Corporate partners sponsor internships through the ENGG 390: MEM Project course. During their internship, students receive course credit while also helping their sponsor solve a real-world problem. Final deliverables may include defining a need and proposing a solution, describing appropriate analytical, experimental, and economic evaluations, and providing recommendations for further action. Projects normally have an industrial context or may be related to a specific design objective within a research program at Thayer.

Example projects:

• Develop a predictive analytics/forecast model and estimate future revenue streams based on learnings from existing data and considering multiple input parameters.
• Improve inefficiencies and optimize the production process associated with manufacturing a product that was encountering challenges with on-time delivery in a life sciences company.
• Create an end-to-end product roadmap for a new low-latency database storage solution for a communications technology company.
• Manage inventory variance by identifying the cause for the variance, performing an audit of that process, presenting findings to upper-management, and proposing solutions at a manufacturing company.
• Build a new platform for monitoring supplier quality for all global programs at an automotive company to ensure the timely qualification of the highest quality parts.
• Provide end-to-end data analysis on the current status of content consumption diversity and user engagement in different user segments for a popular social media app.
• Create automation, tracking, and monitoring tools to facilitate visibility in the customer acquisition pipeline, reduce bottlenecks within key operational procedures, and monitor company resources during daily operation at an energy company.

Teams of two to four students are matched with projects proposed by industry organizations that seek to address a business challenge or need.

ENGM 178: Technology Assessment

‍ENGM 178 projects involve analyzing two or more technology options against a set of technical and business requirements. There is no cost to the organization for the project. The ideal project is important but not urgent, and includes weekly one-hour meetings to give the students advice, keep them on track, and steer them away from dead ends. Projects are finalized with a case description by August 17.

Example projects:

• Investigate the use of artificial intelligence (AI) technology for identifying patients with undiagnosed medical conditions in healthcare settings.
• Assess the range of IoT technologies that can be incorporated into commercial high purity water systems to improve service delivery.
• Assess available technologies for cooling high-power electrical components used in computing and industrial applications.
• Study the state of the art in off-wire light rail vehicles and recommend a cost-effective, climate-friendly technology that is FRA compliant with respect to crashworthiness and minimizes adverse noise impacts to suburban environments.
• Assess the current state of spatial audio adoption across the signal chain for mobile (smartphone) based content consumption. Craft a hypothesis for how adoption will evolve over the next five years. Recommend what, if any, activities the company should undertake to maintain a premium, competitive position in the market.

ENGM 190: Platform Design, Management, and Strategy

This class is taught in the Fall by Professor Geoff Parker, a recognized expert in platform companies, such as Amazon and AirBNB.

"A platform is a business based on enabling value-creating interactions between external producers and consumers. The platform provides an open, participative infrastructure for these interactions and sets governance conditions for them." —Geoff Parker interview in The Marketing Journal.

Example projects:

• Work with the leadership team to develop the company’s metaverse strategy, including evaluating metaverse vendor partners, key use cases, revenue streams, and network effects.
• For an existing platform company, explore unmet user needs for what other services could be integrated into the current platform and fit with the company’s vision. Determine how the company should manage the extended platform ecosystem and how any organizational gaps should be filled.
• Topics from the book Platform Revolution, including architecture and design, managing network effects, launch, monetization, open vs closed, competition and strategy, governance, strategy and competition, and policy and regulation.
• For brick-and-mortar companies, determine if a start-up platform would have any implications for your customers and explore a complementary platform business offering.

ENGM 204: Data Analytics Project Lab (DAPL)

‍This class is held in the Spring and shows students how data analytics, machine learning, and artificial intelligence create value for organizations. The proliferation of IT-mediated economic activity generates an abundance of micro-level market data which has led to the competitive use of analytics, experimentation, and fact-based decision-making. Today, most organizations rely extensively on data analysis to predict outcomes and guide executive decision-making.

DAPL provides a great opportunity for students to work on a real question and gain access to talented engineers who understand business by matching student teams with a business challenge involving analytics and machine learning. Projects are sourced from both commercial and non-profit as well as government organizations. The ideal project has a reasonable chance of a valuable outcome for the company and a data set (at least 50,000) sufficient for training AI and ML models.

Example projects:

• Add real-time social media data to existing data assets; build a model to identify the likelihood that legislation would pass into law.
• Analyze price elasticities across complex consumer goods product lines to make pricing recommendations and focus offerings by eliminating unprofitable segments.
• Apply machine learning to expense forecasting for complex multi-billion-dollar division and significantly improve accuracy and demonstrated potential to dramatically reduce analyst labor.