the business need for flexibility, allowing products to be made on any configuration whenever needed as determined by the capability to deliver closest-to-completion requirements.

To read this entire column, which appeared in the November 2019 issue of SMT007 Magazine, click here.

01/27/2021 | Dana Korf, Korf Consultancy

I-007e recently released a highly informative series of short webinars called Implementing Digital Twin Best Practices from Design Through Manufacturing presented by industry expert Jay Gorajia, the Director of Siemens Global Digital Manufacturing Services. The webinar is an excellent overview how data that is generated using a digital twin model can be effectively utilized to improve business execution using the Siemens tool suite.

12/30/2020 | Sagi Reuven and Zac Elliott, Siemens Digital Industries Software

Accurate data is required to adjust processes and to ensure quality over time. This is difficult because not all data is in the same format, and not all sensors perform the same over time. How do you know what the best data to collect is and how to filter out the junk data from useful or smart data? This is not an easy task when the interfaces to data collection sources are complex, and they do not speak the same language, often requiring the vendor’s help to get data out of the machine and then spending time normalizing the data to turn it into something useful. This is a challenge for companies trying to set up a custom data collection system themselves.

12/16/2020 | Sagi Reuven and Zac Elliott, Siemens Digital Industries Software

Whenever we discuss data, keep in mind that people have been collecting data, verifying it, and translating it into reports for a long time. And if data is collected and processes are changed automatically, people still will be interpreting and verifying the accuracy of the data, creating reports, making recommendations, solving problems, tweaking, improving, and innovating. Whatever data collection system is used, any effort to digitalize needs to engage and empower the production team at the factory. Their role is to attend to the manufacturing process but also to act as the front line of communications and control.