Predictive Modeling for Digitally-enabled, Multi-Criteria Decision Making in Innovative Product Design and Analysis with Total Lifecycle Sustainability

The tools currently available for product and system design have very limited capability for total lifecycle data-driven decision making. This limits capability for alternate design assessment, 6R-based performance analysis and, in general, evaluating design requirements referred to as 'ilities' - usability, serviceability, recyclability, maintainability, etc. Inability to perform such assessments limits companies' ability to plan for second generation and beyond, often leading to the design and marketing of products with rapid obsolescence and, therefore, poor overall sustainability performance. While LCA is the most commonly used tool, it provides limited opportunities for total sustainability analysis and does not offer product or system optimization capabilities for a rigorous consideration of trade-offs or uncertainties. While product data management tools have grown in sophistication, they are not fully integrated with other tools to be of value for total lifecycle decision making. Thus, the major challenges to improving total lifecycle product sustainability are two-fold. They are: 1) lack of collaboration across the supply chain and the absence of seamlessly interfaced product data systems that can provide the digital thread of the product from pre-manufacturing through post-use stages (shown as Digital Interfacing phase) and, 2) lack of integrated multi-criteria decision making tools that use information from disparate sources to collectively evaluate performance, conduct optimization and what-if analyses during product design (shown as Computational Modeling phase). Next generation product development processes will be digitally-enabled, linking and sharing information related to the total product lifecycle from all supply chain partners and end-users to enable multi-criteria decision making and predictive modeling to design and market multi-lifecycle products for improved sustainability performance. This integrated capability will enable product designers and manufacturers to evaluate design alternatives, investigate trade-off decisions to optimally select materials, processes and system requirements and conduct risk analyses with seamless information sharing between partners. Achieving the future state described above requires developing capabilities in two broad areas: digitally interfacing systems across the product lifecycle to provide access to requisite information and developing integrated modeling tools for multi-criteria, predictive modeling-based decision support. The work proposed in this project will address the latter task, identified as the 'Computational Modeling' phase in the figure below. This will be a university-industry collaborative project and will bring together academic researchers, software developers as well as OEMs who will become end-users of the capabilities developed. This collaboration will enable leveraging existing software platforms for product design, LCA capabilities as well as sustainability assessment tools and academic research to develop a digital thread-enabled modeling capability as shown. Industry partners/testbeds will be used to demonstrate the business benefits of multi-criteria decision making tools developed and how they can help reduce total lifecycle costs, enable better sustainability performance and increased multi-lifecycle performance. The predictive capabilities will build in design-make feedback loops for what-if analyses and help evaluate various manufacturing process and operations 'ilities' as well as later lifecycle 'ilities'.