Siemens Digital Industries Software has submitted this post.

The automotive industry is being reshaped by a convergence of trends. Technology advancements, such as automated driving systems, 5G connectivity and electrified powertrains are transforming the cars we know into more sustainable, accessible and convenient transportation machines.

Meanwhile, regulatory bodies are levying increasingly strong emissions and sustainability directives for both vehicles and the production facilities in which they are made. Finally, a rash of new entrants to the automotive market are driving up competition, putting pressure on product development and introduction cycles as both startups and legacy automakers compete to bring the vehicles of tomorrow to market today.

Risk in Traditional Manufacturing

Despite the widespread transformation occurring in the automotive industry, established automotive manufacturers are deeply invested in traditional manufacturing processes, equipment and facilities. However, that increases the risk of falling behind in manufacturing efficiency and flexibility.

Most established automotive factories today are brownfield facilities, meaning that they contain a blend of older and newer equipment. These newer machines are built to share data across a network, whereas older machines are typically data islands. As a result, traditional manufacturing approaches are not able to leverage data in an intelligent way.

While manufactures are updating their processes, it is generally not to the extent that these companies can withdraw the necessary insight from their plant to meet the challenges of today. Especially in today’s market, this can jeopardize an automaker’s business in multiple ways:

• Potential product line disruptions. Unanticipated breakdowns, maintenance delays and quality issues can result in late deliveries, cost overruns and failure to meet financial metrics.
• Lack of flexibility and resilience. Not being able to adapt quickly to changes and new requirements, or not being able to recover from unanticipated downtime or quickly change over from one product to another, can definitely have an impact on business operations.
• Noncompliance to regulations and sustainability targets. The modern manufacturing environment is under an escalating number of regulatory mandates and requirements. This is further complicated by fines, penalties and even criminal prosecution, ultimately leading to imprisonment and a drop in share prices.​
• Risk of cyber-attacks. Recent news highlights that ransomware can shut down entire manufacturing eco-systems, resulting in financial loss, intellectual technology leakage and a reduction in customer confidence.​

Overall, the traditional manufacturing process is rapidly falling behind, exposing companies to significant production challenges that may result in a loss of reputation, competitive position and even business.​ Today, companies must be able to quickly relocate or duplicate production lines globally as regional politics and economies dictate. They must also deal with increasing pressure to innovate and bring new features to market faster, increasing complexity and decreasing launch timelines, and comply with new sustainability requirements and regulations.

In traditional approaches to manufacturing design, mechanical, electrical, software and controls teams work on optimizing their operations independently and in parallel, often with limited communication among domains. When it is time to commission a manufacturing line, multiple engineering teams must synchronize various system designs in multiple formats (e.g., mechanical, electrical systems, software and network design files all use different formats).

While their respective designs work well in isolation, these teams do not have an opportunity to test the integration of their designs in advance, often resulting in emergent issues and problems in the combined system. Resolving these unforeseen problems delays the commissioning process and puts a successful launch at risk. And of course, many of these lines are brownfield projects, meaning that companies must design, commission and maintain a production line with a mixture of new and legacy equipment.

The Advantage of Smart Manufacturing

To succeed, automakers must become more agile and resilient to successfully manage increased complexity, helping them to embrace the future of manufacturing. To do so, automakers need to modernize their manufacturing process by connecting the data of all engineering disciplines to the knowledge of their shop floor. This includes incorporating new machines that have built-in sensors and processing capabilities, and integrating sensors and processing power into legacy equipment.

The integration of sensors and intelligence into production facilities will enable companies to build a closed-loop of engineering and manufacturing data, helping them to monitor and improve manufacturing performance and product quality. This closed-loop of data can also enable companies to predict operational issues in real-time.

While new smart manufacturing machines will be crucial to this closed-loop, the modernization of legacy equipment and the convergence of information and operational technologies (IT/OT) will also serve as a valuable source of insights that can inform the commissioning of new machines and accelerate the modernization of the entire plant.

