The evolution of quality: from inspection to quality 4.0

Broday, Evandro Eduardo

doi:10.1108/ijqss-09-2021-0121

Cited by 34 publications

(35 citation statements)

References 34 publications

(37 reference statements)

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“…Broday expects quality methods to change, but not be replaced, with Quality 4.0 leading to quicker results from combining big data and AI with more traditional quality methods [24]. Industry 4.0 also reduces the need for labor and organizations will need to ensure their employees are able to gain new skills [25] as new competencies are needed in Quality 4.0 [26].…”

Section: Resultsmentioning

confidence: 99%

Root Cause Analysis in Quality 4.0: A Scoping Review of Current State and Perspectives

Barsalou¹

2023

TEM Journal

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This paper describes a subset of Industry 4.0, namely, Quality 4.0. Industry 4.0, has brought with it many changes to industry and changes that will directly impact root cause analysis. Massive amounts of data are being generated by industry 4.0 and previous quality tools and methods are no longer up to the task of handling such large amounts of data. Old root cause analysis tools and methods can still be used in Quality 4.0, but they must undergo adaptation to remain usable and high-quality professionals will need to update their skills.

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Section: Resultsmentioning

confidence: 99%

Root Cause Analysis in Quality 4.0: A Scoping Review of Current State and Perspectives

Barsalou¹

2023

TEM Journal

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“…Big data has come to prominence due to Industry 4.0, with Cyber-Physical-Systems (CPS) linked together though the Internet of Things (IoT) [42]. Industry 4.0 has also led to Quality 4.0, where quality professionals must concern themselves with Artificial Intelligence (AI) [43] and machine learning [44] for the analysis of big data [45]. Ou et al [46] have proposed process for the analysis of big data in Quality 4.0 consisting of data collection, preparation of data, EDA and modeling of data, visualization of results and creation of an analysis report, and then decision making based on the results.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Exploratory Data Analysis: An Empirical Test of Run Chart Utility

Barsalou,

Saraiva,

Henriques

2023

Management Systems in Production Engineering

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This paper explores Exploratory Data Analysis (EDA). Graphical methods are used to gain insights in EDA and these insights can be useful for forming tentative hypotheses when performing a root cause analysis (RCA). The topic of EDA is well addressed in the literature; however, empirical studies of the efficacy of EDA are lacking. We therefore aim to evaluate EDA by comparing one group of students identifying salient features in a table against a second group of students attempting to identify salient features in the same data presented in the form of a run chart, and then extracting relevant conclusions from such a comparison. Two groups of students were randomly selected to receive data; either in the form of a table or a run chart. They were then tasked with visually identifying any data points that stood out as interesting. The number of correctly identified values and the time to find the values were both evaluated by a two-sample t-test to determine if there was a statistically significant difference. The participants with a graph found the correct values that stood out in the data much quicker than those that used a table. Those using the data in the form of a table too much longer and failed to identify values that stood out. However, those with a graph also had far more false positives. Much has been written on the topic of EDA in the literature; however, an empirical evaluation of this common methodology is lacking. This paper confirms with empirical evidence the effectiveness of EDA.

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“…Several studies on Quality 4.0 have associated it with the impact of technology on TQM (Sader, Husti and Daróczi, 2019;Chiarini, 2020;Nenadál, 2020;Carvalho et al, 2021). Quality 4.0 is founded on empirical learning, empirical knowledge discovery as well as real-time data generation, collection and analysis to enable intelligent operations decisions (Nenadál, 2020;Rifqi et al, 2021;Broday, 2022). Quality 4.0 ensures that pertinent information is communicated continuously via the system.…”

Section: Development Of Quality 40mentioning

confidence: 99%

“…The management role remains critical with the changing quality environment and the implementation of Quality 4.0. With Quality 4.0 grounded on the technologies of the 4IR (Rifqi et al, 2021;Broday, 2022), management must be committed to technology and innovation, with this journey demanding a mental and cultural shift. Thus, top management is a crucial enabler of Quality 4.0 and individuals must be receptive to external ideas and willing to share their knowledge.…”

Section: Management Commitment To Technology and Innovationmentioning

confidence: 99%

“…Research on Quality 4.0 has gained traction in the last five years, driven by technological advances which increased the accessibility of the Fourth Industrial Revolution (4IR) technologies, as well as the COVID-19 pandemic which changed the face of society and the workplace (Sony and Aithal, 2020;Broday, 2022). Together with such research, it is critical to also develop a Quality 4.0 maturity assessment model that can be employed to help improve operational efficiency and business performance.…”

Section: Introductionmentioning

confidence: 99%

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Developing a Quality 4.0 Maturity Index for Improved Business Operational Efficiency and Performance

Mtotywa

2022

QIP Journal

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Purpose: Technological advances and increased environmental turbulence require a transition in quality management. The study aimed at developing a Quality 4.0 maturity index for improved business operational efficiency and performance. Methodology/Approach: This conceptual paper introduces a theoretical business evaluative model that allows an integrated analysis of technology-driven, quality management dimensions. The model is based on theoretical and empirical information and describes Quality 4.0 business analysis by a theoretical central business dimensional concept, formal statistical analytical methods and uses these data to assign a maturity index score to the business. Findings: The study builds the Quality 4.0 maturity index following the analysis of seven continuous quality improvement dimensions. The maturity of these dimensions in the business is assessed with a five-point maturity level. The effectiveness of the index should be confirmed with fit as covariation and a composite score for the level of Quality 4.0 maturity. Research Limitation/Implication: The research is based on theory and has not been validated with empirical data. It is recommended that a validation study be conducted based on the approach and guidelines provided in the paper. Originality/Value of paper: The study helped develop a theoretical aspect of total quality management during an era of the fourth industrial revolution. It also aimed at practically benefiting a business by focussing on improved business capacity and capability to mitigate the environmental turbulence associated with pandemics. The paper provides novel work, as it describes one of the first Quality 4.0 maturity index models that may be used to improve business.

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The evolution of quality: from inspection to quality 4.0

Cited by 34 publications

References 34 publications

Root Cause Analysis in Quality 4.0: A Scoping Review of Current State and Perspectives

Root Cause Analysis in Quality 4.0: A Scoping Review of Current State and Perspectives

Exploring Exploratory Data Analysis: An Empirical Test of Run Chart Utility

Developing a Quality 4.0 Maturity Index for Improved Business Operational Efficiency and Performance

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