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.
Deep Learning Models of Scanner/Vision Tunnel Performance in Sortation Subsystems
Felix Dumont (LGO ’21)
Problem: At Amazon’s large crossbelt sorter sites, the goal is 98% scanner performance. However, the average read rate success is 80-90%, contributing to a large amount of manual rework and recirculation impacting sorter utilization. The mechanisms to deep-dive scanner issues make it extremely difficult to categorize no-reads (unsuccessful scans) into operational or actual equipment issues. As a result, Amazon has very little visibility as to no-read causes across sites and cannot properly put together a plan to improve the situation.
Approach: To address the no-read issues that Felix witnessed across the fulfillment network at Amazon, he built a pipeline on Amazon Web Services (AWS) to process scanner images. Then, he developed a deep learning ResNet model through AWS SageMaker to assign fault reasons for each image. A user interface finally allowed operations managers to see which sites are lagging behind, launch deep-dives and test operational or equipment fixes.
Impact: Felix’s solution allowed engineers and operations managers to understand the cause of no-reads at their respective sites and empowered them to address the issues. Despite the subjective nature of multiple labels, the models Felix developed showed less than 2% aggregated error across his validation set and less than 5% error across previously unseen scanners or sites, effectively correctly reporting all of the site-specific trends and issues. A conservative entitlement is approximately $2.2MM for the pilot sites in annual savings with the potential to save significantly more if actively adopted across the network.
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.