To attend Hannover-Messe 2018, Industry 4.0, is to walk through paradoxes of the large and small, solid and wave-like, human and technological. While it is a temporary event, it is also a solid snapshot of the near-future, exhibiting the jittery flashing and indeterminate solidity of a quantum environment, but at human scale.
On the one hand, the show is overwhelmingly massive. The exhibits are gleaming and spaceship-sized, overlaid with movie theater sized screens proffering a vision of radical transformation. Here companies demonstrate robot arms that use artificial intelligence (AI) and video eyes to play and win against humans in a game of ping pong. Everywhere techno-beat music infuses a collective thrum throughout the huge hallways reinforcing the notion of a future of manufacturing automation.
Interplay of Sensors, Solutions, and People
On the other hand, tiny sensors are abound collecting and collating data. We, the fleshy humans, ply our handshakes, smiles, and agendas amidst a mashup of stainless steel manufactured goods and digitized capitalism. At the level of nation-states, Germany’s Chancellor Merkel and Mexico’s President Nieto opened the event with appeals to investment, interconnectivity and collaboration for the 200,000+ visitors and nearly 6,000 exhibitors.
Amidst and penetrating the humans and robots, the flow of quantum theory is actualized in the form of wave patterns running the electronics in our pockets and electromagnetic signals from, well, everywhere.
IoT Predictive Maintenance: Stepping Up
Predictive maintenance is where a lot of this noise comes together into a common-sense business case. Ultimately manufacturing is not looking for robots for the sake of robots, but for operational efficiencies in manufacturing to drive profits. Data used to predict and improve machine health and reduce downtime makes sense to process engineers tasked to derive increasing operational value at scale.
Analysts estimate a compound annual growth rate (CAGR) of predictive maintenance of 25-40% quickly scaling into the multiple billions. Depending on the size of the manufacturer and industry, manufacturers are embracing predictive maintenance at different speeds, but according to the analyst firm Frenus’s “Customers’ Voice: Predictive Maintenance in Manufacturing, Western Europe,” presentation at Industry 4.0, 50% of manufactures have a current predictive maintenance presence and 46% have a predictive maintenance implementation scheduled for the next 1-3 years.
Moreover, as the Frenus report noted and Chief Research Officer Stuart Carlaw describes in his round-up piece on the Industry 4.0 event (page 9) the benefits of predictive maintenance can be substantial.
Carlaw describes how industrial automation giant ABB condition monitoring (using Cassia long-range Bluetooth gateways) claimed reductions in downtime of up to 70%, motor life extensions of up to 30%, and energy reductions of up to 10%. For operational efficiency and process engineers, those kinds of numbers are compelling.
Quick Predictive Maintenance Takeaways from Hannover-Messe 2018 Industry 4.0:
1. Nordic countries (Denmark, Sweden, Norway, etc.) appear to be 1-5 years ahead (depending on manufacturing industry niche) of German-speaking countries (Germany, Belgium, Austria, and to include France etc.) in market adoption of wireless connectivity and IoT. The UK is ahead of Germany, but behind Nordic countries.
2. German manufacturing production needs are over 100% capacity in the past several quarters, therefore many manufactures are looking for operational efficiencies. However, a combination of a “if it isn’t broke, don’t fix it” satisfaction with German manufacturing high-quality engineering/brand approach and existing “wired” connectivity in German manufacturing production as the “status quo” is slowing adoption of wireless IoT.
3. And yet, German process engineers at manufacturing plants are seeking to address the capacity issue. They are willing to experiment with new operational efficiencies, specifically in supply lines “anything that moves” (rather than production lines). Note, the IT department is the blocker in these initiatives based on concerns over IoT and wireless reliability and security.
4. In general, awareness of wireless IoT in predictive maintenance is still at the stage where protocol selection (Bluetooth, Wifi, LoRA…) is being debated. Both the ABI Research and Frenus analyst firms touch on the current state of flux in the industrial IoT connectivity layer.
The ABI Research event report notes a “generally conservative attitude” toward wireless technologies even while noting a paradigm shifting “long-range Bluetooth for industrial applications (was) highlighted at the show.” Moreover, the Frenus analyst report notes several wireless protocol options used in predictive maintenance and a fast-evolving wireless landscape.
5. Adoption of general “best practices” in German manufacturing is often provided by public/private/academic organizations. For example, large and small German manufacturers take guidance on standards and new approaches such as wireless and IoT from the Fraunhofer-Gesellschaft Institute.
Industry 4.0: Multiple Components of an Ever-Evolving Near Future
As author William Gibson noted, “The future is already here, its just not very evenly distributed.” If any cue may be taken from Hannover-Messe Industry 4.0, the industrial near-future has a fractal quality, with small and large components resembling each other in similar patterns.
For example, at Industry 4.0, the circuit boards on display and logic trees of the demonstration robots are laid out in empirical decision matrixes similar to their scaled-up counterparts – the grid like lay-out of exhibition booths and walkways. The random and indeterminant electronics of circuitry act in their “wave-like” patterns similar to the meandering yet purposeful flows of people traversing the event walkways.
In the space where commerce happens, IoT predictive maintenance has evolved out of the condition-based maintenance past and is pulling the industry-as-data admixture forward with exemplars like ABB leading the way with paradigm shifting technologies.
On the horizon, notes Frenus, of the next industrial data value-add iteration is edge capabilities and prescriptive maintenance. Moreover, as noted, the small and large coverage when it comes to market making. Variations in nation-states with differing cultures and market access leverage points mean differing strategies regarding the type and timing of predictive maintenance offerings.