Manufacturing Next

Why Simulation Data Management is Vital for Product Lifecycle Management

 
September 28, 2020

Upfront digital prototyping through engineering, functional, and behavioral simulation has become a core business practice in product lifecycle management (PLM), particularly in complex product and systems development. The reason: simulation-led design is a key enabler of the shift left approach in model-based system engineering and development. Insights provided by CIM data, a PLM-focused consulting and research firm, indicate that simulation has been registering a compounded annual growth rate (CAGR) of around 10% (nearly $6.5 billion in 2018) over the past five years, and this growth has made simulation the ‘star’ of the PLM industry. 

Despite a significant increase in the use of simulation applications, simulation process and data management (SPDM) are often neglected in mainstream PLM and are used by less than 5% of simulation engineers. The low adoption of SPDM across industries is mainly due to the wide availability of a variety of simulation apps and high upfront costs that affect the return on investment, and the traditional mindset of engineers. 

The Need for Simulation and Data Management in PLM

Enterprises are increasingly adopting simulation as it plays a crucial role in complex product development, thereby increasing the need for SPDM, as illustrated below:

Proliferation and traceability of simulation data: Complex product environments encompassing multiple domains drive the volume of simulations, resulting in the proliferation of simulation data. This data is crucial for compliance, certification, validation, and data management, and is fundamental to ensuring traceability across the product lifecycle. 

Globalization and templatization of simulation: Global collaboration among stakeholders is critical for successful product development. SPDM enables templatization of the process and data model, empowering users to leverage global schema and make local changes in short cycles while ensuring quality.

Democratization of simulation: Advances in tools with high fidelity models, improved user interface, and a decline in the scripting or command-based approach has made simulation more user friendly. Business functions beyond engineering and research and development can adopt simulation. It can also be used to implement different operational and business models. 

Configuration of digital twin and traceability in digital thread: Configuring and managing the right set of virtual models and simulations, from development, manufacturing, and service, are vital for establishing digital thread - a crucial imperative in the digital twin era. Organizations that add SPDM into their digital thread can build future business models with better configuration control of simulation data.

Critical decision-making capability: Simulation has expanded to the left side of the ‘V’ in model-based system engineering, making it a key enabler in decision making in the product development. SPDM has thus become an essential tool for collaboration, achieving ‘first time right’.  

Implementing SPDM in PLM

To successfully deploy SPDM in their PLMs, an enterprise must consider the following:  

⦁ Openness to heterogeneous data: In most cases, an organization cannot use a single tool or set of processes to cater to its end-to-end simulation needs in a multi-domain and multi-physics environment. SPDM requires heterogeneous data from different software vendors.

⦁ Creating user-based metadata: Accurate metadata is crucial for organizing and retrieving data. Developing metadata based on simulation analysis ensures accuracy and ease of creation. 

⦁ Integrating with a PLM innovation platform: Integrating SPDM with high performance computing (HPC) clusters and a PLM platform will ensure successful data interoperability in digital thread. This enables holistic configuration control of simulation data in the systems of other enterprises.

⦁ Flexibility in predefined processes:  As simulation helps in arriving at an optimum solution with simulation users typically using a what-if approach, enterprises must ensure there are easy ways to exit the predefined processes and create new steps. 

⦁ Agile principles: Due to the complexity of various parameters and attributes, organizations are advised to follow the agile approach in implementing SPDM, that is, in iterative and incremental steps. Cultural change is also essential for successful agile adaptation. 

Integrating SPDM with other enterprise ecosystems will enable end-to-end traceability and configuration control. It will also act as a foundation for new-age technologies like digital thread and digital twin. The end result is an enhanced engineering agility for successful and rapid product development.

Prashant Chouhan heads the New Product Innovation and Lifecycle Process business practice for the Europe Manufacturing Innovation and Transformation Group (ITG) within TCS’ Manufacturing business unit. He has 17 years of experience and has worked on business consulting and advisory services in the area of PLM and master data management for global manufacturing customers. He holds a Master’s degree in Manufacturing Management from the Indian Institute of Management – Calcutta, India, and a Bachelor’s degree in Mechanical Engineering from MBM Engineering College, Jodhpur, India.

 

Bhanu Prakash Ila is a consultant for New Product Development in the Europe Manufacturing Industry Advisory Practice at Tata Consultancy Services. He has 14 years of experience in new product development with strong working knowledge in simulation and verification and validation. He holds a master’s degree in machine design from IIT Madras and a bachelor’s degree in mechanical engineering.