How To Choose The Right Industrial IoT Platform For Smart Manufacturing
As a manufacturing company that is looking to leverage industrial Internet of Things for smart manufacturing, or, AI-enabled connected asset offerings, companies are faced with a plethora of platform choices. Wading through the marketing talk and figuring out what approach to pick can be quite a task. Crafting the right platform strategy is a key consideration for a successful initiative.
The complexity is driven by the fact that many IIoT platforms are complementary but also offer overlapping functionality. So for the industrial customer it becomes a bit of a nightmare to try and understand the future implications of one platform vs. an other. A decision might suit immediate needs but be the wrong choice for what lies ahead. In a previous article, I covered the 4 dimensions of Digital Transformation with IIoT and the Manufacturing Intelligence ROI drivers. Let’s look at how these can be mapped to help make a decision when it comes to making a platform choice.
The 3 layers of a typical IIoT solution correspond to three types of platform choices and players that an enterprise needs to consider. An Edge platform focuses on connecting machines and providing a framework for intelligent local applications that can connect to an adjacent core IOT platform. This adjacent layer is the Core platform provider who acts as data aggregator and processor and typically hosts the manufacturing data lake. All business value realization applications feed off the Core and Edge layers to create a new breed of the Application Platforms.
The emergence of these 3 distinct platform layers, and the players that provide them, have turbocharged innovation for smart manufacturing and connected assets. Currently, ecosystem plays that mix and match each of these layers abound. A melting pot of new solution offerings and business models is now available. This is excellent news for enterprises as innovation opportunities are now multiplied in terms of functionality, commercial and deployment models as platforms migrate upstream. All of these are feasible because they adhere to the Platform Dharma of open APIs that make them interoperable with each other. There are very few players that are proven to provide all 3 layers as a part of their offerings.
As we dive deeper into the layers here are some platform characteristics that can further help characterize them by segmentation:
Edge platforms live close to the machines they connect, and have evolved from being machine- and protocol-oriented to sophisticated intelligent gateways that can interact with local applications and cloud data aggregators. The first examples of edge platforms were OPCs and Historians that opened up interfaces for local and global applications to leverage. Some OT players have offerings that cater to their strengths and can be classified as machine-based. Protocol-based platforms have a wider applicability for machines and expose API interfaces but are limited in their analytics and distributed ML functionality. An intelligent edge platform product includes the ability to talk to a diverse set of machines and protocols. It also can perform advanced analytics locally so that the insights are actionable faster. These platforms might have preferred cloud services that they interoperate with, and allow the flexibility to augment them with other providers. All these platforms however, form one component of the business value driver for the manufacturer. They typically need to interact with higher order applications that are better positioned to provide an ROI for a business case. An increasing productivity OEE will typically require not just live dashboards but also need to pull in production plans from an ERP, overlaying historical data and other systems. An intelligent edge platform layer acts as an excellent conduit for a portion of this functionality but cannot create the entire solution without working closely with the core platform layer.
At its most basic level, the IoT Infra-as-a-Service (IoT IaaS) platforms provide the glue that enables all the adjacent layers to leverage each other. But there is a lot more to the Core than just being the manufacturing data lake. IoT Platform-as-a-Service (IoT PaaS) players typically pre-process all the machine data and make it very efficient to write applications and easy to connect to edge platforms. The variant of these platforms are the IT ERP-oriented players who are more focused on easing the integrations with their brands. These integration platforms are not exactly self-service friendly. While they will provide a boost for their own brands the interoperability in real world hybrid scenarios may be constrained. Both IoT PaaS and Integration platforms provide a preferred set of applications from their own stable or via partners for kick-starting business value.
Application Platforms began as verticalized offerings that were applicable to specific business cases. Over time they expanded their footprint to include multiple use cases and applications. These evolved into becoming platforms for manufacturing intelligence. These platforms provide interoperability with intelligent edge and IoT PaaS players to provide an immediate starting point for the digital transformation journey. The evolution of these application platforms into operational intelligence data as a service providers is anticipated. In some cases the applications are heavily tied to certain assets types. This has led to them going deeper and creating possibilities for digital twins that could model in the virtual world the behavior of these assets in the field. The asset-as-a-service offering depends on depth in asset modeling to succeed. Typically manufacturing intelligence platforms provide a richer set of APIs for integration with ERPs and other 3rd party applications. This in turn accelerates ROI achievement for enterprises. In order to help accelerate deployment, they have taken the necessary steps to ensure tighter coupling with preferred IoT PaaS and intelligent edge providers.
To make a decision regarding a platform strategy or provider, it is important to first define your digital transformation roadmap as per the 4 dimensions noted earlier. Based on the functional coverage, time frame, budget and extensibility, ROI can vary extensively. A prudent choice, with an eye towards longer-lasting value, is one that maximizes the ROI in a shorter timeframe while also delivering on the roadmap objectives.
More on tech trends in manufacturing: 5 Ways the Cloud Can Improve Your Manufacturing Operations
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