Whether you’re an experienced engineer or a member of a new generation just launching your professional career, you can make full use of Industry 4.0 technology through a step-by-step approach.
Peter Fischbach, Business Development New Technologies, Domain Expert i4.0 and IoT, Bosch Rexroth Corp.
Today’s manufacturing and automation industry segments are constantly leveraging new technologies and processes to become more agile, more responsive to fast-changing global markets and more innovative in their products and solutions.
There’s also another “new” element to consider in current manufacturing environments: a new generation of engineers, many of whom are entering the industrial workplaces with skills, backgrounds and experiences that are significantly different from previous cohorts of manufacturing engineers.
This newer generation is starting to work alongside existing automation engineering teams that may be grounded in production systems, plants and processes. In many cases, these legacy technologies are less digital, less connected and less “intelligent” than modern production systems and technologies being introduced. These new technologies, commonly referred to as Industry 4.0 or Factory of the Future systems, offer the potential to revolutionize global manufacturing operations — in fact, their rapid adoption is already having a significant impact in many industries.
Whether they’re experienced engineers or part of a newer generation that just launched their professional careers, today’s manufacturing and automation engineers can make full use of i4.0 technology by targeting key productivity, throughput or quality issues and building a step-by-step approach to make smart, incremental investments to move forward on the path to the Factory of the Future.
Combining innovation with experience
The current generation of engineers entering manufacturing operations have backgrounds and experiences that differ in a number of ways from engineers with thirty, twenty, or even ten years on the job. Mechanical and process engineers fresh out of school grew up surrounded by and interacting with digital information technologies. They are used to having in-depth data on hand, or easily searched and accessed, to make decisions.
They use more computer-aided design tools and three-dimensional simulations of systems, creating virtual prototypes of components or devices and testing them using those platforms before using a 3D printer to fabricate an actual prototype. Even if they studied electrical or mechanical engineering, they may have also taken basic programming classes and have experience with commercial programming languages (as opposed to standard PLC programming methods using ladder logic), as well as designing and programming user interfaces.
They also tend to be more experienced with thinking about and designing systems that are networked and highly connected. That means they may assume that technology incorporates the ability to connect with other systems and share data horizontally and vertically, including the use of cloud-based applications and artificial intelligence-type systems.
In contrast, there are large segments of today’s manufacturing systems that are less networked and data-rich than the new generation of engineers might expect. The older engineering staffs in these operations draw more on their experience designing, building and running these systems, compensating for the comparative dearth of information. These more experienced engineers know their machines: how they sound, how products move through the equipment and where potential choke points, common areas of stress, and potential breakdowns are located.
Combining the skills and expertise of both veteran and new engineers can help companies implement i4.0 technologies more effectively as they seek to build the Factory of the Future. The older generation of engineers can contribute their experience and insights about how existing manufacturing systems perform, while the newer generation can contribute their skills at organizing and analyzing data and optimizing how digital technology like sensors, visualization systems and controls can be leveraged to maximum value.
Starting the Factory of the Future journey
Implementing the vision of the Factory of the Future requires a careful selection of targets for technology investment. In order to transform production systems — both existing equipment, as well as machines and production lines designed today or are still on the drawing board — data and connectivity both play a crucial role.
In the Factory of the Future, everything is connected, from the individual machine components with embedded sensors and intelligence up through machine-level and plant-level communications architectures to a cloud-based solution. Sophisticated software collects, transfers and processes data in ways to provide both production transparency and actionable answers to questions about production bottlenecks, inefficient workflows and equipment in need of preventive maintenance.
This is the level of data that many of the newer engineers will expect to have — but may not initially have access to. Many manufacturing plants contain a mix of machines, from legacy equipment with little or no intelligent controls, sensors or communications capabilities to state-of-the-art systems fully equipped for Industry 4.0 operation. It may be necessary to develop a step-by-step strategy that starts small and is scalable — going after the most obvious issues and problems.
