Quality 4.0 :- Technology and Data-Driven Quality Management
Over the last decade, manufacturing practices, processes, and technologies have undergone significant changes. These modifications have the potential to resurrect their engineering and manufacturing activities. This phenomenon is known as the Fourth Industrial Revolution or Industry 4.0. It is founded on advanced manufacturing and engineering technologies, massive digitization, big data analytics, advanced robotics, adaptive automation, additive and precision manufacturing (e.g., 3D printing), modeling and simulation, artificial intelligence, and material nano-engineering. This revolution presents both challenges and opportunities for the disciplines of systems, manufacturing, analytics, and process engineering.
Throughout modern history, quality models, approaches, and practices have evolved from inspection to quality control, assurance, management, and quality by design. Total Quality Management, Six Sigma, Lean Sigma, and Quality by Design are examples of well-known quality initiatives that have been implemented around the world. These quality movements were led by well-known experts such as Shewhart, Deming, Juran, Taguchi, and others, who laid the groundwork for the quality approach used in industry, business, and government. However, it appears that the quality discipline has entered a state of stagnation in recent years, with very few innovative quality models being proposed.
Quality Assurance and Quality Control
Competition among fashion brands is extremely fierce in the fashion industry. If your quality isn’t up to par, you risk failing to meet industry and customer expectations, wasting a lot of time, money, and resources, and eventually falling behind your competition. Many apparel brands are also under pressure to increase output and maximize profits, which causes issues with their quality management systems.
Quality assurance ensures that the end result of the manufacturing process is a high-quality, dependable product. It is made up of all the planned, and systematic operations put in place to produce a product that successfully satisfies the brand’s and customers’ requirements. A level of consistency and unity will not be maintained during the various stages of soft lines manufacturing if quality assurance methods are not used.
Quality control is a component of quality assurance that occurs from the raw material sourcing stage all the way through to the final stages of production. It is concerned with the product, whereas quality assurance is concerned with the process. It entails putting in place a set of activities to identify and correct any defects in the actual final products being produced before they are released.
The Cost of Quality
The cost of quality is the money spent by a factory on garment and material inspections, quality personnel training, and repair labor to ensure that products meet acceptable quality standards. In other words, it is the total amount of money spent by a fashion brand to avoid nonconformance. Furthermore, the COQ can be divided into the Cost of Good Quality (COPQ), which includes the costs of prevention and appraisal, and the Cost of Poor Quality (COPQ), which includes the costs of internal and external failure.
The cost of quality is critical because it enables you to determine the extent and costs of resources used for activities that prevent poor quality, evaluate the quality of your garments, and are the result of internal and external failures. As a result, you will be able to calculate the cost savings that will result from implementing efficient quality management systems. Furthermore, the COPQ is far greater than the cost of implementing and improving quality.
Traditional Quality Inspections
Traditionally, quality inspections are performed at the end of a production cycle, or alternatively, after the stitching operation is completed. This results in additional costs because defects are only addressed after they occur, and it can be difficult to determine which workstation or process on the assembly line was responsible for the defect at this stage.
Furthermore, many traditional quality control inspections are performed manually using checklists, and the results of the inspections are only entered into data management systems once all quality inspections have been completed.
Quality 4.0: Data-Driven Quality Control
Quality management practices are changing due to digitalization, and this is where Q.4.0 (Quality 4.0) can add value.
Data-driven quality control is an alternative to traditional quality inspections, which involve conducting many individualized tests on each product after production. It requires the systematic collection and analysis of historical and real-time quality data and data from products and machinery on the factory floor. This information is used to create quality profiles and models, which can assist factories in improving product quality and lowering repair and rejection rates. This can reduce both the cost of high-quality operations and the cost of poor quality.
Data-driven quality control enables the real-time integration of multiple external quality-related data sources. A factory could incorporate real-time customer responses or reports on defects encountered by customers. Customers can become involved in the factory’s quality processes, allowing the factory to address production issues more quickly. Customers will feel that their suggestions and feedback are being considered, positively impacting the overall customer experience.
Value Proposition For Quality 4.0
- Human intelligence can be supplemented (or improved).
- Improve decision-making speed and quality.
- Transparency, traceability, and auditability should all be improved.
- Anticipate changes, expose biases, and adapt to new situations and knowledge.
- Relationships, organizational boundaries, and the concept of trust must all evolve in order to reveal opportunities for continuous improvement and new business models.
- Learn how to learn by developing self-awareness and other types of awareness as skills.
Quality 4.0 Principal Establishment and Implementation
To embrace the future of quality by achieving excellence through quality is to embrace the future of quality. It is critical that quality professionals assist their organizations in making the vital link between quality excellence and their ability to thrive in the face of change, utilizing quality principles to facilitate transformation and growth.
Quality 4.0 is about more than just technology. It’s a new way to manage quality using today’s digital tools and figure out how to achieve excellence through quality. Quality professionals can elevate their role from enforcers to navigators, successfully guiding organizations through digital disruption and toward excellence by speaking the digital language and making a case for quality in disruption.
The need for flawless processes remains the same, if not more important, as more work is automated. Existing methods will be disrupted, necessitating the training of the next generation of workers to implement new processes and strategies, which will be critical to the quality and business operations. Quality is a crucial link in the digital transformation process, and it should be considered at the strategic level to ensure its long-term viability.
Organizations’ platforms, such as processes, systems, data, operations, and governance, must keep up with technology, which is growing ten times faster than it used to. Technology is also a great leveler because it allows anyone with the right idea and intent access to capabilities that were previously only available to large corporations. By engaging with new technologies, understanding these technological advancements and the potential outputs they create, and determining how and when to use them, quality professionals can progress from data analyst to data wrangler roles.
Tools Of Quality 4.0
The implementation of a company’s digital strategy will not be without hiccups. Some challenges will be universal across industries and digital maturity levels, while others will be unique to an organization. In addition to the time-tested and well-known quality tools and principles, the Quality 4.0 tools listed below should be used to address these issues when implementing and deploying systems to support digital transformation.
Artificial intelligence: Computer vision, language processing, chatbots, personal assistants, navigation, robotics, auditing, and making complex decisions are all examples of artificial intelligence.
Big data: Infrastructure, more accessible access to data sources, and tools for managing and analyzing large data sets without supercomputers are all examples of big data.
Blockchain: increasing transaction transparency and auditability (for assets and information), monitoring conditions so that transactions do not occur unless quality objectives are met.
Deep learning: Image classification, complex pattern recognition, time series forecasting, text generation, sound and art creation, fictitious video creation from real video, image heuristic adjustment are examples of deep learning.
Enabling technologies: include low-cost sensors and actuators, cloud computing, open-source software, augmented reality (AR), mixed reality, virtual reality (VR), data streaming, 5G networks, IPv6, and Internet of Things (IoT).
Machine learning applications: include text analysis, recommendation systems, email spam filters, fraud detection, grouping objects, and forecasting.
Data science: The practice of combining disparate data sets to make predictions, perform classifications, find patterns in large data sets, reduce large sets of observations to the most significant predictors, and apply traditional sound techniques (such as visualization, inference, and simulation) to generate viable models and solutions.