Quality 4.0
Quality 4.0
Quality 4.0 is based on the technologies and paradigms of the 4th industrial revolution, allowing smart decisions using real-time data generation, collection and analysis of information to reach the best practices in quality management.[1][2]. It became the 5th wave in the quality movement, preceded by statistical quality control, total quality management, Six Sigma, and design for Six Sigma[3]. Traditional quality approaches use scientific methodologies and statistics for problem solving. Meanwhile, quality 4.0 is based on big data and artificial intelligence to rise its conformance levels.
Decay of traditional quality control methods
Some of the traditional approaches for quality improvement are not capable of handling all the dynamics present in smart manufacturing. An increase in complexity of the data analyzed (including hyperdimensional feature spaces, data volume, and transient sources of variation)[4] have left Six Sigma already behind, creating a new line of action towards quality in Industry 4.0. Therefore, in the last decade, quality engineers went into stagnation stage with little innovation to offer to the manufacturing industry using traditional quality philosophies, until the development of Quality 4.0 practices[5].
Value propositions
To be able to create some value regarding what already exists, a combination of complimentary initiatives or goals has to be developed[6][7]. The American Society of Quality mentions these value propositions[8].
- Detect rare quality events
- Predict quality issues
- Eliminate visual an manual inspections
- Augmentation of human intelligence
- Increase the speed and quality of decision making processes
- Improve traceability, transparency and auditability
- Creation of new business models
If a company is able to follow and implement Quality 4.0 practices, they will position themselves in an advantageous position as some of the most advanced and influential companies in the world, as the majority has not followed this path yet or are not prepared to do so.
Areas of study (Six areas of knowledge)
Quality 4.0 has its foundations in six main areas of knowledge[9]:
- Quality
- Statistics
- Programming
- Optimization
- Learning
- Manufacturing
Having a deep understanding of each of the areas and being able to apply them will lead to a successful implementation of Quality 4.0 initiatives.
Technologies
Previous industrial revolutions depended in its majority of machinery upgrades and improvements of mechanical parts[10]. In the 4th revolution, most of the value depends on data and technology and the advantages that can be taken from them. Smart manufacturing technologies that are used to enable this Quality improvement are: Artificial intelligence, industrial internet of things, and Cloud storage and computing[11].
Building blocks
To be able to create a link between the physical and digital systems, and optimize, control, monitor and automate processes, four main building blocks have been developed[12]: Cyber-physical systems, real-time interactions, data driven approaches and self-learning adaptations and executions. Cyber-physical systems are the smart solutions which are called smart manufacturing, they take the traditional engineering processes and convert them into digital and computational fundamentals to be analyzed. Real-time interactions are the smart sensors deployed to have the process monitoring in real-time for observation; technology and solutions are created based on this data. Data-driven approaches are models that could surpass the capacity of a physical system, deriving models and systems from the data of the process, and it is not limited by the data of the physical model itself. Finally, self-learning adaptations will come into place when the process becomes slightly modified withing the adequate parameters, regulating its sources of variation and self execute in case needed.
Quality 4.0 initiative
Rare quality event detection is one of the most important challenges in Quality 4.0. Manufacturing data tends to be highly unbalanced, as the majority class is non-defective and defective items are difficult to detect. Defect detection is formulated as a binary classification problem, where machine learning algorithms are trained to identify the defective class[13].
7-step Problem Solving Strategy implementation
The traditional problem solving strategies, such as PDCA (Plan, Do, Check, Act), and DMAIC (Define, Measure, Analyze, Improve, Control) have evolved to cope with the challenges posed by Industry 4.0[14]
A 7-step strategy has been developed to increase the likelihood of success in the deployment of Quality 4.0 systems.
- Identify
To select the most relevant project with high chances of success.
- Acsensorize
To deploy cameras or sensor in order to monitor the process.
- Discover
Create features in the data.
- Learn
Develop classifier using Big Models.
- Predict
Optimize classifier prediction.
- Redesign
Derive knowledge from results.
- Relearn
As the name states, to create a relearning strategy for new distribution classes.
Quality 4.0 certifications
As professionals struggle to develop a successful Quality 4.0 strategy, certifications become necessary to establish a common ground for deployment and technology implementation[15] [16]. Around 80% of engineers have to follow additional training to obtain the skills needed for Industry 4.0. As quality engineers, an equivalence and certification curricula has to be made for Green, Black and Master Black Belt in Quality 4.0. This will allow an open and common strategy for problem solving.
It is important for quality professionals to certificate in new technologies to differentiate themselves as capable for working and show their competences in Quality 4.0/Industry 4.0. The competences acquired will create a higher earning potential and more career opportunities[17]
References
- ↑ "GM to go all-electric by 2035, phase out gas and diesel engines". NBC News. Retrieved 2022-04-05.
