Read our experts' newest Digital Factory Optimization insightsRecent economic uncertainty and volatility in global supply chains are enough to put even the strongest manufacturing companies on edge. But where there is a challenge, there is also significant opportunity! Through this series of articles, we offer insights how you can maximize your production with Digital Factory Optimization. A manufacturer that can rapidly adapt to new circumstances and optimize production will grow and expand its market share - even as market forces shift.
The manufacturing industry is poised for significant growth - but organizations still face a range of complex challenges.
Between the pandemic, geopolitical turmoil, and talent shortages, the road ahead for manufacturers is anything but easy. These challenges emphasize the importance of each manufacturer’s ability to optimize their production cycle, and nowhere is this more true than on the production line itself.
If you work in manufacturing, you know better than anyone that it’s getting more and more complicated to optimize your production line. Let’s take a look at some of the complex challenges you may be facing and why they are so difficult to address using traditional methods.
Maximizing Output While Maintaining Quality
Of course, the biggest challenge facing manufacturers is maximizing output while maintaining or ideally improving product quality. Your team may be focused on reducing downtime, especially when it’s unplanned. Alternatively, your team may struggle more with eliminating bottlenecks and alleviating their impact on production and revenue.
Unfortunately, anticipating the causes of downtime on each production line is an extremely difficult task. If you’re like most organizations, you have no trouble identifying a bottleneck. The real challenge lies in rapidly identifying a bottleneck before it cuts into production quotas.
Reducing Scrap to Mitigate the Impacts of Supply Chain Disruptions
Recent disruption and volatility in the supply chain has made it more important than ever for manufacturers to minimize scrap and waste. According to the American Productivity and Quality Center, scrap and rework costs you up to 2.2% of your annual revenue. That figure doesn’t even include the impacts from reduced production capacity, scrap handling and disposal costs, and other indirect effects on your business.
Similar to maximizing output, you may be struggling with identifying and anticipating the causes of scrap in advance. Whether it’s faulty machine operation, adjustment errors, or another issue, few companies are able to spot these issues early enough to address the problem before costly scrap generation.
Optimizing Material Handling & Material Flow
Ideally, you’ll always have the right parts in the right place just-in-time. However, material shortages and supply chain disruptions have made this more difficult than ever. On the production line level, elements like automated guided vehicles (AGVs) pose challenges as well. You must determine not only how many AGVs are needed, but how they can best handle pathfinding and how these vehicles can be seamlessly integrated into the rest of your production cycle.
If you manage larger facilities, simply tracking materials and where they are placed in storage is another problem. There’s no doubt manufacturers are eager for solutions that optimize material handling and material flow to support efficient production of quality goods.
The Solution - Leverage Data Analytics and AI
The good news is that manufacturers have two powerful tools at their disposal to address all of the above challenges - and more. Most every manufacturer collects and analyzes data, but few organizations use data analytics to its fullest potential. If you’re serious about increasing output, reducing scrap and optimizing production lines, you have to make sure you’re getting the most from your data.
Firstly, you must shift from simply collecting data to drawing meaningful conclusions and creating actionable insights. Oftentimes, the data collected is either too siloed or too complex for manufacturers to rapidly draw insights. This means that many manufacturers overlook tremendous opportunities for greater quality, production, and efficiency.
Secondly, you need to shift from manual data analysis to AI data analysis. In today’s fast-paced, ever-changing manufacturing environment, manual data analysis is simply too time-consuming. AI is able to provide rapid insights on how to avert bottlenecks, avoid machine failures, and implement optimization opportunities.
Unlocking the Full Potential of Production Line Optimization
In the last few years, we’ve seen a vast increase in the adoption of digital technologies in the manufacturing industry. Manufacturers like yourself are overcoming the complex challenges of today’s marketplace with cutting-edge technologies that would have been impossible just a decade prior. It’s an exciting time to be in manufacturing, and the best part is that we are just scratching the surface.
About the author
Christoph Inauen is Head of Siemens Digital Enterprise and Digital Services Group in the U.S. Prior, he was working at SAP as the VP of IoT Business Incubation. He has over 30 years of hands-on experience in digitalization across various industries in Europe, Asia and North America. Christoph’s passion is to help customers achieve their business goals by leveraging digitalization. He has lived in Dallas, TX for the past 19 years.
