Manual visual inspection is no longer viable. To overcome human limitations, inconsistent results and the global increase in labor costs, manufacturers seek smart automated solutions.
As Industry 4.0 takes hold, industrial automation and robotics are replacing many manual tasks in manufacturing. However, when it comes to visual quality inspection, most production lines still employ human workers in the tedious task of examining products and judging defects.
The biggest drawback of manual visual inspection is that humans make mistakes. Tired workers often miss defects that “escape” the quality screens on the production floor and leak into finished goods packages or into integrated systems. When these defects are discovered or surface at a later stage often by end customers, users or consumers, it is too late and very costly to fix. The Cost of Poor Quality (CoPQ) in these cases is significant. It includes – among other elements – the costs of returned or rejected goods (RMA), scrap, rework and in many cases the negative impact on brand reputation and end customer dissatisfaction.
Kitov.ai is paving the way towards smart manufacturing, by developing the right technology to enable smart computer-driven visual inspection and support manufacturers along their digital transformation path.
We are the Smart Visual Inspection Pioneers!
Human-based inspection fails to produce consistent and standard results, which is a major challenge for modern manufacturing. Manufacturers strive to apply a standard measurement for the inspection of their products regardless of the time, the site and the worker performing the inspection.
Based on our research, manufacturers spend over $18B every year on manual visual inspection to ensure they meet customer requirements. In most markets and across most industries, labor costs are on constantly rising. In some markets regulators encourage manufacturers to automate tedious inspection processes for various reasons. Often, it is not only a matter of cost. In many markets it has become a question of availability of skilled workers, who have become a scarce resource. Shortages in quality control workers often create a bottleneck in production. In a competitive marketplace, time-to-market is a critical factor in the successful introduction of new products and should not be delayed due to quality control issues. At the same time, manufacturers would prefer to engage their skilled workforce in higher-value activities with better return on investment and higher economic gains, such as analyzing and eliminating defects.
As part of their Industry 4.0 strategy, smart manufacturers are looking for ways to leverage artificial intelligence, robotics and big data to stay ahead of the game. In recent years manufacturers have shown increased interest in machine vision technology as an enabler of Good Manufacturing Practices (GMP) as well as trackability.
An automated, smart visual inspection solution can offer smart manufacturers access to data that was not available in the past. A comprehensive database that includes inspection results with images of defect and defect-free products is a gold-mine of information that can be transformed into actionable intelligence. With the help of big data analytics technology manufacturers, OEMs and ODMs can analyze and track quality, trends, common defects and evolving quality issues, as well as proactively introducing improvements in the production process, product design and supply chain management.
In a survey conducted by Tata Consulting Services (TCS) “Product quality and tracking defects” was ranked with the highest priority among the other potential drivers and benefits of using big data in manufacturing.
As manufacturers increasingly adopt mass customization and endorse personalization in production, they seek ways to deal with increased variance in their production environment. Variance in products, models and design, as well as in materials and components is becoming a major challenge. In order to address increased variance, manufacturers require flexible inspection technology as a key step in their journey towards comprehensive digital transformation.
Our “secret sauce” is our ability to master and leverage four technological disciplines to create a powerful, end-to-end solution.
Kitov harnesses 3D Computer Vision Algorithms and Artificial Intelligence, such as machine learning and deep learning, to reliably and consistently detect and classify critical defects without human intervention.
Kitov’s solution is based on a unique design that includes a robotic arm and a high-accuracy rotating table. This design imitates the way humans look for defects and facilitates the collection of multiple images of the inspected product from various angles, positions and illumination conditions. Powered by advanced 3D computer vision algorithms, our software effectively and reliably finds all defects including those that are often missed by humans.
KITOV DETECTION ENGINES
Kitov.ai has developed powerful AI-driven algorithms that process images and apply a wide range of tools and methods to reliably and accurately detect all defects. These algorithms are embedded in the Kitov software. However, unlike the human eyes and brain that get tired and bored over time, resulting in declined detection, Kitov’s AI-driven software gets smarter and better the longer it works, achieving constant improvement in detection over time.
ARTIFICIAL INTELLIGENCE (AI)
By using AI to connect the dots among a cluster of technology elements, Kitov creates a total solution that works.
We harness a variety of technological disciplines and synergize them to drive our smart visual inspection solution. Moreover, in recent years we have developed unique, patented capabilities that continue to support our customers and help them manufacture at the highest levels of quality.
Kitov.ai has been recognized by CIO Applications Magazine, as one of the TOP 10 MACHINE VISION PROVIDERS OF 2019
In 2018 KITOV ONE received a platinum-level award for innovation by the Vision Systems Design Magazine in recognition of its breakthrough technology