How AI Reduces Waste in the Supply Chain

Supply chain professionals are integrating artificial intelligence (AI) into their workflows. With countless promises of enhanced productivity, safety, and financial security, many may overlook how AI could contribute to sustainable operations. Waste seeps from every corner of the chain, from bathroom sinks to diesel-powered fleets. What can AI do to lower carbon footprints and make supply chains greener?

Predictive Maintenance

Sensors are installable throughout a supply chain. They can go into vehicles, lighting systems, and conveyors. In the electronics sector, these workers use AI predictive maintenance for acoustic monitoring, ultrasonic testing, and temperature oversight. Every opportunity to obtain data is a chance to transition from reactive or preventive maintenance to predictive. AI will inform the workforce of failures before they happen. It prevents supply chains from replacing entire pieces of equipment, which will end up in landfills.

Additionally, it stops organizations from stocking up on too many parts that would otherwise go to preventive maintenance. Storerooms will waste fewer resources, like energy, holding onto excess stock because repair schedules were too frequent before. Streamlining maintenance schedules also minimizes downtime.

Instead, AI informs the company how frequently technologies genuinely need attention, keeping them working at peak performance and optimization for more hours than not.

Inventory Management

AI, combined with robots and the Internet of Things (IoT), can change how inventory managers work. They have demand forecasting potential, determining what stock is necessary to keep and order without second guessing. This prevents unnecessary buildup on the shelves from using square footage and energy for potentially years. It also prevents spoilage if housing perishables.

Supply chains must also leverage the timeliness of demand forecasting to have greater visibility over volatile times of the year. These include holidays, market shifts, and economic fluctuations. AI datasets can also use historical data to predict if past trends will return. Stockouts are another event AI can predict, which allows supply chains to hold onto clients and maintain a positive reputation.

Transportation Optimization

On average, supply chain emissions are around 11.4 times higher than a company’s direct emissions. This results from a combination of factors, but it is largely due to transportation inefficiencies. The best way to reduce waste is through electrification, which AI can help logistics and supply chain teams budget for by analyzing market pricing. It can dock up plans for a gradual or immediate transition while considering other necessary renewable energy assets, like chargers, that the company will need to sustain its elimination of fuel dependence.

While this happens, AI can review the drivers’ routes and suggest optimizations. It may take paths in high-traffic areas or drive through regions where elevation changes put undue stress on the car or require excess gas.

AI also helps workers on the road by suggesting where to stop for fuel — if the vehicle uses it — for the lowest price. The savings can go to other waste-saving initiatives. Additionally, AI-informed GPS systems will reroute drivers if they are hitting traffic, preventing them from wasting resources unintentionally by taking the most eco-friendly path.

Waste Analysis

Supply chain workers see various types of materials enter the trash every day. Packaging comprises a third of plastics produced, yet only 14% of it hits recyclers. AI-powered cameras can detect what is recyclable as it passes through the building, letting stakeholders know what to train employees on when it comes to waste sorting.

The data also keeps tabs on the concentrations of specific waste streams. Managers will find ways to intervene in the most wasteful parts of their processes to make them more efficient and environmentally friendly. AI can also suggest avenues for responsibly discarding specific types of supply chain waste as organizations implement new disposal plans.

Quality Control

The food and beverage sector uses 30% of its electricity consumption to freeze stock to keep it as high-quality as possible. While quality control is sometimes in the product itself, supply chains go to many lengths to preserve quality which may not be the most eco-conscious. AI could reveal the data through visualizations after capturing information from the company for a while. It informs stakeholders how to take greener actions to keep quality high.

AI notices defects as early as they are detectable. It could help find the cause earlier than manual intervention. It may lead to initiating repairs on a specific machine or editing a workflow process to prevent future issues.

It also improves the quality of packaging and materials. Using innovations like computer vision through cameras, AI can identify problem products that are fragile, notifying loaders how to care for them. It can also scan the packaging and suggest to designers how to craft something more suitable for its protection. AI can also consider weight when analyzing the material and shape, suggesting optimized designs to waste fewer resources during transportation.

Green AI

Supply chain management can benefit greatly from introducing AI into its ecosystem. The opportunities for carbon footprint savings are too monumental to ignore, especially as demand and sectors like e-commerce continue to rise. Supply chains will become busier and more pressured than ever, and now is the best time to shave off emissions-producing operations with the insights AI provides.