Businesses are constantly optimizing, particularly in the design and operation of complex systems. The engineering and business combination in the LGO curriculum uniquely prepares students to work on systems optimization problems in a variety of industries. For example, LGO students are uniquely qualified to work on complex healthcare systems engineering problems that can effect how many patients a hospital can treat. These problems all use large-scale analytics models to produce results.
Developing a Capacity Analysis Tool in a Vertically Integrated, High Mix, Low Volume Engineering Landscape
Hans Nowak II (LGO ’20)
Problem: Raytheon’s Circuit Card Assembly (CCA) factory is the largest Department of Defense CCA manufacturer in the world. Recently, two factors have increased the demand of the CCA factory: large-scale consolidation from three manufacturing factories into a single CCA factory; a rapidly growing market from new Raytheon technology, resulting in high volume of new product introductions. With Raytheon’s recent merger with UTC, the ability to continuously analyze factory capacity for new contracts and variable demand is needed.
Approach: For this project, Hans worked on three primary objectives. He first created a sustainable capacity analysis tool that automatically and continuously updated projected capacity utilization. Second, he used machine learning and other techniques to predict cycle times of future demand. Lastly, he provided data-driven recommendations from capacity analysis to maximize optimal strategic capacity planning decisions.
Impact: Hans’ strategy combined the capability of automated data mining algorithms, the predictive power of machine learning, and the optimization ability of mathematical programming. The models built enabled Raytheon to make better decisions when planning factory capacity in the long term, and get a clearer picture of operational health in the short term. It’s important to note they must be properly implemented and scaled to have sustainable effect on the company.
A Systems-Based Analysis Method for Safety Design in Rocket Testing Controllers
Jeremy Paquin (LGO ’19)
Problem: NASA’s Space Launch System (SLS), is an advanced launch vehicle for a new era of exploration beyond Earth’s orbit into deep space. Boeing is the prime contractor to build the Core Stage, and backbone of the SLS. The primary objective of Jeremy’s project was to improve Boeing’s ability to perform test firing of the current and future iterations of the SLS in a way that minimizes schedule risk and cost.
Approach: Jeremy’s project modeled the current stage controller processes. This included interviewing key stakeholders and subject matter experts, and top-down systems model development of key interactions. He also defined hazards/accidents, and key interactions, and held stakeholder In-Progress Reviews (IPRs) throughout. Jeremy then completed a Systems-Theoretic Process Analysis (STPA) on the Stage Controller Design, comparing unsafe control actions with previous analysis, comparing key performance metrics on previous analysis with STPA results and metrics.
Impact: The outcome of the project provided a framework for Stage Controller safety analysis for safer testing and pre-launch operations of the SLS core phase in a way that minimized cost and schedule risk. This framework is applicable to future space launch architecture development. Additionally, non-technical recommendations were suggested in the areas of testing operations and organization. Future work will examine extensions of STPA to software, and automation of certain steps.
Modulariziation of Highly Customized High Voltage Test Equipment for Project Business
Paige Blok (LGO ’18)
Problem: MR is the world market leader in transformer switching and HIGHVOLT GmbH, a subsidiary of MR, has over 110 years specializing in design, development and manufacture of high-voltage test equipment. HIGHVOLT’s highly specialized test products are built to exceedingly individual customer specifications with a repetition rate near zero leading to high cost and long delivery time. Paige’s project focused on reducing cost and delivery time through complexity management.
Approach: Paige employed two primary techniques to understand the complex processes: core process flow mapping and pain-point data collection. Process flow mapping is critical to understanding where complexity enters the process, resides, and is correlated to other process steps. Pain-point collection and analysis aim to reveal inefficiencies to target causal factors. The synthesis of process flow mapping and pain-point data highlights challenge areas that, if alleviated, would reduce complexity.
Impact: Common threads discovered in the collection of key stakeholder data suggest specific high-complexity areas to investigate. 155 pain-points were collected, analyzed, classified and mapped to the process flow, showing concentration areas, large-impact points, and cause-victim relationships. Thirteen critical high-impact areas were identified with five significantly correlated projects proposed to executive level leadership. Senior level leadership agreed with project recommendations and a six-month implementation plan was enacted.