Manufacturing is no stranger to robotics and automation. Long before all the studies by Oxford professors on how robots would impact the industry, manufacturing has been there and done it. So it’s no surprise that 62% of manufacturers still see automation and robotics as key investments for the future, followed closely by data analytics (60%), according to Deloitte’s Manufacturing Industry Outlook for 2023. The problem is that so many manufacturers are still operating with data silos, undermining their potential to not just effectively embrace new technologies but improve efficiencies and product performance.
The ‘smart factory’ is an idea predicated on automation, data analytics, and how this can drive emerging technologies, such as digital twins. But with data silos, this becomes increasingly difficult. All organizations need a backbone of key data running through all departments so that decisions are made on a full picture of intelligence. While front-end data virtualization capabilities can unify disparate and isolated data, essential to a manufacturer’s ability to deliver modern lifecycle management is service and asset data.
The ability to track and predict machine performance in real-time adds a layer of intelligence that can feed into product design and production. Understanding how machines are used by customers offers valuable insight into how products can be optimized or improved. According to a Harvard Business Review report, this digitizing of service “enables the collection of key data that the rest of the organization can use to build, finance, market, and support the product life cycle,” it says, adding “Asset centricity through service can also equip the organization to better respond to circularity and sustainability initiatives.”
Extending The Concept of ‘Smart’ Outside The Factory Walls
This idea of extending the ‘smart’ concept outside of the factory walls and into the field brings advantages to customer service, product delivery, supply chain and inventory management. This is about maximizing outcomes. It’s an approach shared by Gartner, which recently claimed that “the context of IT spending is changing as buyers increasingly value and make investments in business outcomes rather than buying solutions.”
We’ve seen this in service for some time now and most manufacturers understand the notion of servitization, but this extension into the field, into the customers, is about completing the circle. If manufacturers can have a 360-degree vision of a product in real time, it’s totally transformational and brings extra value to servitization programs.
This qualitative asset and service data is rich with valuable information. As the HBR report details, it includes “insights and context about customers, how the asset supports customer goals, the user experience, the level of feature adoption, what activities led to a service issue, asset aesthetics, such as rust buildup, renewal plans, and so on.” For example, contamination, environmental influences, working patterns, and machine utilization vs specification all hold valuable data insights into machine performance. This also helps with managing warranty claims and identifying the best service plans or the next performance-level product.
Smart Solutions Bring Competitive Advantage
It is this level of detail that will be required to deliver smart manufacturing ideas, such as digital twins. As manufacturers pursue ‘smart’ solutions to find efficiencies and competitive advantage, the digital data thread is key to its success. The richness of asset and service data is such, that it can help organizations reshape supply chains, easing pressures on inventory but it can also add a layer that makes everything relevant within a digital twin. It re-ignites the idea of just-in-time but at a more nuanced level, where factory designs, products, personnel, and logistics are all working in tandem.
As Deloitte suggests, investments in the right technologies “can help manufacturers pivot quickly,” with enhanced data and analytics capabilities improving forecasting. “It could also accelerate the value mapping of suppliers and raw materials along with the impact of a shortage. This advanced analysis can expedite the implementation of preventive measures during uncertain periods.”
The point is that without asset and service data, manufacturers do not have a complete picture of products and services. As smart manufacturing matures and extends beyond the four walls of the factory into the field, we are on the cusp of smart service. With AI and ML-driven capabilities, the technology is now there to help manufacturers make this a reality. Whether it’s smart factories or even the industrial metaverse, the future will need both asset and service data—from inside and outside the factory walls—and the more relevant, qualitative data a manufacturer has, the better.