Michael Donauer, MIT Portugal Ph.D. Student, has been recently distinguished with the Best Paper Award at the International Conference on Flexible Automation and Intelligent Manufacturing (FAIM). Michael developed a tool that helps identifying the causes of nonconformities in environments similar to mass production. FAIM is the leading international forum to disseminate, to all branches of automation and manufacturing, information on the most recent and relevant research, theories and practices. This year's edition focused on The Challenge of Sustaining Global Competitive Manufacturing Systems.
In the mass production industry it is not uncommon to have large amounts of data at hand. But how to make the most of its usage? And what can you learn from it to increase production and profit? What Michael Donauer has developed is a new tool that, based on the large amounts of data, helps identifying root causes of nonconformities in mass production environments. There are large challenges in this process concerning the requirements for data storage and processing, in addition to time consuming sessions for the interpretation of saved data. Therefore these tools need to be tailored to specific industry or even individual company requirements.
The obtained results are not only of interest for academics, but also for practitioners such as quality engineers. In order to devise the quality tool, the novel approach integrates different disciplines namely IT, quality control and economics. To access this new methodology, Michael Donauer presented a real life case study, a company, which is part of the automotive supply chain, where he has been tailoring and implementing this new solution.
Firstly, he understood and analyzed the problem through full and part time placements at the company's plant. During this period he gathered data from the IT system and through expert interviews, conducted across all hierarchical levels. In order to retrieve data in a reliable manner he created a data input file, which also performed treatment to align the input as an interface for the quality tool. The algorithms of the tool transform the treated input data into a pattern visualization that identifies individual machines of previous production steps that appear to be the nonconformity originator.
Besides providing quality engineers with a tool to facilitate their ambition to constantly improve manufacturing processes, Michael Donauer's work is an example on how researchers in the field of engineering systems can develop interdisciplinary approaches that contribute both to academia and industry.
Michael Donauer is currently on the 4th year of his Ph.D. in Leaders of Technology Industries (LTI) at Faculdade de Engenharia da Universidade do Porto, FEUP, under the MIT Portugal Program. During the last year, he was at MIT to conduct part of his research project.