2015 in review: the top 10 lessons from Industry 4.0 (1/2)
In 2015, more blog posts were devoted to manufacturing, and Industry 4.0 specifically, than any other subject. On average, these posts also met with the greatest interest among our readers. Industry 4.0 was a motif running through all topics the whole year long. I’d like to look back at what happened in 2015 from an Industry 4.0 standpoint and summarize where we are now. And now, my personal Top 10 list of Industry 4.0 lessons in 2015:
When manufacturing companies in Germany undertake continuous improvement projects, they focus heavily on their value streams. This is in essence a perfect fit for Industry 4.0, as it requires connectivity across boundaries: a value stream by definition represents the flow of parts from supplier to production to the customer. However, the primary focus of many projects in 2015 – whether for lean production in general or for Industry 4.0 specifically – was on the benefits produced by optimizing certain individual processes. If you’re looking for total, all-encompassing connectivity – covering all links in the supply chain and reaching out across multiple plants – you’ll still have to wait a few years. Over the next couple of years, organizations will increasingly (have to) ensure that their structure reflects their value streams ; this means moving towards value stream organization. This does not mean, however, that Industry 4.0 offered no optimization potential in 2015. Quite the opposite: for companies that reviewed their value streams specifically to pinpoint where problems occur and how these can be improved or resolved with software, there were immediate benefits to be had.
Lesson #2: Condition monitoring and predictive maintenance: where do Germany’s manufacturers stand in 2015?
In 2015, condition monitoring was named most frequently as the application that production experts truly need. Predictive maintenance is not far behind – at least, that’s the plan of German production experts. I’m curious to see if we’ll be talking about this in 2016 as one of the blog’s topics of the year.
Condition monitoring generally refers to solutions that collect, analyze, and monitor machine, process, or quality data. Cross-machine connectivity of data is the crucial prerequisite for making machine status transparent and in turn for taking action (e.g. having service technicians recalibrate the parameters) if values fall outside an acceptable range. See our cycle time monitoring example to learn how it pays off.
The analysis incorporates data from MES, data sent directly from the machines, data from sensors, and – depending on how automated the derived actions are – it might also include data from ERP systems, for example.
People often ask us: so what’s new about any of that? Here’s the answer in full detail:
- The fact that connected systems are the data sources for analysis.
- The way results are presented visually and in a central monitoring view. This is often the first time that experts have had convenient access to – and an overview and direct control of – more than just individual machines (e.g. an entire production line).
- Only the right information is passed on to the right people at the right time via the right medium.
- Experts can now take their knowledge of production, maintenance, repairs, etc. and feed it directly into the software solutions. They can use existing data and define rules on when to react to certain conditions – and don’t need to wait for IT experts to be available for implementation.
- These systems have to be scalable (start small, think big) and interoperable (open interfaces, compliant with existing Industry 4.0 standards, integrated into the existing IT landscape of the enterprise).
For many companies, the biggest challenge of 2015 was to identify Industry 4.0 projects that can start delivering benefits and providing a competitive edge today – which meant fitting themselves into an Industry 4.0 “big picture” that is only just starting to take shape at each company. What’s needed here? Three things are essential (see also the Industry 4.0 Innovation Cycle) – but none of it is rocket science.
- To get started, companies need to equip their production experts with the right tools. These include rules technology for their business experts and big data processing, e.g. using data analytics tools, so they are in a position to exploit the huge quantities of existing production data.
- The infrastructure setup has to be “Industry 4.0-ready.” This includes connected machines, a data collection infrastructure, suitable security mechanisms, etc.
- Last but not least, your organization should already have a rough sketch or initial concept of what Industry 4.0 means for your business.
The best way for you to learn “the rest” is in an actual project! Our experience from over 100 Industry 4.0 projects at Bosch shows that companies achieve the greatest success when they view Industry 4.0 as an evolutionary development ; in other words, when it unfolds step by step. A retail and logistics company might for example start with a PoC in which a small number of connected sensors (temperature) are deployed to monitor the cold chain. The goal of this pilot phase is to generate measurable quick wins. Once this phase has been completed successfully, the next step is scaling. In this phase, the number of sensors is massively increased; now thousands of them monitor the cold chain. The company can integrate more sensor data (vibrations, geolocation) and develop completely new applications. In further phases, this sensor data can be put to other uses, for instance to optimize the ordering processes. This step-by-step approach is well-suited to small and medium enterprises (SME), too.
Since we usually talk about new Industry 4.0 applications for which there are currently no off-the-shelf solutions, it is essential to choose the right partners: ones that offer pooled expertise with which to develop these sorts of new solutions. This expertise primarily includes know-how in plant engineering, product development, production processes, sensor technology, data analytics, software solutions and integration, UX, legal issues concerning the IoT, and sometimes even more.
Lesson #4: What was the biggest obstacle in 2015 that kept companies from moving toward Industry 4.0?
This was a question posed by Bosch Software Innovations when it conducted a survey in summer 2015 among 180 production experts in Germany, Austria, and Switzerland. According to the survey results, the biggest challenges concern
- Experts: Oftentimes there are simply not enough experts with the necessary combination of skills. This is particularly true for the combination of manufacturing and IT expertise.
- Standards: Many companies are waiting for standards to be instituted. The fear is that they will make the wrong investment decisions today and then have to start from scratch again tomorrow.
- Security: “Production data in the cloud” – this idea sets off alarm bells for the majority of production experts. Public cloud, private cloud or no cloud at all? How should I be conducting risk and threat analyses? What security mechanisms do I need to use? And what else should I do to secure systems and machines, and protect our intellectual property – not just to preserve know-how that sets us apart from competitors, but also to keep it safe from manipulation?
- Benchmarks: There is a need to establish benchmarks of finalized projects. The insights (e.g. calculations of RoI) offered by such benchmarks can be helpful when launching an in-house Industry 4.0 project that is intended to yield the greatest possible benefit in the core business. This can often be something simple, like a practical device that provides production experts with information relevant to their work.
There’s a good chance that “production data security” will become the topic of the year in 2016. Without a doubt, security is crucial to the success of Industry 4.0.
Security by design has long been an established principle in software engineering. Secure engineering processes have always included risk and threat analyses as an essential part of software development.
What’s new here is the IoT context. An Industry 4.0 IT setup involves any number of stakeholders, systems, and other factors. Moreover, because these systems evolve, you need to be prepared for scenarios you can’t even envision yet. The best way to cope with this is to find partners who specialize in the IoT: partners that are involved in the development of appropriate IT security concepts, architectures, and standards and can help you establish these in your organization.
Suitable partners will also be familiar with software architectures in manufacturing from years of practical experience. For example, if you’re looking to implement remote services, the necessary infrastructures are already available for you to build on. What’s more, many initiatives that are now just getting off the ground are tackling this topic in full. Over the next few years, they will create dedicated blueprints, reference architectures, best practices, and more for security in Industry 4.0.
These are the first five lessons that I take away for Industry 4.0 in 2015. Stay tuned for my second part, in which I discuss the role of data in Industry 4.0, new business models, the rule of ten, how standards can help and what Industry 4.0 is not about! Any comments so far?