Industry: Difference between revisions

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=== Optimizing supply chains ===

* [[Freight consolidation|'''Freight Consolidation''']]: Optimizing the shipping and transportation of goods across today's globalized supply chains is a complex, multifaceted challenge – especially for perishable goods. Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.

* '''[[Logistics Optimization]]'''

* '''[[Goods Demand Forecasting]]:''' The production, shipment, and climate-controlled warehousing of excess products is a major source of industrial GHG emissions. ML may be able to mitigate overproduction and/or the overstocking of goods by improving models for forecasting consumer demand, especially for perishable goods or "fashionable" items that quickly become obsolete.

* '''[[Goods Demand Forecasting]]'''

* '''[[Greenhouse Gas Emissions Detection|Greenhouse gas emissions mapping]]''' '''for supply chains''': While some factories release publicly-available data on their emissions, this data is not available (or is misreported) in many cases, especially in emerging markets. ML can help map greenhouse gas emissions using a combination of remote sensing and on-the-ground data, leading both consumers and upstream suppliers to make greener decisions in sourcing products.

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* '''[[Accelerated Science|Accelerated science]] for clean energy technologies''': Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.

* '''[[Generative Design]]:''' ML-enabled 3D-modelling software can can help create new designs for physical structures that reduce the need for carbon-intensive materials – especially cement and steel.

* '''[[Generative Design]]'''

* '''[[Additive Manufacturing]]''': Novel manufacturing techniques such as 3D printing allow for the production of unusual shapes that use less material or different types of material, but may be impossible to produce through traditional manufacturing methods.

=== Optimizing factory operations ===

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*'''[[Electricity Supply Forecasting|Supply]] and [[Energy Demand Forecasting|demand]] forecasting''': The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.

* '''[[Methane Leak Detection]]''': Fertilizer factories and some other chemical plants often leak methane, a powerful greenhouse gas. ML can help detect and prevent these leaks.

* '''[[Adaptive Systems Control]]'''

* '''[[Adaptive Systems Control]]: ....'''

*[[Predictive Maintenance|'''Predictive Maintenance''']]: Quickly detecting machinery faults can help reduce electricity and materials waste. ML can help detect faults in real time from machinery sensor data, or even forecast them ahead of time to enable preemptive repair or replacement.

* [[Demand response|'''Demand response''']]

* [[Demand response|'''Demand response''']]: '''<u>by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives.</u>'''

== Background Readings ==