Artificial intelligence (AI) technology has quickly become an invaluable tool in heavy industry, and startups are constantly developing new AI tools to help companies overcome existing challenges.
Facility waste management, for example, is increasingly important as manufacturers seek to increase efficiency, improve productivity and make their manufacturing processes more sustainable.
However, it can be difficult to identify sources of waste and potential solutions. New AI and machine learning (ML) solutions could help manufacturers optimize waste management at their facilities.
Current challenges in facility waste management
For every facility that uses raw materials or components to create a product, waste will be a serious issue. These wastes can be solid or in the form of chemical wastes, water and fumes. Waste can be toxic, meaning it can harm the environment around the facility if not disposed of properly.
There are strategies companies can use to better identify and reduce facility waste. Lean Manufacturing, for example, is a popular manufacturing method that includes techniques such as Value Stream Mapping (VSM) and Quality at Source (QATS) that help reduce waste through quality interventions. top-down and bottom-up processes.
Many regulatory agencies, such as the US EPA, also publish best practices for waste management in commercial facilities. Some industry organizations also publish their own recommendations for implementing site remediation plans or improving waste management practices.
However, although these techniques provide tools for waste management, implementation and identification of waste can often be difficult. Waste patterns can be difficult to identify as critical without sufficient facility process data. Waste management strategies may work in theory but fail in practice, or they may require too much extra work from site personnel to be functional.
Waste management has become more important
At the same time, manufacturers face rapidly changing market conditions. Steady increases in demand, supply shortages and changing customer expectations have made facility waste management more critical than ever.
More than a third of global consumers are willing to pay more for sustainable products, and some studies have shown that consumers will actively avoid brands they view as unsustainable.
Most consumers willing to pay more for sustainability are younger, whether Millennials or Gen Z, indicating that this trend may become even more relevant as purchasing power of these generations increases.
An optimized waste management process not only helps a company save money, it can also help improve the company’s public image. As many brands attempt to go green and demonstrate their environmental commitments to consumers, effective waste management has become essential.
How companies are using AI in waste management
New AI-powered tools can help facility managers identify and effectively manage sources of site waste. These solutions work both at a high level, helping managers make more efficient decisions, and directly in the production line, where they can help floor workers identify and eliminate waste.
Industrial vision for automated waste recognition and sorting
An example of a new solution that uses both robotics and AI innovations comes from a London-based startup, Greyparrot. The company is developing an artificial vision tool that has been trained to identify and sort different types of waste, such as “glass, paper, cardboard, newspapers, cans and different types of plastics”.
Information from the sorting algorithm can be transmitted to workers, allowing them to more efficiently sort waste into different waste streams that can be more easily recycled. The company’s waste recognition API can also be used in conjunction with a robotic arm or similar tool to automatically sort waste with little or no human supervision required.
For businesses that already recycle but spend a lot of time, labor and money sorting waste for recycling, this tool combined with facility robotics could help dramatically speed up waste management while making the process a lot cheaper.
A similar startup, Winnow Vision, offers a similar platform designed for use in commercial kitchens and food processing facilities. Their machine vision solution tracks and measures food waste, assigning a monetary value to all the food and ingredients that a company sends to landfill without fully utilizing them.
Reduce waste by improving product quality
Poor quality products can be a major source of waste. Process errors and poor quality materials can lead to defective products that companies cannot sell but have invested resources in.
Some of the resources used in a product can be recovered through recycling or other programs, but it will always be more efficient to eliminate waste at the source.
AI quality control systems use a combination of pattern recognition and computer vision to eliminate defective products from the manufacturing process earlier. These control systems, combined with other Industry 4.0 technologies (such as IoT devices), can help improve manufacturing methods that reduce waste, such as the Lean manufacturing approach.
Top-Down AI Approaches for Facility Waste
A growing number of startups are offering AI products that help analyze business systems from the top down, rather than being integrated directly into the production process like a machine vision waste recognition system.
An example of such startups is WINT Water Intelligence, the developer of an AI-powered water management system. An AI solution from WINT helps tackle one of the biggest sources of water waste: leaks.
Plumbing in facilities is often complex and difficult to monitor, which means that small leaks can go undetected for long periods of time, resulting in significant water waste. With AI pattern matching, it is possible to more effectively monitor and detect water leaks as they occur. With this technology, companies could significantly reduce water waste without making major changes to facility processes.
Using AI to optimize facility waste management
Waste management is often a challenge for industrial facilities, but new AI tools can help reduce the work needed to minimize waste.
Waste recognition and sorting systems, AI for quality control, and facility monitoring technology can all help reduce waste in a facility.