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Why Manufacturers Are Turning to Seebo for Process-Based AI

While the manufacturing sector is leading the way in IoT adoption, many companies are finding it difficult to gain actionable insight from the data they’re collecting.

By using AI techniques such as artificial neural networks and machine learning, manufacturers have a golden opportunity to impact the bottom line through improved efficiency, safer work environments, and better quality products.

The hurdle:

Most manufacturing operations don’t have the relevant skills and knowledge in-house, and hiring additional professionals such as data scientists is often not an option. Providing a solution to this problem is Seebo, a SaaS company helping manufacturers advance to Industry 4.0.

The Seebo platform was developed to enable manufacturers to solve complex quality and downtime issues with simple tools and without the need for expert analytical knowledge.

What is Process-Based AI?

Process-Based AI is an approach that takes into account the entire production lifecycle. Data across every stage of production — from the design phase all the way to delivery and beyond — is captured and analyzed.

This level of data resolution allows for in-depth analysis, which can identify the true root cause of unplanned downtime events and quality issues, not just symptoms.

Read More: AI is Revolutionizing Property Management to Make Your Job Easier

How Manufacturers Benefit from AI

The number of production robots in manufacturing is rising daily and every aspect of the industry is being affected by the growing number of smart factory technologies.

From maintenance and inventory management to quality control and the supply chain, AI solutions such as the ones offered by Seebo are being used to optimize manufacturing in a myriad of ways:

Maintenance

It was only natural that industrial AI would first be targeted towards maintenance since inefficiency in this field is such a common pain point amongst manufacturers.

Preventive scheduled maintenance has been the go-to approach since the 1960s, but this method has a number of serious flaws including wasted work hours and materials. In most cases, scheduled maintenance is completely ineffective in actually preventing unplanned downtime.

Predictive maintenance has become a goal of manufacturers who understand the value of being able to predict failures in a component, machine, or system. A predictive maintenance system alerts relevant personnel so that focused repairs can take place, but not too early as to cut short the remaining useful life (RUL) of a part, or to go into downtime unnecessarily.

With process-based AI, machine learning algorithms are used to formulate predictions based on a very wide data group. The entire manufacturing process is analyzed including archival data and current data captured via sensors across the production floor.

The result is hyper-optimized maintenance that prevents secondary damage and requires smaller labor forces to perform maintenance procedures.

Quality 4.0

The quality of products is crucial to market positioning and customer retention. High defect percentages and product recalls are extremely damaging to brand value.

The term “Quality 4.0” refers to the leveraging of Industry 4.0 techniques including process-based AI in order to reach new levels of product quality.

Using AI algorithms, correlations between process deviations and drops in quality are discovered. These correlations are sometimes counter-intuitive and can go undetected when the analysis is based purely on human knowledge and experience.

Quality 4.0 allows manufacturers to improve the quality of their output without having to waste raw materials, energy, or human work hours — quality deterioration can be predicted and prevented.

Read More: Will Business Development Ever be Automated?

Generative Design

Process-based AI can also be utilized in the design phase. By constructing a well-defined design brief and feeding it through an artificial neural network, designers and engineers are able to explore all the possible configurations of a solution.

The brief should include definitions and restrictions for materials, production techniques, and budget and time constraints.

The solutions generated by the neural network can then be tested, providing additional data about which ideas worked best. Additional iterations are then generated until an optimal solution is found.

Supply Chain & Market Adaptation

Process-based AI can be used to significantly improve the flow in a supply chain. AI algorithms can help manufacturers anticipate, and better respond to, changes in the market.

AI algorithms can identify demand patterns based on date, location, socio-economic parameters, political status, macro-economic attributes, weather patterns and more.

Manufacturers can use this information to streamline inventory, energy consumption, and the use of raw materials, and make better financial decisions regarding company strategy.

Using the Right Tools for Process-Based AI

Deploying process-based AI usually demands significant engineering and budgetary resources. Companies that already have sensors and data capturing tools in place (historians, PLC, SCADA etc.), will have a head start, but ensuring a positive ROI is a real challenge.

Seebo’s approach is to offer a fast track to initial value by quickly deploying a digital twin of the entire process. Using code-free, visual tools, digital twin deployment can be completed in a matter of weeks. Then, a specific business problem is targeted for a quick-win pilot. Finally, after a successful pilot, the solution can be scaled across other manufacturing processes within the facility.

This approach makes sense since creating a data lake for the entire enterprise could be a lengthy and very costly project. Introducing process-based AI to a single production line and perfecting the algorithms will allow for a high level of accuracy and build confidence amongst project stakeholders.

Read More: The AI-Powered Shift in the Food and Beverage Industry

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