1. The year of the hybrid cloud’s arrival in Industry 4.0
The combination of cloud and industrial (especially production-related) data has been a thorny issue in Industry 4.0 for years. Manufacturing data that affords insights into business processes will always be sensitive. They will not be made available in the cloud in the foreseeable future. However, a sharper distinction is being made: Is it machine process data that reveals nothing about what that machine makes and how it works? Such data is increasingly being seen as unproblematic for processing in the cloud. This distinction is a basic prerequisite for new services aimed to minimize downtime, for example, alarming and ticketing.
Learning in the cloud is also gaining traction in intralogistics. One such use case is predictive maintenance for forklifts in fleet management. Intelligence will increasingly be decentralized to do things like detect error patterns. Companies still conduct overall equipment effectiveness analyses and the like locally, on the premises. This requires hybrid solutions. The tech to make that happen is out there – fog computing, local clouds, or even edge computing.
New data-driven applications and services will not need to handle streamed data on a continuous basis. This development towards more decentralized, hybrid, selective and defined data analysis in Industry 4.0 applications will, in my opinion, characterize 2017 as the year that opened the cloud in Industry 4.0.
2. Digital twins: The year of the gateway to Industry 4.0
Digital twins are now part and parcel of every Industry 4.0 digitalization effort. They are to be understood as a standardized concept, allowing the standardization of technical interfaces independent of physical assets, providing information about energy consumption, for example, independently of the machine. Digital twins enable engineers to implement software solutions irrespective of the machine’s make or model. Why is that a big deal in Industry 4.0? The development of software solutions and of machines (including systems integration) can then be decoupled from each other. And that brings all the benefits of scalability: speed, independence from manufacturers, and solutions that coalesce horizontally, all across manufacturing and logistics value streams.
Production digital twins are used in physical plants where things are actually made. They are linked to IoT-enabled components by means of product digital twins. These are interfaced with performance digital twins to simulate or scrutinize the behavior of components; say, a vibrating motor in a faulty pump. This means we can consolidate, replicate, and analyze information on all manufacturing assets, their component parts, and other system-related processes – in real time and on one platform. This information is sourced via standardized protocols.
In 2017, Industry 4.0 is hard to imagine without digital twins. That is why I see it as the year of digital twins’ gateway to Industry 4.0.
3. The year of Industry 4.0 solutions: Converging value streams
The factory of the future could rise up out of a greenfield project, but that is by no means a must. Assuming the more prevalent brownfield situation, what do we need to build tomorrow’s connected factory, alongside the above-mentioned approaches and concepts of a hybrid cloud and digital twins? The solution will have to factor all value streams into the equation. Yet island solutions still abound. Most are vertical and geared to special use cases in manufacturing or logistics.
Lately, though, we have been seeing pilot value streams converging in Industry 4.0 integration projects. For example, a new transport/order/management solution is connecting the intelligent supermarket, milk runs, and manufacturing orders in SAP.
When the ideal end-to-end solution arrives, it will unify
- Manufacturing: The various functional lines will converge in the work order with the aim of achieving 100% on-time delivery and quality. Data will be tapped from every source, including the shop floor, the warehouse, tracking and tracing applications, and intelligent energy management systems.
- Intralogistics: Smart systems will connect milk runs with shelves and the fleet.
- Inbound/Outbound logistics: Since these are already highly optimized, further integration remains limited for now.
It is only through the intelligent connectivity of manufacturing, logistics, customers, and even product development that we gain holistic insights, for example, to get to the bottom of quality defects. In fact, this is how Industry 4.0 was originally defined.
Of course, Industry 4.0 solutions that cut across value streams will need to become more mature. One such integration based on digital twins and a pilot value stream is already underway at Bosch’s Rexroth plant in Homburg. This facility is setting up a dynamic dispatching system to manufacture smaller quantities, with the ultimate goal of economically producing customers’ very specific needs down to a lot size of one.
With all this in mind, I believe 2017 deserves to be called the year of converging value streams.
