2015 in review: the top 10 lessons from Industry 4.0 (2/2)
In my first blog about the 10 lessons that I take away for Industry 4.0 in 2015, I focused on the following five topics: Value stream organizations, condition monitoring, how companies can best get started with Industry 4.0, which roadblocks they see and the role that security concerns play in Industry 4.0. Now there come the next five of my top 10 lessons.
Since at least 2015, if not earlier, it’s been clear how important this topic is for production: data is the oil of the connected factory. One of the core competencies in Industry 4.0 is the ability to transform collected data into new information, consolidate this into knowledge, and then incorporate it into production management; in other words, reap new benefits. Companies in Germany are moving towards analyzing data at several points along the production process and gathering this new information. Doing so allows them to, for example, improve production cycle times and detect deviations early on to avoid unplanned machine downtimes. The quantities of data associated with these activities is certain to continue growing.
To implement this approach, we need experts who are equally at home with data analysis, manufacturing and software engineering. It’s the combination of skills that’s essential. What do we mean by that? Allow me to explain using an example: as a rule, data scientists look at a problem from a data-centric perspective. Their approach is neutral and flows from the data. Production experts, on the other hand, focus on the technical side of the problem. Merging these two methods is the key to the art of efficiently resolving the problem using data analytics. Particularly in the business understanding phase, experts need the right kind of production know-how, yet shouldn’t lose their neutral perspective on the data to be analyzed.
Several blog posts have provided examples of the benefits resulting from such analytics projects in manufacturing. Based on the analysis of data from 30,000 hydraulic valves, Bosch was able to cut the time spent on end-of-line testing of these valves by more than 17%. Data analytics showed that, provided the results of several earlier steps were positive, certain subsequent testing steps in the inspection process could be eliminated. The outcome of those subsequent steps could be reliably predicted by analyzing the earlier steps.
Discussions about Industry 4.0 frequently mention new services and business models. However, as of 2015, few of these had made it into actual practice.
One example of an Industry 4.0 service in production is Bosch Rexroth tightening systems, which come with a software-based cockpit for the intelligent analysis of safety-relevant tightening data. This extra software enables the supplier to sell an innovative additional service that sets it apart from the competition. Users benefit from the solution’s ability to detect faults in production early on so they can be avoided. As important as the software-based service itself is the fact that it comes with an open interface. The implication: data from different tightening systems can be integrated and visualized within the cockpit.
New machine maintenance services are made possible through a combination of configurable production rules and open PLC interfaces for direct access to the machines’ control units. This means service technicians’ know-how can be integrated into predictive maintenance services. For example, service technicians know that changes in engine torque indicate mechanical wear and tear. This knowledge can be translated into a rule that the software then executes: if an engine’s torque changes by a given amount, the system notifies the maintenance group accordingly. The constant rule-based monitoring of the operating data means that service intervals can be adjusted dynamically to accommodate the actual wear and tear. As a result, suppliers can organize their maintenance services more efficiently, and users benefit from increased machine availability and with it, better plant productivity.
Liability claims were certainly a cost-intensive issue for manufacturing companies in 2015, but not the only one. Quality problems can surface before the product has even left the factory, and the further along in the value chain a quality problem is detected, the higher the company’s costs. The relationship between the two is called the “rule of 10”. At each step of the value chain, fixing a problem is approximately 10 times faster and cheaper than doing so in the next phase.
Industry 4.0 software can have a huge effect on reaction times when it comes to process quality management. Instead of waiting for a quality report from somewhere along the value chain before identifying and solving problems in production, process quality is checked every time the process is run. If a quality problem is detected, troubleshooting can be started instantly.
You can use this principle to meet your zero defects quality targets. Now, with the increasing virtualization of production landscapes, you can collect data at any point in time from any location and analyze it accordingly.
As Industry 4.0 is developed further, much will depend on the collaboration among companies, universities, associations, consortia, and standardization organizations. Many of the initiatives in 2015 were geared towards developing standards for Industry 4.0. I’ve provided some representative examples below.
Partners are collaborating on new solutions for various Industry 4.0 issues and making use of IIC (Industrial Internet Consortium) testbeds. One of these solutions is for an aircraft landing gear predictive maintenance use case.
A range of Eclipse communities are working collectively in open source projects on services relevant for the IoT and Industry 4.0, e.g. an IoT software update service and an IoT information model repository.
Other major cross-sector collaborations (particularly in Germany) include IUNO, which develops blueprints for secure Industry 4.0 projects, and Plattform Industrie 4.0. One of the latter’s goals is to draw up recommendations and initiate suitable standards.
There are also plenty of bilateral partnerships that focus on IoT and Industry 4.0 topics. For example, PTC and Bosch Software Innovations have teamed up to efficiently develop IoT and Industry 4.0 applications and make the management of machine connectivity secure, flexible, and transparent.
Lesson #10: Last but not least: what Industry 4.0 is not
Looking back at 2015, you could get the impression that all manufacturing activities carry the Industry 4.0 label. But now that I’ve summarized the top Industry 4.0 topics, allow me to list a few facts that make it clear what Industry 4.0 is not:
- Despite the concerns of the public or labor representatives, analyzing personal employee data is not at all a target of Industry 4.0 projects, according to manufacturing experts in Germany.
- Also, Industry 4.0 is not about reducing labor costs; instead, it’s about creating new working conditions, new career profiles, and training opportunities. Ultimately, all Industry 4.0 projects aim to improve competitiveness and in turn secure jobs (especially in countries with high labour costs).
- It’s about more than just lean production activities that have been in place for many, many years in our highly optimized manufacturing plants.
- It doesn’t necessarily involve “the cloud.” Condition monitoring can rely on data from connected machines without it having to be managed in the cloud.
- It’s not a technical topic. It’s a business topic, just as other IT projects are – with software, hardware, sensors, and more technology being the enablers.
- It’s not just about digitalizing production. Industry 3.0 was the information technology phase, and Industry 4.0 is about leveraging connectivity – and the benefits that result from analyzing connected data.
The discussion on Industry 4.0 in 2015 is open now. Did I miss out on a particular learning? Please add to my list, I am open for an extension of my blog series.