Optimization of the production process is always one of the priorities of every manufacturer. The world has already witnessed 3 industrial revolutions, each of them has redefined how goods are being made, and each of them has established the new market leaders. Now, the 4th revolution, called Industry 4.0, is happening in front of us. The emergence of new technologies like AI, Big Data and Cloud services changed the ways modern production plants look like.

Here we present the examples of 4 modern AI and Big Data solutions for industrial applications.

1. IoT and Big Data – from predictive maintenance to better business decisions

Predictive maintenance has been a well-known term for years. But thanks to modern technology the efficient solutions are much easier to implement and way more affordable.

Using Big Data solutions, the data gathered from the sensors can be analyzed and used not only to monitor the machines and predict its failures, but also to find the weak spots in the production processes, and finally to improve the business decisions.

Currently, there are several products and platforms that can be used out-of-the-box by companies. But in the majority, the most effective approach might be the tailor-fit solution adjusted to the companies needs and goals. A data lake implemented on the cloud infrastructure is an example of high throughput, scalable solution for that kind of application.

2. Visual defects detection

Nowadays, due to the increasing production volume, products’ defects have to be detected faster and more precisely than ever before. Strict quality requirements had to be met during all of the stages.
A video inspection system to monitor the semi-finished products during the production process and to recognize the broken ones can significantly increase the cost-effectiveness. The computer vision solutions can operate significantly faster than humans while providing at least as good results.

The key elements of such a system are:

  • Proper infrastructure for gathering and processing the data online and on the spot
  • A real-time deep learning model for visual inspection
  • Integration with an existing production line and determine new business processes.

What are the benefits of the incorporation of such a solution?

Primarily, the company is able to reduce the number of complaints. Moreover, the defects recognized in the early stages of the production process significantly reduce the number of malfunctioning end products – the production is easier to scale up.

3. Detection of anomalies in CCTV

The manufacturing companies often have a number of production plants and hire thousands of workers. As the production volume and number of technical employees have grown, safety supervision has become really hard and requires lots of people to constantly monitor the CCTV records. Checking archive records would also be really time-consuming without any tags.

That kind of issue can be solved with a computer vision system that would work with CCTV cameras mounted above the workstations. Such a system can monitor a workflow, detect malfunctions of a production process and alert about dangerous or suspicious events.

What are the parts of such a solution?

  • A data pipeline that can collect data from the CCTV, preprocess it and stream it to the model;
  • A Machine Learning model that will be able to detect anomalies in videos.

The model should work in real-time and alert a shift manager in case of any unusual events.

What kind of profits a company can gain by investing in such a system?

Health and safety at work would be enhanced. The shift managers could be immediately informed about the disturbances in the workflow. In the case of unwanted events, the reaction can be faster and more direct. Finally, the number of people watching all of the CCTV streams can be reduced.

4. Demand forecasting to optimize the production

Optimization of the supplying process is an essential part of production planning. It is crucial to order the correct amount of resources. In many cases, the orders need to be placed weeks or months ahead. What’s more, in the case of new products, their new models or types, it’s hard to predict how big the demand for them will be.

In such a situation, a sales forecasting system may be split into two parts:

  • A forecasting model that can predict the sales of the products based on the historical records
  • A separate, auxiliary algorithm that will be able to estimate the future sales of the brand new product in the distributor portfolio.

What’s more, the nowadays deep learning models can be used to extract important features from the products images and create the forecasting systems that take a lot of factors into consideration making predictions.

There are several reasons to adopt such kind of system in the company. Revenue could be maximized thanks to the optimized size of production. The right amount of the new types of products could be ordered which can reduce the risks of wasting the resources. Due to the increasing amount of data, every year prediction will be more accurate.

Of course, there are a lot more ways to use modern AI solutions to improve, in this post we presented only a small fraction of the possible applications. If you’d love to learn more, just book a call with us!