The standard IQ test for people has been established for many years now. Though the content within these tests has been cleverly and carefully put together, there are different kinds of “smarts” among people.
It is likely that people who are putting together IQ tests will favor the kinds of “smarts” that they themselves have. Humans are very complex, however. Everyone is different, with natural skills and abilities in a multitude of disciplines, technical, artistic, social and others. Put us all together as a society, and we make a pretty good team, at least when we all work together and support each other.
You would think that in the case of manufacturing technology, the whole process should be a lot simpler. How would we assess the IQ of a Smart factory? There are many different kinds of “smarts” that should be considered, as many as there are machine technologies and software automation opportunities. In humans, it is quite a challenge to try to improve our IQ, but in factories, it has become increasingly easy through the greater availability of data, and evolving software technologies. In humans also, people with higher IQs tend to be specialists in a particular way. In our factories, we need a good balance of intelligence, for example, not only machine learning on a single machine, or closed-loop feedback on a specific line configuration, or automated logistics decision on some of the materials.
In order to assess the “AIQ” (Artificial Intelligence Quotient) of the factory, we need to look into all of the main factors that potentially contribute, that is, the degree to which we are utilising available and practical technologies, so that we can look for areas of improvement, that typically, for example, would utilise existing data in a new way in order to automate an additional function, or gain insight into potential problems with greater detail.
A major challenge is the relationship between data acquisition and utilisation, being very similar to the chicken and egg conundrum. Gathering data from automation represents many difficulties, including the electrical connection, protocol, data encoding, and differing language definitions and implementations between vendors, even when following familiar legacy industry standards. The IPC Connected Factory Exchange (CFX) is the first standard that addresses all of these issues, founded in electronics, but applicable in all forms of discrete manufacturing. Whichever method of data acquisition is chosen however, significant costs are involved, which in themselves lack a business purpose, as data itself represents little value until utilised.
On the other hand, why develop Smart factory applications when there is a lack of data, or more seriously, where the data is not practicable due to lack of context and even consistently defined meaning. To achieve the Smart factory business case, both data acquisition in a cost-effective way needs to be present, as well as a clear value-driven roadmap of data utilisation potential.
The “Discover Your Smart Factory IQ” white paper sets out the most common aspirations of those who have discovered Smart manufacturing applications, using a defined set of rules that guide the reader through the assessment of the current level of Smart factory achievement, as well as immediate readiness for the next stage of technologies, and then on to the ultimate roadmap towards all of the benefits that data-driven manufacturing can offer.
Any factory operation that is seeking to improve the level of digitalisation, to make digitalisation more cost-effective and affordable in terms of Return On Investment (ROI), or, simply benchmarking differing operations within an organisation in order to determine strategy, will find this white paper an invaluable tool in the practical measurement and assessment for Smart factory technology.