Cliff Rice is an automotive, electric vehicle (EV), and battery industry consultant at Rockwell Automation.
Automotive manufacturers understand that they must transform digitally to keep up with demand and stay competitive while needs throughout the industry continue to evolve.
Digitalized factories promote connectivity by using systems that can efficiently communicate data throughout the production floor. This allows manufacturers to identify opportunities for optimization, enabling automakers to continuously improve production. Additionally, the technology can alert manufacturers to errors or issues that may compromise product quality. To take advantage of these production capabilities, many automakers are looking to adapt their current production practices to incorporate digital connectivity. However, it can be difficult to know where to start.
Today’s Motor Vehicles (TMV): Focusing on the importance of this digital future, how can it help automotive manufacturers meet the changing industry needs?
Cliff Rice (CR): When we talk about changing industry needs, I focus on electric vehicles (EVs). That’s a huge market shift we’ve seen across the board; everyday it seems like you hear about a new investment in EVs. One of the unique aspects is that the vehicle is a really rich source of data and has an incredibly long lifespan. If we can take that data and tie it back to the manufacturing process, we can learn a lot, such as where quality problems may be interjected or where things may be getting missed. We can improve the customer experience by limiting the scope of recalls and really honing in our operations and in production by looking at the data from the vehicle.
TMV: The industry realizes the need for these technologies. Yet, there are some hiccups. So what challenges are auto manufacturers facing when they go to adopt these technologies?
CR: It’s not a completely linear process for most, and I’d clump the challenges into two main categories – organizational and technical challenges. From an organizational standpoint, I see a lot of lack of alignment. Individual plants or even lines can have conflicting needs. And the difference in scale of a challenge and the return on solving that challenge between an individual line and before organization is drastic. For example, sometimes the biggest need on one plant line is not going to translate well across all plants. So you get individual initiatives going on that solve very specific use cases, but can’t scale and can’t really change the way the business operates.
And then technologically, I’d say the biggest concern is cybersecurity. It’s something we talk a lot about. Everything is connected so that’s why we, as a company, have really expanded our offerings and portfolios with remote threat detection, vulnerability monitoring, secure remote access for vendors, and we have an end-to-end encrypted communication protocol. We follow IEC 62443-4-1 which defines the security standard for the design of products and processes. Not only does the product need to be secure, but the way it’s designed needs to be secure.
TMV: Being aware of these challenges, what tips could you offer manufacturers looking to incorporate Industry 4.0 technologies into their productions?
CR: There are three important considerations: One is to start with business value, the second is to make sure you have organizational alignment, and the third is to build momentum. When I say start with business value, I mean look at the problem and define what the return on solving that problem will be. Then look at all of the key players who need to be involved not only in that one line, but across the entire plant, and include executive engagement so that we can scale. We do all of that before we even start to consider technology because there’s risk involved with trying to force a piece of technology to fit as a solution. We want to start with a value workshop where we just look at what the problem is and if we solve this, how it would scale. We can examine all of that and define what its success criteria will look like before we even start to consider what technologies are going to use to solve that problem. It’s about making sure we have people from different roles, ranks, and locations involved in the process. This will help ensure we have adoption and usability. We can take those lessons learned and deployed across the whole organization. That dovetails to building momentum. That first digital twin isn’t going to be your last, so think of that very first specific challenge that can be solved quickly that has real quantified return on investment (ROI). Rockwell Automation has the FactoryTalk Innovation Suite of software which is scalable and compatible with other products, and can be used as inputs into other systems. It’s not only a one-off tool you use to build and solve problems, but you can still use that platform to solve the next problem and build the next digital twin. Going from pilot to enterprise-wide is how real transformation happens.
TMV: How can manufacturers assess their digital readiness before they take these next steps to actually implementing?
CR: That assessment is an important and complicated process because it will change with everybody. But generally, starting with that value workshop is going to uncover a lot of these gaps. And we’re going to want to look at everything kind of holistically. So I think where most people are going to start is networks and infrastructure. But we’re also going to want to look at people, processes, capabilities, and challenges within those organizations. At Rockwell, the way we go about assessing organizations’ digital readiness, and helping them is always by combining domain expertise first. We’d bring in a team that’s focused on digital transformations and advising, and guiding organizations. Every organization can take on some of these digital twin initiatives, even completely manual operations with little to no automation. There are opportunities through things such as augmented reality (AR), but depending upon the challenge at hand, then there’s going to be specific things such as networking requirements, or server infrastructure or capabilities to support these digital twins. It’s no good creating an incredibly complex machine learning (ML) algorithm if the organization can’t tweak or train it because they don’t have the right resources. And that’s where we’ll bring in the teams from our digital transformation consultants to assess and advise based on what the company’s needs are.