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.

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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.

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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.

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