Respecting the power of knowledge: Reducing design weakness in factory equipment by facilitating knowledge reuse, with a maintenance perspective
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Aas, Rickard
Furborg, Fredric
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Earlier research, at the company this thesis has been carried out, has shown that
design weakness is a major contributor to machine breakdowns, and struggles with
knowledge re-use is believed to be a cause for this. Therefore, this thesis covers two areas:
(1) To try to find if there is a correlation between breakdown losses and the project
documentation, and (2) Find a solution to improve the existing acquisition process in the
aspect of knowledge management.
The analysis of project documentation was conducted in a manual and qualita tive
manner, which leaves room for improvement. In the end, it did not give clear evidence
for a correlation, but a few indications, such as delays and lacking docu mentation
for testing, seemed to cause more breakdowns.
To improve lessons learned, a tool was developed which served to replace the existing
method which had been deemed too cumbersome and not a worthwhile task from
earlier research at the company. This tool consists of an app created in Power Apps,
connected to a database of lessons, which is believed to improve several aspects of
lessons learned as well as eliminate existing pitfalls the existing method had fallen
into relating to lessons learned.
It is recommended to continue exploring the reasons for poor machine performance, as this
thesis could not conclude with certainty that documentation is the root cause.
Furthermore, the analysis-process could be improved greatly as there was no predefined
way of conducting it, and conducting it in a more systematic manner
could yield better results. Finally, lessons learned within the case company can
be further improved in the future by involving elements from industry 4.0 by, for
example implementing direct connections to the machines to increase the amount of data; increasing automatic communications between softwares; using machine learning to
automatically create lessons and ultimately make decisions based on these lessons.
Following through with these imp rovement s, and other as well, for increased knowledge
re-use can be incredibly beneficial for any company as less resources will have to be
put into reinventing the wheel and helps avoid making mistakes over and over again.