Manufacturers are under more pressure than ever to create highly automated and flexible production processes. As organizations navigate supply chain disruptions and labor challenges, more teams are looking at new ways to reduce costs and improve efficiency while retaining agility for inevitable changes.
Digital twins offer manufacturers tremendous advantages here. In brief, a digital twin is a virtual model designed to represent a real-world object or system. Digital twins provide manufacturers with an extraordinary ability to virtually commission new plants, validate designs, and identify opportunities for improvement.
In this blog post, we’ll be looking at several digital twin applications at the plant level, line/human level, and machine level. We’ll also explore how forward-thinking companies are already leveraging this exciting new technology for stronger production processes.
Optimization at the Plant Level with Digital Twins
Optimizing at the plant level is an extraordinarily complex challenge for plant managers and related roles. Whether you’re commissioning a new plant or optimizing an existing plant, there are countless variables to consider including production sequences, number of workers, number of AGVs, buffer sizes, and more.
Digital twins allow you to create a virtual representation of this plant in-detail to experiment with different configurations. However, the real power of digital twins comes when you pair them with artificial intelligence. AI allows for rapid analysis of millions of different configurations to find the optimal solution for your specific needs.
Whether your goal is to reduce costs, increase output, or even become more energy efficient, digital twins paired with AI can help you realize phenomenal results. As an additional benefit, digital twins are a powerful tool for visually showcasing a new design to stakeholders, allowing for more confident decisions and mitigation of investment risks.
As an example, Siemens recently partnered with Airborne International to develop a highly automated composite line. Airborne International has traditionally manufactured composite parts for the aerospace, marine and automotive industries, but they wanted to enter the consumer goods market as well. This was a challenge because composite parts typically require extensive manual work, which was simply too expensive for the consumer goods market.
With a digital twin and AI analysis, Airborne was able to identify bottlenecks, optimize production planning, and achieve a faster time-to-market - all without ever building a physical prototype. Overall, Airborne was able to increase output by 18% using this process.
Optimization at the Line and Human Level with Digital Twins
With the complexity of today’s manufacturing plants, you can’t simulate every aspect of a plant in one digital twin. That’s why companies create digital twins of different levels to optimize each.
At the line level, digital twins can be leveraged to design more efficient cells at lower costs and minimize resource utilization. Furthermore, digital twins allow you to virtually commission new lines, test PLC logic, perform collision checks, and simulate human vs robotic performance. You can even go a step further and identify ergonomic issues like reachability. In brief, digital twins allow companies to experiment and address faults with their line design in the virtual world rather than in the real world.
Consider the example of SGAR, a Spanish engineering and industrial automation company. SGAR specializes in software design, PLCs, robot programming and hardware engineering. With our digital twins, SGAR has been able to validate the data provided by their mechanical suppliers and completely simulate manufacturing environments.
With the help of these simulations, SGAR now identifies bottlenecks at the very beginning of their projects. They can also debug PLC code before downloading it to the actual environment via virtual commissioning. Overall, these tools have allowed SGAR to reduce project time by 30% and complete projects that wouldn’t have been impossible before.
Optimization at the Machine Level with Digital Twins
When you think of digital twins for machines, the first thing that jumps to mind is a 3D model of the physical machine. While this is an important aspect of a machine digital twin, this is only scratching the surface. Digital twins combine the physical model with kinematic, automation, and electrical models as well. By extension, a virtual machine model encompasses the logic of the PLC program, active components like drives and valves, and the behavior of peripherals in addition to mechanical components.
These models offer a range of benefits to manufacturers. First and foremost, manufacturers can test all aspects of a machine in a virtual environment before building the hardware. You can simulate and validate machine-oriented applications without ever building a physical prototype. In doing so, project planning errors can be detected at an early stage, and you can detect where collisions may occur digitally. Software, mechanical and electrical engineers can work in parallel instead of step-by-step, adjusting their progress on the digital model. Lastly, you can even train machine operators on how to use a new machine before the real machine exists.
