Recently, we were asked to participate in a blog post from one of our partner companies. The format provided was for Questions and Answers, from a group of companies regarding Smart Manufacturing initiatives. I have been fortunate, over the past several months, to be invited to participate with several of their clients as they develop their strategy for adopting smart manufacturing principals, so I was asked to provide some of the answers.
The questions start by showing an Infographic about Industry 4.0, with the associated emerging technologies.
- Digital Twin
- Horizontal and Vertical System Integration
- Autonomous Machines
- Machine Learning/AI
- Big Data
- Augmented Reality
- Additive Manufacturing
- Cyber Security
- Industrial Internet of Things
Does your company implement solutions in an of these above areas?. If so, what might you consider your top three?
- Analytics and AI with Digital Twin and Big Data-driven by IIoT – It is difficult to separate because they are all legs on the same stool.
- Cyber Security
Which areas do you think shows the most market interest right
Analytics and AI are the biggest wins for our clients. Whether this is driven by traditional data sources or by IIoT/Big Data, the goal is to use data to unlock additional value, drive continuous improvement projects, and provide new insights into the process, quality, and customer demand
Could you briefly describe a solution you developed or saw
that represents a unique or innovative implementation of Industry 4.0 this year technologies ?
One example is an enterprise-wide project to deploy a standard set of infrastructure consisting of firewall and edge devices to enable data collection at all company manufacturing sites. The solution included connectivity to existing control systems, but it also included deployment of IIoT sensing for vibration, temperature, and power consumption through third party providers. Additionally, it included a standard setup to deploy sensing for machine utilization where the existing controls did not provide any data and the cost of traditional IO was significantly more expensive.
The goal is to provide a standardized toolset that provides machine performance data, digital twin, and analytics tools for Machine Learning/Deep Learning on process data.
Were there any lessons learned that you’d like to share from your perspective as the developer or from your client as the user?
The focus on these projects should remain on the business and how the business will get additional value from the solution. Much is said about the technology, but the real achievements are in finding ways to liberate the data and make it actionable. Early wins with a project will be in utilizing data in traditional control systems and legacy solutions.
Much of the IIoT focus is on streaming live data (telemetry data), but there are likely other data sources with valuable data and context that will be necessary. Batching systems, existing performance solutions, quality systems, etc. all add to the richness and quality of the data set available for analysis.