Tire producers are blessed with a healthy market outlook. But they face unprecedented challenges, too.
In fact, global tire demand is expected to increase about four percent each year through 2019. At the same time, manufacturers increasingly compete in a global marketplace – and must produce more variations to address an automotive landscape that includes internal combustion, hybrid and full-electric vehicles.
How Can Tire Makers Best Address Market Conditions?
To ramp-up productivity and agility, leading manufacturers have embraced smarter plant-floor technology. But many are challenged to create an information-enabled production environment from the patchwork of digital assets on their manufacturing floor.
True digital transformation requires not only collecting relevant data from intelligent assets. But also converting that data into information that enables people at all levels of the organization to work smarter and more productively.
Expanding Beyond Conventional Cloud-Based Platforms
As smart assets began to proliferate on plant floors in recent years, so too did cloud-based analytics platforms designed to transform generated data into useful intelligence.
Conventional cloud-based platforms can aggregate data from multiple sources. However, gaining manufacturing and business insights from that data takes time – and may require data architects, business intelligence engineers and other in-house data management expertise.
In addition, sending data to the cloud and back is not practical for every application. A traditional cloud-based approach simply cannot provide contextualized information quickly enough to immediately impact plant processes – and people as they go about their work each day.
A Cost-Effective, Pragmatic Approach to Distributed Intelligence
A scalable analytics platform can provide a cost-effective and pragmatic way to distribute actionable intelligence across all levels of the organization – on the edge, on-premises or in the cloud.
For example, take a look at this new analytics solution, which embeds analytics and machine learning capabilities at the edge – and closest to the source of the information and plant-level decision makers.
Delivered on a plug-in appliance, set-up is simple. No rewiring of existing smart sensors is required. Connect Ethernet and wait about five minutes while the solution crawls the industrial network and discovers smart assets. As devices are discovered, data is collected, and health and diagnostic dashboards are built and delivered.
As the appliance uncovers information about how the devices are related to each other, such as fault causality, it starts to understand the system on which it is deployed – and can make prescriptive recommendations. For example, it can send an “action card” to a user’s smartphone or tablet.
A scalable approach to analytics can have an immediate impact in many areas of tire manufacturing. And will become increasingly important in complex applications like mixing and curing, where machine learning can have a dramatic effect on product quality, manufacturing agility and energy efficiency.
But that’s just part of the solution. The scalable platform extends beyond the edge to compute engines and tools that automatically orchestrate data from multiple sources – and allow users to see commonalities and gain operational and business insights faster.
The bottom line? A scalable approach enables tire producers to more deeply engage each level of the organization in optimizing its manufacturing process – from local engineering and maintenance to the top levels of the company. And from the device to the cloud.