By leveraging digital twins for virtual commissioning, Norwegian machine builder Tronrud Engineering was able to reduce their design phase by 10% and their commissioning phase by 25%.
About the author
Steffen Klawitter is a Digital Enterprise Lead Architect with Siemens Digital Industries. He provides well-balanced visionary & practical leadership for strategic digital transformations in highly competitive industries, cutting-edge markets, and fast-paced environments. He has a proven track record of leading international teams to create long-lasting business value for enterprises in areas like solution & enterprise architecture, digital twin & virtual commissioning, industrial internet of things, blockchain-based trusted traceability and edge AI.
To be successful in today’s marketplace, manufacturers must be more agile than ever. Shifting customer demands, short product life cycles, new technologies and other factors all put pressure on manufacturers to create highly flexible yet efficient production systems.
In the effort to address these challenges, virtual commissioning is invaluable. In brief, virtual commissioning is the design and testing of a virtual model (including PLC, Behavior models and 3D simulation) of a production machine or line. By using a 3D simulation to create a digital copy of the machine and connecting it with an automation system, manufacturers can commission, revise and validate the machine design all before creating a prototype.
Efficiently Validate Automation Programs, Component Communication, Production Machines and More
When commissioning new machines, it’s important that machines are ready to operate the day they arrive at your facility. Virtual commissioning allows you to create a single, cohesive model of your machine including an automation model, an electrical model, behavior patterns, and a physical/kinematic model. This makes it possible to validate each of these aspects of design at a very early stage.
By identifying errors and design flaws early in the process, it’s easier than ever to ramp-up or configure machines without creating production stoppages. Automation engineers can ensure that code is properly configured in the event of failure and safety protocols are working correctly, while mechanical and systems engineers can experiment with the sequence of operations and identify any risk of collisions.
Integrate Different Design Departments Early in the Process
One of the biggest challenges in commissioning new machines or production lines is enabling effective collaboration between different engineering teams. Typically, mechanical, electrical, controls and systems engineers are all somewhat siloed from one another, resulting in a stop-and-start process that limits collaboration.
With virtual commissioning, every engineering team can collaborate and develop designs simultaneously at an early stage of the production system lifecycle. By enabling these teams to work in parallel, you can dramatically reduce the time required for both the design and commissioning phases.
This increased collaboration is hugely beneficial for automotive manufacturers, who must manage several different vendors when commissioning a new line or facility. Now, many of our automotive customers require their vendors to virtually commission their machines, allowing the customer to ensure that machines and line components from different vendors mesh together seamlessly.
We’ve also seen machine builders leverage virtual commissioning for a faster, more efficient production lifecycle. Virtual commissioning allows machine builders to verify PLC code before the machine is built in reality, expediting the process and allowing them to verify that the machine behavior is correct at a much earlier stage.
Start Optimization Before Even Making a Prototype
The advantages of virtually commissioning a new machine or line don’t stop at the commissioning phase, either. With a comprehensive virtual model of your new machine or line, you can leverage this model to further optimize your facility and production life cycle.
By pairing the model with a digital twin of your production, you can experiment with different sequences, traveling paths, the speed of motors, the position of sensors, and more to refine your process for best results.
A key advantage Siemens offers is that all of these solutions seamlessly integrate. You can connect virtually commissioned machines directly with PLC hardware and simulation, making it easier than ever to optimize production.
Bringing Commissioning Into the Digital Age
In a competitive, rapidly evolving manufacturing environment, companies need every advantage they can get to keep production flexible without sacrificing quality or cost-efficiency. Virtual commissioning is an incredibly powerful tool to achieve these goals in the commissioning phase, but it’s really just a single piece of an exciting transition happening in manufacturing. When combined with digital twins, AI, and other cutting-edge technologies, the sky's the limit for production optimization.
About the author
Peter Papperger is Head of Digital Engineering with Siemens Digital Industries. He has over 20 years of experience developing and leading projects in automation, IT/OT, and digitalization in various industries. Peter has a background in engineering and holds MBAs from Germany and the U.S.
One of the biggest challenges facing manufacturers today is the need to continuously optimize production and operations.
It starts with optimizing production facilities in real-time, navigating changing circumstances like material availability, supply chain disruptions, machine maintenance and so forth.
