All LGO students master data analytics skills at LGO. The summer curriculum incorporates analytics coursework using Python and R, and many electives popular with LGOs have a data analysis focus. Some students choose an analytics internship, which have a large component of the research work devoted to large data sets and complex analysis.
Many internship projects that LGOs complete have a significant data analysis component. Students use data to make decision recommendations on supply chains, new products, optimized systems, and more. But some projects go above and beyond using data. These projects create analytics tools and predictive frameworks to make statistically sound business decisions.
Predictive analysis of Installation Quality vs Process Severity Events
Bidusha Poudyal (LGO ’20)
Engineering Department: Electrical Engineering and Computer Science
Problem: Bidusha’s internship company is building many new Fulfillment Centers (FCs) to support rapid demand growth. Their Installation and Operational Qualification (IOQ) process reduces early operational failures, but these have not been completely eliminated. Inadequate IOQ and tighter installation timelines lead to degraded installation quality, resulting in operational challenges and increased costs. As the FC network continues to grow, they need to improve installation quality to reduce early operational issues.
Approach: Bidusha focused on improving IOQ coverage, reprioritizing the testing schedule, introducing threshold metrics for installation quality, and exploring predictive/ preventative maintenance opportunities. She developed analytical frameworks and machine-learning models to uncover the most problematic FC equipment. Bidusha implemented topic modeling and word frequency analysis on free-text descriptive data from severity events and root cause analysis, to discover the most problematic equipment and identify high failure-prone areas within FCs so installation effort can be allocated appropriately, eliminating early operational challenges.
Impact: Bidusha’s project uncovered FC installation issues that led to costly problems during the start-up phase after FCs went live, reducing operational efficiency. She developed updated programs to simulate real operational conditions in an FC prior to go–live in order to perform integration, load, and stress tests on critical equipment. Her programmatic data-driven method allowed teams to leverage a preset metric for installation quality to compel vendors to improve the pre-handover processes.
Improving Asset Utilization and Manufacturing Production Capacity Using Analytics
Noa Ghersin (LGO ’20)
Problem: Boeing wanted to leverage digital solutions to improve Overall Equipment Effectiveness (OEE) for the thousands of machines it uses to manufacture airplane components. Noa’s project focused on increasing machine utilization and manufacturing production capacity in support of its vertical integration strategy. With increasing competition and an impetus to lower manufacturing costs, manufacturers like Boeing’s Interiors Responsibility Center (IRC) were looking to leverage IoT technology to transform not only what they manufacture, but how.
Approach: Noa’s analysis included personnel interviews, observational time studies, review of historical machine data, and value stream mapping. An analytical tool based on mixed integer programming techniques was built to dictate optimal job allocations in the IRC’s CNC router workstation, replacing a previously manual task. A discrete event simulation of operations in the CNC router workstation was built and tested for further analysis of efficiencies gained from the tool. Additional operational inefficiencies were uncovered by resource state analyses of simulated operations. Noa also offered a methodology for data-based strategic decision-making, leveraging linear programming methods to account for ordered strategic priorities.
Impact: Discrete event simulation projected that replacing human-dictated job allocations with an analytical tool would yield higher throughputs, enabling Boeing to better utilize its existing assets. A survey following a pilot of the tool revealed that analytics-based job allocations increased job satisfaction among 70% of employees in the CNC router workstation. What-if analyses simulating other potential interventions led to identification of alternative staffing and material storage schemes associated with 65% to 100% reduction in overtime hours.
Improving Project Timelines Using AI / ML To Detect Forecasting Errors
David Goldberg (LGO ’19)
Problem: Across industries and functions, data is a core ingredient driving our decisions and actions but can we trust our data? Accurate and robust forecasting is critical for optimizing recommendations and decisions around biotechnology companies’ drug pipelines. David’s project targeted the recurring errors in Amgen’s data that needed to be better detected and corrected to improve capacity management and decision making.
Approach: David developed a novel data analytics tool to detect and flag potential errors within Amgen’s capacity management forecasting. He generated the tool in an automated manner using statistical analysis, artificial intelligence and machine learning. The framework, approach and techniques can more broadly be applied to detect anomalies and errors in other sets of data from across industries and functions. User interaction allowed the tool to learn from past experiences improving over time. Flagging and correcting this data allowed for overcoming errors, which could have ultimately hampered Amgen’s ability to efficiently roll out drugs for patients.
Impact: At the time David completed his project, the tool identified 893 corrected errors with a 99.2% accuracy rate and an estimated business impact of $77.798M in optimized resources. Using the paradigm of intelligent augmentation (IA), his tool empowered employees by saving them time sifting through thousands of lines and hundreds of thousands of data points. The human user could now make decisions based on the tool provided output.