IT/OT convergence is a crucial step that essentially involves the integration of communication standards that enable information to be passed between pieces of equipment (OT) and the business systems that monitor machines and facilities at a higher level (IT). The result is improved operational efficiencies, both at the factory and the machine level. At the same time, ensuring engineering disciplines are integrated throughout the design process will support faster and easier commissioning of production lines, leading to a flawless launch.

So how do they get there? Digitalizing the manufacturing planning, design and commissioning is the key. Digitalization enables automotive manufacturers to design, test and commission production machines, lines and even entire facilities quickly and with minimal risk. In addition, digitalization helps create a continuous flow of information throughout the manufacturing lifecycle, from engineering to the production floor.

This digital backbone enables companies to make the most of connected machines by gathering valuable data from the shop floor for use in multiple processes, such as advanced analytics or predictive maintenance programs. With these capabilities, automakers will be able to implement a complete smart manufacturing approach that connects engineering, information technologies, operational technologies and the shop floor. As a result, these companies will be more adaptive and resilient to market changes while also supporting future product innovation through manufacturing flexibility.

Connectedness in manufacturing design and engineering starts at process planning. By connecting the process planning through a digital thread, initial plans for work cells and production lines are captured and made accessible to each discipline and team, and to downstream processes. This is the first step in building a virtual version, or digital twin, of a smart manufacturing line.

Automakers can then begin to simulate production processes using the virtual representations of the cells developed in process planning. Through these simulations, engineers can evaluate different line configurations, test the integration of new and old machines and identify where upgrades are warranted based on results. Furthermore, modern digital twin simulations can run automation logic, enabling companies to virtually commission production lines that have already been verified and validated, ensuring that machines and cells work together as they should to produce high quality parts at launch. The data collected from these simulations then becomes the backbone of the verification and validation of plant operations.

Once the production line has been designed, commissioned and installed, the automaker can begin production while capturing valuable data from the shop floor. With real-time access to operational data, companies can easily manage and adjust the manufacturing environment as needed. For example, real-time manufacturing data can help identify bottlenecks or pinpoint the source of quality issues on the production line. As this information feeds into the data backbone, it creates a closed loop of manufacturing planning, engineering and actual operation. In addition, the production data being captured on the line feeds AI, machine learning and simulation platforms. These can help companies perform predictive maintenance on machinery, prevent quality failures and initiate corrective actions through the entire production lifecycle.

Smart Manufacturing Begins with Digitalization

Automotive manufacturers can certainly embark on a digital transformation on their own, but a strong technology partner can provide support, services and digital transformation expertise to help accelerate this transformation while reducing risk. Software and technology vendors, such as Siemens, can offer comprehensive software and digital solutions to help with the digitalization of various processes.

For example, they can supply edge devices to connect legacy production machines. These edge devices integrate with IT and OT systems to support constant monitoring, forecasting and self-organization of the manufacturing environment by using artificial intelligence and machine learning. In addition to a robust portfolio of digital capabilities, these companies can provide consulting and engineering services to reduce the risk of a digital transformation program, while also accelerating progress to key milestones. Finally, a strong partner can deploy technologies in various ways to support their customers: that means on-premises, in the cloud or through a hybrid cloud approach as needed by their program.

Ultimately, automakers must take the first steps towards smart manufacturing. The starts by updating legacy equipment with intelligent technologies to enable the company to receive actionable insights from their existing production systems. This information can then be leveraged in the design and commissioning of new production lines to rapidly modernize manufacturing plants. This actual production data can also help with the development of a virtual representation of production lines and establishing an interconnection of all engineering disciplines.

With a fully modernized plant, companies can then leverage production intelligence to create a closed-loop of manufacturing data, engineering and execution. This will enable automakers to monitor machine performance and predict operational issues to drive continuous improvement of products and processes, improving line speed and throughput as a result.

Overall, the benefits of digitalization will help automotive manufacturers to create truly smart manufacturing ecosystems, aiding in their ability to execute flawless launches, accelerate startup times, and meet production throughput targets.

Learn more at Siemens Digital Industries Software.