Machine learning is revolutionizing apparel inspections by providing prescriptive insights into every step of the process and improving quality, reducing risk and increasing transparency.
The greatest challenge to improving the product quality apparel factories are delivering today is capturing accurate, credible inspection data. With many factories relying on decades-old methods of recording inspections on paper, in Microsoft Word, Excel or Adobe PDF documents, they’re finding these methods can’t keep up with the pace brands and retailers need to produce products at.
Factories, brands, retailers and the customers they serve know whether inspections are succeeding or not by the quality of the garments produced. Inspections make it possible for a brand or retailer to make a positive impression instantly by providing customers with the highest quality garments possible. Automating inspections using mobile technologies, aggregating and analyzing data on a secure, cloud-based inspection platform, and gaining prescriptive insights from the data using machine learning is revolutionizing apparel manufacturing.
Machine Learning Is Driving Inspections’ Inflection Point
Capturing real-time data streams from inspections, quickly analyzing and taking prescriptive actions on the insights gained using machine learning is driving inspections’ inflection point today. By using constraint-based algorithms and logic to understand why there are large differences in inspection and product quality between factories, it’s possible to improve an entire supply chains’ product quality and delivery performance. Machine learning algorithms also “learn” or optimize their performance over time based on repetitive patterns in data. By continually learning from data patterns provided by automated inspections, machine learning provides a roadmap for continuous improvement.
Capitalizing on the data gained by automating the inspection process, brands, retailers, factories, vendors and suppliers are all benefitting from the prescriptive insights machine learning-based inspection platforms provide.
Automating inspections with machine learning is delivering the following benefits:
- Reducing risk, the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. One of the most powerful catalysts driving inspections’ inflection point is the combination of automated workflows that deliver high-quality data that machine learning produces prescriptive insights from. And those insights are shared on performance dashboards across every brand, retailer, supplier, vendor and factory involved in shared production strategies today.
- Matching the most experienced inspector for a given factory and product inspection drastically increases accuracy and quality. When machine learning is applied to the inspector selection and assignment process, the quality, and thoroughness of inspections increase. For the first time, brands, retailers and factories have a clear, quantified view of Inspector Productivity Analysis across the entire team of inspectors available in a given region or country. Inspections are uploaded in real-time to the Inspectorio platform where advanced analytics and additional machine learning algorithms are applied to the data, providing greater prescriptive insights that would have ever been possible using legacy manual methods.
- Capture inspection data in real-time and know about potential and real quality problems faster, using machine learning to spot anomalies in the data quickly. Using configurable workflows on mobile devices, inspectors can more quickly capture any potential material or production defect and have the results uploaded to the Inspectorio platform instantly. The platform can analyze the data in less than a second using machine learning algorithms to find any aberrations or anomalies in the data and then alert quality managers in the factory, brand and/or retailer. Machine learning can avert costly recalls, and low-quality production runs as a result.
- Improving track-and-trace accuracy, speed, and scale by being able to isolate the specific root causes of assembly, materials and product quality problems early. Machine learning excels at solving very complex problems that require balancing multiple constraints at the same time. That’s why it’s a perfect technology to use in solving supply chains’ greatest challenges, with track-and-trace being one of the most difficult. By having inspection data captured and secure on the Inspectorio’s cloud-based platform, brands, retailers, factories, suppliers, and vendors all can perform track-and-trace inquiries and also collaborate in real-time to improve product quality.
- Knowing why specific factories and products generated more Corrective Action/Preventative Action (CAPA) than others and how fast they have been closed in the past and why is now possible. Machine learning is making it possible for entire production networks to know why specific factory and product combinations generate the most CAPAs. Using constraint-based logic, machine learning can also provide prescriptive insights into what needs to be improved to reduce CAPAs, including their root cause.
- Machine learning is making recommendations to inspectors on which defects to look for first based on the data patterns obtained from previous inspections. Capitalizing on machine learning’s ability to learn from data over time, Inspectorio has launched a Defect Recommender as part of its automated inspection platform that provides iterative, real-time guidance to inspectors as they are completing garment and factory questionnaires. These recommendations are further improving the accuracy, efficiency, and productivity of inspections.