IntroductionGE Aviation, an operating subsidiary of General Electric Company operates in Aerospace and defense industry. The company manufactures jet, turboprop, and turboshaft engines and related replacement parts for commercial and military aircrafts. The company also manufactures global aerospace systems and equipment which include platform computing systems, power generation and distribution products, mechanical actuation products and landing gear, and various engine components.
The company also provides repair and maintenance services for all categories of aircrafts. The company’s engines are used in wide range of commercial and military aircrafts which includes executive and regional aircrafts, fighters, bombers and surveillance aircraft, as well as marine applications. The company operates in more than 50 locations worldwide. GE Aviation is headquartered in Evendale, Cincinnati, Ohio, US.
SituationGE – Aviation built a new facility in Batesville, Mississippi, in 2008. In addition to designing a lean and efficient line, GE needed to determine the personnel required to meet their forecasted demand and assess what impact changes in that demand could have on their production and personnel requirements. The company contacted the Mississippi Manufacturing Extension Partnership (MEP.ms), a NIST MEP network affiliate, for help.
SolutionA baseline FlexSim model of the facility was developed to help the facility’s team evaluate alternate system designs. The Mississippi State team worked closely with the facility to share their simulation modeling expertise and provide training. Each model run simulated the 374,400 minutes covered in a one-year period of plant operation, which totals 52 weeks of five 24-hour work days.
Discrete event simulation software excels at accounting for the impact of variability on a system, but there was no history of variability at the Batesville plant due to the prevalence of new processes in the system. The model was reconfigured several times for different variability levels so the plant team could see the effect on production performance. For example, 13 weekly product demand patterns were created to represent 13 possible product mixes; the simulation model estimated the time it would take to complete production for each pattern, and considered the different levels of variability on each scenario.
A MEP.ms team from Mississippi State University, composed of engineers from the Center for Advanced Vehicular Systems Extension and the Department of Industrial and Systems Engineering, provided operations analysis support through the development and application of discrete-event simulation models before the plant construction was completed.
These simulation models were used to analyze and ‘optimize’ plant layout and performance by identifying and eliminating bottlenecks, improving the overall efficiency of the production line, evaluating the line’s sensitivity to product mix changes and evaluating product ramp-up scenarios. In addition, five GE engineers were trained on the basics of simulation modeling and analysis using Flexsim simulation software.
ResultsThe project achieved more than $400,000 in cost savings, and the plant appears to be flourishing today. One hundred and ten people were employed at the facility when the simulation project was conducted, but the plant employs over 450 personnel as of April 2013. Conclusion
The model was an integral part of the facility design process. It was used as a decision support system to help designers quickly assess the performance of various alternative production configurations and resource allocations. One of the analyses conducted during the project was an examination of the sensitivity of manual processing times to various levels of variability.
The analysis clearly showed the significant negative effect on system throughput and cycle time when even a relatively small amount of variability is introduced into the proposed lean manufacturing system. The model proved to be an effective design and planning tool.
Flexsim is a powerful tool that impacted the decisions in regards to capacity, inventory, manpower and equipment. The Flexsim simulation gave a chance to see how the line would perform and react to various situations. This allowed to make better decisions and make improvements that would not have been possible until years later without the model created with the MSU team.
References / Bibliographyhttp://www.flexsim.com/blog/a-look-back-at-an-msu-project-with-ge-aviation/ http://www.mep.ms/ge.htmlge_aviation_swot_analysis_and_company_profile.pdf