Systems Optimization

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.

Ordering Policy and Supply Chain Responsiveness

Ana María Ortiz García (LGO ’16)

Company: Verizon
Location: Basking Ridge, NJ

systems optimization internship
Ana’s system analysis of how different policies would effect Verizon’s business.

Problem: Verizon purchases Customer Premise Equipment (CPE) from various suppliers. They also create long-term equipment purchase forecasts. Supplier contracts establish ordering policies that restrict how much managers can adjust forecasted order quantities (OQ), despite known or anticipated changes in demand. Historically, this policy creates greater variability on inventory levels and strains production plans. Verizon asked Ana to improve the supply chain’s responsiveness by changing only the ordering policy.

Approach: Ana used historical demand data to investigate how alternate policies would impact key metrics. She used this approach on one pilot product, and found that the Verizon can use the sample as a framework to evaluate contract terms.

Impact: For each simulation, Ana ranked all policies across metrics to create a visual representation of policy performance. Ana recommended an alternative approach that improves the prioritized metrics. For the piloted product, changing the ordering policy reduced inventory by $3.4M, saved $500,000 per year and reduced OQ variability by half.  These results can be extended to other products for additional benefit to both suppliers and Verizon.

Developing a Competent Reuse Strategy for Space Launch Vehicles

Marissa Good (LGO ’17)

Company: Boeing
Location: Huntsville, AL

Problem: Rocket hardware is traditionally very expensive to design, develop, certify, and produce because it needs to perform extreme environments with high reliability. Most rocket components are built to execute a specific mission on a single platform and are operated accordingly. Therefore, very few components are used across product platforms, a common technique used in other industries (e.g. automotive and consumer electronics) to decrease the overall cost, schedule and risk of new product introduction. Boeing asked Marissa: Is there something inherently unique about rockets that prevents reuse? If not, are there design strategies to help reuse the rocket components?

Marissa Boeing Space Systems Engineering
Marissa’s research focused on how different components in a space system could be engineered with an eye toward reuse.

Approach: To develop a reuse strategy that improves cost and schedule, Marissa performed a case study. She completed conducted interviews, reviewed process documents, and developed an understanding of how Boeing implements reuse.

Impact: Through her research, Marissa developed the following suggestions:

  • Focus on modularity within launch vehicles rather than reuse across vehicles.
  • Treat reuse as the baseline, rather than an opportunity. This requires aligning incentives and architecting hand-offs/boundaries/ownership to promote reuse.
  • Plan for forward reuse. Consider future requirements when designing the current vehicle. Although this will never be the highest performance/cheapest option, reuse will not happen by coincidence. It must be designed into the system.

Productivity Optimization of an Automated Material Handling System

Willow Primack (LGO ’15)

Company: Amazon
Location: Los Angeles, CA

Problem: With the expansion of its fulfillment center network, Amazon developed material-handling robotics systems that use conveyance, sensors, and software to work with associates. These systems improve productivity and labor efficiency and standardize operations between fulfillment centers. Amazon asked Willow to examine an automated material-handling system at a fulfillment center. Her project is a case study on queuing theory and simulation techniques for modern warehouse systems.

primack-chartApproach: Amazon’s warehouse system is organized around three central stations: item induction, processing, and batch processing. The system exists in parallel lines linked by a central sorter that routes items to different stations. Designers programmed the central sorter to balance work uniformly between production lines, which have different capacities due to varying skills of the associates.

Willow’s model matched product arrival rates to each line’s individual capacity using utilization ratios. She analyzed data on item arrival and processing at different stations to characterize the how much time Amazon needed for each activity. She then conducted an experiment using a Monte Carlo simulation model.

Impact: Willow’s initial results suggested that productivity could improve by 5-10% if Amazon only changed their software logic. This would be a savings of several million dollars per year across Amazon’s fulfillment center network. The adaptive system response will allow exceptionally skilled associates to work at full pace. The project’s approach to system characterization and modeling will also contribute to the company’s continuous improvement process.

Saving Lives by Increasing Patient Throughput

Wendi Rieb (LGO ’15)

Company: Massachusetts General Hospital, Cancer Infusion Unit
Location: Boston, MA

Problem: Massachusetts General Hospital’s Cancer Infusion Unit has about 40,000 patient visits a year (145 a day). They come for chemotherapy, blood transfusions, infused antibiotics, and hydration treatments. Patients wait for a long time, and the hospital faces a shortage of nursing staff and resources. Yet throughout any given day, the hospital uses only about 55% of chairs because most visits happen from 10 a.m. to 2 p.m. At other times during the day, the unit is underutilized. Meanwhile, MGH is growing at 2.5% annually. It is critical for the hospital to use existing facilities more efficiently. Wendi increased the Cancer Center’s throughput by smoothing the Infusion Unit’s intraday utilization.

rieb imageApproach: Wendi used retrospective systems optimization modeling and prospective simulation modeling techniques. Using her algorithm, she proposed new scheduling guidelines to generate more predictable and balanced patient flow, which optimized the scheduling process for infusion-only visits and practice-to-chemotherapy appointments.

Impact: Wendi’s algorithm demonstrated the potential to recover 20 chairs—33% of capacity—at mid-day while smoothing throughput throughout the day. In total, the scheduling technique allows for 10,000 additional chemotherapy treatments annually. The algorithm also suggested that MGH could achieve the optimal state with minimal adjustments to staffing and no adjustment to the Oncology Practice. The algorithm also respects the existing primary nursing model and treatment-specific limitations.

Wendi’s system allows the unit to absorb growth and relieve existing resource shortages.  The Cancer Center could consolidate infusion operations currently housed in other facilities into the unit to generate more operational efficiency.