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How to implement shift left testing in engineering?
These are the CAE software, tools and techniques needed to facilitate an agile shift left testing approach
The Shift Left approach in engineering and product development relies on a suite of crucial software and methodologies to facilitate agile, early testing, validation, and team collaboration. Let's take a look at what is needed for the successful implementation of new, digital testing strategies and efforts:
- Simulation and modeling software for computer-aided engineering (CAE) provide cross-functional teams with insights into the behavior of systems, sub-systems, and components under various conditions.
- Computer-aided design software (CAD) aids in the creation of new features and detailed product ideas.
- Virtual prototyping technologies allow for the quick creation of digital models for early-stage testing.
- Immersive, close collaboration and communication tools foster teamwork, fast feedback loops, and alignment among multidisciplinary teams,
- while data analytics and visualization software help engineers make sense of large datasets.
- Project management software ensures projects stay on track and within budgeted cost.
Let's take a closer look at some key facilitators to move performance testing activities from later stages to the left side of the V-model.
Why is an end-to-end digital thread key for shifting performance testing left?
Central to the Shift Left and Virtual Prototype Testing approach are end-to-end digitalized processes. This entails leveraging digital twins and simulation-led methodologies to model and validate product designs in a virtual environment. By creating digital replicas of physical products, manufacturers can conduct comprehensive testing and analysis without the need for costly physical prototypes.
From our work with industries, we know that in order to get the shift-left right, you need to seamlessly integrate predictive, real-time, immersive virtual testing software into your digital thread. Only then you will gain the unique capability to foresee not just the expected performance but also accurately predict how your product will perform in real-world conditions, accounting for manufacturing intricacies.
Aberdeen Research & Strategy
The key for facilitating the Shift Left approach effectively is seamless integration and collaboration among these software technologies within an end-to-end digital thread. A digital thread provides a connected path that enables the seamless flow of data and information across the different teams working along in the V-model of the product development lifecycle, from initial concept to final production. It ensures that all stakeholders have access to the most up-to-date information and insights, allowing for real-time decision-making and collaboration. By chaining virtual prototyping and simulation tools (CAE software), design tools (CAD software), collaboration platforms, and other key technologies within one digital thread, organizations can streamline development processes, reduce time-to-market, and improve product quality.
What is the role of Artificial Intelligence (AI) in enhancing accuracy and effectiveness of a Shift Left testing methodology?
New technologies like artificial intelligence (AI) play a pivotal role in facilitating the Shift Left and Virtual Prototype Testing approach. AI-powered tools and algorithms enable engineers to analyze vast amounts of data and simulate complex scenarios with unprecedented accuracy and efficiency. Machine learning algorithms can identify patterns and anomalies in data, helping to uncover potential issues early in the development process. Moreover, AI-driven predictive analytics enable manufacturers to anticipate product performance and reliability with greater confidence, allowing for proactive decision-making and risk mitigation. By harnessing the power of AI, organizations can enhance the effectiveness of Shift Left testing, accelerate innovation, and drive continuous improvement across the product lifecycle.
Why is the single-core model amongst the first things needed for a successful implementation of the shift left approach?
Amongst the first things manufacturers need to facilitate an incremental shift to the left side is certainly the single-core model: The term "single core" signifies that all simulations stem from a central, shared model, rather than disparate models for each engineering discipline. This model encapsulates various engineering domains such as structural analysis, fluid dynamics, thermal behavior, and other relevant factors. By consolidating all simulation activities into one comprehensive model, engineers can assess the performance and behavior of a product across different domains simultaneously. This allows for a holistic understanding of how design decisions impact various aspects of the product, facilitating faster identification of potential issues and enabling corrective actions to be taken earlier in the development process. Its adoption is crucial for implementing shift left testing at all, as it facilitates seamless collaboration and synchronization across multiple engineering domains, ensuring consistency and accuracy in simulations.
Why is concurrent engineering a key principle and mindset for any move left strategy?
Concurrent engineering is not a technology, rather a foundational principle that underpins the Shift Left approach. It emphasizes collaboration and integration across disciplines, enabling teams to work concurrently rather than sequentially. By breaking down silos and fostering cross-functional communication, concurrent engineering facilitates early problem identification and resolution.
In the context of Shift Left, the main benefit of concurrent engineering is the fact that continuous performance testing and validation activities are seamlessly integrated into the development process right from the beginning of the V-model. This allows for rapid iteration and feedback loops, enabling teams to address potential problems immediately and make informed decisions on new product features based on data-driven insights.
What is the current landscape of Shift Left implementation?
