Many of LGO’s partner companies are in the business of manufacturing things – from pharmaceuticals to heavy machinery. Students in the MIT LGO program have traditionally done multiple internships looking at how to optimize the manufacturing process. LGO internship have also been at the fore of cutting edge technology in the manufacturing sector, like robotics and automation into production lines.
Lean Transformation for Biomanufacturing Operations
Josh Jensen (LGO ’16)
Problem: Amgen Singapore Manufacturing (ASM) wanted to optimize their manufacturing footprint to respond to fluctuating customer demand and decrease operating costs. It needed to stabilize its operations to meet the supply plan established for the site. Amgen viewed implementing lean principles throughout ASM’s operations was key to achieving that stability.
Approach: Josh implemented lean principles through training, tool creation and process development. He developed and monitored a site metric plan to monitor efficacy and created a strategy for long-term sustainability of lean practices at ASM.
Impact: Josh designed a cohesive, site-wide lean implementation plan from the start of manufacturing operations. Initially, the strategy stabilized operations at ASM. To support long-term benefits, he made recommendations to ASM’s site leadership and Amgen’s Corporate Lean group on improved methods of conducting lean transformations for future Amgen facilities.
Peter Kimball (LGO ’15)
Company: General Motors
Location: Detroit, MI
Problem: Automotive sealing processes prevent corrosion, reduce cabin noise and keep water, fumes, and dust from entering a vehicle cabin. Customers have high expectations for vehicle quality and also expect vehicles to last longer than ever. This has made long-term corrosion resistance more important than ever. To achieve these goals, manufacturing vehicles requires more sealing content.
Automotive paint shops have lifetimes in the range of 25 to 35 years, and those built in the 1980s and ’90s often lack space to support increased sealing requirements. GM asked Peter to reduce the footprint of future sealing lines. Any solution should reduce footprint by at least 25%, reduce investment cost by 10 to 20%, and meet all throughput and quality targets. Primarily, GM asked Peter for a facility layout, initial research, and designs that the company could use for future paint shop facilities.
Approach: Peter’s research and analysis yielded multiple possible concepts, from which he developed three high-potential solutions. To do this, he used practices from other aspects of vehicle manufacturing and included new approaches to conveyance, tooling, and automated cell design.
The final phase comprised a review of three potential sealing line solutions. Peter selected a primary solution based on metrics covering impact and ease of implementation using three criteria:
- Increasing system flexibility
- Eliminating the need for non-value-add conveyance
- Increasing the density of automation through buffer analysis and work-cell stacking
Impact: The solution reduced the sealing line’s footprint by 33% and enabled capital cost reductions roughly 10% of the baseline facility. It provides ongoing operational savings of roughly $100,000 per year.
Measuring Engineering Quality in Airplane Development
Ammar Asfour (LGO ’15)
Location: Seattle, WA
Problem: Airplane Development, a recently formed organization within the Boeing Company, would like to focus on development processes, which will help improve reliability and reduce the cost of development programs. Boeing asked Ammar to apply quality metrics to multiple stages of the airplane development process.
Approach: Ammar identified integration and process discipline as most critical for the final quality of the engineering work. Ammar studied integration, defined as the path between teams and activities. To do this, he analyzed performance of a small engineering support team. He then developed a system dynamics model using data from components and suppliers to understand the effects of early-stage quality on later stages.
The case study focused on four steps in the engineering work: inputs, engineering activities, output, and customer review. Ammar used his framework to observe a five- to seven-member engineering team. The study tracked the process step at which the error was first caused.
Results indicated that 21% of unplanned engineering rework was caused by inadequate delivery to the requested engineering work. Furthermore, the 21% of unplanned engineering rework had the highest hours of any stages. Overall, 75% of engineering rework was due mainly to the process rather than the actual technical engineering work.
Impact: The system dynamic modeling achieved two main results. First, it showed the necessity to simplify the process. Second, Ammar highlighted the importance of accounting for iterations in engineering. After discussing group-modeling with the process owner, Ammar concluded that clear checkpoints and engineering work reviews were needed. Ammar defined the methodology to work with engineering teams to measure their quality performance. His research has the potential to show the quality thresholds in different stages of Airplane Development.