MBSE Gains Ground in Heavy Equipment Industry

The use of data is growing in industries where growing is the primary industry. And data mining is unearthing new insights, greater productivity and better lifecycle management—even in an industry where mining is the daily task.

The heavy equipment (HE) industry moves more slowly than others in the industrial space, but the potential for improvement is great. Like the machines themselves, the heavy equipment industry’s move toward a better-connected equipment fleet is gaining forward momentum. Industry leaders have taken notice and are designing equipment that matches their speedier colleagues in other industries.

“In other industries, most companies are further ahead—however, companies in the HE industry are learning fast from their peers in leading industries like aerospace and automotive,” said Hendrik Lange, senior director, Heavy Equipment Industry, Siemens Digital Industries Software. “They have to, as their equipment turns into software-defined products that are highly connected systems of systems. Farms, mines and construction sites become highly data-driven and automated. Compared to five years ago, there is definitely an increased sense of urgency at HE OEMs to move forward in those areas.”

READ MORE: Aerodynamic Design: Balancing Material Composition with Manufacturing Methods

One of the most dynamic growth areas is model-based systems engineering (MBSE), which creates a digital model of systems that allows for design, simulation, operations, maintenance and lifecycle management of system components. Since the same model is available throughout the organization, the use of MBSE creates a single point of truth for the system and aligns all parts of the organization under a single model.

“There’s been a steady increase in demand for system modeling and model-based systems engineering over the last five years,” said Patrick Ollerton, senior principal for ALM Products at PTC. We see this being driven by increasingly complex products, products with high levels of variability, market shifts to electrification and software-defined products, industry regulations such as DoD 5000.97 and, more recently, updated standards such as SysML v2 with new supporting software tools.”

The HE customer also has seen the value of an MBSE model…and they want more. “Dealing with today’s customer expectations and product trends is one reason,” Lange said. “But they also see that AI delivers opportunities to disrupt their industry once again. The only way to get prepared for AI is to digitalize processes, making sure data is available, has context and is accessible.”

Barriers to Adoption

Change can be elusive, especially if the operations, maintenance and management teams aren’t aligned around the MBSE solution and its potential value.

“Our experience is that a successful implementation mandates an enterprise-wide, top-down change initiative,” Lange said. “HE OEMs are usually large, global corporations that grew both organically and through acquisitions to the industry giants they are today. Across an enterprise, you will have many distributed legacy processes, methods and systems. There are a lot of stakeholders that need to be directionally aligned.”

Ollerton notes that aligning standards within an organization can be a hurdle as well. “The main barriers to MBSE adoption identified by recent market research include a steep learning curve, with software tools, methods and standards such as SysML perceived as difficult to learn and use,” he said. “This perception can result in cultural resistance and a lack of management buy-in. Another challenge commonly raised is integrations, with tool interoperability and model exchange not meeting market expectations.

READ MORE: Reshape Agricultural Practices with Technology Advancements

“Engineering department acceptance is dependent on the value and output MBSE delivers to engineering processes: generated data and models with traceability enabling immediate usage rather than documents,” Lange added.

He also noted that the sheer size of the equipment involved—everything from massive earth movers to reapers and combines—also can be an issue. “HE is about making highly complex products that come in relatively low volumes in a business that is very cyclical,” Lange said. “Therefore, margins are typically thin, which puts a lot of pressure on product development. For many organizations, it’s not easy to invest time and budget in deploying new technologies while their key priority is also to keep their daily business running.”

To address these, companies such as PTC have been working to develop integrated solutions to create a lifecycle management system. “These inputs have been instrumental in driving PTC’s MBSE strategy and product roadmap for PTC Modeler,” said Ollerton. “We have been striving to solve these problems with tools for easier, simpler modeling, automation for faster diagram creation and easier and robust integrations with other lifecycle tools such as Codebeamer and Windchill, as well as third-party solutions.”

Defining the Benefits

Manufacturing has embraced a systemic approach to operational data, and the size of the finished product shouldn’t be the issue. Ollerton said the benefits for manufacturers and OEMs have aligned around these ideas.

“MBSE can benefit all engineering teams, including operators, by providing an unambiguous view of a product’s architecture including interfaces and variability, as well as specifying in detail the expected functional behavior of a complex system,” Ollerton said. “This information is critical in allocating functions to design solutions to guide downstream engineering performed by PLM, CAD and CAM users.”

Lange identified 11 key benefits of MBSE for the R&D, engineering and operation teams:

• Accuracy and confidence in results, acceleration, complexity management, shift left, early and virtual validation of the complete system, re-use strategies (modularity), and the enabling of complex software-defined products.
• Exchange of models with the value chain for unambiguous definition of requirements.
• Reduced late issues during system integration.
• Service and operations.
• MBSE definition is critical for long term (20+ years) operation, service and maintenance of equipment to understand how electrical and electronic components can be replaced or upgraded.
• Owner operator.
• Right sized, sustainable, durable and safe equipment with lower total cost of ownership.
• Having all those stages integrated into a single system.
• Feedback loops to continuously improve current and future-generation products.
• Access to serial-number-specific data throughout the lifecycle to enable advanced capabilities (for example, predictive maintenance).
• Moving from product sales to value-based business models.

Lange noted the benefits stretch throughout the organization and throughout the lifecycle management of the manufacturing equipment and the products. “MBSE helps to accelerate product development, especially for those products where there is typically a tight (and complex) interaction between subsystems,” he said. “Therefore, it is particularly suitable to support the introduction of new technologies that are more sustainable, such as battery electric systems, hydrogen and hybrid systems.”

“There is accuracy throughout development, manufacturing and inventory management,” Lange added. “Each system is completely modeled and therefore all defined buildable product configurations are consistent and traceable from engineering, through manufacturing and service.”

The Advent of AI

The discussion of artificial intelligence is both ubiquitous and muddled. The concept is on everyone’s agenda but implementation is still in the working stages.

Ollerton said the real benefits of AI in design, manufacturing and operations are still coming together, but he sees several early adoptions that can deliver on its promise. “AI has great potential to advance systems engineering and we see numerous potential use cases, including modeling assistance to check correctness/completeness; standards compliance; requirements coverage; and helping to create, modify or restructure system models,” he said.

“AI can help to improve design quality by automating the analysis, simulation and optimization of models as well as enabling automatic creation of traceability links, interface identification, allocation of functions to candidate solutions and detecting opportunities for design reuse,” Ollerton added.

READ MORE: What’s up with model-based engineering?

Lange sees that growth in AI continuing to grow. “Today, AI already accelerates individual design and engineering activities. Siemens is heavily investing in this, both in R&D and through acquisitions. As a result, we have AI already available in design/engineering products.” As an example, Lange said that the Siemens NX system has an adaptive user interface/command prediction. This will dramatically speed up the work of individual designers and shorten the learning curve for new employees.

“AI will reach its true transformative power in design and engineering when it can be applied on a higher level, driving top-level requirements like brand values, quality, sustainability, etc.,” Lange added, “typically areas that are impacted by many development activities within a company and even beyond.”

“OEMs who already manage all those activities in a common environment with shared data and end-to-end traceability, like we envision with MBSE, will be ideally positioned to take the lead.”