How Artificial Intelligence & Machine Learning Impact Waste Management

About the author:

Emily Newton is an industrial journalist. She regularly covers stories for the utilities and energy sectors. Emily is also Editor-in-Chief of Revolutionized.

Artificial intelligence (AI) technology has quickly become an invaluable tool in heavy industry, and startups are constantly developing new AI tools to help businesses overcome existing challenges.

Facility waste management, for example, is increasingly important as manufacturers look to boost efficiency, improve productivity and make their manufacturing processes more sustainable.

Identifying sources of waste and potential solutions can be difficult, however. New AI and machine learning (ML) solutions could help manufacturers optimize their facility waste management.

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. This waste may be solid or in the form of waste chemicals, water and fumes. The waste may be toxic, meaning that it can harm the environment around the facility if not disposed of properly.

There are strategies that businesses can use to get better at identifying and reducing facility waste. Lean manufacturing, for example, is a popular manufacturing method that includes techniques — such as value stream mapping (VSM) and Quality at the Source (QATS) — that help to reduce waste both through top-down and bottom-up process interventions.

Many regulatory agencies, like the U.S. EPA, also publish best practices for managing waste at commercial facilities. Some industry organizations also publish their own recommendations for implementing site sanitation plans or upgrading waste management practices.

However, while these techniques provide tools for waste management, implementation and identification of waste can often be difficult. Patterns of waste may be hard to spot as essential without enough data on facility processes. Waste management strategies may work in theory but fail in practice, or they may require too much additional labor from site staff to be functional.

Waste Management Has Become More Important

At the same time, manufacturers face rapidly changing market conditions. Steadily rising demand, supply shortages and new customer expectations have all 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 found that consumers will actively avoid brands they view as unsustainable.

Most of the consumers willing to pay more for sustainability are younger, either Millennials or Gen Z — signaling that this trend may become even more relevant as the buying power of those generations grows.

An optimized waste management process does not just help a business save money, it can also help improve the business’s public image. At a time when many brands are trying to go green and demonstrate environmental commitments to consumers, effective waste management has become essential.

How Businesses are Using AI in Waste Management

New AI-powered tools may help facility managers to effectively identify and manage sources of site waste. These solutions both work at a high level, helping managers make more effective decisions, and directly in the production line, where they can help floor workers identify and eliminate waste.

Machine Vision for Automated Waste Recognition and Sorting

One example of a new solution that uses both robotics and AI innovations comes from a London-based AI startup, Greyparrot. The company develops a machine 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 passed on to workers, allowing them to more effectively sort waste products into different waste streams that can be more easily recycled. The company’s waste recognition API can also be used in conjunction with a robot arm or similar tool to automatically sort waste with little to no human oversight necessary.

For businesses that already recycle but dedicate a significant amount of time, labor and money to sorting waste for recycling, this tool combined with facility robotics could help significantly accelerate waste management while also making the process much 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 dollar value to all the food and ingredients a business sends to landfill without using it fully.

Reducing Waste by Improving Product Quality

Low-quality products can be a major source of waste. Process errors and low-quality materials can result in defective products that businesses cannot sell but have invested resources in.

Some of the resources used in a product can be recaptured 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 models and machine vision to remove defective products from the manufacturing process earlier. These control systems, combined with other Industry 4.0 technology (like IoT devices), can help to improve waste-reduction manufacturing methods, like the Lean manufacturing approach.

Top-Down AI Approaches to Facility Waste

A growing number of startups offer AI products that help to analyze business systems from the top-down, rather than being embedded directly into the production process like a machine vision waste recognition system.

One example of these startups is WINT Water Intelligence, the developer of an AI-powered water management system. An AI solution from WINT helps to tackle one of the largest sources of water waste — leaks.

Plumbing in facilities is often complex and challenging to monitor, meaning that small leaks may go undetected for long periods of time — generating significant water waste. With AI pattern-matching, it’s possible to more effectively monitor and detect water leaks as they occur. Using the technology, businesses could significantly reduce water waste without major changes to facility processes.