13 Mar 2020 | DataProphet| • General News
It is essential to lay the proper groundwork in order to achieve the benefits of artificial intelligence (AI) enabled factory optimization. This entails the digitization of existing data, the automation of data collection, and the implementation of a proper storage mechanism. We outline these initial steps to prepare your factory for AI optimization.
The process of digitization requires some specialist know-how to get right.
A historic dataset that is valid in its paper form can, for example, be corrupted during digitization. The right approach boils down to a careful combination of data capturing and automation.
Perhaps the most crucial area of factory preparation concerns data collection. Segal’s law, “A man with a watch knows what time it is. A man with two watches is never sure”, should be taken seriously and it is a nice reminder that when it comes to manufacturing processes and particularly in the age of Industrial Internet of Things (IIoT) sensors and low-cost data storage, less can be more.
Once captured, the process data from your factory will need to be stored. The basic requirement for storage, given the commercial value and sensitivity of historic process data, is security and reliability. This requirement can be met with relatively inexpensive yet mature and scalable off-the-shelf storage solutions.
While secure and reliable storage solutions are cheap, you will, however, need to invest some careful planning into how you organize your data “at rest”. Another important aspect of how manufacturers should organize their data for storage is the extent to which this digital schema reflects a complete operational picture. This is particularly the case with quality data.
Having the right data to start with and managing that data effectively is at the heart of any successful AI deployment. It’s important to get these basics right first.
Read the full paper here.
13 May 2020 DataProphet
When considering the implementation of AI and how a manufacturer can work towards achieving autonomous ...