Preventing data loss in PFMEA; a digital solution
singh, manmeet (2021-05-04)
04.05.2021
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2021050428740
https://urn.fi/URN:NBN:fi-fe2021050428740
Tiivistelmä
Kaizen, a Japanese term, defines continual improvement or a scope of improvement that exists in each process, has motivated the author for this research. The fourth industrial revolution has high demands for lean productivity, reducing waste activities and eliminating defect generation possibilities. PFMEA, a tool known for analysing potential risks is not much developed in the past few years in its knowledge handling capabilities. One of the significant input required for PFMEA generation is PTDB; perhaps, the PTDB is maintained on spreadsheets or Microsoft Excel in general by many organisations and has been proven ineffective in research and events globally in the past few years.
However, no development is witnessed in PTDB knowledge handling processes, both in academia and industry. Thus the current thesis aims to answer the basic research question of the study – “What are the advantages of using software-based PTBD over traditional spreadsheet-based PTDB for PFMEA”?
The study empirically evaluated the disruptiveness in spreadsheets and its impact on data quality and decision making, linking the possible challenges for PFMEA. In addition, the study intends to capture industry voice and opinion on a digital solution for PTDB. Semi-structured interviews and online surveys were conducted with industry professionals globally to answer the research question. Data has been analysed through content analysis and it was found that, spreadsheets are inefficient in big data handling due to its list of risks, such as; calculation and formatting errors, data security and data transfer issues. Whereas, the industry respondents welcomed the idea of a better but economical digital solution.
To overcome the challenges, the author has designed a conceptual framework capable of big data handling, delivers security and flexibility. The framework has an inbuilt PFMEA template that eliminates the possibility of data loss, saves time and deliver quality PFMEA. The future scope exits with the design and trial run of the framework.
However, no development is witnessed in PTDB knowledge handling processes, both in academia and industry. Thus the current thesis aims to answer the basic research question of the study – “What are the advantages of using software-based PTBD over traditional spreadsheet-based PTDB for PFMEA”?
The study empirically evaluated the disruptiveness in spreadsheets and its impact on data quality and decision making, linking the possible challenges for PFMEA. In addition, the study intends to capture industry voice and opinion on a digital solution for PTDB. Semi-structured interviews and online surveys were conducted with industry professionals globally to answer the research question. Data has been analysed through content analysis and it was found that, spreadsheets are inefficient in big data handling due to its list of risks, such as; calculation and formatting errors, data security and data transfer issues. Whereas, the industry respondents welcomed the idea of a better but economical digital solution.
To overcome the challenges, the author has designed a conceptual framework capable of big data handling, delivers security and flexibility. The framework has an inbuilt PFMEA template that eliminates the possibility of data loss, saves time and deliver quality PFMEA. The future scope exits with the design and trial run of the framework.