While the benefits of Shift Left testing are clear, the adoption of this approach varies among manufacturers. Many organizations have fully embraced Shift Left principles, integrating testing and validation activities into every stage of the development process. These companies leverage advanced simulation tools and end-to-end digitalized workflows to drive efficiency and quality.
Those who still operate under traditional reactive, linear approach and relegate testing to right part of the development process face costly setbacks and delays - particularly in industries where compliance and safety are paramount. Nonetheless, as product complexity continues to grow, more manufacturers are exploring ways to implement concurrent ways of working and continuous testing across multiple groups of developers and engineers.
Despite the initial investment and upfront effort, particularly in upskilling and training the workforce, simulation-led decision-making stands as a crucial leap forward in industry innovation."
Aberdeen Research & Strategy
Applications and best practice example for shift left testing in the automotive industry
Shift left testing has profound applications in the automotive industry, particularly in addressing the evolving demands of consumers and the complex challenges faced by manufacturers. From ensuring vehicle safety to meeting stringent emissions regulations, automakers are under increasing pressure to deliver high-quality, sustainable mobility solutions in less time. By embracing shift left testing methodologies and chaining the different simulation software, automotive R&D organizations can streamline their product development cycles, reduce reliance on physical prototypes, and make informed decisions on new features early in the design process. This approach allows them to validate design changes across multiple engineering domains, such as material stiffness, crash safety, NVH, and durability, ensuring that the vehicle meets performance and safety standards before physical testing.
Projects in the field of Design-Space Exploration are a specific set applications in the automotive industry: The development of a product involves considering numerous variables that form the product’s design space. Exploring and assessing all possible combinations of variables is impractical and costly with conventional methods, such as Design of Experiments (DoE) and traditional CAE optimization. These methods have limitations in scope, time consumption, and cost, requiring specialized skills.
Here at ESI, we have been developing options to equip our vehicle models with cutting-edge model order reduction functionality, powered by smart machine learning technology to create a parametric model of the design space with fewer and more affordable simulation runs. We work towards empowering both skilled and non-skilled engineers to swiftly grasp the variables’ effects and pinpoint significant combinations that yield desired results, allowing them to prioritize load cases and design parameters for in-depth examination.
Fast parametric design space exploration could then be used for:
- virtual crash test results, optimizing vehicle safety, weight, and cost in a limited timeframe,
- low-speed RCAR insurance assessment, optimizing crash repairability and safety ratings.
To replace physical prototypes with virtual ones, it's crucial to integrate the physics seamlessly, connecting the entire digital thread from design to operation. It's not enough to wait 10 hours to get a crash simulation result. Customers want faster results. Our concept of Hybrid Twin is crucial, as it accelerates the simulation process. Hybrid AI and simulation together are a game changer.
Emmanuel LeroyChief Product & Technology Officer at ESI Group
The user story about Toyota's TILT Lab stands as a best practice example of embracing the shift left approach for a multitude of test cases in product development. By proactively integrating the advanced virtual reality software IC.IDO, Toyota has revolutionized its design and validation processes in the metaverse for several applications e.g. immersive reviews of product integration and worker safety in assembly processes and its overall production environment. This shift has enhanced collaboration and decision-making across its distributed teams, leading to greater efficiency and productivity. Furthermore, the ability to conduct physical assessments virtually has reduced the company's carbon footprint and promoted a better work-life balance for its employees.
In a nutshell: How to perform shift left testing?
Thanks to digitization and the rise of advanced technologies like AI and machine learning, the concept of "Shift Left Testing" is getting a significant push to truly rewire engineering practices. Originally rooted in the IT domain, this approach has been gaining traction among engineers across various disciplines for its agile, collaborative way of working and its huge potential for solving complex simulation problems at scale.
Register to watch ESI TALKS 2024 to delve further into what lies behind the Shift Left paradigm and how virtual prototype testing is reshaping product development as we know it.
Author
Denise Börner
Sr. Marketing Content Specialist
Denise is a seasoned media and communication professional with over 15 years of experience in the IT industry, spanning logistics and asset management software to system simulation and virtual prototyping (CAE & PLM). With a deep passion for technological innovation and sustainability, Denise is a steadfast ambassador and fervent advocate for Virtual Prototyping, utilizing her extensive expertise to steer companies through the conundrum of terminology in the era of digital and AI. As the Sr. Marketing Content Specialist at ESI, Denise creates insightful publications that help businesses understand the technologies, methodologies, and value of shifting from physical to virtual prototype testing – a transition that is facilitated through the utilization of CAE software, augmented with immersive tools and hybrid AI technologies.