Many LGO partner companies manufacture things – from pharmaceuticals to heavy machinery. LGO students who chose to complete their MBA internships in manufacturing facilities find a number of interesting opportunities. LGO students integrate cutting edge technologies in the manufacturing sector, bringing robotics and automation into production lines. They also work on projects to minimize health risks for employees in facilities or to optimize a production line for maximum productivity.
Cost of Complexity: Mitigating Transition Complexity in Mixed-Model Assembly Lines
Robert Addy (LGO ’20)
Problem: Nissan’s mixed-model assembly strategy enables production level adjustment of different vehicles to match changing market demand, but necessitates a trained workforce familiar with the different parts and processes for each vehicle. This strategy has assembly line technicians switch between assembling different vehicles several times hourly. When such a switch takes place between different models, variations in defect rates occur as technicians familiarize themselves with the different sets of parts and processes.
Approach: Robert analyzed in-plant defect data for each vehicle produced over a 12-month period, combined with actual build sequence data. A time period was selected, representing steady production with no major model changes occurring. He also compared defect data and other characteristics of the two assembly lines to determine systematic behavior. Assembly line supervisors were interviewed to understand the process and challenges of switching between different products.
Impact: Robert concluded that transition complexity is an important factor in determining the performance of the assembly system (with respect to defect rates), and could supplement existing models of complexity measurement in assembly systems. He recommended several mitigation measures at the assembly plant level including improvements to the offline kitting system to reduce errors, such as reconfiguring the physical layout, and implementing a visual error detection system.
Improve lead time for the Medium Wheel Loader using core configuration and delayed product differentiation
Bi Zan Valery Lorou (LGO ’19)
Problem: The commercial value chain for Caterpillar’s Medium Wheel Loaders (MWL) contained several challenges to efficient product delivery, including long lead time for dealers, high demand variability, and complex configuration. Bi Zan developed a supply chain model to improve the lead time of the MWL from 20 weeks to 2–6 weeks, by reducing the complexity of configuration and stocking at the distribution center for late stage differentiation.
Approach: Focusing on the 950GC machines sold in North America, Bi Zan determined that a total of 140 current configurations could be reduced to 8 core configurations to increase inventory. While the core strategy incurs an increase in inventory costs, it reduces the hidden cost of stock-out. Bi Zan proposed a hybrid model, segmenting the market into 70% using the current varied configurations, and 30% using core concept configurations.
Impact: Implementing a limited core configuration concept to reduce lead time within a hybrid strategy allowed for a lower inventory holding cost than solely using the core strategy. Caterpillar will be able to serve the dealers options of machines with many configuration options and a shorter lead time, potentially increasing sales, as the machines sold through the core strategy can now be sold at premium cost.
Analysis of Differences and Optimization of Burn-in Siliconization Processes from Different Cartridge Filling Lines
Alex Unger (LGO ’18)
Problem: Application of silicone inside glass insulin cartridges helps reduce injection forces during drug delivery. The injection force is a critical parameter for patient comfort and satisfaction. Cartridges produced on different lines have different injection force results making designing new products with tighter specifications challenging. Alex’s project evaluated differences between production methods on each line and provided recommendations for standardizing the process to improve force consistency across production areas.
Approach: Alex built on the research of past LGO projects as part of a multi-year initiative. She evaluated current processes in three production areas and developed a hypothesis and lab experiments to test her premises. Each production line was mapped from loading of empty cartridges through the end of the heating tunnel. She tested the three different processes for treating the siliconization and created a model explaining the phenomena observed.
Impact: Results from these experiments showed that some production processes have a greater effect than others on silicone layer thickness and subsequent gliding forces. Alex proposed solutions to standardize performance focusing on air pressure reduction, evaluation of the start/stop conditions, and the collaboration between production and research teams. With this work Sanofi became one step closer to breaking the barrier with automated injections.