In the realm of maintenance analytics, people talk a lot about physics-based vs. machine learning-based models. The old guard tends to favor physics, while those (often younger) folks with a penchant for data science tend to be more excited about machine learning. Let’s first set out to define the difference between these two paradigms, and then also take some time to break down the distinction between these two worlds to show that maybe they’re not so different…
Somehow IoT & AI have helped to make equipment maintenance an incredibly sexy topic in the realm of digital innovation – who would have thought? Predictive maintenance (“PdM”) is now on every digital agenda for equipment operators, manufacturers, servicers, and pure-play analytics vendors. In this article, we’ll expand upon the themes of a previous article with a focus on vendor differentiation and various deployment models.
Internet of Things stakeholders talk a lot about the edge these days – some of the noise is just hype, but some of it is real. What are we even talking about and why do people care about the edge? The edge typically implies being at or near the physical location (machine, facility, etc) where data is generated, and the term is often used to imply direct contrast to the cloud: “edge vs. cloud” is a common phrase when discussing IoT architectures. You might (might) consider the backhaul network (fiber, cellular, LoRa, etc) to be the line where the edge ends. Much of the tech development for the edge is focused on enabling operators to run applications, process data, and perform analytics locally.
Digital transformation products (IoT platforms, predictive analytics, etc) are still largely considered a “nice to have” for industrial operators. That said, there is one solution set in this realm that has moved into the “need to have” category: cyber security tools for industrial control systems, SCADA, and OT environments. This is now a board-level topic, and most of the world’s industrial operators are either rolling out solutions globally or working hard right now to determine the best approach (time is critical here).
Equipment manufacturers are increasingly bringing connected machines to market with value-added digital services for their customers. These customers are end-user industrial operator customers who deploy the equipment into their internal operational environments. Let’s discuss how the equipment manufacturer charges its operator customers for the use of these digital services. There are parallels between how digital solution vendors and equipment manufacturers are monetizing their products & services.
Every day, I ask an exec at a manufacturing company what keeps him or her up at night. There is one thing that almost all of them say: My skilled technical workforce is retiring too quickly. Digital vendors have begun emerging with solutions for this problem – but it’s a race against time to see whether they can plug the gaps before the tribal knowledge of that generation evaporates, only to be replaced by a generation of Instagrammers who can’t change a tire.
The cost of sensors has fallen, connectivity is ubiquitous, and machine learning tools are widely available… so why aren’t all industrial companies pushing forward with their digital agendas? Organizations have made progress on technology and solution architecture, but business model development and monetization schemes are still obstacles.
Predictive maintenance is a killer use case for IoT & predictive analytics technologies – how did a grimy old concept for equipment maintenance become such a hot digital trend? Let’s discuss…
In recent years, IIoT and I4.0 startups have been founded under the premise that many industrial operators are “data rich and information poor”. The idea was to put an analytics layer on top of these troves of data to boost productivity and make businesses more predictable. It’s true that most industrial companies are information poor – but sadly, precious few should be considered data rich.
There are a lot of things you can say about Gartner. One of them is that Gartner is a successful company, and it is unreasonable to say otherwise. The stock price continues to grow – revenue has doubled since 2015. But beyond the objective fact that Gartner’s financial performance is strong, opinions vary tremendously around the quality and inherent bias in their research products.
We are most of the way through 2019 – and are we still talking about IoT platforms? Yes, we are. Despite how poorly defined this term still is, and how much investor money has been thrown away, the platforms are here to stay and constitute the core of most IoT projects.