Methodology for Data-Informed Process Improvement to Enable Automated Manufacturing in Current Manual Processes

Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 9; p. 3889
Main Authors Adrita, Mumtahina Mahajabin, Brem, Alexander, O’Sullivan, Dominic, Allen, Eoin, Bruton, Ken
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 2021
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ISSN2076-3417
2076-3417
DOI10.3390/app11093889

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Summary:Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11093889