LGO interns interested in making something new can do an internship in product design and development. Projects in this category allow students to combine their engineering and MBA knowledge in a unique way to solve problems related to design, product implementation, and new supply chain systems.
Leveraging Flexible Manufacturing to Streamline New Product Introduction Processes
Taylor Robinson (LGO ’20)
Problem: Johnson & Johnson Vision Care (JJV) is committed to launching new ACUVUE® Contact Lens products yearly, but current manufacturing lines operate at high utilization rates to meet growing demand. This limits the opportunity to test and validate new lenses for commercial-scale production. Using the JJV Flexible Manufacturing Platform (FMP), which applies innovative and flexible manufacturing technologies, Taylor built a case study to explore strategically leveraging FMP to enable quicker transitions from pilot-line to commercial-scale production.
Approach: Taylor’s FMP case study focused on the heat seal manufacturing step, which was chosen due to its critical importance in maintaining a sterile barrier. The case study also provided further evaluation of the overall manufacturing line, which previously lacked consistency and reliability due to obstacles arising with integrating a new technology. Testing to measure the impacts of contact time, temperature and pressure, the case study showed that time and temperature have the highest influence on heat seal integrity.
Impact: Results from the heat seal case study were used to create a process capability model that ultimately improved decision quality and reduced product failures by 80%. In the long term, the FMP evaluation showed that there is capacity to launch new contact lens products yearly while creating less waste during the product design and development stage. FMP can become a strategic and efficient asset to continuously launch new products for companies such as JJV that have high-volume, high-mix product lines.
Process Enablers for Successful Reverse Engineering inside Large Organizations
Casey Boyle (LGO ’20)
Problem: Reverse engineering can be a productive and strategically advantageous process for large companies to stay competitive and improve their business and expertise. Yet only a small percentage of reverse engineering projects actually reach completion. Casey’s project sought to understand and outline the characteristics of an effective reverse engineering workflow and organization.
Approach: Casey identified key enablers to successfully promote reverse engineering at scale through literature review, assessments of Boeing’s teardown process, and overall component design flow. He determined that reverse engineering should be a system of interconnected dependent events to build a standard workflow around the teardown process guided by functional analysis and clear communication among stakeholders.
Impact: The “pull-” more than “push-” focused process Casey built establishes clear communication between functions and can prevent rework, shorten flow time, and increase quality. He created a framework for implementing these recommendations and tools to mitigate project risks, increase the efficiency of reverse engineering teams, and reduce the occurrences of downstream rework. While no single enabler is in itself novel, when aggregated into a system of best practices, the organization becomes more capable.
Picking Winners: Predictive Modeling for Cell Line Selection
Yucen Xie (LGO ’19)
Problem: In the discovery-to-market development cycle for biopharmaceuticals, the manual selection of a cell line for the Master Cell Bank in product manufacturing for clinical and commercial use is a time-consuming, complex, and resource-intensive process. The selection process is largely conducted on a per-experiment basis to select a single cell line that can yield drug products of consistently high quality and titers.
Approach: Yucen’s project aggregated pre-clinical data to create analytic tools utilizing machine learning algorithms, to produce insights and predictions for cell line selection. He built and implemented an integrated modular virtual experimentation package through data extraction, feature engineering, model evaluation, and user interface design to reduce prediction errors by 38% to 90% for bioreactor end-point titer and product quality metrics.
Impact: Yucen’s models generated parallel in silico predictions with high accuracy, leading to better knowledge of key attributes affecting titer and product outcome, more productive cell lines of higher quality, and reduced development cycle times. This modular algorithmic framework and novel application of machine learning also promotes a scalable and transferable digital platform for analogous applications within the biopharmaceutical industry. Amgen has since submitted a patent application as a result of Yucen’s work.