Basic structure of lessons learned approach to improve manufacturing processes: A case study


Bracke S., Ulutas B.

9th IFAC/IFIP/IFORS/IISE/INFORMS Conference on Manufacturing Modelling, Management and Control (IFAC MIM), Berlin, Almanya, 28 - 30 Ağustos 2019, cilt.52, ss.1010-1015 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 52
  • Doi Numarası: 10.1016/j.ifacol.2019.11.327
  • Basıldığı Şehir: Berlin
  • Basıldığı Ülke: Almanya
  • Sayfa Sayıları: ss.1010-1015
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Manufacturers of complex mass consumer products and industrial capital goods - like automobiles, washing machines, machine tools - aim long-term product reliability during the usage phase. A large percentage of failing units in the product fleet caused by manufacturing faults can lead to significant risks for the manufacturer. Depending on failure mode and number of failing units, these can be related to reliability, financial, or image risks. Preconditions are controlled and capable manufacturing technologies and processes to avoid failures regarding the product. The learning process that is based on manufacturing weak points and failures (process and technology) of the past is one of the possibilities to ensure a continuous improvement of manufacturing processes from previous to subsequently following manufacturing generation. This means saving of structured knowledge regarding a long manufacturing period, depending on the products generation and manufacturing phases (e.g., automobiles: 5 - 6 years, smart phones: 2 years). This paper outlines a Lesson Learned approach on how to analyse, save and prioritise Lesson Learned issues - based on structured weak point data and information - out of an actual manufacturing process period to improve manufacturing control, capability, and quality of the subsequently manufacturing process generation. Furthermore, the paper shows a case study regarding the industrial application of the Lesson Learned approach for manufacturing planning based on previous manufacturing experience. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.