4. The year of very attractive Industry 4.0 open standards and open protocols
Manufacturers want open standards and open protocols; open source plays just a secondary role for them. Their primary aim is to avoid locking in vendors, opting instead to use a broad variety of solutions and machines. In 2017, traditional standards issued by ISO, IEC, and DIN/DKE seemed foreign, as the implementation of specific solutions took center stage. The Production Performance Management Protocol is an attractive semantic standard independent of communication format. The specifications are available to the Eclipse community.
There is increasing awareness of comprehensive Industry 4.0 management protocols. Why is this the case? Nowadays, there are many connected solutions and use cases in the Industry 4.0 market. They appeal equally to users and providers wanting to put together new Industry 4.0 packages that add value. All of this depends largely on how easy it is to integrate Industry 4.0 solutions and, consequently, open standards and open protocols, as well.
A lot has happened in the Industry 4.0 market in 2017. The Production Performance Management Protocol is one example; another is the strategic alliance ADAptive Manufacturing Open Solutions, or ADAMOS. To date, however, there are no market-leading standards. Against this backdrop, 2017 is also the year of great attractiveness.
5. The year of big starts in Industry 4.0 greenfield projects
In my eyes, 2017 is also the year greenfield projects in Industry 4.0 truly got off to a big start. Not only in Asia, but everywhere. Many companies asked Bosch this year to help them with Industry 4.0 greenfield projects. In particularly high demand is our real-world know-how as users and providers of Industry 4.0 solutions. Businesses also turn to Bosch for help with digital transformation – which can include strategies, consulting, implementation, and even full-service support. This implies matters such as the working environments of tomorrow, architectural measures for securing plant premises and, of course, all key aspects of connectivity.
These lighthouse projects aim to create milestones and to serve as reference implementations for the existing plants of industrial manufacturers.
By contrast, Industry 4.0 brownfield projects emphasize connecting existing assets, such as old test benches, as simply as possible. A brownfield approach also relies on plug & play, quick commissioning, and no changes to the existing IT infrastructure.
I regard 2017 as the year Industry 4.0 greenfield projects blossomed.
6. The year of learning Industry 4.0 data analytics
Hybrid Industry 4.0 solutions with distributed intelligence require extensive analytics of both historical data and real-time data, which serve as the basis for new, data-driven Industry 4.0 services that machinery vendors can, in turn, market as added value.
Industrial companies are increasingly open to learning in the cloud. To this end, more companies are investing in digital twins to serve as enablers. There has been great progress on use cases based on the analysis of historical data, especially manufacturing data. In coming years, we will see in the next step how machinery manufacturers return artificial intelligence back to machines, in the form of algorithms for the real-time analysis of process data. This advancement will benefit predictive maintenance as well as the monitoring and assurance of quality at production facilities (example: spot welding).
In this instance as well, machine operators will retain all production-related data. The focus now shifts to analyzing process data, especially from different machines. In the years to come, more and more machinery vendors will create multi-purpose solutions that will drive the availability of machines closer and closer to an overall equipment effectiveness of 100%. It will soon become possible to supply the right replacement parts before installed parts even fail. Before this can happen, however, mass quantities of data will be needed. And that will not happen overnight. In short, a sufficient database will allow proper analysis of data and the identification of insights that will improve processes. Initial pay-per-use approaches are emerging in the market; examples include pay per tightening and pay per cut meter.
One thing is clear. There is tremendous potential in data analytics for Industry 4.0. For this reason, Bosch founded a Center for Artificial Intelligence in 2017. This will help the company expand its expertise in all regards, including research, enabling, services, and key product challenges.
In my opinion, 2017 is the year of learning as regards the application of data analytics as well as Industry 4.0 products and services driven by data.
7. The Industry 4.0 forecast? Spring 2018 is coming.
I recently heard an expert at a conference say: “Industry 4.0 is like gardening in the spring.” It may be winter now, but a great deal will happen in spring 2018 and thereafter. The major suppliers to industry will sharpen their Industry 4.0 portfolios, restructure, and offer entirely new solutions and products. Bosch will continue to benefit from its in-house expertise ranging from sensors to cloud solutions – which we ourselves use in both experimental and productive ways.
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