Six Sigma DMAIC: Process Improvements towards BetterIT Customer Support

Ramly, Noor Nashriq

doi:10.7763/ijeeee.2012.v2.146

Cited by 1 publication

(3 citation statements)

References 1 publication

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“…Safwat and Ezzat [19] decreased the scrap rate in a plastic injection molding plant by using the Six Sigma methodology. IT departments have also benefited from using Six Sigma by reducing customer support issues [20]. Tchidi and He [21] investigated the existing problems in a software industry and proposed the improved engineering process improvement model (using an engineering design process called Design for Six Sigma).…”

Section: Data Driven Process Improvementmentioning

confidence: 99%

“…Value stream mapping and DMAIC [19] Plastic injection molding industry Reduce the scrap rate in the plant DMAIC [20] IT industry Reduce support issues with the IT infrastructure DMAIC [21] Software industry…”

Section: Referencementioning

confidence: 99%

“…Digitization of data produces large quantities of unorganized data, which can be difficult and time consuming to analyze manually. The Lean and Six Sigma implementations listed in Table 1 in Section 2.1 were specific to particular issues in their respective industries with no automation intention [16][17][18][19][20][21][22][23][24][25][26][27][28]. The Strategic Lean Six Sigma Framework proposed to reduce build time in an aerospace industry that required validation from new areas within the industry [25].…”

Section: Proposed Problem Statement and Researchmentioning

confidence: 99%

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

et al. 2021

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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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Section: Data Driven Process Improvementmentioning

confidence: 99%