Scale up efficiency in manufacturing

Scale up efficiency in manufacturing

Artificial intelligence automation in manufacturing assembly lines is becoming more important to bring higher efficiency in speed, quality, and cost. One example is how AI automation on defect detection to prevent faulty components to be assembled. As inaccuracy could impact on production cost and profitability, AI models need to be trained with high quality labelled manufacturing product images. Data annotation process to label these images then becomes crucial.

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Kili is trusted to become their data annotation tool partner


Main manufacturing AI use cases

  • Defect detection

    Kili Technology has facilitated manufacturing companies to automate its defect detection in production assembly lines with AI, to prevent faulty components for being assembled and defective products for being packaged and shipped. This improves manufacturing productivity and cost effectiveness.

  • Waste management

    Kili has scaled up waste management practices with artificial intelligence robots to separate recyclables, solid wastes, and hazardous materials. Speeding up the sorting process of waste increases cost efficiency and sustainability of the manufacturing process.

  • Customer order processing

    Kili Technology has enabled manufacturing customer order process became quicker and more efficient through AI automation model. Text classification and identification of order quantities, SKUs purchased, and delivery dates are done quickly to streamline the ordering process.

We ensure accuracy of AI model for manufacturing efficiency

Kili Technology makes image, video, and text annotation fast and simple to improve your manufacturing operations. Import your data in bulk: images of products and components, videos of assembly lines, customer orders and invoices, and more. Detect even the smallest cracks in product components, identify order quantities, delivery time, and specifications within customer order documents. Build your training datasets with highly customizable interfaces that allow you to combine tasks to improve manufacturing productivity.

However, manual data labelling can be expensive, laborious, and time-consuming. In addition, manufacturing companies need to put an extra-detailed attention to on the data annotation quality, as missing details on product defects or customer orders can have an impact in inefficiencies, quality issues, increased lead time, and rising cost.

This is where data annotation tool such as Kili Technology comes in. The powerful computer vision and NLP features along with customizable interface to perform image, video, and text labelling such as object detection, document classification, and entity recognition will simplify the process while enhancing the quality of building training dataset for manufacturing machine learning applications.


Create training datasets from data annotation

As quality and efficiency is highly important in production lines, manufacturing companies around the world are looking to use artificial intelligence models to automate tedious tasks in mass production assembly lines, such as inspecting and detecting defects in product components, customer order processing, separating production wastes, among others. However, creating and training these AI models requires access to large amounts of annotated data of relevant images, videos, and texts.

It is not a big problem to find certain datasets. For instance, to train manufacturing AI automation model “manufacturing datasets” in your favorite search engine.
However, in order for a model to be able to make accurate predictions – especially in specific cases for instance defect detection – it must be trained on a large amount of high-quality data that is specialized in the problem you want to address.

Furthermore, as manufacturing productions deal with various type of products with unique details, to perform on a real use case specific to your needs, you will have no choice but to collect data from your database and label it. Be aware that labelling can be expensive and of poor quality. That’s where Kili Technology comes in.


What makes Kili Technology different?

Expertise

Kili Technology delivers a stellar performance to annotate image, video, text, PDF, image, and OCR. We offer specialized interfaces for all annotation tasks related to object detection and classification for product in assembly lines, entity recognition to identify specific phrases in customer orders and invoices, relations extraction, and more. 

Quality

Kili Technology’s state of the art quality management system allow an intensive collaboration and a rigorous review throughout the life of the project to ensure clean, high-quality manufacturing data training datasets.

Freedom

At Kili Technology, you can annotate data wherever you want with whoever you like. On premise or in SaaS, with your internal annotators or with our labelling workforce, remotely or in your premises, we adapt to your constraints!

Cost

Data annotation can be expensive. By allowing the use of online learning, active learning, weakly supervised learning or data augmentation, Kili Technology allows you to drastically reduce the cost of data annotation!

Scale

Kili Technology has access to a unique network of professionals around the world who are able to accurately translate, transcribe, and annotate manufacturing data, so we can quickly create large, custom training datasets for use cases in manufacturing and industrial production.



Training data interfaces for manufacturing

Object detection for product defects

Add structure to your product images with Bounding Box Annotation, Semantic Annotation, Polygon Annotation, and more around even the most subtle cracks to detect defects on your products. Use nested classification feature to define type of defects on your labels.

Object classification for waste management

Structure images of your production wastes to identify recyclables, solid waste, and hazardous materials. Leverage our nested classification feature to define granularity to the type of waste for advancement of your model.

Named-Entity-Recognition for quicker order processing

Apply structure and semantic information to unstructured text of customer orders at the document and word level. Take advantage of our weakly supervised learning service to use business rules such as regular expressions and dictionaries to annotate massively.

A last but not least, create your own interfaces for your specific tasks with Kili’s interface builder!




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