However, that’s only the tip of the iceberg. Manufacturers also need optimization solutions that are accessible for their workforce. Advanced optimization technologies like AI, edge/cloud computing, and digital twins are incredibly valuable - but unfortunately experts on these platforms are few and far between.
In this article, we’ll explore some of the key challenges around real-time optimization, and the opportunities presented by digital plant shadow technologies.
Optimizing Production Day-in, Day-Out
Simulating the static design or initial blueprint of a factory is one thing. But anyone with experience in manufacturing can tell you that a factory is a living, evolving environment, making ongoing optimization a serious challenge. In order to stay competitive and adapt to new market trends while manufacturing quality products, companies must consistently modify their facilities and operations.
Managing material flow, adjusting production schedules to account for delayed deliveries or delayed supply, and planning for shifting capacity in terms of workers and shift schedules are just a handful of real-time optimization hurdles. Traditionally, plant managers have to decide how to respond to these circumstances based on gut feel and experience. However, this is hardly the most efficient method.
By harnessing AI, cloud computing, and digital twins, companies can create a digital plant shadow for vastly more efficient operations. In short, a digital plant shadow is what you get when you combine digital twins with real-time data to connect the simulation with the shop floor. In doing so, manufacturers are able to minimize running costs, optimize plant logistics and resources, and more.
Leveraging Live Data
By collecting and analyzing live data in real-time, manufacturers are able to unlock a wealth of optimization opportunities.
Consider the example of one of our clients, a major e-commerce retailer in Germany. As you can imagine, they face significant logistical challenges in terms of warehousing and shipping. At their logistics centers, they have a central conveyor line that is crucial to this process. When a customer orders a product, the product is collected from the warehouse and put on the conveyor belt, where it is transported to be packaged and shipped. Given the high volume of their sales, the client struggled with frequent blockages on the conveyor belt. These blockages necessitated a near-total shutdown of packaging and shipping processes as staff rushed to clear the blockage and new packages could not be added to the line. Given that a single hour of downtime cost the company an average of €65,000, this was a serious problem.
To alleviate the issue, the client leveraged Siemens digital plant shadow technology to continuously observe how many boxes were put on the conveyor line and forecast blockages by simulating their future paths. The platform is able to predict when and where there will be a jam, providing their staff with 10-20 minutes of advance notice when a jam is likely to occur. As a result, the client has saved hundreds of thousands of Euros and significantly streamlined their warehousing and shipping logistics.
This example demonstrates the power of using live data for real-time ongoing optimization with a digital plant shadow. In a matter of minutes, live data is turned into actionable insights to maximize operational efficiency.
Making AI-Driven, Cloud-Based Optimization Simple and Easy for the End-User
A major hurdle in real-time optimization is expert dependency. Your technology is only as valuable as your ability to use it for actionable insights and practical takeaways.
Our digital plant shadow platform uses a straightforward, intuitive app. Users can visualize simulation results right on the shop floor, as well as view recommended optimization solutions and suggestions for streamlining production.
In brief, the app and platform eliminates the need for costly experts to be involved in every step of the process. It allows everyday plant managers and manufacturers to take full advantage of the optimization opportunities afforded by cutting-edge AI and cloud computing technologies.
Using Digital Plant Shadows to Not Just Survive But Thrive Through Change
Recent economic uncertainty and volatility in global supply chains are enough to put even the strongest manufacturing firms on edge. The silver lining is that this disruption offers significant opportunities for manufacturers to grow and expand their market share - if they can rapidly adapt to new circumstances and optimize production as market forces shift.
About the author
Dr. Daniel Klein is the Head of Digital Twin Applications at the Digital Industries Customer Services in Fürth, Germany. Together with his team he helps customers understand the broad field of the Digital Twin, how the different digital technologies can tackle their specific business challenges and gives hands-on support with dedicated experts during the implementation. Prior, he was working for KUKA Industries, a leading German robot manufacturer, where he was responsible for the introduction of new digital technologies in the engineering. He's holding a M.Sc. in mechanical engineering and a Ph.D. in virtual product development from the FAU Erlangen-Nuremberg.