You may begin by asking, “Where is my value? Where can I gain the most in productivity?” Start by focusing on production bottlenecks and quality issues — for example, a production part that consistently has quality issues and high scrap rates.
How do you get the information to address these quality issues? Improving connectivity between production units and upgrading sensors can help provide the data. Many plants today have island productions, where one machine produces one part. Then it goes to another machine for another production step and they are not connected — that is, the data about the production of the part in one machine doesn’t follow the part over to the next machine in any automated fashion.
To decide what sensors to install and where, you need to define what data you need in order to get at the root causes of quality issues. It’s important to start by applying lean principles to accomplish this goal, such as mapping the value stream and identifying where time is wasted or scrap is created. Lean manufacturing programs and processes are well established in many production environments and the older, more experienced engineers have long-standing, hands-on expertise applying lean to their plants.
It’s a perfect framework for collaborating with the new generation of engineers; lean principles can be used to point to where initial investments in sensor and data-collecting technologies can have the greatest impact. By applying simple sensors and simple data storage for these targeted, high-level, high-volume areas, you begin collecting historic data and start visualizing it. It also provides the right framework for capturing and analyzing historic and trending production data for a more detailed and comprehensive approach to understanding how to improve operations.
With that insight, you have the information you need to apply continuous improvement process (CIP) tools that will gain value and correct your quality, cost or wasted time and effort issues. This approach can be applied to a range of production systems, not just the legacy ones with minimal sensors and connectivity. You can begin with sensors, but many machines also have smart motion drives and controls to deepen and enrich the data you’re capturing. For example, intelligent servo drives can capture feedback data on motion characteristics, such as torque and velocity. This data can be integrated with data from other devices and sensors to assess whether the load or resistance on a particular axis matches established parameters, or whether there are mechanical issues to be addressed.
Making smart use of connected data
As manufacturers augment sensors, data collection and connectivity, machine by machine or production cell by production cell, there is the risk of “too much data, not enough insight.” One reason some companies resist investing in i4.0 technology is the fear that they will be overwhelmed by a mountain of data and not really know how to extract the right insights to improve their performance.
It’s a legitimate concern — and while the new generation of engineers has developed skills for mining and analyzing this volume of data, configuring how that information is captured and channeled is just as important. A logical step to consider is embedding an intelligent “edge” or gateway that collects the right data from the factory floor.
These gateways normalize the data streams to give you a coherent and actionable portrait of your production in real time. An IoT gateway, for example, makes real-time monitoring of process data, such as temperature, pressure, vibration, power consumption or other parameters, easier to set up without intervening in the automation logic. Data can be centralized at the plant level with local machine state monitoring systems and eventually scale up to using the gateway to connect all production locations through the cloud and using cloud-based analytical tools.
With these tools, it is possible to have much more data-driven solutions to help companies improve a number of critical productivity tools and strategic programs. These include:
–Predictive maintenance: having better insight about when and how to schedule machine service intervals and equipment upgrades to minimize downtime
–Data analytics: making smarter use of production data and incorporating them into operational and strategic decision-making
–Visualization and notification: feeding digital Kanban boards to visualize production data in real time and network with IT applications for production planning and quality data management and communicating throughout plants to provide information as the basis for decisions and process improvements
–OEE optimization: using data from across the enterprise to better quantify overall equipment effectiveness (OEE) and compare different production platforms to help guide investments in future equipment
–Business intelligence: enterprise-wide strategic planning to address long-range investments in new production systems, plants, personnel and markets can be guided with solid historical data and better insight from cloud-based analytical tools
Scalable pathways to the Factory of the Future
It’s important to appreciate that the Factory of the Future is already being built. Seasoned engineers and the new generation just joining today’s manufacturing companies both have important contributions to make. Together they can chart a scalable, step-by-step approach that adds the layers of sensors, intelligence and connectivity to existing manufacturing platforms that can yield actionable insights to solve current issues. They can also collaborate with forward-looking OEMs and technology suppliers to determine the best way to incorporate i4.0 technology into the next generation of production platforms.
Bosch Rexroth Corp.