AI tools like ChatGPT are grabbing headlines, but other AI techniques and tools?specifically designed for enterprises?are quietly helping companies meet their sustainability goals. Classic AI is already being used widely today in various use cases, and generative AI is evolving rapidly to address new classes of use cases.
- Asset management:?Whether it’s for utility infrastructure or factory floor machinery,?timely intervention can prolong the life of an asset;?reducing the volume of waste sent to landfill and the environmental impact of creating a replacement. AI solutions work by collecting asset performance data and feeding it into machine learning models, which can?predict?asset health and risk of failure.
- Inventory management:?Transportation uses energy;in addition, perishable goods may need to be refrigerated in?transit and storage.?Inventory optimization?is important to ensure you have enough stock while also meeting customer demand. At the same time, you want to reduce the carbon footprint associated with moving and storing stock. AI helps address this problem by combining aspects like demand forecasting, last–mile delivery,?and routing optimization.
- Schedule optimization:?This use case is like inventory management but addresses the challenge of ensuring that you have the appropriate alignment of talent. If we think about asset maintenance, for example, the questions are which technicians are available, where, and how should their work be prioritized. It’s not about minimizing travel.?Instead, it’s better to?prioritize?a more distant asset for repair because that asset has a higher cost or could fail sooner. AI can tackle problems like asset maintenance efficiently.
- Anomaly detection:?Some manufacturers have zero-defect goals. If a part is defective or assembled incorrectly, it might not be possible to salvage or recycle it. Image and video recognition systems can use AI to monitor each stage of manufacture,?catching any?discrepancies?as early as possible.?As well as wasted materials, additional energy is consumed when parts have to be reworked or remade. This use case shows how AI can help by processing unstructured image and video data in addition to structured data in the previous examples.
- Compute optimization:?Data centers consume a huge amount of electricity. By using AI to understand compute demand over time, it becomes possible to?optimize?the use of computing and cooling resources. ?Matching resources to demand more closely helps to save energy.
At Magnus Code we help you build, and deploy modern applications to help solve your paint points. Contact us now to learn how we can help integrate AI into your business workflow and achieve your sustainability goals.