- ↑ Escobar, Carlos A.; McGovern, Megan E.; Morales-Menendez, Ruben (2021). "Quality 4.0: A review of big data challenges in manufacturing". Journal of Intelligent Manufacturing. 32 (8): 2319–2334. doi:10.1007/s10845-021-01765-4. Unknown parameter
|s2cid=ignored (help) - ↑ Escobar, Carlos A.; McGovern, Megan E.; Morales-Menendez, Ruben (2021). "Quality 4.0: A review of big data challenges in manufacturing". Journal of Intelligent Manufacturing. 32 (8): 2319–2334. doi:10.1007/s10845-021-01765-4. Unknown parameter
|s2cid=ignored (help) - ↑ Wuest, Thorsten; Irgens, Christopher; Thoben, Klaus-Dieter (2013-03-24). "An approach to monitoring quality in manufacturing using supervised machine learning on product state data". Journal of Intelligent Manufacturing. 25 (5): 1167–1180. doi:10.1007/s10845-013-0761-y. ISSN 0956-5515. Unknown parameter
|s2cid=ignored (help) - ↑ Zonnenshain, Avigdor; Kenett, Ron S. (2020). "Quality 4.0—the challenging future of quality engineering". Quality Engineering. 32 (4): 614–626. doi:10.1080/08982112.2019.1706744. Unknown parameter
|s2cid=ignored (help) - ↑ Escobar, Carlos A.; Morales-Menendez, Ruben; Macias, Daniela (September 2020). "Process-monitoring-for-quality — A machine learning-based modeling for rare event detection". Array. 7: 100034. doi:10.1016/j.array.2020.100034. ISSN 2590-0056. Unknown parameter
|s2cid=ignored (help) - ↑ M., Radziwill, Nicole (2018-10-17). Quality 4.0: Let's Get Digital - The many ways the fourth industrial revolution is reshaping the way we think about quality. OCLC 1106315321. Retrieved 2022-04-05. Search this book on
- ↑ "Quality 4-0 | ASQ". asq.org. Retrieved 2022-04-05.
- ↑ Escobar, Carlos A.; Chakraborty, Debejyo; McGovern, Megan; MacIas, Daniela; Morales-Menendez, Ruben (2021). "Quality 4.0 — Green, Black and Master Black Belt Curricula". Procedia Manufacturing. 53: 748–759. doi:10.1016/j.promfg.2021.06.085. Unknown parameter
|s2cid=ignored (help) - ↑ "Industry 4.0: How to navigate digitization of the manufacturing sector | McKinsey". www.mckinsey.com. Retrieved 2022-04-05.
- ↑ Carvalho, Adriana; Chouchene, Amal; Lima, Tânia; Charrua-Santos, Fernando (2020). "Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework". Applied System Innovation. 3 (4): 55. doi:10.3390/asi3040055.
- ↑ Lee, Edward A. (2008). "Cyber Physical Systems: Design Challenges". 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC). pp. 363–369. doi:10.1109/isorc.2008.25. ISBN 978-0-7695-3132-8. Unknown parameter
|s2cid=ignored (help) Search this book on
- ↑ Escobar, Carlos A.; MacIas, Daniela; Morales-Menendez, Ruben (2021). "Process monitoring for quality — A multiple classifier system for highly unbalanced data". Heliyon. 7 (10): e08123. doi:10.1016/j.heliyon.2021.e08123. PMC 8517167 Check
|pmc=value (help). PMID 34693053 Check|pmid=value (help). Unknown parameter|s2cid=ignored (help) - ↑ Escobar, Carlos A.; McGovern, Megan E.; Morales-Menendez, Ruben (2021). "Quality 4.0: A review of big data challenges in manufacturing". Journal of Intelligent Manufacturing. 32 (8): 2319–2334. doi:10.1007/s10845-021-01765-4. Unknown parameter
|s2cid=ignored (help) - ↑ "Quality 4.0". www.qualitydigest.com. 2017-07-18. Retrieved 2022-04-05.
- ↑ Escobar, Carlos A.; Chakraborty, Debejyo; McGovern, Megan; MacIas, Daniela; Morales-Menendez, Ruben (2021). "Quality 4.0 — Green, Black and Master Black Belt Curricula". Procedia Manufacturing. 53: 748–759. doi:10.1016/j.promfg.2021.06.085. Unknown parameter
|s2cid=ignored (help) - ↑ Kosowatz, John (2014). "Spinning Liquid Gold". Mechanical Engineering. 136 (7): 32–37. doi:10.1115/1.2014-jul-1.
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