Why slow down? Factors affecting speed loss in process manufacturing
| Autor: | Alexandria Lee Trattner, Lars Hvam, Anders Haug |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Production line
Overall equipment effectiveness 0209 industrial biotechnology Computer science Mechanical Engineering Scale (chemistry) Speed loss Total productive maintenance 02 engineering and technology Work in process Industrial and Manufacturing Engineering Computer Science Applications Product (business) Process industry 020901 industrial engineering & automation Risk analysis (engineering) Control and Systems Engineering Production (economics) Industrial and production engineering Productivity Software |
| Zdroj: | Trattnera, A, Hvam, L & Haug, A 2020, ' Why slow down? Factors affecting speed loss in process manufacturing ', International Journal of Advanced Manufacturing Technology, vol. 106, no. 5-6, pp. 2021-2034 . https://doi.org/10.1007/s00170-019-04559-4 Trattner, A L, Hvam, L & Haug, A 2020, ' Why slow down? Factors affecting speed loss in process manufacturing ', International Journal of Advanced Manufacturing Technology, vol. 106, pp. 2021-2034 . https://doi.org/10.1007/s00170-019-04559-4 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-019-04559-4 |
| Popis: | Loss of production speed is an unavoidable reality for process manufacturers. Reduced production speeds are shown to consume 9–15% of available production capacity in various production contexts and create substantial costs for capital-intensive process industries. Amongst the least examined of the six big efficiency losses measured within total productive maintenance, speed loss presents significant opportunities for potential efficiency improvements in manufacturing companies. Based on the literature, this paper presents a framework of the factors related to speed loss, including three overall dimensions: technology factors, human factors and product factors. Next, a case study of two production lines to investigate this framework and quantify the scale of speed loss for the factors identified in the case study. For quantification, generalised least squares regression is performed to study the relationship between each factor and speed loss. The analysis of the production data reveals that technology and human factors have the strongest correlations with speed losses in this industry and account for the most speed loss. This research can directly support operational improvement initiatives in practice by identifying the factors with the strongest relationships to speed loss, aiding practitioners to select the most relevant means to improve speed and identify appropriate overall equipment effectiveness targets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink